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azureml-sd
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azureml-sd
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@@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -50,7 +50,22 @@
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"* `joblib`\n",
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"* `shap`\n",
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"\n",
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"\n",
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"Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install --upgrade scikit-learn>=0.22.1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id=\"LoadingData\"></a>\n",
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"## Loading the Data\n",
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"We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
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@@ -1,8 +0,0 @@
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name: fairlearn-azureml-mitigation
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dependencies:
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- pip:
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- azureml-sdk
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- azureml-contrib-fairness
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- fairlearn==0.4.6
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- joblib
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- shap
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@@ -52,9 +52,22 @@
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"* `joblib`\n",
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"* `shap`\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install --upgrade scikit-learn>=0.22.1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id=\"LoadingData\"></a>\n",
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"## Loading the Data\n",
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"We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
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@@ -1,8 +0,0 @@
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name: upload-fairness-dashboard
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dependencies:
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- pip:
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- azureml-sdk
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- azureml-contrib-fairness
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- fairlearn==0.4.6
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- joblib
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- shap
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@@ -1,500 +0,0 @@
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668
|
||||
0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663
|
||||
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|
||||
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|
||||
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|
||||
0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974
|
||||
1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953
|
||||
1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821
|
||||
1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021
|
||||
1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379
|
||||
1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991
|
||||
1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865
|
||||
1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677
|
||||
0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620
|
||||
0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779
|
||||
1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910
|
||||
1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685
|
||||
1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755
|
||||
0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270
|
||||
0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898
|
||||
0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901
|
||||
0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087
|
||||
0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925
|
||||
1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843
|
||||
1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811
|
||||
0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102
|
||||
0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788
|
||||
0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149
|
||||
1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012
|
||||
0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718
|
||||
0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652
|
||||
0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729
|
||||
0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240
|
||||
1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443
|
||||
0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648
|
||||
1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824
|
||||
1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539
|
||||
0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173
|
||||
1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742
|
||||
1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826
|
||||
1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747
|
||||
0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920
|
||||
1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790
|
||||
1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843
|
||||
1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493
|
||||
1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762
|
||||
0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863
|
||||
1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856
|
||||
0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904
|
||||
1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883
|
||||
0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956
|
||||
1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809
|
||||
0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184
|
||||
1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822
|
||||
1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948
|
||||
0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885
|
||||
1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871
|
||||
1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794
|
||||
0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679
|
||||
1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903
|
||||
1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840
|
||||
0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909
|
||||
1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892
|
||||
1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836
|
||||
1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886
|
||||
1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661
|
||||
1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883
|
||||
1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783
|
||||
1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838
|
||||
0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106
|
||||
0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895
|
||||
0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352
|
||||
1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836
|
||||
1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645
|
||||
1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866
|
||||
0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706
|
||||
0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750
|
||||
0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720
|
||||
0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006
|
||||
1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826
|
||||
1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908
|
||||
0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041
|
||||
0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898
|
||||
1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823
|
||||
0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498
|
||||
0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062
|
||||
0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863
|
||||
1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358
|
||||
0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655
|
||||
0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089
|
||||
1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806
|
||||
0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779
|
||||
1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832
|
||||
1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713
|
||||
1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977
|
||||
0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749
|
||||
1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926
|
||||
0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150
|
||||
0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107
|
||||
1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791
|
||||
1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781
|
||||
0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692
|
||||
1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824
|
||||
0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754
|
||||
0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969
|
||||
0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786
|
||||
1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791
|
||||
1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798
|
||||
1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777
|
||||
0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687
|
||||
0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806
|
||||
1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694
|
||||
1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906
|
||||
1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770
|
||||
1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656
|
||||
1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785
|
||||
0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827
|
||||
0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745
|
||||
0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650
|
||||
1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489
|
||||
1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876
|
||||
0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041
|
||||
1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242
|
||||
0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769
|
||||
1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078
|
||||
1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623
|
||||
1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137
|
||||
1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589
|
||||
0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816
|
||||
1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638
|
||||
0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057
|
||||
0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774
|
||||
1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526
|
||||
1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958
|
||||
0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902
|
||||
0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707
|
||||
1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740
|
||||
1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949
|
||||
0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815
|
||||
1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474
|
||||
0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183
|
||||
1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715
|
||||
0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094
|
||||
1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777
|
||||
0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008
|
||||
1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807
|
||||
1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879
|
||||
1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798
|
||||
0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661
|
||||
0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870
|
||||
1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963
|
||||
1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705
|
||||
1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866
|
||||
0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280
|
||||
1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866
|
||||
1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878
|
||||
0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676
|
||||
0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608
|
||||
1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726
|
||||
0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578
|
||||
0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059
|
||||
1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939
|
||||
0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035
|
||||
0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145
|
||||
0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276
|
||||
0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466
|
||||
1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776
|
||||
0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278
|
||||
0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934
|
||||
0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430
|
||||
1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841
|
||||
1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906
|
||||
0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325
|
||||
1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786
|
||||
0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950
|
||||
0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762
|
||||
0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861
|
||||
0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851
|
||||
0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079
|
||||
1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724
|
||||
1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744
|
||||
0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912
|
||||
0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012
|
||||
0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665
|
||||
1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034
|
||||
0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095
|
||||
0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221
|
||||
1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777
|
||||
1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917
|
||||
1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771
|
||||
0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395
|
||||
1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865
|
||||
1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890
|
||||
1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815
|
||||
0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131
|
||||
0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154
|
||||
1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717
|
||||
0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775
|
||||
1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893
|
||||
0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901
|
||||
0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769
|
||||
1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828
|
||||
0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748
|
||||
1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599
|
||||
0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120
|
||||
1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833
|
||||
0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051
|
||||
1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126
|
||||
1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881
|
||||
1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135
|
||||
1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797
|
||||
0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612
|
||||
0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556
|
||||
0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789
|
||||
1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708
|
||||
0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878
|
||||
0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087
|
||||
0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846
|
||||
1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028
|
||||
0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994
|
||||
0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868
|
||||
1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913
|
||||
0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291
|
||||
0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123
|
||||
1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878
|
||||
0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511
|
||||
0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076
|
||||
0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112
|
||||
1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046
|
||||
1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009
|
||||
1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891
|
||||
1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140
|
||||
0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744
|
||||
1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238
|
||||
0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306
|
||||
0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758
|
||||
1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172
|
||||
1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992
|
||||
1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786
|
||||
1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922
|
||||
0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609
|
||||
1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351
|
||||
1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836
|
||||
0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252
|
||||
1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122
|
||||
0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042
|
||||
0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662
|
||||
1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626
|
||||
1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920
|
||||
1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059
|
||||
1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722
|
||||
1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941
|
||||
0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072
|
||||
1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053
|
||||
0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310
|
||||
0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847
|
||||
0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873
|
||||
0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183
|
||||
1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807
|
||||
0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936
|
||||
1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013
|
||||
1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822
|
||||
1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803
|
||||
0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176
|
||||
0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714
|
||||
0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815
|
||||
0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918
|
||||
0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056
|
||||
0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513
|
||||
0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773
|
||||
0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799
|
||||
1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813
|
||||
1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978
|
||||
1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743
|
||||
1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817
|
||||
1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875
|
||||
0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078
|
||||
1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619
|
||||
1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726
|
||||
0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950
|
||||
0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860
|
||||
0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034
|
||||
0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846
|
||||
0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920
|
||||
1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668
|
||||
0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313
|
||||
0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671
|
||||
1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224
|
||||
1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779
|
||||
0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851
|
||||
1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860
|
||||
0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958
|
||||
1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671
|
||||
1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127
|
||||
1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807
|
||||
1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823
|
||||
1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872
|
||||
1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061
|
||||
1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833
|
||||
1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780
|
||||
0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143
|
||||
1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709
|
||||
1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858
|
||||
1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091
|
||||
0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838
|
||||
0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661
|
||||
0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729
|
||||
0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878
|
||||
1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838
|
||||
1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792
|
||||
0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702
|
||||
0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068
|
||||
1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931
|
||||
0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522
|
||||
0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807
|
||||
1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054
|
||||
1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606
|
||||
1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961
|
||||
0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308
|
||||
0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146
|
||||
1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709
|
||||
0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180
|
||||
1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868
|
||||
1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779
|
||||
0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213
|
||||
1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168
|
||||
1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238
|
||||
0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891
|
||||
0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561
|
||||
1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911
|
||||
0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303
|
||||
1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094
|
||||
1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825
|
||||
1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995
|
||||
1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151
|
||||
1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280
|
||||
1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893
|
||||
1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043
|
||||
0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226
|
||||
1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851
|
||||
0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733
|
||||
0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764
|
||||
0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028
|
||||
1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774
|
||||
1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661
|
||||
1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776
|
||||
0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675
|
||||
1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931
|
||||
1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715
|
||||
0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889
|
||||
1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886
|
||||
0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087
|
||||
0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760
|
||||
0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006
|
||||
1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811
|
||||
0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176
|
||||
0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096
|
||||
0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721
|
||||
1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732
|
||||
1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874
|
||||
1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753
|
||||
0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883
|
||||
0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018
|
||||
1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806
|
||||
0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472
|
||||
0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827
|
||||
1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835
|
||||
1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826
|
||||
1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640
|
||||
1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764
|
||||
1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025
|
||||
1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223
|
||||
1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169
|
||||
1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003
|
||||
1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908
|
||||
1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836
|
||||
0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833
|
||||
0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294
|
||||
1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845
|
||||
1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510
|
||||
0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445
|
||||
1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926
|
||||
1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933
|
||||
1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853
|
||||
0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678
|
||||
1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038
|
||||
1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197
|
||||
1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746
|
||||
0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772
|
||||
1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983
|
||||
1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759
|
||||
1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825
|
||||
1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776
|
||||
0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892
|
||||
0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110
|
||||
1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762
|
||||
0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756
|
||||
1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831
|
||||
1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748
|
||||
0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756
|
||||
1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993
|
||||
1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881
|
||||
1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786
|
||||
0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983
|
||||
0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983
|
||||
1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867
|
||||
0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937
|
||||
0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293
|
||||
1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105
|
||||
0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949
|
||||
1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895
|
||||
0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722
|
||||
1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809
|
||||
0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007
|
||||
1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717
|
||||
0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656
|
||||
0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856
|
||||
1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832
|
||||
1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753
|
||||
0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954
|
||||
1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874
|
||||
0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848
|
||||
1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518
|
||||
1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787
|
||||
1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834
|
||||
0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722
|
||||
1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040
|
||||
0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758
|
||||
1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854
|
||||
1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828
|
||||
1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745
|
||||
0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701
|
||||
1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215
|
||||
1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837
|
||||
1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867
|
||||
0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119
|
||||
1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914
|
||||
0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373
|
||||
1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623
|
||||
1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133
|
||||
0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079
|
||||
0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919
|
||||
1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651
|
||||
0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207
|
||||
0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660
|
||||
1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951
|
||||
1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910
|
||||
0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715
|
||||
1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026
|
||||
0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239
|
||||
1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696
|
||||
0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662
|
||||
1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910
|
||||
0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050
|
||||
0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258
|
||||
1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884
|
||||
0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026
|
||||
1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119
|
||||
1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926
|
||||
0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079
|
||||
1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674
|
||||
0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733
|
||||
0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795
|
||||
0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329
|
||||
0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161
|
||||
0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200
|
||||
1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875
|
||||
0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071
|
||||
1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993
|
||||
1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866
|
||||
1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978
|
||||
1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982
|
||||
1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853
|
||||
1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122
|
||||
1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719
|
||||
1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739
|
||||
0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690
|
||||
1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643
|
||||
0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142
|
||||
1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758
|
||||
1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823
|
||||
0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258
|
||||
1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836
|
||||
0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349
|
||||
0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,500 +0,0 @@
|
||||
1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796
|
||||
0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116
|
||||
0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696
|
||||
1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234
|
||||
0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 1.336 -0.965 0.972 2.215 0.671 1.021 -1.439 0.000 0.493 -2.019 -0.289 0.000 0.805 0.930 0.984 1.430 2.198 1.934 1.684
|
||||
0 0.756 1.126 -0.945 2.355 -0.555 0.889 0.800 1.440 0.000 0.585 0.271 0.631 2.215 0.722 1.744 1.051 0.000 0.618 0.924 0.698 1.551 0.976 0.864 0.988 0.803 0.234 0.822 0.911
|
||||
0 1.141 -0.741 0.953 1.478 -0.524 1.197 -0.871 1.689 2.173 0.875 1.321 -0.518 1.107 0.540 0.037 -0.987 0.000 0.879 1.187 0.245 0.000 0.888 0.701 1.747 1.358 2.479 1.491 1.223
|
||||
1 0.606 -0.936 -0.384 1.257 -1.162 2.719 -0.600 0.100 2.173 3.303 -0.284 1.561 1.107 0.689 1.786 -0.326 0.000 0.780 -0.532 1.216 0.000 0.936 2.022 0.985 1.574 4.323 2.263 1.742
|
||||
1 0.603 0.429 -0.279 1.448 1.301 1.008 2.423 -1.295 0.000 0.452 1.305 0.533 0.000 1.076 1.011 1.256 2.548 2.021 1.260 -0.343 0.000 0.890 0.969 1.281 0.763 0.652 0.827 0.785
|
||||
0 1.171 -0.962 0.521 0.841 -0.315 1.196 -0.744 -0.882 2.173 0.726 -1.305 1.377 1.107 0.643 -1.790 -1.264 0.000 1.257 0.222 0.817 0.000 0.862 0.911 0.987 0.846 1.293 0.899 0.756
|
||||
1 1.392 -0.358 0.235 1.494 -0.461 0.895 -0.848 1.549 2.173 0.841 -0.384 0.666 1.107 1.199 2.509 -0.891 0.000 1.109 -0.364 -0.945 0.000 0.693 2.135 1.170 1.362 0.959 2.056 1.842
|
||||
1 1.024 1.076 -0.886 0.851 1.530 0.673 -0.449 0.187 1.087 0.628 -0.895 1.176 2.215 0.696 -0.232 -0.875 0.000 0.411 1.501 0.048 0.000 0.842 0.919 1.063 1.193 0.777 0.964 0.807
|
||||
1 0.890 -0.760 1.182 1.369 0.751 0.696 -0.959 -0.710 1.087 0.775 -0.130 -1.409 2.215 0.701 -0.110 -0.739 0.000 0.508 -0.451 0.390 0.000 0.762 0.738 0.998 1.126 0.788 0.940 0.790
|
||||
1 0.460 0.537 0.636 1.442 -0.269 0.585 0.323 -1.731 2.173 0.503 1.034 -0.927 0.000 0.928 -1.024 1.006 2.548 0.513 -0.618 -1.336 0.000 0.802 0.831 0.992 1.019 0.925 1.056 0.833
|
||||
1 0.364 1.648 0.560 1.720 0.829 1.110 0.811 -0.588 0.000 0.408 1.045 1.054 2.215 0.319 -1.138 1.545 0.000 0.423 1.025 -1.265 3.102 1.656 0.928 1.003 0.544 0.327 0.670 0.746
|
||||
1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668
|
||||
0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663
|
||||
1 1.789 -0.583 1.641 0.897 0.799 0.515 -0.100 -1.483 0.000 1.101 0.031 -0.326 2.215 1.195 0.001 0.126 2.548 0.768 -0.148 0.601 0.000 0.916 0.921 1.207 1.069 0.483 0.934 0.795
|
||||
1 1.332 -0.571 0.986 0.580 1.508 0.582 0.634 -0.746 1.087 1.084 -0.964 -0.489 0.000 0.785 0.274 0.343 2.548 0.779 0.721 1.489 0.000 1.733 1.145 0.990 1.270 0.715 0.897 0.915
|
||||
0 1.123 0.629 -1.708 0.597 -0.882 0.752 0.195 1.522 2.173 1.671 1.515 -0.003 0.000 0.778 0.514 0.139 1.274 0.801 1.260 1.600 0.000 1.495 0.976 0.988 0.676 0.921 1.010 0.943
|
||||
0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974
|
||||
1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953
|
||||
1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821
|
||||
1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021
|
||||
1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379
|
||||
1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991
|
||||
1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865
|
||||
1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677
|
||||
0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620
|
||||
0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779
|
||||
1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910
|
||||
1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685
|
||||
1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755
|
||||
0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270
|
||||
0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898
|
||||
0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901
|
||||
0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087
|
||||
0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925
|
||||
1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843
|
||||
1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811
|
||||
0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102
|
||||
0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788
|
||||
0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149
|
||||
1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012
|
||||
0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718
|
||||
0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652
|
||||
0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729
|
||||
0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240
|
||||
1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443
|
||||
0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648
|
||||
1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824
|
||||
1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539
|
||||
0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173
|
||||
1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742
|
||||
1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826
|
||||
1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747
|
||||
0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920
|
||||
1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790
|
||||
1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843
|
||||
1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493
|
||||
1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762
|
||||
0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863
|
||||
1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856
|
||||
0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904
|
||||
1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883
|
||||
0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956
|
||||
1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809
|
||||
0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184
|
||||
1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822
|
||||
1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948
|
||||
0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885
|
||||
1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871
|
||||
1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794
|
||||
0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679
|
||||
1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903
|
||||
1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840
|
||||
0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909
|
||||
1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892
|
||||
1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836
|
||||
1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886
|
||||
1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661
|
||||
1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883
|
||||
1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783
|
||||
1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838
|
||||
0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106
|
||||
0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895
|
||||
0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352
|
||||
1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836
|
||||
1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645
|
||||
1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866
|
||||
0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706
|
||||
0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750
|
||||
0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720
|
||||
0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006
|
||||
1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826
|
||||
1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908
|
||||
0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041
|
||||
0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898
|
||||
1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823
|
||||
0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498
|
||||
0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062
|
||||
0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863
|
||||
1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358
|
||||
0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655
|
||||
0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089
|
||||
1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806
|
||||
0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779
|
||||
1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832
|
||||
1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713
|
||||
1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977
|
||||
0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749
|
||||
1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926
|
||||
0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150
|
||||
0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107
|
||||
1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791
|
||||
1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781
|
||||
0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692
|
||||
1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824
|
||||
0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754
|
||||
0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969
|
||||
0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786
|
||||
1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791
|
||||
1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798
|
||||
1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777
|
||||
0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687
|
||||
0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806
|
||||
1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694
|
||||
1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906
|
||||
1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770
|
||||
1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656
|
||||
1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785
|
||||
0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827
|
||||
0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745
|
||||
0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650
|
||||
1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489
|
||||
1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876
|
||||
0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041
|
||||
1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242
|
||||
0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769
|
||||
1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078
|
||||
1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623
|
||||
1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137
|
||||
1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589
|
||||
0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816
|
||||
1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638
|
||||
0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057
|
||||
0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774
|
||||
1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526
|
||||
1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958
|
||||
0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902
|
||||
0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707
|
||||
1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740
|
||||
1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949
|
||||
0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815
|
||||
1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474
|
||||
0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183
|
||||
1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715
|
||||
0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094
|
||||
1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777
|
||||
0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008
|
||||
1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807
|
||||
1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879
|
||||
1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798
|
||||
0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661
|
||||
0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870
|
||||
1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963
|
||||
1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705
|
||||
1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866
|
||||
0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280
|
||||
1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866
|
||||
1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878
|
||||
0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676
|
||||
0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608
|
||||
1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726
|
||||
0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578
|
||||
0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059
|
||||
1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939
|
||||
0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035
|
||||
0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145
|
||||
0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276
|
||||
0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466
|
||||
1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776
|
||||
0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278
|
||||
0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934
|
||||
0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430
|
||||
1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841
|
||||
1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906
|
||||
0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325
|
||||
1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786
|
||||
0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950
|
||||
0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762
|
||||
0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861
|
||||
0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851
|
||||
0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079
|
||||
1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724
|
||||
1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744
|
||||
0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912
|
||||
0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012
|
||||
0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665
|
||||
1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034
|
||||
0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095
|
||||
0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221
|
||||
1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777
|
||||
1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917
|
||||
1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771
|
||||
0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395
|
||||
1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865
|
||||
1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890
|
||||
1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815
|
||||
0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131
|
||||
0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154
|
||||
1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717
|
||||
0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775
|
||||
1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893
|
||||
0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901
|
||||
0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769
|
||||
1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828
|
||||
0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748
|
||||
1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599
|
||||
0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120
|
||||
1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833
|
||||
0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051
|
||||
1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126
|
||||
1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881
|
||||
1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135
|
||||
1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797
|
||||
0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612
|
||||
0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556
|
||||
0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789
|
||||
1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708
|
||||
0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878
|
||||
0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087
|
||||
0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846
|
||||
1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028
|
||||
0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994
|
||||
0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868
|
||||
1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913
|
||||
0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291
|
||||
0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123
|
||||
1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878
|
||||
0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511
|
||||
0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076
|
||||
0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112
|
||||
1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046
|
||||
1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009
|
||||
1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891
|
||||
1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140
|
||||
0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744
|
||||
1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238
|
||||
0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306
|
||||
0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758
|
||||
1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172
|
||||
1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992
|
||||
1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786
|
||||
1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922
|
||||
0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609
|
||||
1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351
|
||||
1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836
|
||||
0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252
|
||||
1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122
|
||||
0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042
|
||||
0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662
|
||||
1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626
|
||||
1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920
|
||||
1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059
|
||||
1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722
|
||||
1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941
|
||||
0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072
|
||||
1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053
|
||||
0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310
|
||||
0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847
|
||||
0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873
|
||||
0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183
|
||||
1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807
|
||||
0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936
|
||||
1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013
|
||||
1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822
|
||||
1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803
|
||||
0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176
|
||||
0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714
|
||||
0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815
|
||||
0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918
|
||||
0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056
|
||||
0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513
|
||||
0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773
|
||||
0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799
|
||||
1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813
|
||||
1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978
|
||||
1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743
|
||||
1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817
|
||||
1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875
|
||||
0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078
|
||||
1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619
|
||||
1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726
|
||||
0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950
|
||||
0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860
|
||||
0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034
|
||||
0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846
|
||||
0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920
|
||||
1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668
|
||||
0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313
|
||||
0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671
|
||||
1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224
|
||||
1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779
|
||||
0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851
|
||||
1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860
|
||||
0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958
|
||||
1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671
|
||||
1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127
|
||||
1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807
|
||||
1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823
|
||||
1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872
|
||||
1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061
|
||||
1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833
|
||||
1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780
|
||||
0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143
|
||||
1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709
|
||||
1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858
|
||||
1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091
|
||||
0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838
|
||||
0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661
|
||||
0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729
|
||||
0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878
|
||||
1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838
|
||||
1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792
|
||||
0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702
|
||||
0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068
|
||||
1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931
|
||||
0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522
|
||||
0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807
|
||||
1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054
|
||||
1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606
|
||||
1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961
|
||||
0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308
|
||||
0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146
|
||||
1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709
|
||||
0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180
|
||||
1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868
|
||||
1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779
|
||||
0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213
|
||||
1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168
|
||||
1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238
|
||||
0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891
|
||||
0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561
|
||||
1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911
|
||||
0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303
|
||||
1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094
|
||||
1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825
|
||||
1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995
|
||||
1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151
|
||||
1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280
|
||||
1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893
|
||||
1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043
|
||||
0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226
|
||||
1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851
|
||||
0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733
|
||||
0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764
|
||||
0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028
|
||||
1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774
|
||||
1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661
|
||||
1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776
|
||||
0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675
|
||||
1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931
|
||||
1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715
|
||||
0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889
|
||||
1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886
|
||||
0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087
|
||||
0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760
|
||||
0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006
|
||||
1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811
|
||||
0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176
|
||||
0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096
|
||||
0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721
|
||||
1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732
|
||||
1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874
|
||||
1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753
|
||||
0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883
|
||||
0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018
|
||||
1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806
|
||||
0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472
|
||||
0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827
|
||||
1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835
|
||||
1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826
|
||||
1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640
|
||||
1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764
|
||||
1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025
|
||||
1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223
|
||||
1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169
|
||||
1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003
|
||||
1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908
|
||||
1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836
|
||||
0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833
|
||||
0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294
|
||||
1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845
|
||||
1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510
|
||||
0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445
|
||||
1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926
|
||||
1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933
|
||||
1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853
|
||||
0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678
|
||||
1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038
|
||||
1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197
|
||||
1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746
|
||||
0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772
|
||||
1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983
|
||||
1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759
|
||||
1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825
|
||||
1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776
|
||||
0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892
|
||||
0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110
|
||||
1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762
|
||||
0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756
|
||||
1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831
|
||||
1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748
|
||||
0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756
|
||||
1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993
|
||||
1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881
|
||||
1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786
|
||||
0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983
|
||||
0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983
|
||||
1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867
|
||||
0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937
|
||||
0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293
|
||||
1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105
|
||||
0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949
|
||||
1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895
|
||||
0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722
|
||||
1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809
|
||||
0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007
|
||||
1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717
|
||||
0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656
|
||||
0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856
|
||||
1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832
|
||||
1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753
|
||||
0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954
|
||||
1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874
|
||||
0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848
|
||||
1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518
|
||||
1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787
|
||||
1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834
|
||||
0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722
|
||||
1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040
|
||||
0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758
|
||||
1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854
|
||||
1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828
|
||||
1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745
|
||||
0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701
|
||||
1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215
|
||||
1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837
|
||||
1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867
|
||||
0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119
|
||||
1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914
|
||||
0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373
|
||||
1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623
|
||||
1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133
|
||||
0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079
|
||||
0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919
|
||||
1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651
|
||||
0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207
|
||||
0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660
|
||||
1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951
|
||||
1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910
|
||||
0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715
|
||||
1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026
|
||||
0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239
|
||||
1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696
|
||||
0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662
|
||||
1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910
|
||||
0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050
|
||||
0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258
|
||||
1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884
|
||||
0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026
|
||||
1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119
|
||||
1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926
|
||||
0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079
|
||||
1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674
|
||||
0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733
|
||||
0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795
|
||||
0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329
|
||||
0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161
|
||||
0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200
|
||||
1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875
|
||||
0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071
|
||||
1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993
|
||||
1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866
|
||||
1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978
|
||||
1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982
|
||||
1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853
|
||||
1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122
|
||||
1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719
|
||||
1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739
|
||||
0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690
|
||||
1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643
|
||||
0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142
|
||||
1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758
|
||||
1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823
|
||||
0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258
|
||||
1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836
|
||||
0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349
|
||||
0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,270 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Use LightGBM Estimator in Azure Machine Learning\n",
|
||||
"In this notebook we will demonstrate how to run a training job using LightGBM Estimator. [LightGBM](https://lightgbm.readthedocs.io/en/latest/) is a gradient boosting framework that uses tree based learning algorithms. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"This notebook uses azureml-contrib-gbdt package, if you don't already have the package, please install by uncommenting below cell."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install azureml-contrib-gbdt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Run, Experiment\n",
|
||||
"import shutil, os\n",
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"from azureml.contrib.gbdt import LightGBM\n",
|
||||
"from azureml.train.dnn import Mpi\n",
|
||||
"from azureml.core.compute import AmlCompute, ComputeTarget\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you are using an AzureML Compute Instance, you are all set. Otherwise, go through the [configuration.ipynb](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML Workspace"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Set up machine learning resources"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cluster_vm_size = \"STANDARD_DS14_V2\"\n",
|
||||
"cluster_min_nodes = 0\n",
|
||||
"cluster_max_nodes = 20\n",
|
||||
"cpu_cluster_name = 'TrainingCompute2' \n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
|
||||
" if cpu_cluster and type(cpu_cluster) is AmlCompute:\n",
|
||||
" print('found compute target: ' + cpu_cluster_name)\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('creating a new compute target...')\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = cluster_vm_size, \n",
|
||||
" vm_priority = 'lowpriority', \n",
|
||||
" min_nodes = cluster_min_nodes, \n",
|
||||
" max_nodes = cluster_max_nodes)\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
|
||||
" \n",
|
||||
" # can poll for a minimum number of nodes and for a specific timeout. \n",
|
||||
" # if no min node count is provided it will use the scale settings for the cluster\n",
|
||||
" cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
|
||||
" \n",
|
||||
" # For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
|
||||
" print(cpu_cluster.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"From this point, you can either upload training data file directly or use Datastore for training data storage\n",
|
||||
"## Upload training file from local"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scripts_folder = \"scripts_folder\"\n",
|
||||
"if not os.path.isdir(scripts_folder):\n",
|
||||
" os.mkdir(scripts_folder)\n",
|
||||
"shutil.copy('./train.conf', os.path.join(scripts_folder, 'train.conf'))\n",
|
||||
"shutil.copy('./binary0.train', os.path.join(scripts_folder, 'binary0.train'))\n",
|
||||
"shutil.copy('./binary1.train', os.path.join(scripts_folder, 'binary1.train'))\n",
|
||||
"shutil.copy('./binary0.test', os.path.join(scripts_folder, 'binary0.test'))\n",
|
||||
"shutil.copy('./binary1.test', os.path.join(scripts_folder, 'binary1.test'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
|
||||
"validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
|
||||
"lgbm = LightGBM(source_directory=scripts_folder, \n",
|
||||
" compute_target=cpu_cluster, \n",
|
||||
" distributed_training=Mpi(),\n",
|
||||
" node_count=2,\n",
|
||||
" lightgbm_config='train.conf',\n",
|
||||
" data=training_data_list,\n",
|
||||
" valid=validation_data_list\n",
|
||||
" )\n",
|
||||
"experiment_name = 'lightgbm-estimator-test'\n",
|
||||
"experiment = Experiment(ws, name=experiment_name)\n",
|
||||
"run = experiment.submit(lgbm, tags={\"test public docker image\": None})\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use data reference"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.datastore import Datastore\n",
|
||||
"from azureml.data.data_reference import DataReference\n",
|
||||
"datastore = ws.get_default_datastore()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"datastore.upload(src_dir='.',\n",
|
||||
" target_path='.',\n",
|
||||
" show_progress=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
|
||||
"validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
|
||||
"lgbm = LightGBM(source_directory='.', \n",
|
||||
" compute_target=cpu_cluster, \n",
|
||||
" distributed_training=Mpi(),\n",
|
||||
" node_count=2,\n",
|
||||
" inputs=[datastore.as_mount()],\n",
|
||||
" lightgbm_config='train.conf',\n",
|
||||
" data=training_data_list,\n",
|
||||
" valid=validation_data_list\n",
|
||||
" )\n",
|
||||
"experiment_name = 'lightgbm-estimator-test'\n",
|
||||
"experiment = Experiment(ws, name=experiment_name)\n",
|
||||
"run = experiment.submit(lgbm, tags={\"use datastore.as_mount()\": None})\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# uncomment below and run if compute resources are no longer needed\n",
|
||||
"# cpu_cluster.delete() "
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "jingywa"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,7 +0,0 @@
|
||||
name: lightgbm-example
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-contrib-gbdt
|
||||
- azureml-widgets
|
||||
- azureml-core
|
||||
@@ -1,111 +0,0 @@
|
||||
# task type, support train and predict
|
||||
task = train
|
||||
|
||||
# boosting type, support gbdt for now, alias: boosting, boost
|
||||
boosting_type = gbdt
|
||||
|
||||
# application type, support following application
|
||||
# regression , regression task
|
||||
# binary , binary classification task
|
||||
# lambdarank , lambdarank task
|
||||
# alias: application, app
|
||||
objective = binary
|
||||
|
||||
# eval metrics, support multi metric, delimite by ',' , support following metrics
|
||||
# l1
|
||||
# l2 , default metric for regression
|
||||
# ndcg , default metric for lambdarank
|
||||
# auc
|
||||
# binary_logloss , default metric for binary
|
||||
# binary_error
|
||||
metric = binary_logloss,auc
|
||||
|
||||
# frequence for metric output
|
||||
metric_freq = 1
|
||||
|
||||
# true if need output metric for training data, alias: tranining_metric, train_metric
|
||||
is_training_metric = true
|
||||
|
||||
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
|
||||
max_bin = 255
|
||||
|
||||
# training data
|
||||
# if exsting weight file, should name to "binary.train.weight"
|
||||
# alias: train_data, train
|
||||
data = binary.train
|
||||
|
||||
# validation data, support multi validation data, separated by ','
|
||||
# if exsting weight file, should name to "binary.test.weight"
|
||||
# alias: valid, test, test_data,
|
||||
valid_data = binary.test
|
||||
|
||||
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
|
||||
num_trees = 100
|
||||
|
||||
# shrinkage rate , alias: shrinkage_rate
|
||||
learning_rate = 0.1
|
||||
|
||||
# number of leaves for one tree, alias: num_leaf
|
||||
num_leaves = 63
|
||||
|
||||
# type of tree learner, support following types:
|
||||
# serial , single machine version
|
||||
# feature , use feature parallel to train
|
||||
# data , use data parallel to train
|
||||
# voting , use voting based parallel to train
|
||||
# alias: tree
|
||||
tree_learner = feature
|
||||
|
||||
# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu.
|
||||
# num_threads = 8
|
||||
|
||||
# feature sub-sample, will random select 80% feature to train on each iteration
|
||||
# alias: sub_feature
|
||||
feature_fraction = 0.8
|
||||
|
||||
# Support bagging (data sub-sample), will perform bagging every 5 iterations
|
||||
bagging_freq = 5
|
||||
|
||||
# Bagging farction, will random select 80% data on bagging
|
||||
# alias: sub_row
|
||||
bagging_fraction = 0.8
|
||||
|
||||
# minimal number data for one leaf, use this to deal with over-fit
|
||||
# alias : min_data_per_leaf, min_data
|
||||
min_data_in_leaf = 50
|
||||
|
||||
# minimal sum hessians for one leaf, use this to deal with over-fit
|
||||
min_sum_hessian_in_leaf = 5.0
|
||||
|
||||
# save memory and faster speed for sparse feature, alias: is_sparse
|
||||
is_enable_sparse = true
|
||||
|
||||
# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
|
||||
# alias: two_round_loading, two_round
|
||||
use_two_round_loading = false
|
||||
|
||||
# true if need to save data to binary file and application will auto load data from binary file next time
|
||||
# alias: is_save_binary, save_binary
|
||||
is_save_binary_file = false
|
||||
|
||||
# output model file
|
||||
output_model = LightGBM_model.txt
|
||||
|
||||
# support continuous train from trained gbdt model
|
||||
# input_model= trained_model.txt
|
||||
|
||||
# output prediction file for predict task
|
||||
# output_result= prediction.txt
|
||||
|
||||
# support continuous train from initial score file
|
||||
# input_init_score= init_score.txt
|
||||
|
||||
|
||||
# number of machines in parallel training, alias: num_machine
|
||||
num_machines = 2
|
||||
|
||||
# local listening port in parallel training, alias: local_port
|
||||
local_listen_port = 12400
|
||||
|
||||
# machines list file for parallel training, alias: mlist
|
||||
machine_list_file = mlist.txt
|
||||
@@ -230,6 +230,15 @@ You may check the version of tensorflow and uninstall as follows
|
||||
2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
|
||||
3) If the listed version is a not a supported version, `pip uninstall tensorflow` in the command shell and enter y for confirmation.
|
||||
|
||||
## KeyError: 'brand' when running AutoML on local compute or Azure Databricks cluster**
|
||||
If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken:
|
||||
|
||||
1) Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0):
|
||||
`pip install --upgrade azureml-sdk[automl]`
|
||||
|
||||
2) Downgrade the installed version of py-cpuinfo to 5.0.0:
|
||||
`pip install py-cpuinfo==5.0.0`
|
||||
|
||||
## Remote run: DsvmCompute.create fails
|
||||
There are several reasons why the DsvmCompute.create can fail. The reason is usually in the error message but you have to look at the end of the error message for the detailed reason. Some common reasons are:
|
||||
1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.` Note that underscore is not allowed in the name.
|
||||
|
||||
@@ -6,7 +6,7 @@ dependencies:
|
||||
- python>=3.5.2,<3.6.8
|
||||
- nb_conda
|
||||
- matplotlib==2.1.0
|
||||
- numpy>=1.16.0,<=1.16.2
|
||||
- numpy~=1.16.0
|
||||
- cython
|
||||
- urllib3<1.24
|
||||
- scipy==1.4.1
|
||||
@@ -14,6 +14,7 @@ dependencies:
|
||||
- pandas>=0.22.0,<=0.23.4
|
||||
- py-xgboost<=0.90
|
||||
- conda-forge::fbprophet==0.5
|
||||
- holidays==0.9.11
|
||||
- pytorch::pytorch=1.4.0
|
||||
- cudatoolkit=10.1.243
|
||||
|
||||
@@ -26,6 +27,5 @@ dependencies:
|
||||
- azureml-pipeline
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- pyarrow==0.17.0
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@ dependencies:
|
||||
- python>=3.5.2,<3.6.8
|
||||
- nb_conda
|
||||
- matplotlib==2.1.0
|
||||
- numpy>=1.16.0,<=1.16.2
|
||||
- numpy~=1.16.0
|
||||
- cython
|
||||
- urllib3<1.24
|
||||
- scipy==1.4.1
|
||||
@@ -15,6 +15,7 @@ dependencies:
|
||||
- pandas>=0.22.0,<=0.23.4
|
||||
- py-xgboost<=0.90
|
||||
- conda-forge::fbprophet==0.5
|
||||
- holidays==0.9.11
|
||||
- pytorch::pytorch=1.4.0
|
||||
- cudatoolkit=9.0
|
||||
|
||||
@@ -27,5 +28,4 @@ dependencies:
|
||||
- azureml-pipeline
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- pyarrow==0.17.0
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
|
||||
@@ -57,7 +57,7 @@
|
||||
"9. Test the ACI service.\n",
|
||||
"\n",
|
||||
"In addition this notebook showcases the following features\n",
|
||||
"- **Blacklisting** certain pipelines\n",
|
||||
"- **Blocking** certain pipelines\n",
|
||||
"- Specifying **target metrics** to indicate stopping criteria\n",
|
||||
"- Handling **missing data** in the input"
|
||||
]
|
||||
@@ -89,7 +89,7 @@
|
||||
"from azureml.automl.core.featurization import FeaturizationConfig\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.train.automl import AutoMLConfig\n",
|
||||
"from azureml.explain.model._internal.explanation_client import ExplanationClient"
|
||||
"from azureml.interpret._internal.explanation_client import ExplanationClient"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -105,7 +105,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -314,8 +314,8 @@
|
||||
"|**task**|classification or regression or forecasting|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
|
||||
"| **whitelist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n",
|
||||
"|**blocked_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
|
||||
"|**allowed_models** | *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blocked_models** allowed for **allowed_models**.|\n",
|
||||
"|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
|
||||
"|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
|
||||
"|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
|
||||
@@ -349,7 +349,7 @@
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" experiment_exit_score = 0.9984,\n",
|
||||
" blacklist_models = ['KNN','LinearSVM'],\n",
|
||||
" blocked_models = ['KNN','LinearSVM'],\n",
|
||||
" enable_onnx_compatible_models=True,\n",
|
||||
" training_data = train_data,\n",
|
||||
" label_column_name = label,\n",
|
||||
@@ -362,7 +362,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -733,24 +733,6 @@
|
||||
"print(aci_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Delete a Web Service\n",
|
||||
"\n",
|
||||
"Deletes the specified web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#aci_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -775,7 +757,9 @@
|
||||
"source": [
|
||||
"## Test\n",
|
||||
"\n",
|
||||
"Now that the model is trained, run the test data through the trained model to get the predicted values."
|
||||
"Now that the model is trained, run the test data through the trained model to get the predicted values. This calls the ACI web service to do the prediction.\n",
|
||||
"\n",
|
||||
"Note that the JSON passed to the ACI web service is an array of rows of data. Each row should either be an array of values in the same order that was used for training or a dictionary where the keys are the same as the column names used for training. The example below uses dictionary rows."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -815,10 +799,27 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = fitted_model.predict(X_test)\n",
|
||||
"import json\n",
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"X_test_json = X_test.to_json(orient='records')\n",
|
||||
"data = \"{\\\"data\\\": \" + X_test_json +\"}\"\n",
|
||||
"headers = {'Content-Type': 'application/json'}\n",
|
||||
"\n",
|
||||
"resp = requests.post(aci_service.scoring_uri, data, headers=headers)\n",
|
||||
"\n",
|
||||
"y_pred = json.loads(json.loads(resp.text))['result']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"actual = array(y_test)\n",
|
||||
"actual = actual[:,0]\n",
|
||||
"print(y_pred.shape, \" \", actual.shape)"
|
||||
"print(len(y_pred), \" \", len(actual))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -827,8 +828,7 @@
|
||||
"source": [
|
||||
"### Calculate metrics for the prediction\n",
|
||||
"\n",
|
||||
"Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
|
||||
"from the trained model that was returned."
|
||||
"Now visualize the data as a confusion matrix that compared the predicted values against the actual values.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -838,12 +838,45 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib notebook\n",
|
||||
"test_pred = plt.scatter(actual, y_pred, color='b')\n",
|
||||
"test_test = plt.scatter(actual, actual, color='g')\n",
|
||||
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
|
||||
"from sklearn.metrics import confusion_matrix\n",
|
||||
"import numpy as np\n",
|
||||
"import itertools\n",
|
||||
"\n",
|
||||
"cf =confusion_matrix(actual,y_pred)\n",
|
||||
"plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
|
||||
"plt.colorbar()\n",
|
||||
"plt.title('Confusion Matrix')\n",
|
||||
"plt.xlabel('Predicted')\n",
|
||||
"plt.ylabel('Actual')\n",
|
||||
"class_labels = ['no','yes']\n",
|
||||
"tick_marks = np.arange(len(class_labels))\n",
|
||||
"plt.xticks(tick_marks,class_labels)\n",
|
||||
"plt.yticks([-0.5,0,1,1.5],['','no','yes',''])\n",
|
||||
"# plotting text value inside cells\n",
|
||||
"thresh = cf.max() / 2.\n",
|
||||
"for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
|
||||
" plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Delete a Web Service\n",
|
||||
"\n",
|
||||
"Deletes the specified web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aci_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
@@ -93,7 +93,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -232,7 +232,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -88,7 +88,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -204,7 +204,6 @@
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n",
|
||||
" conda_packages=['numpy==1.16.2'], \n",
|
||||
" pin_sdk_version=False)\n",
|
||||
"#cd.add_pip_package('azureml-explain-model')\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd\n",
|
||||
"\n",
|
||||
"print('run config is ready')"
|
||||
|
||||
@@ -114,7 +114,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -217,7 +217,7 @@
|
||||
"\n",
|
||||
"**Time column** is the time axis along which to predict.\n",
|
||||
"\n",
|
||||
"**Grain** is another word for an individual time series in your dataset. Grains are identified by values of the columns listed `grain_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
|
||||
"**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
|
||||
"\n",
|
||||
"This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
|
||||
]
|
||||
@@ -269,7 +269,7 @@
|
||||
"source": [
|
||||
"target_column_name = 'BeerProduction'\n",
|
||||
"time_column_name = 'DATE'\n",
|
||||
"grain_column_names = []\n",
|
||||
"time_series_id_column_names = []\n",
|
||||
"freq = 'M' #Monthly data"
|
||||
]
|
||||
},
|
||||
@@ -329,7 +329,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"max_horizon = 12"
|
||||
"forecast_horizon = 12"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -352,8 +352,6 @@
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
"|**enable_dnn**|Enable Forecasting DNNs|\n",
|
||||
"\n",
|
||||
"This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results.\n",
|
||||
"\n",
|
||||
"This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)."
|
||||
]
|
||||
},
|
||||
@@ -366,11 +364,10 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_settings = {\n",
|
||||
" 'time_column_name': time_column_name,\n",
|
||||
" 'max_horizon': max_horizon,\n",
|
||||
" 'enable_dnn' : True,\n",
|
||||
"}\n",
|
||||
"from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
|
||||
"forecasting_parameters = ForecastingParameters(\n",
|
||||
" time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting', \n",
|
||||
" primary_metric='normalized_root_mean_squared_error',\n",
|
||||
@@ -382,7 +379,8 @@
|
||||
" compute_target=compute_target,\n",
|
||||
" max_concurrent_iterations=4,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" **automl_settings)"
|
||||
" enable_dnn=True,\n",
|
||||
" forecasting_parameters=forecasting_parameters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -392,7 +390,7 @@
|
||||
"hidePrompt": false
|
||||
},
|
||||
"source": [
|
||||
"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required."
|
||||
"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -583,7 +581,7 @@
|
||||
"source": [
|
||||
"from helper import run_inference\n",
|
||||
"\n",
|
||||
"test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset, valid_dataset, max_horizon,\n",
|
||||
"test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset, valid_dataset, forecast_horizon,\n",
|
||||
" target_column_name, time_column_name, freq)"
|
||||
]
|
||||
},
|
||||
@@ -605,7 +603,7 @@
|
||||
"from helper import run_multiple_inferences\n",
|
||||
"\n",
|
||||
"summary_df = run_multiple_inferences(summary_df, experiment, test_experiment, compute_target, script_folder, test_dataset, \n",
|
||||
" valid_dataset, max_horizon, target_column_name, time_column_name, freq)"
|
||||
" valid_dataset, forecast_horizon, target_column_name, time_column_name, freq)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -87,7 +87,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -238,6 +238,22 @@
|
||||
"test.to_pandas_dataframe().head(5).reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Forecasting Parameters\n",
|
||||
"To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
|
||||
"|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
|
||||
"|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
|
||||
"|**drop_column_names**|Name(s) of columns to drop prior to modeling|"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -250,20 +266,16 @@
|
||||
"|-|-|\n",
|
||||
"|**task**|forecasting|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
|
||||
"|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
|
||||
"|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
|
||||
"|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
"|**compute_target**|The remote compute for training.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
|
||||
"|**time_column_name**|Name of the datetime column in the input data|\n",
|
||||
"|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
|
||||
"|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
|
||||
"|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
|
||||
"|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
|
||||
"|**forecasting_parameters**|A class that holds all the forecasting related parameters.|\n",
|
||||
"\n",
|
||||
"This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
|
||||
"This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -281,7 +293,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"max_horizon = 14"
|
||||
"forecast_horizon = 14"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -297,17 +309,18 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"time_series_settings = {\n",
|
||||
" 'time_column_name': time_column_name,\n",
|
||||
" 'max_horizon': max_horizon, \n",
|
||||
" 'country_or_region': 'US', # set country_or_region will trigger holiday featurizer\n",
|
||||
" 'target_lags': 'auto', # use heuristic based lag setting \n",
|
||||
" 'drop_column_names': ['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
|
||||
"}\n",
|
||||
"from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
|
||||
"forecasting_parameters = ForecastingParameters(\n",
|
||||
" time_column_name=time_column_name,\n",
|
||||
" forecast_horizon=forecast_horizon,\n",
|
||||
" country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
|
||||
" target_lags='auto', # use heuristic based lag setting \n",
|
||||
" drop_column_names=['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting', \n",
|
||||
" primary_metric='normalized_root_mean_squared_error',\n",
|
||||
" blacklist_models = ['ExtremeRandomTrees'], \n",
|
||||
" blocked_models = ['ExtremeRandomTrees'], \n",
|
||||
" experiment_timeout_hours=0.3,\n",
|
||||
" training_data=train,\n",
|
||||
" label_column_name=target_column_name,\n",
|
||||
@@ -317,7 +330,7 @@
|
||||
" max_concurrent_iterations=4,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" **time_series_settings)"
|
||||
" forecasting_parameters=forecasting_parameters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -422,7 +435,7 @@
|
||||
"source": [
|
||||
"We now use the best fitted model from the AutoML Run to make forecasts for the test set. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
|
||||
"\n",
|
||||
"The scoring will run on a remote compute. In this example, it will reuse the training compute.|"
|
||||
"The scoring will run on a remote compute. In this example, it will reuse the training compute."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -439,7 +452,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieving forecasts from the model\n",
|
||||
"To run the forecast on the remote compute we will use two helper scripts: forecasting_script and forecasting_helper. These scripts contain the utility methods which will be used by the remote estimator. We copy these scripts to the project folder to upload them to remote compute."
|
||||
"To run the forecast on the remote compute we will use a helper script: forecasting_script. This script contains the utility methods which will be used by the remote estimator. We copy the script to the project folder to upload it to remote compute."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -453,15 +466,14 @@
|
||||
"\n",
|
||||
"script_folder = os.path.join(os.getcwd(), 'forecast')\n",
|
||||
"os.makedirs(script_folder, exist_ok=True)\n",
|
||||
"shutil.copy('forecasting_script.py', script_folder)\n",
|
||||
"shutil.copy('forecasting_helper.py', script_folder)"
|
||||
"shutil.copy('forecasting_script.py', script_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For brevity we have created the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target."
|
||||
"For brevity, we have created a function called run_forecast that submits the test data to the best model determined during the training run and retrieves forecasts. The test set is longer than the forecast horizon specified at train time, so the forecasting script uses a so-called rolling evaluation to generate predictions over the whole test set. A rolling evaluation iterates the forecaster over the test set, using the actuals in the test set to make lag features as needed. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -472,8 +484,7 @@
|
||||
"source": [
|
||||
"from run_forecast import run_rolling_forecast\n",
|
||||
"\n",
|
||||
"remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, max_horizon,\n",
|
||||
" target_column_name, time_column_name)\n",
|
||||
"remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, target_column_name)\n",
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
@@ -537,7 +548,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The MAPE seems high; it is being skewed by an actual with a small absolute value. For a more informative evaluation, we can calculate the metrics by forecast horizon:"
|
||||
"Since we did a rolling evaluation on the test set, we can analyze the predictions by their forecast horizon relative to the rolling origin. The model was initially trained at a forecast horizon of 14, so each prediction from the model is associated with a horizon value from 1 to 14. The horizon values are in a column named, \"horizon_origin,\" in the prediction set. For example, we can calculate some of the error metrics grouped by the horizon:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -557,7 +568,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"It's also interesting to see the distributions of APE (absolute percentage error) by horizon. On a log scale, the outlying APE in the horizon-3 group is clear."
|
||||
"To drill down more, we can look at the distributions of APE (absolute percentage error) by horizon. From the chart, it is clear that the overall MAPE is being skewed by one particular point where the actual value is of small absolute value."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -567,7 +578,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
|
||||
"APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
|
||||
"APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, forecast_horizon + 1)]\n",
|
||||
"\n",
|
||||
"%matplotlib inline\n",
|
||||
"plt.boxplot(APEs)\n",
|
||||
@@ -631,5 +642,5 @@
|
||||
"version": 3
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,99 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pandas.tseries.frequencies import to_offset
|
||||
|
||||
|
||||
def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
|
||||
predicted_column_name='predicted',
|
||||
horizon_colname='horizon_origin'):
|
||||
"""
|
||||
Demonstrates how to get the output aligned to the inputs
|
||||
using pandas indexes. Helps understand what happened if
|
||||
the output's shape differs from the input shape, or if
|
||||
the data got re-sorted by time and grain during forecasting.
|
||||
|
||||
Typical causes of misalignment are:
|
||||
* we predicted some periods that were missing in actuals -> drop from eval
|
||||
* model was asked to predict past max_horizon -> increase max horizon
|
||||
* data at start of X_test was needed for lags -> provide previous periods
|
||||
"""
|
||||
|
||||
if (horizon_colname in X_trans):
|
||||
df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
|
||||
horizon_colname: X_trans[horizon_colname]})
|
||||
else:
|
||||
df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
|
||||
|
||||
# y and X outputs are aligned by forecast() function contract
|
||||
df_fcst.index = X_trans.index
|
||||
|
||||
# align original X_test to y_test
|
||||
X_test_full = X_test.copy()
|
||||
X_test_full[target_column_name] = y_test
|
||||
|
||||
# X_test_full's index does not include origin, so reset for merge
|
||||
df_fcst.reset_index(inplace=True)
|
||||
X_test_full = X_test_full.reset_index().drop(columns='index')
|
||||
together = df_fcst.merge(X_test_full, how='right')
|
||||
|
||||
# drop rows where prediction or actuals are nan
|
||||
# happens because of missing actuals
|
||||
# or at edges of time due to lags/rolling windows
|
||||
clean = together[together[[target_column_name,
|
||||
predicted_column_name]].notnull().all(axis=1)]
|
||||
return(clean)
|
||||
|
||||
|
||||
def do_rolling_forecast(fitted_model, X_test, y_test, target_column_name,
|
||||
time_column_name, max_horizon, freq='D'):
|
||||
"""
|
||||
Produce forecasts on a rolling origin over the given test set.
|
||||
|
||||
Each iteration makes a forecast for the next 'max_horizon' periods
|
||||
with respect to the current origin, then advances the origin by the
|
||||
horizon time duration. The prediction context for each forecast is set so
|
||||
that the forecaster uses the actual target values prior to the current
|
||||
origin time for constructing lag features.
|
||||
|
||||
This function returns a concatenated DataFrame of rolling forecasts.
|
||||
"""
|
||||
df_list = []
|
||||
origin_time = X_test[time_column_name].min()
|
||||
while origin_time <= X_test[time_column_name].max():
|
||||
# Set the horizon time - end date of the forecast
|
||||
horizon_time = origin_time + max_horizon * to_offset(freq)
|
||||
|
||||
# Extract test data from an expanding window up-to the horizon
|
||||
expand_wind = (X_test[time_column_name] < horizon_time)
|
||||
X_test_expand = X_test[expand_wind]
|
||||
y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
|
||||
y_query_expand.fill(np.NaN)
|
||||
|
||||
if origin_time != X_test[time_column_name].min():
|
||||
# Set the context by including actuals up-to the origin time
|
||||
test_context_expand_wind = (X_test[time_column_name] < origin_time)
|
||||
context_expand_wind = (
|
||||
X_test_expand[time_column_name] < origin_time)
|
||||
y_query_expand[context_expand_wind] = y_test[
|
||||
test_context_expand_wind]
|
||||
|
||||
# Make a forecast out to the maximum horizon
|
||||
y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)
|
||||
|
||||
# Align forecast with test set for dates within the
|
||||
# current rolling window
|
||||
trans_tindex = X_trans.index.get_level_values(time_column_name)
|
||||
trans_roll_wind = (trans_tindex >= origin_time) & (
|
||||
trans_tindex < horizon_time)
|
||||
test_roll_wind = expand_wind & (
|
||||
X_test[time_column_name] >= origin_time)
|
||||
df_list.append(align_outputs(y_fcst[trans_roll_wind],
|
||||
X_trans[trans_roll_wind],
|
||||
X_test[test_roll_wind],
|
||||
y_test[test_roll_wind],
|
||||
target_column_name))
|
||||
|
||||
# Advance the origin time
|
||||
origin_time = horizon_time
|
||||
|
||||
return pd.concat(df_list, ignore_index=True)
|
||||
@@ -1,37 +1,21 @@
|
||||
import argparse
|
||||
import azureml.train.automl
|
||||
from azureml.automl.runtime.shared import forecasting_models
|
||||
from azureml.core import Run
|
||||
from sklearn.externals import joblib
|
||||
import forecasting_helper
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--max_horizon', type=int, dest='max_horizon',
|
||||
default=10, help='Max Horizon for forecasting')
|
||||
parser.add_argument(
|
||||
'--target_column_name', type=str, dest='target_column_name',
|
||||
help='Target Column Name')
|
||||
parser.add_argument(
|
||||
'--time_column_name', type=str, dest='time_column_name',
|
||||
help='Time Column Name')
|
||||
parser.add_argument(
|
||||
'--frequency', type=str, dest='freq',
|
||||
help='Frequency of prediction')
|
||||
|
||||
args = parser.parse_args()
|
||||
max_horizon = args.max_horizon
|
||||
target_column_name = args.target_column_name
|
||||
time_column_name = args.time_column_name
|
||||
freq = args.freq
|
||||
|
||||
run = Run.get_context()
|
||||
# get input dataset by name
|
||||
test_dataset = run.input_datasets['test_data']
|
||||
|
||||
grain_column_names = []
|
||||
|
||||
df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
|
||||
|
||||
X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
|
||||
@@ -39,14 +23,12 @@ y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[targ
|
||||
|
||||
fitted_model = joblib.load('model.pkl')
|
||||
|
||||
df_all = forecasting_helper.do_rolling_forecast(
|
||||
fitted_model,
|
||||
X_test_df,
|
||||
y_test_df.values.T[0],
|
||||
target_column_name,
|
||||
time_column_name,
|
||||
max_horizon,
|
||||
freq)
|
||||
y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
|
||||
|
||||
# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
|
||||
assign_dict = {'horizon_origin': X_trans['horizon_origin'].values, 'predicted': y_pred,
|
||||
target_column_name: y_test_df[target_column_name].values}
|
||||
df_all = X_test_df.assign(**assign_dict)
|
||||
|
||||
file_name = 'outputs/predictions.csv'
|
||||
export_csv = df_all.to_csv(file_name, header=True)
|
||||
|
||||
@@ -5,8 +5,7 @@ from azureml.core.run import Run
|
||||
|
||||
|
||||
def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
|
||||
max_horizon, target_column_name, time_column_name,
|
||||
freq='D', inference_folder='./forecast'):
|
||||
target_column_name, inference_folder='./forecast'):
|
||||
condafile = inference_folder + '/condafile.yml'
|
||||
train_run.download_file('outputs/model.pkl',
|
||||
inference_folder + '/model.pkl')
|
||||
@@ -20,10 +19,7 @@ def run_rolling_forecast(test_experiment, compute_target, train_run, test_datase
|
||||
est = Estimator(source_directory=inference_folder,
|
||||
entry_script='forecasting_script.py',
|
||||
script_params={
|
||||
'--max_horizon': max_horizon,
|
||||
'--target_column_name': target_column_name,
|
||||
'--time_column_name': time_column_name,
|
||||
'--frequency': freq
|
||||
'--target_column_name': target_column_name
|
||||
},
|
||||
inputs=[test_dataset.as_named_input('test_data')],
|
||||
compute_target=compute_target,
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -288,7 +288,20 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"max_horizon = 48"
|
||||
"forecast_horizon = 48"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Forecasting Parameters\n",
|
||||
"To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -297,28 +310,27 @@
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the name of the time column and the maximum forecast horizon.\n",
|
||||
"Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**task**|forecasting|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
|
||||
"|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
|
||||
"|**experiment_timeout_hours**|Maximum amount of time in hours that the experiment take before it terminates.|\n",
|
||||
"|**training_data**|The training data to be used within the experiment.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
"|**compute_target**|The remote compute for training.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
|
||||
"|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**max_horizon**|The number of periods out you would like to predict past your training data. Periods are inferred from your data.|\n"
|
||||
"|**forecasting_parameters**|A class holds all the forecasting related parameters.|\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
|
||||
"This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -327,14 +339,14 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_settings = {\n",
|
||||
" 'time_column_name': time_column_name,\n",
|
||||
" 'max_horizon': max_horizon,\n",
|
||||
"}\n",
|
||||
"from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
|
||||
"forecasting_parameters = ForecastingParameters(\n",
|
||||
" time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting', \n",
|
||||
" primary_metric='normalized_root_mean_squared_error',\n",
|
||||
" blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'], \n",
|
||||
" blocked_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'], \n",
|
||||
" experiment_timeout_hours=0.3,\n",
|
||||
" training_data=train,\n",
|
||||
" label_column_name=target_column_name,\n",
|
||||
@@ -342,7 +354,7 @@
|
||||
" enable_early_stopping=True,\n",
|
||||
" n_cross_validations=3, \n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" **automl_settings)"
|
||||
" forecasting_parameters=forecasting_parameters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -550,7 +562,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Advanced Training <a id=\"advanced_training\"></a>\n",
|
||||
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
|
||||
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -558,9 +570,9 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Using lags and rolling window features\n",
|
||||
"Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
|
||||
"Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `forecast_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
|
||||
"\n",
|
||||
"This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
|
||||
"This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -569,16 +581,14 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_advanced_settings = {\n",
|
||||
" 'time_column_name': time_column_name,\n",
|
||||
" 'max_horizon': max_horizon,\n",
|
||||
" 'target_lags': 12,\n",
|
||||
" 'target_rolling_window_size': 4,\n",
|
||||
"}\n",
|
||||
"advanced_forecasting_parameters = ForecastingParameters(\n",
|
||||
" time_column_name=time_column_name, forecast_horizon=forecast_horizon,\n",
|
||||
" target_lags=12, target_rolling_window_size=4\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting', \n",
|
||||
" primary_metric='normalized_root_mean_squared_error',\n",
|
||||
" blacklist_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blacklisted for tutorial purposes, remove this for real use cases. \n",
|
||||
" blocked_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blocked for tutorial purposes, remove this for real use cases. \n",
|
||||
" experiment_timeout_hours=0.3,\n",
|
||||
" training_data=train,\n",
|
||||
" label_column_name=target_column_name,\n",
|
||||
@@ -586,7 +596,7 @@
|
||||
" enable_early_stopping = True,\n",
|
||||
" n_cross_validations=3, \n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" **automl_advanced_settings)"
|
||||
" forecasting_parameters=advanced_forecasting_parameters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -635,7 +645,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Advanced Results<a id=\"advanced_results\"></a>\n",
|
||||
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
|
||||
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -94,7 +94,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -142,15 +142,15 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"TIME_COLUMN_NAME = 'date'\n",
|
||||
"GRAIN_COLUMN_NAME = 'grain'\n",
|
||||
"TIME_SERIES_ID_COLUMN_NAME = 'time_series_id'\n",
|
||||
"TARGET_COLUMN_NAME = 'y'\n",
|
||||
"\n",
|
||||
"def get_timeseries(train_len: int,\n",
|
||||
" test_len: int,\n",
|
||||
" time_column_name: str,\n",
|
||||
" target_column_name: str,\n",
|
||||
" grain_column_name: str,\n",
|
||||
" grains: int = 1,\n",
|
||||
" time_series_id_column_name: str,\n",
|
||||
" time_series_number: int = 1,\n",
|
||||
" freq: str = 'H'):\n",
|
||||
" \"\"\"\n",
|
||||
" Return the time series of designed length.\n",
|
||||
@@ -161,9 +161,8 @@
|
||||
" :type test_len: int\n",
|
||||
" :param time_column_name: The desired name of a time column.\n",
|
||||
" :type time_column_name: str\n",
|
||||
" :param\n",
|
||||
" :param grains: The number of grains.\n",
|
||||
" :type grains: int\n",
|
||||
" :param time_series_number: The number of time series in the data set.\n",
|
||||
" :type time_series_number: int\n",
|
||||
" :param freq: The frequency string representing pandas offset.\n",
|
||||
" see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
|
||||
" :type freq: str\n",
|
||||
@@ -174,14 +173,14 @@
|
||||
" data_train = [] # type: List[pd.DataFrame]\n",
|
||||
" data_test = [] # type: List[pd.DataFrame]\n",
|
||||
" data_length = train_len + test_len\n",
|
||||
" for i in range(grains):\n",
|
||||
" for i in range(time_series_number):\n",
|
||||
" X = pd.DataFrame({\n",
|
||||
" time_column_name: pd.date_range(start='2000-01-01',\n",
|
||||
" periods=data_length,\n",
|
||||
" freq=freq),\n",
|
||||
" target_column_name: np.arange(data_length).astype(float) + np.random.rand(data_length) + i*5,\n",
|
||||
" 'ext_predictor': np.asarray(range(42, 42 + data_length)),\n",
|
||||
" grain_column_name: np.repeat('g{}'.format(i), data_length)\n",
|
||||
" time_series_id_column_name: np.repeat('ts{}'.format(i), data_length)\n",
|
||||
" })\n",
|
||||
" data_train.append(X[:train_len])\n",
|
||||
" data_test.append(X[train_len:])\n",
|
||||
@@ -197,8 +196,8 @@
|
||||
" test_len=n_test_periods,\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" target_column_name=TARGET_COLUMN_NAME,\n",
|
||||
" grain_column_name=GRAIN_COLUMN_NAME,\n",
|
||||
" grains=2)"
|
||||
" time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
|
||||
" time_series_number=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -228,7 +227,7 @@
|
||||
"whole_data = X_train.copy()\n",
|
||||
"target_label = 'y'\n",
|
||||
"whole_data[target_label] = y_train\n",
|
||||
"for g in whole_data.groupby('grain'): \n",
|
||||
"for g in whole_data.groupby('time_series_id'): \n",
|
||||
" plt.plot(g[1]['date'].values, g[1]['y'].values, label=g[0])\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
@@ -297,7 +296,7 @@
|
||||
"source": [
|
||||
"## Create the configuration and train a forecaster <a id=\"train\"></a>\n",
|
||||
"First generate the configuration, in which we:\n",
|
||||
"* Set metadata columns: target, time column and grain column names.\n",
|
||||
"* Set metadata columns: target, time column and time-series id column names.\n",
|
||||
"* Validate our data using cross validation with rolling window method.\n",
|
||||
"* Set normalized root mean squared error as a metric to select the best model.\n",
|
||||
"* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
|
||||
@@ -312,21 +311,22 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
|
||||
"lags = [1,2,3]\n",
|
||||
"max_horizon = n_test_periods\n",
|
||||
"time_series_settings = { \n",
|
||||
" 'time_column_name': TIME_COLUMN_NAME,\n",
|
||||
" 'grain_column_names': [ GRAIN_COLUMN_NAME ],\n",
|
||||
" 'max_horizon': max_horizon,\n",
|
||||
" 'target_lags': lags\n",
|
||||
"}"
|
||||
"forecast_horizon = n_test_periods\n",
|
||||
"forecasting_parameters = ForecastingParameters(\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" forecast_horizon=forecast_horizon,\n",
|
||||
" time_series_id_column_names=[ TIME_SERIES_ID_COLUMN_NAME ],\n",
|
||||
" target_lags=lags\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the model selection and training process."
|
||||
"Run the model selection and training process. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -352,7 +352,7 @@
|
||||
" max_concurrent_iterations=4,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" label_column_name=target_label,\n",
|
||||
" **time_series_settings)\n",
|
||||
" forecasting_parameters=forecasting_parameters)\n",
|
||||
"\n",
|
||||
"remote_run = experiment.submit(automl_config, show_output=False)"
|
||||
]
|
||||
@@ -482,7 +482,7 @@
|
||||
"# use forecast_quantiles function, not the forecast() one\n",
|
||||
"y_pred_quantiles = fitted_model.forecast_quantiles(X_test)\n",
|
||||
"\n",
|
||||
"# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
|
||||
"# quantile forecasts returned in a Dataframe along with the time and time series id columns \n",
|
||||
"y_pred_quantiles"
|
||||
]
|
||||
},
|
||||
@@ -492,7 +492,7 @@
|
||||
"source": [
|
||||
"#### Destination-date forecast: \"just do something\"\n",
|
||||
"\n",
|
||||
"In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
|
||||
"In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the forecast horizon from training."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -519,7 +519,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per grain, so each grain can have a different forecast origin. \n",
|
||||
"The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per time-series, so each time-series can have a different forecast origin. \n",
|
||||
"\n",
|
||||
"The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)."
|
||||
]
|
||||
@@ -536,13 +536,13 @@
|
||||
" test_len=4,\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" target_column_name=TARGET_COLUMN_NAME,\n",
|
||||
" grain_column_name=GRAIN_COLUMN_NAME,\n",
|
||||
" grains=2)\n",
|
||||
" time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
|
||||
" time_series_number=2)\n",
|
||||
"\n",
|
||||
"# end of the data we trained on\n",
|
||||
"print(X_train.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
|
||||
"print(X_train.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
|
||||
"# start of the data we want to predict on\n",
|
||||
"print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
|
||||
"print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -569,7 +569,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
|
||||
"How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the forecast horizon. We need to provide a define `y` value to establish the forecast origin.\n",
|
||||
"\n",
|
||||
"We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
|
||||
]
|
||||
@@ -584,7 +584,7 @@
|
||||
"\n",
|
||||
" \"\"\"\n",
|
||||
" This function will take the full dataset, and create the query\n",
|
||||
" to predict all values of the grain from the `forecast_origin`\n",
|
||||
" to predict all values of the time series from the `forecast_origin`\n",
|
||||
" forward for the next `horizon` horizons. Context from previous\n",
|
||||
" `lookback` periods will be included.\n",
|
||||
"\n",
|
||||
@@ -654,8 +654,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(X_context.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"print(X_context.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"X_context.tail(5)"
|
||||
]
|
||||
},
|
||||
@@ -685,7 +685,7 @@
|
||||
"n_lookback_periods = max(lags)\n",
|
||||
"lookback = pd.DateOffset(hours=n_lookback_periods)\n",
|
||||
"\n",
|
||||
"horizon = pd.DateOffset(hours=max_horizon)\n",
|
||||
"horizon = pd.DateOffset(hours=forecast_horizon)\n",
|
||||
"\n",
|
||||
"# now make the forecast query from context (refer to figure)\n",
|
||||
"X_pred, y_pred = make_forecasting_query(fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME,\n",
|
||||
@@ -701,7 +701,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note that the forecast origin is at 17:00 for both grains, and periods from 18:00 are to be forecast."
|
||||
"Note that the forecast origin is at 17:00 for both time-series, and periods from 18:00 are to be forecast."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -716,7 +716,7 @@
|
||||
"# show the forecast aligned\n",
|
||||
"X_show = xy_away.reset_index()\n",
|
||||
"# without the generated features\n",
|
||||
"X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
|
||||
"X_show[['date', 'time_series_id', 'ext_predictor', '_automl_target_col']]\n",
|
||||
"# prediction is in _automl_target_col"
|
||||
]
|
||||
},
|
||||
@@ -724,14 +724,14 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Forecasting farther than the maximum horizon <a id=\"recursive forecasting\"></a>\n",
|
||||
"When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified maximum horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
|
||||
"## Forecasting farther than the forecast horizon <a id=\"recursive forecasting\"></a>\n",
|
||||
"When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified forecast horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
|
||||
"\n",
|
||||
"To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the maximum horizon given at training time.\n",
|
||||
"To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the forecasting horizon given at training time.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first max-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next max-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
|
||||
"Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first forecast-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next forecast-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
|
||||
"\n",
|
||||
"A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n",
|
||||
"\n",
|
||||
@@ -745,16 +745,17 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# generate the same kind of test data we trained on, but with a single grain/time-series and test period twice as long as the max_horizon\n",
|
||||
"# generate the same kind of test data we trained on, but with a single time-series and test period twice as long\n",
|
||||
"# as the forecast_horizon.\n",
|
||||
"_, _, X_test_long, y_test_long = get_timeseries(train_len=n_train_periods,\n",
|
||||
" test_len=max_horizon*2,\n",
|
||||
" test_len=forecast_horizon*2,\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" target_column_name=TARGET_COLUMN_NAME,\n",
|
||||
" grain_column_name=GRAIN_COLUMN_NAME,\n",
|
||||
" grains=1)\n",
|
||||
" time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
|
||||
" time_series_number=1)\n",
|
||||
"\n",
|
||||
"print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
|
||||
"print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
|
||||
"print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
|
||||
"print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -775,8 +776,8 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following. \n",
|
||||
"y_pred1, _ = fitted_model.forecast(X_test_long[:max_horizon])\n",
|
||||
"y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(max_horizon, np.nan))))\n",
|
||||
"y_pred1, _ = fitted_model.forecast(X_test_long[:forecast_horizon])\n",
|
||||
"y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(forecast_horizon, np.nan))))\n",
|
||||
"np.array_equal(y_pred_all, y_pred_long)"
|
||||
]
|
||||
},
|
||||
@@ -785,7 +786,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Confidence interval and distributional forecasts\n",
|
||||
"AutoML cannot currently estimate forecast errors beyond the maximum horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the maximum horizon. "
|
||||
"AutoML cannot currently estimate forecast errors beyond the forecast horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the forecast horizon. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -801,7 +802,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Similarly with the simple senarios illustrated above, forecasting farther than the max horizon in other senarios like 'multiple grain', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
|
||||
"Similarly with the simple senarios illustrated above, forecasting farther than the forecast horizon in other senarios like 'multiple time-series', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
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@@ -82,7 +82,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -178,7 +178,7 @@
|
||||
"source": [
|
||||
"Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred. \n",
|
||||
"\n",
|
||||
"The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
|
||||
"The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -187,8 +187,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"grain_column_names = ['Store', 'Brand']\n",
|
||||
"nseries = data.groupby(grain_column_names).ngroups\n",
|
||||
"time_series_id_column_names = ['Store', 'Brand']\n",
|
||||
"nseries = data.groupby(time_series_id_column_names).ngroups\n",
|
||||
"print('Data contains {0} individual time-series.'.format(nseries))"
|
||||
]
|
||||
},
|
||||
@@ -207,7 +207,7 @@
|
||||
"source": [
|
||||
"use_stores = [2, 5, 8]\n",
|
||||
"data_subset = data[data.Store.isin(use_stores)]\n",
|
||||
"nseries = data_subset.groupby(grain_column_names).ngroups\n",
|
||||
"nseries = data_subset.groupby(time_series_id_column_names).ngroups\n",
|
||||
"print('Data subset contains {0} individual time-series.'.format(nseries))"
|
||||
]
|
||||
},
|
||||
@@ -216,7 +216,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Data Splitting\n",
|
||||
"We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the grain columns."
|
||||
"We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -227,15 +227,15 @@
|
||||
"source": [
|
||||
"n_test_periods = 20\n",
|
||||
"\n",
|
||||
"def split_last_n_by_grain(df, n):\n",
|
||||
" \"\"\"Group df by grain and split on last n rows for each group.\"\"\"\n",
|
||||
"def split_last_n_by_series_id(df, n):\n",
|
||||
" \"\"\"Group df by series identifiers and split on last n rows for each group.\"\"\"\n",
|
||||
" df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
|
||||
" .groupby(grain_column_names, group_keys=False))\n",
|
||||
" .groupby(time_series_id_column_names, group_keys=False))\n",
|
||||
" df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
|
||||
" df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
|
||||
" return df_head, df_tail\n",
|
||||
"\n",
|
||||
"train, test = split_last_n_by_grain(data_subset, n_test_periods)"
|
||||
"train, test = split_last_n_by_series_id(data_subset, n_test_periods)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -301,11 +301,11 @@
|
||||
"For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
|
||||
"* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
|
||||
"* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
|
||||
"* Create grain-based features to enable fixed effects across different series\n",
|
||||
"* Create features based on time series identifiers to enable fixed effects across different series\n",
|
||||
"* Create time-based features to assist in learning seasonal patterns\n",
|
||||
"* Encode categorical variables to numeric quantities\n",
|
||||
"\n",
|
||||
"In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please check out the forecasting grouping notebook. \n",
|
||||
"In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n",
|
||||
"\n",
|
||||
"You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: "
|
||||
]
|
||||
@@ -353,6 +353,21 @@
|
||||
"featurization_config.add_transformer_params('Imputer', ['INCOME'], {\"strategy\": \"median\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Forecasting Parameters\n",
|
||||
"To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
|
||||
"|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -361,9 +376,9 @@
|
||||
"\n",
|
||||
"The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters.\n",
|
||||
"\n",
|
||||
"For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If grain columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
|
||||
"For forecasting tasks, there are some additional parameters that can be set in the `ForecastingParameters` class: the name of the column holding the date/time, the timeseries id column names, and the maximum forecast horizon. A time column is required for forecasting, while the time_series_id is optional. If time_series_id columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
|
||||
"\n",
|
||||
"The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning application that estimates the next month of sales should set the horizon according to suitable planning time-scales. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
|
||||
"The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the forecast horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning application that estimates the next month of sales should set the horizon according to suitable planning time-scales. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
|
||||
"\n",
|
||||
"We note here that AutoML can sweep over two types of time-series models:\n",
|
||||
"* Models that are trained for each series such as ARIMA and Facebook's Prophet. Note that these models are only available for [Enterprise Edition Workspaces](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace#upgrade).\n",
|
||||
@@ -389,11 +404,8 @@
|
||||
"|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models|\n",
|
||||
"|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models|\n",
|
||||
"|**debug_log**|Log file path for writing debugging information|\n",
|
||||
"|**time_column_name**|Name of the datetime column in the input data|\n",
|
||||
"|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
|
||||
"|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
|
||||
"|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
|
||||
"|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used.|"
|
||||
"|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -402,11 +414,12 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"time_series_settings = {\n",
|
||||
" 'time_column_name': time_column_name,\n",
|
||||
" 'grain_column_names': grain_column_names,\n",
|
||||
" 'max_horizon': n_test_periods\n",
|
||||
"}\n",
|
||||
"from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
|
||||
"forecasting_parameters = ForecastingParameters(\n",
|
||||
" time_column_name=time_column_name,\n",
|
||||
" forecast_horizon=n_test_periods,\n",
|
||||
" time_series_id_column_names=time_series_id_column_names\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting',\n",
|
||||
" debug_log='automl_oj_sales_errors.log',\n",
|
||||
@@ -420,7 +433,7 @@
|
||||
" n_cross_validations=3,\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" **time_series_settings)"
|
||||
" forecasting_parameters=forecasting_parameters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -428,7 +441,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can now submit a new training run. Depending on the data and number of iterations this operation may take several minutes.\n",
|
||||
"Information from each iteration will be printed to the console."
|
||||
"Information from each iteration will be printed to the console. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -537,9 +550,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The featurized data, aligned to y, will also be returned.\n",
|
||||
"# forecast returns the predictions and the featurized data, aligned to X_test.\n",
|
||||
"# This contains the assumptions that were made in the forecast\n",
|
||||
"# and helps align the forecast to the original data\n",
|
||||
"y_predictions, X_trans = fitted_model.forecast(X_test)"
|
||||
]
|
||||
},
|
||||
@@ -560,7 +572,7 @@
|
||||
"\n",
|
||||
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
|
||||
"\n",
|
||||
"It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
|
||||
"We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -569,9 +581,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from forecasting_helper import align_outputs\n",
|
||||
"\n",
|
||||
"df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
|
||||
"assign_dict = {'predicted': y_predictions, target_column_name: y_test}\n",
|
||||
"df_all = X_test.assign(**assign_dict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -794,5 +805,5 @@
|
||||
"task": "Forecasting"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,98 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pandas.tseries.frequencies import to_offset
|
||||
|
||||
|
||||
def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
|
||||
predicted_column_name='predicted',
|
||||
horizon_colname='horizon_origin'):
|
||||
"""
|
||||
Demonstrates how to get the output aligned to the inputs
|
||||
using pandas indexes. Helps understand what happened if
|
||||
the output's shape differs from the input shape, or if
|
||||
the data got re-sorted by time and grain during forecasting.
|
||||
|
||||
Typical causes of misalignment are:
|
||||
* we predicted some periods that were missing in actuals -> drop from eval
|
||||
* model was asked to predict past max_horizon -> increase max horizon
|
||||
* data at start of X_test was needed for lags -> provide previous periods
|
||||
"""
|
||||
|
||||
if (horizon_colname in X_trans):
|
||||
df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
|
||||
horizon_colname: X_trans[horizon_colname]})
|
||||
else:
|
||||
df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
|
||||
|
||||
# y and X outputs are aligned by forecast() function contract
|
||||
df_fcst.index = X_trans.index
|
||||
|
||||
# align original X_test to y_test
|
||||
X_test_full = X_test.copy()
|
||||
X_test_full[target_column_name] = y_test
|
||||
|
||||
# X_test_full's index does not include origin, so reset for merge
|
||||
df_fcst.reset_index(inplace=True)
|
||||
X_test_full = X_test_full.reset_index().drop(columns='index')
|
||||
together = df_fcst.merge(X_test_full, how='right')
|
||||
|
||||
# drop rows where prediction or actuals are nan
|
||||
# happens because of missing actuals
|
||||
# or at edges of time due to lags/rolling windows
|
||||
clean = together[together[[target_column_name,
|
||||
predicted_column_name]].notnull().all(axis=1)]
|
||||
return(clean)
|
||||
|
||||
|
||||
def do_rolling_forecast(fitted_model, X_test, y_test, target_column_name, time_column_name, max_horizon, freq='D'):
|
||||
"""
|
||||
Produce forecasts on a rolling origin over the given test set.
|
||||
|
||||
Each iteration makes a forecast for the next 'max_horizon' periods
|
||||
with respect to the current origin, then advances the origin by the
|
||||
horizon time duration. The prediction context for each forecast is set so
|
||||
that the forecaster uses the actual target values prior to the current
|
||||
origin time for constructing lag features.
|
||||
|
||||
This function returns a concatenated DataFrame of rolling forecasts.
|
||||
"""
|
||||
df_list = []
|
||||
origin_time = X_test[time_column_name].min()
|
||||
while origin_time <= X_test[time_column_name].max():
|
||||
# Set the horizon time - end date of the forecast
|
||||
horizon_time = origin_time + max_horizon * to_offset(freq)
|
||||
|
||||
# Extract test data from an expanding window up-to the horizon
|
||||
expand_wind = (X_test[time_column_name] < horizon_time)
|
||||
X_test_expand = X_test[expand_wind]
|
||||
y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
|
||||
y_query_expand.fill(np.NaN)
|
||||
|
||||
if origin_time != X_test[time_column_name].min():
|
||||
# Set the context by including actuals up-to the origin time
|
||||
test_context_expand_wind = (X_test[time_column_name] < origin_time)
|
||||
context_expand_wind = (
|
||||
X_test_expand[time_column_name] < origin_time)
|
||||
y_query_expand[context_expand_wind] = y_test[
|
||||
test_context_expand_wind]
|
||||
|
||||
# Make a forecast out to the maximum horizon
|
||||
y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)
|
||||
|
||||
# Align forecast with test set for dates within the
|
||||
# current rolling window
|
||||
trans_tindex = X_trans.index.get_level_values(time_column_name)
|
||||
trans_roll_wind = (trans_tindex >= origin_time) & (
|
||||
trans_tindex < horizon_time)
|
||||
test_roll_wind = expand_wind & (
|
||||
X_test[time_column_name] >= origin_time)
|
||||
df_list.append(align_outputs(y_fcst[trans_roll_wind],
|
||||
X_trans[trans_roll_wind],
|
||||
X_test[test_roll_wind],
|
||||
y_test[test_roll_wind],
|
||||
target_column_name))
|
||||
|
||||
# Advance the origin time
|
||||
origin_time = horizon_time
|
||||
|
||||
return pd.concat(df_list, ignore_index=True)
|
||||
@@ -1,22 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
|
||||
def APE(actual, pred):
|
||||
"""
|
||||
Calculate absolute percentage error.
|
||||
Returns a vector of APE values with same length as actual/pred.
|
||||
"""
|
||||
return 100 * np.abs((actual - pred) / actual)
|
||||
|
||||
|
||||
def MAPE(actual, pred):
|
||||
"""
|
||||
Calculate mean absolute percentage error.
|
||||
Remove NA and values where actual is close to zero
|
||||
"""
|
||||
not_na = ~(np.isnan(actual) | np.isnan(pred))
|
||||
not_zero = ~np.isclose(actual, 0.0)
|
||||
actual_safe = actual[not_na & not_zero]
|
||||
pred_safe = pred[not_na & not_zero]
|
||||
return np.mean(APE(actual_safe, pred_safe))
|
||||
@@ -80,7 +80,7 @@
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.train.automl import AutoMLConfig\n",
|
||||
"from azureml.explain.model._internal.explanation_client import ExplanationClient"
|
||||
"from azureml.interpret._internal.explanation_client import ExplanationClient"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -96,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -354,7 +354,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explanation\n",
|
||||
"In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. We will also show how to run the automl model and the explainer model through deploying an AKS web service.\n",
|
||||
"In this section, we will show how to compute model explanations and visualize the explanations using azureml-interpret package. We will also show how to run the automl model and the explainer model through deploying an AKS web service.\n",
|
||||
"\n",
|
||||
"Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance based on your test data.\n",
|
||||
"\n",
|
||||
@@ -434,7 +434,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Initialize the Mimic Explainer for feature importance\n",
|
||||
"For explaining the AutoML models, use the MimicWrapper from azureml.explain.model package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
|
||||
"For explaining the AutoML models, use the MimicWrapper from azureml-interpret package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -443,7 +443,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
|
||||
"from interpret.ext.glassbox import LGBMExplainableModel\n",
|
||||
"from azureml.interpret.mimic_wrapper import MimicWrapper\n",
|
||||
"explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator,\n",
|
||||
" explainable_model=automl_explainer_setup_obj.surrogate_model, \n",
|
||||
" init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
|
||||
@@ -486,7 +487,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer\n",
|
||||
"from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer\n",
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"# Initialize the ScoringExplainer\n",
|
||||
@@ -507,7 +508,7 @@
|
||||
"source": [
|
||||
"### Deploying the scoring and explainer models to a web service to Azure Kubernetes Service (AKS)\n",
|
||||
"\n",
|
||||
"We use the TreeScoringExplainer from azureml.explain.model package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
|
||||
"We use the TreeScoringExplainer from azureml.interpret package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -529,7 +530,7 @@
|
||||
"source": [
|
||||
"#### Create the conda dependencies for setting up the service\n",
|
||||
"\n",
|
||||
"We need to create the conda dependencies comprising of the azureml-explain-model, azureml-train-automl and azureml-defaults packages."
|
||||
"We need to download the conda dependencies using the automl_run object."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -566,7 +567,7 @@
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import azureml.train.automl\n",
|
||||
"import azureml.explain.model\n",
|
||||
"import azureml.interpret\n",
|
||||
"from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \\\n",
|
||||
" automl_setup_model_explanations\n",
|
||||
"import joblib\n",
|
||||
|
||||
@@ -98,7 +98,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -625,7 +625,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
|
||||
"from azureml.interpret._internal.explanation_client import ExplanationClient\n",
|
||||
"client = ExplanationClient.from_run(automl_run)\n",
|
||||
"engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
|
||||
"print(engineered_explanations.get_feature_importance_dict())\n",
|
||||
@@ -659,7 +659,7 @@
|
||||
"In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n",
|
||||
"\n",
|
||||
"### Register the AutoML model and the scoring explainer\n",
|
||||
"We use the *TreeScoringExplainer* from *azureml.explain.model* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n",
|
||||
"We use the *TreeScoringExplainer* from *azureml-interpret* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n",
|
||||
"In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
|
||||
]
|
||||
},
|
||||
@@ -681,7 +681,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create the conda dependencies for setting up the service\n",
|
||||
"We need to create the conda dependencies comprising of the *azureml-explain-model*, *azureml-train-automl* and *azureml-defaults* packages. "
|
||||
"We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -4,7 +4,7 @@ import pandas as pd
|
||||
import os
|
||||
import pickle
|
||||
import azureml.train.automl
|
||||
import azureml.explain.model
|
||||
import azureml.interpret
|
||||
from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
|
||||
automl_setup_model_explanations
|
||||
import joblib
|
||||
|
||||
@@ -7,12 +7,13 @@ from azureml.core.experiment import Experiment
|
||||
from azureml.core.dataset import Dataset
|
||||
from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
|
||||
automl_setup_model_explanations, automl_check_model_if_explainable
|
||||
from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
|
||||
from azureml.explain.model.mimic_wrapper import MimicWrapper
|
||||
from azureml.automl.core.shared.constants import MODEL_PATH
|
||||
from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer
|
||||
from interpret.ext.glassbox import LGBMExplainableModel
|
||||
from azureml.interpret.mimic_wrapper import MimicWrapper
|
||||
from automl.client.core.common.constants import MODEL_PATH
|
||||
from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer
|
||||
import joblib
|
||||
|
||||
|
||||
OUTPUT_DIR = './outputs/'
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
|
||||
|
||||
@@ -92,7 +92,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -233,7 +233,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -13,32 +13,45 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Automated ML on Azure Databricks\n",
|
||||
"We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
|
||||
"\n",
|
||||
"In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
"2. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"3. Configure Automated ML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using Azure Databricks.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
|
||||
"7. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
|
||||
"**install azureml-sdk with Automated ML**\n",
|
||||
"* Source: Upload Python Egg or PyPi\n",
|
||||
"* PyPi Name: `azureml-sdk[automl]`\n",
|
||||
"* Select Install Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We support installing AML SDK with Automated ML as library from GUI. When attaching a library follow <a href=\"https://docs.databricks.com/user-guide/libraries.html\" target=\"_blank\">this link</a> and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
|
||||
"# AutoML : Classification with Local Compute on Azure DataBricks\n",
|
||||
"\n",
|
||||
"**azureml-sdk with automated ml**\n",
|
||||
"* Source: Upload Python Egg or PyPi\n",
|
||||
"* PyPi Name: `azureml-sdk[automl]`\n",
|
||||
"* Select Install Library"
|
||||
"In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
"2. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"3. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using AzureDataBricks.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"Prerequisites:\n",
|
||||
"Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register Machine Learning Services Resource Provider\n",
|
||||
"Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
|
||||
"Start the Azure portal.\n",
|
||||
"Select your All services and then Subscription.\n",
|
||||
"Select the subscription that you want to use.\n",
|
||||
"Click on Resource providers\n",
|
||||
"Click the Register link next to Microsoft.MachineLearningServices"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -145,31 +158,8 @@
|
||||
" resource_group = resource_group)\n",
|
||||
"\n",
|
||||
"# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
|
||||
"ws.write_config()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a Folder to Host Sample Projects\n",
|
||||
"Finally, create a folder where all the sample projects will be hosted."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"sample_projects_folder = './sample_projects'\n",
|
||||
"\n",
|
||||
"if not os.path.isdir(sample_projects_folder):\n",
|
||||
" os.mkdir(sample_projects_folder)\n",
|
||||
" \n",
|
||||
"print('Sample projects will be created in {}.'.format(sample_projects_folder))"
|
||||
"ws.write_config()\n",
|
||||
"write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -178,7 +168,7 @@
|
||||
"source": [
|
||||
"## Create an Experiment\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -191,6 +181,7 @@
|
||||
"import os\n",
|
||||
"import random\n",
|
||||
"import time\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"from matplotlib.pyplot import imshow\n",
|
||||
@@ -212,7 +203,6 @@
|
||||
"source": [
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-classification'\n",
|
||||
"project_folder = './sample_projects/automl-local-classification'\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"\n",
|
||||
@@ -222,94 +212,11 @@
|
||||
"output['Workspace Name'] = ws.name\n",
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Project Directory'] = project_folder\n",
|
||||
"output['Experiment Name'] = experiment.name\n",
|
||||
"pd.set_option('display.max_colwidth', -1)\n",
|
||||
"pd.DataFrame(data = output, index = ['']).T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Diagnostics\n",
|
||||
"\n",
|
||||
"Opt-in diagnostics for better experience, quality, and security of future releases."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.telemetry import set_diagnostics_collection\n",
|
||||
"set_diagnostics_collection(send_diagnostics = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Registering Datastore"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
|
||||
"\n",
|
||||
"The code below registers a datastore pointing to a publicly readable blob container."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Datastore\n",
|
||||
"\n",
|
||||
"datastore_name = 'demo_training'\n",
|
||||
"container_name = 'digits' \n",
|
||||
"account_name = 'automlpublicdatasets'\n",
|
||||
"Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = datastore_name, \n",
|
||||
" container_name = container_name, \n",
|
||||
" account_name = account_name,\n",
|
||||
" overwrite = True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Below is an example on how to register a private blob container\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = 'example_datastore', \n",
|
||||
" container_name = 'example-container', \n",
|
||||
" account_name = 'storageaccount',\n",
|
||||
" account_key = 'accountkey'\n",
|
||||
")\n",
|
||||
"```\n",
|
||||
"The example below shows how to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_data_lake(\n",
|
||||
" workspace = ws,\n",
|
||||
" datastore_name = 'example_datastore',\n",
|
||||
" store_name = 'adlsstore',\n",
|
||||
" tenant_id = 'tenant-id-of-service-principal',\n",
|
||||
" client_id = 'client-id-of-service-principal',\n",
|
||||
" client_secret = 'client-secret-of-service-principal'\n",
|
||||
")\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -323,9 +230,7 @@
|
||||
"source": [
|
||||
"Automated ML takes a `TabularDataset` as input.\n",
|
||||
"\n",
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
|
||||
"\n",
|
||||
"You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -334,13 +239,12 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.data.datapath import DataPath\n",
|
||||
"\n",
|
||||
"datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
|
||||
"\n",
|
||||
"X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
|
||||
"y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
|
||||
"example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
|
||||
"dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
|
||||
"dataset.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -357,16 +261,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_train.take(5).to_pandas_dataframe()"
|
||||
"training_data = dataset.drop_columns(columns=['FBI Code'])\n",
|
||||
"label = 'Primary Type'"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -384,14 +280,11 @@
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
|
||||
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
|
||||
"|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
|
||||
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -404,15 +297,13 @@
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" primary_metric = 'AUC_weighted',\n",
|
||||
" iteration_timeout_minutes = 10,\n",
|
||||
" iterations = 3,\n",
|
||||
" preprocess = True,\n",
|
||||
" iterations = 5,\n",
|
||||
" n_cross_validations = 10,\n",
|
||||
" max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" spark_context=sc, #databricks/spark related\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
" training_data=training_data,\n",
|
||||
" label_column_name=label)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -433,26 +324,6 @@
|
||||
"local_run = experiment.submit(automl_config, show_output = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Continue experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run.continue_experiment(iterations=2,\n",
|
||||
" X=X_train, \n",
|
||||
" y=y_train,\n",
|
||||
" spark_context=sc,\n",
|
||||
" show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -475,14 +346,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"displayHTML(\"<a href={} target='_blank'>Your experiment in Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following will show the child runs and waits for the parent run to complete."
|
||||
"displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -503,6 +367,7 @@
|
||||
"metricslist = {}\n",
|
||||
"for run in children:\n",
|
||||
" properties = run.get_properties()\n",
|
||||
" #print(properties)\n",
|
||||
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)} \n",
|
||||
" metricslist[int(properties['iteration'])] = metrics\n",
|
||||
"\n",
|
||||
@@ -514,9 +379,11 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model after the above run is complete \n",
|
||||
"## Deploy\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
"### Retrieve the Best Model\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -525,71 +392,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_model = local_run.get_output()\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
|
||||
"Show the run and the model that has the smallest `log_loss` value:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lookup_metric = \"log_loss\"\n",
|
||||
"best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View the engineered names for featurized data\n",
|
||||
"Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View the featurization summary\n",
|
||||
"Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
|
||||
"- Raw feature name\n",
|
||||
"- Number of engineered features formed out of this raw feature\n",
|
||||
"- Type detected\n",
|
||||
"- If feature was dropped\n",
|
||||
"- List of feature transformations for the raw feature"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the featurization summary as a list of JSON\n",
|
||||
"featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
|
||||
"# View the featurization summary as a pandas dataframe\n",
|
||||
"pd.DataFrame.from_records(featurization_summary)"
|
||||
"best_run, fitted_model = local_run.get_output()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -607,11 +410,13 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
|
||||
"X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
|
||||
"y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
|
||||
"images = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
|
||||
"images = np.reshape(images.values, (100,8,8))"
|
||||
"dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
|
||||
"\n",
|
||||
"df_test = dataset_test.to_pandas_dataframe()\n",
|
||||
"df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
|
||||
"\n",
|
||||
"y_test = df_test[['Primary Type']]\n",
|
||||
"X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -628,35 +433,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Randomly select digits and test.\n",
|
||||
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
|
||||
" print(index)\n",
|
||||
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
|
||||
" label = y_test.values[index]\n",
|
||||
" title = \"Label value = %d Predicted value = %d \" % (label, predicted)\n",
|
||||
" fig = plt.figure(3, figsize = (5,5))\n",
|
||||
" ax1 = fig.add_axes((0,0,.8,.8))\n",
|
||||
" ax1.set_title(title)\n",
|
||||
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
|
||||
" display(fig)"
|
||||
"fitted_model.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
|
||||
"\n",
|
||||
"Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -689,10 +468,10 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"name": "auto-ml-classification-local-adb",
|
||||
"notebookId": 587284549713154
|
||||
"notebookId": 1275190406842063
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
|
||||
@@ -27,7 +27,7 @@
|
||||
"source": [
|
||||
"# AutoML : Classification with Local Compute on Azure DataBricks with deployment to ACI\n",
|
||||
"\n",
|
||||
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
@@ -164,30 +164,6 @@
|
||||
"write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a Folder to Host Sample Projects\n",
|
||||
"Finally, create a folder where all the sample projects will be hosted."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"sample_projects_folder = './sample_projects'\n",
|
||||
"\n",
|
||||
"if not os.path.isdir(sample_projects_folder):\n",
|
||||
" os.mkdir(sample_projects_folder)\n",
|
||||
" \n",
|
||||
"print('Sample projects will be created in {}.'.format(sample_projects_folder))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -229,7 +205,6 @@
|
||||
"source": [
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-classification'\n",
|
||||
"project_folder = './sample_projects/automl-local-classification'\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"\n",
|
||||
@@ -239,94 +214,11 @@
|
||||
"output['Workspace Name'] = ws.name\n",
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Project Directory'] = project_folder\n",
|
||||
"output['Experiment Name'] = experiment.name\n",
|
||||
"pd.set_option('display.max_colwidth', -1)\n",
|
||||
"pd.DataFrame(data = output, index = ['']).T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Diagnostics\n",
|
||||
"\n",
|
||||
"Opt-in diagnostics for better experience, quality, and security of future releases."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.telemetry import set_diagnostics_collection\n",
|
||||
"set_diagnostics_collection(send_diagnostics = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Registering Datastore"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
|
||||
"\n",
|
||||
"The code below registers a datastore pointing to a publicly readable blob container."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Datastore\n",
|
||||
"\n",
|
||||
"datastore_name = 'demo_training'\n",
|
||||
"container_name = 'digits' \n",
|
||||
"account_name = 'automlpublicdatasets'\n",
|
||||
"Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = datastore_name, \n",
|
||||
" container_name = container_name, \n",
|
||||
" account_name = account_name,\n",
|
||||
" overwrite = True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Below is an example on how to register a private blob container\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = 'example_datastore', \n",
|
||||
" container_name = 'example-container', \n",
|
||||
" account_name = 'storageaccount',\n",
|
||||
" account_key = 'accountkey'\n",
|
||||
")\n",
|
||||
"```\n",
|
||||
"The example below shows how to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_data_lake(\n",
|
||||
" workspace = ws,\n",
|
||||
" datastore_name = 'example_datastore',\n",
|
||||
" store_name = 'adlsstore',\n",
|
||||
" tenant_id = 'tenant-id-of-service-principal',\n",
|
||||
" client_id = 'client-id-of-service-principal',\n",
|
||||
" client_secret = 'client-secret-of-service-principal'\n",
|
||||
")\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -340,9 +232,7 @@
|
||||
"source": [
|
||||
"Automated ML takes a `TabularDataset` as input.\n",
|
||||
"\n",
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
|
||||
"\n",
|
||||
"You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -351,13 +241,12 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.data.datapath import DataPath\n",
|
||||
"\n",
|
||||
"datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
|
||||
"\n",
|
||||
"X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
|
||||
"y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
|
||||
"example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
|
||||
"dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
|
||||
"dataset.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -374,16 +263,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_train.take(5).to_pandas_dataframe()"
|
||||
"training_data = dataset.drop_columns(columns=['FBI Code'])\n",
|
||||
"label = 'Primary Type'"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -401,14 +282,11 @@
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
|
||||
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
|
||||
"|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
|
||||
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -422,14 +300,12 @@
|
||||
" primary_metric = 'AUC_weighted',\n",
|
||||
" iteration_timeout_minutes = 10,\n",
|
||||
" iterations = 5,\n",
|
||||
" preprocess = True,\n",
|
||||
" n_cross_validations = 10,\n",
|
||||
" max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" spark_context=sc, #databricks/spark related\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
" training_data=training_data,\n",
|
||||
" label_column_name=label)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -475,13 +351,6 @@
|
||||
"displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following will show the child runs and waits for the parent run to complete."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -651,11 +520,13 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
|
||||
"X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
|
||||
"y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
|
||||
"images = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
|
||||
"images = np.reshape(images.values, (100,8,8))"
|
||||
"dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
|
||||
"\n",
|
||||
"df_test = dataset_test.to_pandas_dataframe()\n",
|
||||
"df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
|
||||
"\n",
|
||||
"y_test = df_test[['Primary Type']]\n",
|
||||
"X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -672,20 +543,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"# Randomly select digits and test.\n",
|
||||
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
|
||||
" print(index)\n",
|
||||
" test_sample = json.dumps({'data':X_test[index:index + 1].values.tolist()})\n",
|
||||
" predicted = aci_service.run(input_data = test_sample)\n",
|
||||
" label = y_test.values[index]\n",
|
||||
" predictedDict = json.loads(predicted)\n",
|
||||
" title = \"Label value = %d Predicted value = %s \" % ( label,predictedDict['result'][0]) \n",
|
||||
" fig = plt.figure(3, figsize = (5,5))\n",
|
||||
" ax1 = fig.add_axes((0,0,.8,.8))\n",
|
||||
" ax1.set_title(title)\n",
|
||||
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
|
||||
" display(fig)"
|
||||
"fitted_model.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -703,7 +561,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"myservice.delete()"
|
||||
"aci_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -741,7 +599,7 @@
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"name": "auto-ml-classification-local-adb",
|
||||
"notebookId": 2733885892129020
|
||||
"notebookId": 3772036807853791
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
name: register-model-deploy-local-advanced
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- scikit-learn
|
||||
@@ -1,5 +0,0 @@
|
||||
name: register-model-deploy-local
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- scikit-learn
|
||||
@@ -172,7 +172,7 @@
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'],\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n",
|
||||
" pip_packages=['azureml-defaults'])\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
@@ -465,7 +465,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "shipatel"
|
||||
"name": "gopalv"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
|
||||
@@ -149,7 +149,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"%%writefile score_ssl.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import json\n",
|
||||
@@ -201,7 +201,7 @@
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
|
||||
"inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -109,7 +109,7 @@
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies \n",
|
||||
"\n",
|
||||
"conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults'])\n",
|
||||
"conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
|
||||
"myenv = Environment(name='myenv')\n",
|
||||
"myenv.python.conda_dependencies = conda_deps"
|
||||
]
|
||||
|
||||
@@ -204,108 +204,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Option 1: Provision as a run based compute target\n",
|
||||
"### Option 1: Provision a compute target (Basic)\n",
|
||||
"\n",
|
||||
"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# create a new runconfig object\n",
|
||||
"run_config = RunConfiguration()\n",
|
||||
"\n",
|
||||
"# signal that you want to use AmlCompute to execute script.\n",
|
||||
"run_config.target = \"amlcompute\"\n",
|
||||
"\n",
|
||||
"# AmlCompute will be created in the same region as workspace\n",
|
||||
"# Set vm size for AmlCompute\n",
|
||||
"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
|
||||
"\n",
|
||||
"# enable Docker \n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"# set Docker base image to the default CPU-based image\n",
|
||||
"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
|
||||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'sklearn-pandas', 'azureml-dataprep'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
|
||||
"# In production scenario user would choose their dependencies\n",
|
||||
"import pkg_resources\n",
|
||||
"available_packages = pkg_resources.working_set\n",
|
||||
"sklearn_ver = None\n",
|
||||
"pandas_ver = None\n",
|
||||
"for dist in available_packages:\n",
|
||||
" if dist.key == 'scikit-learn':\n",
|
||||
" sklearn_ver = dist.version\n",
|
||||
" elif dist.key == 'pandas':\n",
|
||||
" pandas_ver = dist.version\n",
|
||||
"sklearn_dep = 'scikit-learn'\n",
|
||||
"pandas_dep = 'pandas'\n",
|
||||
"if sklearn_ver:\n",
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
|
||||
" pip_packages=azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"# Now submit a run on AmlCompute\n",
|
||||
"from azureml.core.script_run_config import ScriptRunConfig\n",
|
||||
"\n",
|
||||
"script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
|
||||
" script='train_explain.py',\n",
|
||||
" run_config=run_config)\n",
|
||||
"\n",
|
||||
"run = experiment.submit(script_run_config)\n",
|
||||
"\n",
|
||||
"# Show run details\n",
|
||||
"run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"# Shows output of the run on stdout.\n",
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Option 2: Provision as a persistent compute target (Basic)\n",
|
||||
"\n",
|
||||
"You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"\n",
|
||||
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
|
||||
"* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
|
||||
@@ -351,18 +252,17 @@
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"# Create a new RunConfig object\n",
|
||||
"run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to AmlCompute target created in previous step\n",
|
||||
"run_config.target = cpu_cluster.name\n",
|
||||
"\n",
|
||||
"# enable Docker \n",
|
||||
"# Enable Docker \n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
|
||||
@@ -382,13 +282,13 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
|
||||
" pip_packages=azureml_pip_packages)\n",
|
||||
"azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"from azureml.core import Run\n",
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
@@ -400,6 +300,13 @@
|
||||
"run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -424,7 +331,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Option 3: Provision as a persistent compute target (Advanced)\n",
|
||||
"### Option 2: Provision a compute target (Advanced)\n",
|
||||
"\n",
|
||||
"You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
|
||||
"\n",
|
||||
@@ -483,18 +390,17 @@
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"# Create a new RunConfig object\n",
|
||||
"run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to AmlCompute target created in previous step\n",
|
||||
"run_config.target = cpu_cluster.name\n",
|
||||
"\n",
|
||||
"# enable Docker \n",
|
||||
"# Enable Docker \n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -516,13 +422,13 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
|
||||
" pip_packages=azureml_pip_packages)\n",
|
||||
"azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"from azureml.core import Run\n",
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
@@ -554,19 +460,6 @@
|
||||
"run.get_metrics()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
|
||||
"\n",
|
||||
"client = ExplanationClient.from_run(run)\n",
|
||||
"# Get the top k (e.g., 4) most important features with their importance values\n",
|
||||
"explanation = client.download_model_explanation(top_k=4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -682,7 +575,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve model for visualization and deployment\n",
|
||||
"# Retrieve model for visualization and deployment\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"import joblib\n",
|
||||
"original_model = Model(ws, 'model_explain_model_on_amlcomp')\n",
|
||||
@@ -703,7 +596,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve x_test for visualization\n",
|
||||
"# Retrieve x_test for visualization\n",
|
||||
"import joblib\n",
|
||||
"x_test_path = './x_test_boston_housing.pkl'\n",
|
||||
"run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
|
||||
|
||||
@@ -7,5 +7,5 @@ dependencies:
|
||||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas
|
||||
- azureml-dataprep
|
||||
- azureml-dataset-runtime
|
||||
- ipywidgets
|
||||
|
||||
@@ -122,7 +122,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get the IBM employee attrition dataset\n",
|
||||
"# Get the IBM employee attrition dataset\n",
|
||||
"outdirname = 'dataset.6.21.19'\n",
|
||||
"try:\n",
|
||||
" from urllib import urlretrieve\n",
|
||||
@@ -163,7 +163,7 @@
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
|
||||
" target, \n",
|
||||
" test_size = 0.2,\n",
|
||||
" test_size=0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=target)"
|
||||
]
|
||||
@@ -223,7 +223,7 @@
|
||||
"# Append classifier to preprocessing pipeline.\n",
|
||||
"# Now we have a full prediction pipeline.\n",
|
||||
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
|
||||
" ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])"
|
||||
" ('classifier', SVC(C=1.0, probability=True))])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -249,7 +249,7 @@
|
||||
"# Append classifier to preprocessing pipeline.\n",
|
||||
"# Now we have a full prediction pipeline.\n",
|
||||
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
|
||||
" ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n",
|
||||
" ('classifier', SVC(C=1.0, probability=True))]) \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -393,7 +393,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature shap values for all features and all data points in the training data\n",
|
||||
"# Feature shap values for all features and all data points in the training data\n",
|
||||
"print('local importance values: {}'.format(global_explanation.local_importance_values))"
|
||||
]
|
||||
},
|
||||
@@ -450,8 +450,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Experiment, Run\n",
|
||||
"from interpret.ext.blackbox import TabularExplainer\n",
|
||||
"from azureml.core import Workspace, Experiment\n",
|
||||
"from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
@@ -576,6 +575,23 @@
|
||||
"ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## End\n",
|
||||
"Complete the run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
@@ -141,7 +141,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get IBM attrition data\n",
|
||||
"# Get IBM attrition data\n",
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
@@ -218,17 +218,17 @@
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
|
||||
" target,\n",
|
||||
" test_size = 0.2,\n",
|
||||
" test_size=0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=target)\n",
|
||||
"\n",
|
||||
"# preprocess the data and fit the classification model\n",
|
||||
"# Preprocess the data and fit the classification model\n",
|
||||
"clf.fit(x_train, y_train)\n",
|
||||
"model = clf.steps[-1][1]\n",
|
||||
"\n",
|
||||
"model_file_name = 'log_reg.pkl'\n",
|
||||
"\n",
|
||||
"# save model in the outputs folder so it automatically get uploaded\n",
|
||||
"# Save model in the outputs folder so it automatically get uploaded\n",
|
||||
"with open(model_file_name, 'wb') as file:\n",
|
||||
" joblib.dump(value=clf, filename=os.path.join('./outputs/',\n",
|
||||
" model_file_name))"
|
||||
@@ -345,13 +345,12 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
|
||||
" pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
|
||||
"myenv = CondaDependencies.create(pip_packages=['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
|
||||
" pin_sdk_version=False)\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
@@ -368,7 +367,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"# retrieve scoring explainer for deployment\n",
|
||||
"# Retrieve scoring explainer for deployment\n",
|
||||
"scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
|
||||
]
|
||||
},
|
||||
@@ -416,11 +415,11 @@
|
||||
"\n",
|
||||
"headers = {'Content-Type':'application/json'}\n",
|
||||
"\n",
|
||||
"# send request to service\n",
|
||||
"# Send request to service\n",
|
||||
"print(\"POST to url\", service.scoring_uri)\n",
|
||||
"resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n",
|
||||
"\n",
|
||||
"# can covert back to Python objects from json string if desired\n",
|
||||
"# Can covert back to Python objects from json string if desired\n",
|
||||
"print(\"prediction:\", resp.text)\n",
|
||||
"result = json.loads(resp.text)"
|
||||
]
|
||||
@@ -431,7 +430,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#plot the feature importance for the prediction\n",
|
||||
"# Plot the feature importance for the prediction\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt; plt.rcdefaults()\n",
|
||||
"\n",
|
||||
|
||||
@@ -156,7 +156,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Submit an AmlCompute run in a few different ways\n",
|
||||
"## Submit an AmlCompute run\n",
|
||||
"\n",
|
||||
"First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
|
||||
"\n",
|
||||
@@ -202,9 +202,43 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Provision as a run based compute target\n",
|
||||
"### Provision a compute target\n",
|
||||
"\n",
|
||||
"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
|
||||
"You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"\n",
|
||||
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
|
||||
"* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your CPU cluster\n",
|
||||
"cpu_cluster_name = \"cpu-cluster\"\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
|
||||
" max_nodes=4)\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
"cpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure & Run"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -217,31 +251,23 @@
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# create a new runconfig object\n",
|
||||
"# Create a new runconfig object\n",
|
||||
"run_config = RunConfiguration()\n",
|
||||
"\n",
|
||||
"# signal that you want to use AmlCompute to execute script.\n",
|
||||
"run_config.target = \"amlcompute\"\n",
|
||||
"# Set compute target to AmlCompute target created in previous step\n",
|
||||
"run_config.target = cpu_cluster.name\n",
|
||||
"\n",
|
||||
"# AmlCompute will be created in the same region as workspace\n",
|
||||
"# Set vm size for AmlCompute\n",
|
||||
"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
|
||||
"\n",
|
||||
"# enable Docker \n",
|
||||
"# Enable Docker \n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"# set Docker base image to the default CPU-based image\n",
|
||||
"# Set Docker base image to the default CPU-based image\n",
|
||||
"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
|
||||
"# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
|
||||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
|
||||
"run_config.auto_prepare_environment = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
" \n",
|
||||
"\n",
|
||||
@@ -263,13 +289,13 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
|
||||
" pip_packages=['sklearn_pandas', 'pyyaml'] + azureml_pip_packages,\n",
|
||||
"azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
|
||||
" pin_sdk_version=False)\n",
|
||||
"# Now submit a run on AmlCompute\n",
|
||||
"from azureml.core.script_run_config import ScriptRunConfig\n",
|
||||
@@ -327,7 +353,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve model for visualization and deployment\n",
|
||||
"# Retrieve model for visualization and deployment\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"import joblib\n",
|
||||
"original_model = Model(ws, 'amlcompute_deploy_model')\n",
|
||||
@@ -341,7 +367,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve global explanation for visualization\n",
|
||||
"# Retrieve global explanation for visualization\n",
|
||||
"from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
|
||||
"\n",
|
||||
"# get model explanation data\n",
|
||||
@@ -355,7 +381,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve x_test for visualization\n",
|
||||
"# Retrieve x_test for visualization\n",
|
||||
"import joblib\n",
|
||||
"x_test_path = './x_test.pkl'\n",
|
||||
"run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
|
||||
@@ -396,13 +422,6 @@
|
||||
"Deploy Model and ScoringExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -435,13 +454,13 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
|
||||
" pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
|
||||
"azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
|
||||
"myenv = CondaDependencies.create(pip_packages=azureml_pip_packages,\n",
|
||||
" pin_sdk_version=False)\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
@@ -457,7 +476,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve scoring explainer for deployment\n",
|
||||
"# Retrieve scoring explainer for deployment\n",
|
||||
"scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
|
||||
]
|
||||
},
|
||||
@@ -496,17 +515,17 @@
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"# create data to test service with\n",
|
||||
"# Create data to test service with\n",
|
||||
"examples = x_test[:4]\n",
|
||||
"input_data = examples.to_json()\n",
|
||||
"\n",
|
||||
"headers = {'Content-Type':'application/json'}\n",
|
||||
"\n",
|
||||
"# send request to service\n",
|
||||
"# Send request to service\n",
|
||||
"print(\"POST to url\", service.scoring_uri)\n",
|
||||
"resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
|
||||
"\n",
|
||||
"# can covert back to Python objects from json string if desired\n",
|
||||
"# Can covert back to Python objects from json string if desired\n",
|
||||
"print(\"prediction:\", resp.text)"
|
||||
]
|
||||
},
|
||||
@@ -536,13 +555,6 @@
|
||||
"1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n",
|
||||
"1. [Inferencing time: deploy a locally-trained keras model and explainer](./train-explain-model-keras-locally-and-deploy.ipynb)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -7,6 +7,6 @@ dependencies:
|
||||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas
|
||||
- azureml-dataprep
|
||||
- azureml-dataset-runtime
|
||||
- azureml-core
|
||||
- ipywidgets
|
||||
|
||||
@@ -342,7 +342,7 @@
|
||||
"## Running a few steps in parallel\n",
|
||||
"Here we are looking at a simple scenario where we are running a few steps (all involving PythonScriptStep) in parallel. Running nodes in **parallel** is the default behavior for steps in a pipeline.\n",
|
||||
"\n",
|
||||
"We already have one step defined earlier. Let's define few more steps."
|
||||
"We already have one step defined earlier. Let's define few more steps. For step3, we are using customized conda-dependency, and job might fail when \"azureml-defaults\" (or other meta package) is not in pip-package list. We need to be aware if we are not using any of the meta packages (azureml-sdk, azureml-defaults, azureml-core), and we recommend installing \"azureml-defaults\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -637,7 +637,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
"version": "3.6.2"
|
||||
},
|
||||
"order_index": 1,
|
||||
"tags": [
|
||||
|
||||
@@ -544,7 +544,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sanpil"
|
||||
"name": "nagaur"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
|
||||
@@ -40,7 +40,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Datastore, Experiment\n",
|
||||
"from azureml.core import Workspace, Datastore, Experiment, Dataset\n",
|
||||
"from azureml.core.compute import AmlCompute\n",
|
||||
"from azureml.core.compute import ComputeTarget\n",
|
||||
"\n",
|
||||
@@ -109,7 +109,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Building Pipeline Steps with Inputs and Outputs\n",
|
||||
"As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline."
|
||||
"A step in the pipeline can take [dataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) as input. This dataset can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -118,13 +118,20 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reference the data uploaded to blob storage using DataReference\n",
|
||||
"# Uploading data to the datastore\n",
|
||||
"data_path = def_blob_store.upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reference the data uploaded to blob storage using file dataset\n",
|
||||
"# Assign the datasource to blob_input_data variable\n",
|
||||
"blob_input_data = DataReference(\n",
|
||||
" datastore=def_blob_store,\n",
|
||||
" data_reference_name=\"test_data\",\n",
|
||||
" path_on_datastore=\"20newsgroups/20news.pkl\")\n",
|
||||
"print(\"DataReference object created\")"
|
||||
"blob_input_data = Dataset.File.from_files(data_path).as_named_input(\"test_data\")\n",
|
||||
"print(\"Dataset created\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -142,8 +149,8 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Define a Step that consumes a datasource and produces intermediate data.\n",
|
||||
"In this step, we define a step that consumes a datasource and produces intermediate data.\n",
|
||||
"#### Define a Step that consumes a dataset and produces intermediate data.\n",
|
||||
"In this step, we define a step that consumes a dataset and produces intermediate data.\n",
|
||||
"\n",
|
||||
"**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** \n",
|
||||
"\n",
|
||||
|
||||
@@ -1,5 +1,13 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -1062,7 +1070,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sanpil"
|
||||
"name": "anshirga"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
|
||||
@@ -6,10 +6,15 @@ import numpy as np
|
||||
from azureml.core.model import Model
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
from azureml_user.parallel_run import EntryScript
|
||||
|
||||
|
||||
def init():
|
||||
global iris_model
|
||||
|
||||
logger = EntryScript().logger
|
||||
logger.info("init() is called.")
|
||||
|
||||
parser = argparse.ArgumentParser(description="Iris model serving")
|
||||
parser.add_argument('--model_name', dest="model_name", required=True)
|
||||
args, unknown_args = parser.parse_known_args()
|
||||
@@ -20,6 +25,9 @@ def init():
|
||||
|
||||
|
||||
def run(input_data):
|
||||
logger = EntryScript().logger
|
||||
logger.info("run() is called with: {}.".format(input_data))
|
||||
|
||||
# make inference
|
||||
num_rows, num_cols = input_data.shape
|
||||
pred = iris_model.predict(input_data).reshape((num_rows, 1))
|
||||
|
||||
@@ -51,7 +51,23 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
@@ -138,7 +154,7 @@
|
||||
"\n",
|
||||
"mnist_data = Datastore.register_azure_blob_container(ws, \n",
|
||||
" datastore_name=datastore_name, \n",
|
||||
" container_name= container_name, \n",
|
||||
" container_name=container_name, \n",
|
||||
" account_name=account_name,\n",
|
||||
" overwrite=True)"
|
||||
]
|
||||
@@ -181,6 +197,13 @@
|
||||
"input_mnist_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The input dataset can be specified as a pipeline parameter, so that you can pass in new data when rerun the PRS pipeline."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -199,15 +222,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Intermediate/Output Data\n",
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
|
||||
"\n",
|
||||
"**Constructing PipelineData**\n",
|
||||
"- name: [Required] Name of the data item within the pipeline graph\n",
|
||||
"- datastore_name: Name of the Datastore to write this output to\n",
|
||||
"- output_name: Name of the output\n",
|
||||
"- output_mode: Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
|
||||
"- output_path_on_compute: For \"upload\" mode, the path to which the module writes this output during execution\n",
|
||||
"- output_overwrite: Flag to overwrite pre-existing data"
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -218,9 +233,7 @@
|
||||
"source": [
|
||||
"from azureml.pipeline.core import Pipeline, PipelineData\n",
|
||||
"\n",
|
||||
"output_dir = PipelineData(name=\"inferences\", \n",
|
||||
" datastore=def_data_store, \n",
|
||||
" output_path_on_compute=\"mnist/results\")"
|
||||
"output_dir = PipelineData(name=\"inferences\", datastore=def_data_store)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -273,11 +286,11 @@
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"# register downloaded model \n",
|
||||
"model = Model.register(model_path = \"models/\",\n",
|
||||
" model_name = \"mnist-prs\", # this is the name the model is registered as\n",
|
||||
" tags = {'pretrained': \"mnist\"},\n",
|
||||
" description = \"Mnist trained tensorflow model\",\n",
|
||||
" workspace = ws)"
|
||||
"model = Model.register(model_path=\"models/\",\n",
|
||||
" model_name=\"mnist-prs\", # this is the name the model is registered as\n",
|
||||
" tags={'pretrained': \"mnist\"},\n",
|
||||
" description=\"Mnist trained tensorflow model\",\n",
|
||||
" workspace=ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -298,11 +311,7 @@
|
||||
" \n",
|
||||
"\n",
|
||||
"#### Dependencies\n",
|
||||
"Helper scripts or Python/Conda packages required to run the entry script or model.\n",
|
||||
"\n",
|
||||
"The deployment configuration for the compute target that hosts the deployed model. This configuration describes things like memory and CPU requirements needed to run the model.\n",
|
||||
"\n",
|
||||
"These items are encapsulated into an inference configuration and a deployment configuration. The inference configuration references the entry script and other dependencies. You define these configurations programmatically when you use the SDK to perform the deployment. You define them in JSON files when you use the CLI."
|
||||
"Helper scripts or Python/Conda packages required to run the entry script."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -332,7 +341,10 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Specify the environment to run the script\n",
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment."
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n",
|
||||
"* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n",
|
||||
"\n",
|
||||
"If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -345,7 +357,7 @@
|
||||
"from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n",
|
||||
" \"azureml-core\", \"azureml-dataprep[fuse]\"])\n",
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
|
||||
"batch_env = Environment(name=\"batch_environment\")\n",
|
||||
"batch_env.python.conda_dependencies = batch_conda_deps\n",
|
||||
"batch_env.docker.enabled = True\n",
|
||||
@@ -401,7 +413,7 @@
|
||||
" parallel_run_config=parallel_run_config,\n",
|
||||
" inputs=[ input_mnist_ds_consumption ],\n",
|
||||
" output=output_dir,\n",
|
||||
" allow_reuse=True\n",
|
||||
" allow_reuse=False\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
@@ -430,7 +442,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Monitor the run"
|
||||
"### Monitor the run\n",
|
||||
"\n",
|
||||
"The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -439,8 +453,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
"# This will output information of the pipeline run, including the link to the details page of portal.\n",
|
||||
"pipeline_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -456,9 +470,40 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait the run for completion and show output log to console\n",
|
||||
"pipeline_run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### View the prediction results per input image\n",
|
||||
"In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"batch_run = pipeline_run.find_step_run(parallelrun_step.name)[0]\n",
|
||||
"batch_output = batch_run.get_output_data(output_dir.name)\n",
|
||||
"\n",
|
||||
"target_dir = tempfile.mkdtemp()\n",
|
||||
"batch_output.download(local_path=target_dir)\n",
|
||||
"result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
|
||||
"df.columns = [\"Filename\", \"Prediction\"]\n",
|
||||
"print(\"Prediction has \", df.shape[0], \" rows\")\n",
|
||||
"df.head(10) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -496,15 +541,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_run_2.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### View the prediction results per input image\n",
|
||||
"In the score.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
|
||||
"# This will output information of the pipeline run, including the link to the details page of portal.\n",
|
||||
"pipeline_run_2"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -513,25 +551,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import shutil\n",
|
||||
"\n",
|
||||
"# remove previous run results, if present\n",
|
||||
"shutil.rmtree(\"mnist_results\", ignore_errors=True)\n",
|
||||
"\n",
|
||||
"batch_run = next(pipeline_run.get_children())\n",
|
||||
"batch_output = batch_run.get_output_data(\"inferences\")\n",
|
||||
"batch_output.download(local_path=\"mnist_results\")\n",
|
||||
"\n",
|
||||
"for root, dirs, files in os.walk(\"mnist_results\"):\n",
|
||||
" for file in files:\n",
|
||||
" if file.endswith('mnist_outputs.txt'):\n",
|
||||
" result_file = os.path.join(root,file)\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
|
||||
"df.columns = [\"Filename\", \"Prediction\"]\n",
|
||||
"print(\"Prediction has \", df.shape[0], \" rows\")\n",
|
||||
"df.head(10) "
|
||||
"# Wait the run for completion and show output log to console\n",
|
||||
"pipeline_run_2.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -49,7 +49,23 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
@@ -168,15 +184,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Intermediate/Output Data\n",
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
|
||||
"\n",
|
||||
"**Constructing PipelineData**\n",
|
||||
"- name: [Required] Name of the data item within the pipeline graph\n",
|
||||
"- datastore_name: Name of the Datastore to write this output to\n",
|
||||
"- output_name: Name of the output\n",
|
||||
"- output_mode: Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
|
||||
"- output_path_on_compute: For \"upload\" mode, the path to which the module writes this output during execution\n",
|
||||
"- output_overwrite: Flag to overwrite pre-existing data"
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -250,11 +258,7 @@
|
||||
" \n",
|
||||
"\n",
|
||||
"#### Dependencies\n",
|
||||
"Helper scripts or Python/Conda packages required to run the entry script or model.\n",
|
||||
"\n",
|
||||
"The deployment configuration for the compute target that hosts the deployed model. This configuration describes things like memory and CPU requirements needed to run the model.\n",
|
||||
"\n",
|
||||
"These items are encapsulated into an inference configuration and a deployment configuration. The inference configuration references the entry script and other dependencies. You define these configurations programmatically when you use the SDK to perform the deployment. You define them in JSON files when you use the CLI.\n",
|
||||
"Helper scripts or Python/Conda packages required to run the entry script.\n",
|
||||
"\n",
|
||||
"## Print inferencing script"
|
||||
]
|
||||
@@ -286,7 +290,11 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Specify the environment to run the script\n",
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment."
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n",
|
||||
"* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n",
|
||||
"* For TabularDataset, add **pandas** as `run(mini_batch)` uses `pandas.DataFrame` as mini_batch type.\n",
|
||||
"\n",
|
||||
"If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -299,7 +307,7 @@
|
||||
"from azureml.core.runconfig import CondaDependencies\n",
|
||||
"\n",
|
||||
"predict_conda_deps = CondaDependencies.create(pip_packages=[\"scikit-learn==0.20.3\",\n",
|
||||
" \"azureml-core\", \"azureml-dataprep[pandas,fuse]\"])\n",
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n",
|
||||
"\n",
|
||||
"predict_env = Environment(name=\"predict_environment\")\n",
|
||||
"predict_env.python.conda_dependencies = predict_conda_deps\n",
|
||||
@@ -326,7 +334,7 @@
|
||||
"parallel_run_config = ParallelRunConfig(\n",
|
||||
" source_directory=scripts_folder,\n",
|
||||
" entry_script=script_file, # the user script to run against each input\n",
|
||||
" mini_batch_size='5MB',\n",
|
||||
" mini_batch_size='1KB',\n",
|
||||
" error_threshold=5,\n",
|
||||
" output_action='append_row',\n",
|
||||
" append_row_file_name=\"iris_outputs.txt\",\n",
|
||||
@@ -357,7 +365,7 @@
|
||||
" output=output_folder,\n",
|
||||
" parallel_run_config=parallel_run_config,\n",
|
||||
" arguments=['--model_name', 'iris-prs'],\n",
|
||||
" allow_reuse=True\n",
|
||||
" allow_reuse=False\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
@@ -383,13 +391,22 @@
|
||||
"pipeline_run = Experiment(ws, 'iris-prs').submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## View progress of Pipeline run\n",
|
||||
"\n",
|
||||
"The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# this will output a table with link to the run details in azure portal\n",
|
||||
"# This will output information of the pipeline run, including the link to the details page of portal.\n",
|
||||
"pipeline_run"
|
||||
]
|
||||
},
|
||||
@@ -397,29 +414,18 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## View progress of Pipeline run\n",
|
||||
"\n",
|
||||
"The progress of the pipeline is able to be viewed either through azureml.widgets or a console feed from PipelineRun.wait_for_completion()."
|
||||
"### Optional: View detailed logs (streaming) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# GUI\n",
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Console logs\n",
|
||||
"## Wait the run for completion and show output log to console\n",
|
||||
"pipeline_run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
@@ -438,19 +444,14 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import shutil\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"shutil.rmtree(\"iris_results\", ignore_errors=True)\n",
|
||||
"prediction_run = pipeline_run.find_step_run(distributed_csv_iris_step.name)[0]\n",
|
||||
"prediction_output = prediction_run.get_output_data(output_folder.name)\n",
|
||||
"\n",
|
||||
"prediction_run = next(pipeline_run.get_children())\n",
|
||||
"prediction_output = prediction_run.get_output_data(\"inferences\")\n",
|
||||
"prediction_output.download(local_path=\"iris_results\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for root, dirs, files in os.walk(\"iris_results\"):\n",
|
||||
" for file in files:\n",
|
||||
" if file.endswith('iris_outputs.txt'):\n",
|
||||
" result_file = os.path.join(root,file)\n",
|
||||
"target_dir = tempfile.mkdtemp()\n",
|
||||
"prediction_output.download(local_path=target_dir)\n",
|
||||
"result_file = os.path.join(target_dir, prediction_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
|
||||
"\n",
|
||||
"# cleanup output format\n",
|
||||
"df = pd.read_csv(result_file, delimiter=\" \", header=None)\n",
|
||||
|
||||
@@ -120,7 +120,7 @@
|
||||
"\n",
|
||||
"def download_model(model_name):\n",
|
||||
" # downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
|
||||
" url=\"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n",
|
||||
" url = \"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n",
|
||||
" local_path = os.path.join(model_dir, model_name)\n",
|
||||
" urllib.request.urlretrieve(url, local_path)"
|
||||
]
|
||||
@@ -415,7 +415,7 @@
|
||||
"parallel_cd.add_conda_package(\"torchvision\")\n",
|
||||
"parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n",
|
||||
"parallel_cd.add_pip_package(\"azureml-core\")\n",
|
||||
"parallel_cd.add_pip_package(\"azureml-dataprep[fuse]\")\n",
|
||||
"parallel_cd.add_pip_package(\"azureml-dataset-runtime[fuse]\")\n",
|
||||
"\n",
|
||||
"styleenvironment = Environment(name=\"styleenvironment\")\n",
|
||||
"styleenvironment.python.conda_dependencies=parallel_cd\n",
|
||||
@@ -461,7 +461,7 @@
|
||||
" output=processed_images, # Output file share/blob container\n",
|
||||
" arguments=[\"--style\", style_param],\n",
|
||||
" parallel_run_config=parallel_run_config,\n",
|
||||
" allow_reuse=True #[optional - default value True]\n",
|
||||
" allow_reuse=False #[optional - default value True]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
@@ -497,7 +497,10 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Monitor using widget"
|
||||
"# Monitor pipeline run\n",
|
||||
"\n",
|
||||
"The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the `pipeline_run` object.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -506,25 +509,25 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Track pipeline run progress\n",
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_run.wait_for_completion()"
|
||||
"# This will output information of the pipeline run, including the link to the details page of portal.\n",
|
||||
"pipeline_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Downloads the video in `output_video` folder"
|
||||
"### Optional: View detailed logs (streaming) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait the run for completion and show output log to console\n",
|
||||
"pipeline_run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -534,6 +537,13 @@
|
||||
"# Download output video"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Downloads the video in `output_video` folder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -541,8 +551,8 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def download_video(run, target_dir=None):\n",
|
||||
" stitch_run = run.find_step_run(\"stitch\")[0]\n",
|
||||
" port_data = stitch_run.get_output_data(\"output_video\")\n",
|
||||
" stitch_run = run.find_step_run(stitch_video_step.name)[0]\n",
|
||||
" port_data = stitch_run.get_output_data(output_video.name)\n",
|
||||
" port_data.download(target_dir, show_progress=True)"
|
||||
]
|
||||
},
|
||||
@@ -674,7 +684,8 @@
|
||||
"from azureml.pipeline.core.run import PipelineRun\n",
|
||||
"published_pipeline_run_candy = PipelineRun(ws.experiments[experiment_name], run_id)\n",
|
||||
"\n",
|
||||
"RunDetails(published_pipeline_run_candy).show()"
|
||||
"# Show detail information of run\n",
|
||||
"published_pipeline_run_candy"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -459,7 +459,7 @@
|
||||
" entry_script='tf_mnist.py',\n",
|
||||
" use_gpu=True,\n",
|
||||
" framework_version='2.0',\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -630,52 +630,44 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Predict on the test set\n",
|
||||
"## Predict on the test set (Optional)\n",
|
||||
"Now load the saved TensorFlow graph, and list all operations under the `network` scope. This way we can discover the input tensor `network/X:0` and the output tensor `network/output/MatMul:0`, and use them in the scoring script in the next step.\n",
|
||||
"\n",
|
||||
"Note: if your local TensorFlow version is different than the version running in the cluster where the model is trained, you might see a \"compiletime version mismatch\" warning. You can ignore it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import tensorflow as tf\n",
|
||||
"imported_model = tf.saved_model.load('./model')"
|
||||
" import tensorflow as tf\n",
|
||||
" imported_model = tf.saved_model.load('./model')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pred =imported_model(X_test)\n",
|
||||
"y_hat = np.argmax(pred, axis=1)\n",
|
||||
" pred = imported_model(X_test)\n",
|
||||
" y_hat = np.argmax(pred, axis=1)\n",
|
||||
"\n",
|
||||
"# print the first 30 labels and predictions\n",
|
||||
"print('labels: \\t', y_test[:30])\n",
|
||||
"print('predictions:\\t', y_hat[:30])"
|
||||
" # print the first 30 labels and predictions\n",
|
||||
" print('labels: \\t', y_test[:30])\n",
|
||||
" print('predictions:\\t', y_hat[:30])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
|
||||
" print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
|
||||
" print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -724,7 +716,7 @@
|
||||
" entry_script='tf_mnist.py',\n",
|
||||
" framework_version='2.0',\n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1055,7 +1047,7 @@
|
||||
" font_color = 'red' if y_test[s] != result[i] else 'black'\n",
|
||||
" clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
|
||||
" \n",
|
||||
" plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)\n",
|
||||
" plt.text(x=10, y=-10, s=result[i], fontsize=18, color=font_color)\n",
|
||||
" plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
|
||||
" \n",
|
||||
" i = i + 1\n",
|
||||
|
||||
@@ -285,7 +285,7 @@
|
||||
" distributed_training=Mpi(),\n",
|
||||
" framework_version='1.13', \n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -5,7 +5,7 @@ dependencies:
|
||||
- azureml-widgets
|
||||
- keras
|
||||
- tensorflow-gpu==1.13.2
|
||||
- horovod==0.16.1
|
||||
- horovod==0.19.1
|
||||
- matplotlib
|
||||
- pandas
|
||||
- fuse
|
||||
|
||||
@@ -442,12 +442,12 @@
|
||||
"\n",
|
||||
"# set up environment\\n\n",
|
||||
"env = Environment('my_env')\n",
|
||||
"# ensure latest azureml-dataprep and other required packages installed in the environment\n",
|
||||
"# ensure latest azureml-dataset-runtime and other required packages installed in the environment\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['keras',\n",
|
||||
" 'azureml-sdk',\n",
|
||||
" 'tensorflow==2.0.0',\n",
|
||||
" 'matplotlib',\n",
|
||||
" 'azureml-dataprep[pandas,fuse]'])\n",
|
||||
" 'azureml-dataset-runtime[pandas,fuse]'])\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies = cd"
|
||||
]
|
||||
|
||||
@@ -300,7 +300,7 @@
|
||||
" script_params=script_params,\n",
|
||||
" entry_script='tf_mnist_with_checkpoint.py',\n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -411,7 +411,7 @@
|
||||
" entry_script='tf_mnist_with_checkpoint.py',\n",
|
||||
" resume_from=model_location,\n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -264,6 +264,58 @@
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Wait 15 minutes for scoring data to be uploaded\n",
|
||||
"\n",
|
||||
"From the Model Data Collector, it can take up to (but usually less than) 15 minutes for data to arrive in your blob storage account. \n",
|
||||
"\n",
|
||||
"Wait 15 minutes to ensure cells below will run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import time\n",
|
||||
"\n",
|
||||
"time.sleep(900)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get scoring dataset thats been created\n",
|
||||
"\n",
|
||||
"Scoring dataset will be created automatically for each model/version/service that has been deployed and registered with name in the format of inference-data-elevation-{model_name}-{model_version}-{service_name}\n",
|
||||
"\n",
|
||||
"Wait 15 minutes to ensure cells below will run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scoring_dataset_name = \"inference-data-{0}-{1}-{2}\".format(model.name, model.version, service_name)\n",
|
||||
"scoring_dataset = Dataset.get_by_name(ws, scoring_dataset_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create datadset monitor for scoring dataset against training dataset\n",
|
||||
"\n",
|
||||
"Check out [datadrift on dataset notebook](../../work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) for more details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -302,26 +354,6 @@
|
||||
" print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Wait 10 minutes\n",
|
||||
"\n",
|
||||
"From the Model Data Collector, it can take up to (but usually less than) 10 minutes for data to arrive in your blob storage account. Wait 10 minutes to ensure cells below will run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import time\n",
|
||||
"\n",
|
||||
"time.sleep(600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -335,22 +367,23 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime, timedelta\n",
|
||||
"from azureml.datadrift import DataDriftDetector, AlertConfiguration\n",
|
||||
"\n",
|
||||
"services = [service_name]\n",
|
||||
"start = datetime.now() - timedelta(days=2)\n",
|
||||
"feature_list = X_features\n",
|
||||
"alert_config = AlertConfiguration(['user@contoso.com'])\n",
|
||||
"alert_config = AlertConfiguration(['user@contoso.com']) # replace with your email to recieve alerts from the scheduled pipeline after enabling\n",
|
||||
"monitor_name = \"monitor_model_demo\"\n",
|
||||
"baseline = dset # training dataset\n",
|
||||
"target = scoring_dataset # scording dataset\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" monitor = DataDriftDetector.create_from_model(ws, model.name, model.version, services, \n",
|
||||
" frequency='Day', \n",
|
||||
" schedule_start=datetime.utcnow() + timedelta(days=1), \n",
|
||||
" alert_config=alert_config, \n",
|
||||
" compute_target='cpu-cluster')\n",
|
||||
" monitor = DataDriftDetector.create_from_datasets(ws, monitor_name, baseline, target, \n",
|
||||
" compute_target='cpu-cluster', # compute target for scheduled pipeline and backfills \n",
|
||||
" frequency='Day', # how often to analyze target data\n",
|
||||
" feature_list=None, # list of features to detect drift on\n",
|
||||
" drift_threshold=None, # threshold from 0 to 1 for email alerting\n",
|
||||
" latency=0, # SLA in hours for target data to arrive in the dataset\n",
|
||||
" alert_config=alert_config) # email addresses to send alert\n",
|
||||
"except KeyError:\n",
|
||||
" monitor = DataDriftDetector.get(ws, model.name, model.version)\n",
|
||||
" monitor = DataDriftDetector.get_by_name(ws, monitor_name)\n",
|
||||
" \n",
|
||||
"monitor"
|
||||
]
|
||||
@@ -362,7 +395,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# many monitor settings can be updated \n",
|
||||
"monitor = monitor.update(drift_threshold = 0.1)\n",
|
||||
"monitor = monitor.update(drift_threshold = 0.1, feature_list = X_features)\n",
|
||||
"\n",
|
||||
"monitor"
|
||||
]
|
||||
@@ -371,7 +404,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Run the monitor on today's scoring data\n",
|
||||
"## Analyze today's scoring data\n",
|
||||
"\n",
|
||||
"Perform a data drift run on the data sent to the service earlier in this notebook. If you set your email address in the alert configuration and the drift threshold <=0.1 you should recieve an email alert to drift from this run.\n",
|
||||
"\n",
|
||||
@@ -384,16 +417,37 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"now = datetime.utcnow()\n",
|
||||
"target_date = datetime(now.year, now.month, now.day)\n",
|
||||
"run = monitor.run(target_date, services, feature_list=feature_list, compute_target='cpu-cluster')"
|
||||
"analysis_run = monitor.backfill(target_date, target_date)\n",
|
||||
"analysis_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get and view results and metrics\n",
|
||||
"## Query metrics and show results in Python\n",
|
||||
"\n",
|
||||
"The below cell will plot some key data drift metrics, and can be used to query the results. Run `help(monitor.get_output)` for specifics on the object returned."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"analysis_run.wait_for_completion(wait_post_processing=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Get and view results and metrics\n",
|
||||
"\n",
|
||||
"For enterprise workspaces, the UI in the Azure Machine Learning studio can be used. Otherwise, the metrics can be queried in Python and plotted. "
|
||||
]
|
||||
@@ -404,11 +458,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The run() API initiates a pipeline run for each service in the services list. \n",
|
||||
"# Here we retrieve the individual service run to get its output results and metrics. \n",
|
||||
"\n",
|
||||
"child_run = list(run.get_children())[0]\n",
|
||||
"child_run"
|
||||
"# get results from Python SDK after the analysis run finishes\n",
|
||||
"results, metrics = monitor.get_output(start_time=target_date, end_time=target_date)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -417,25 +468,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"child_run.wait_for_completion(wait_post_processing=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results, metrics = monitor.get_output(run_id=child_run.id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"drift_plots = monitor.show()"
|
||||
"# plot the results from Python SDK \n",
|
||||
"monitor.show(start_time=target_date, end_time=target_date)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -453,7 +487,11 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"monitor.enable_schedule()"
|
||||
"# enable the pipeline schedule and recieve email alerts\n",
|
||||
"monitor.enable_schedule()\n",
|
||||
"\n",
|
||||
"# disable the pipeline schedule \n",
|
||||
"#monitor.disable_schedule()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -484,13 +522,6 @@
|
||||
" * [Send requests or feedback](mailto:driftfeedback@microsoft.com) on data drift directly to the team\n",
|
||||
" * Please open issues with data drift here on GitHub or on StackOverflow if others are likely to run into the same issue"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@@ -514,7 +545,7 @@
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Data drift on aks",
|
||||
"index_order": 1.0,
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -530,7 +561,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.4"
|
||||
"version": "3.6.10"
|
||||
},
|
||||
"star_tag": [
|
||||
"featured"
|
||||
|
||||
@@ -35,6 +35,7 @@ Using these samples, you will learn how to do the following.
|
||||
| [cartpole_sc.ipynb](cartpole-on-single-compute/cartpole_sc.ipynb) | Notebook to train a Cartpole playing agent on an Azure Machine Learning Compute Cluster (single node) |
|
||||
| [pong_rllib.ipynb](atari-on-distributed-compute/pong_rllib.ipynb) | Notebook for distributed training of Pong agent using RLlib on multiple compute targets |
|
||||
| [minecraft.ipynb](minecraft-on-distributed-compute/minecraft.ipynb) | Notebook to train an agent to navigate through a lava maze in the Minecraft game |
|
||||
| [particle.ipynb](multiagent-particle-envs/particle.ipynb) | Notebook to train policies in a multiagent cooperative navigation scenario based on OpenAI's Particle environments |
|
||||
|
||||
## Prerequisites
|
||||
|
||||
|
||||
@@ -22,7 +22,7 @@
|
||||
"source": [
|
||||
"# Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Compute Instance\n",
|
||||
"\n",
|
||||
"Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100\u00e2\u20ac\u2122s of nodes if needed.\n",
|
||||
"Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100s of nodes if needed.\n",
|
||||
"\n",
|
||||
"This example shows how to use Reinforcement Learning in Azure Machine Learning to train a Cartpole playing agent on a compute instance."
|
||||
]
|
||||
@@ -582,7 +582,7 @@
|
||||
" rl_framework=Ray(),\n",
|
||||
" \n",
|
||||
" # Additional pip packages to install\n",
|
||||
" pip_packages = ['azureml-dataprep[fuse,pandas]'])"
|
||||
" pip_packages = ['azureml-dataset-runtime[fuse,pandas]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -22,7 +22,7 @@
|
||||
"source": [
|
||||
"# Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Single Compute\n",
|
||||
"\n",
|
||||
"Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100\u00e2\u20ac\u2122s of nodes if needed.\n",
|
||||
"Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100s of nodes if needed.\n",
|
||||
"\n",
|
||||
"This example shows how to use Reinforcement Learning in Azure Machine Learning to train a Cartpole playing agent on a single compute. "
|
||||
]
|
||||
@@ -254,6 +254,7 @@
|
||||
" dockerfile=f.read()\n",
|
||||
"\n",
|
||||
" xvfb_env = Environment(name='xvfb-vdisplay')\n",
|
||||
" xvfb_env.docker.enabled = True\n",
|
||||
" xvfb_env.docker.base_image = None\n",
|
||||
" xvfb_env.docker.base_dockerfile = dockerfile\n",
|
||||
" \n",
|
||||
@@ -547,29 +548,18 @@
|
||||
"source": [
|
||||
"import shutil\n",
|
||||
"\n",
|
||||
"# A helper function to download (copy) movies from a dataset to local directory\n",
|
||||
"# A helper function to download movies from a dataset to local directory\n",
|
||||
"def download_movies(artifacts_ds, movies, destination):\n",
|
||||
" # Create the local destination directory \n",
|
||||
" if path.exists(destination):\n",
|
||||
" dir_util.remove_tree(destination)\n",
|
||||
" dir_util.mkpath(destination)\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" print(\"Trying mounting dataset and copying movies.\")\n",
|
||||
" # Note: We assume movie paths start with '\\'\n",
|
||||
" mount_context = artifacts_ds.mount()\n",
|
||||
" mount_context.start()\n",
|
||||
" for movie in movies:\n",
|
||||
" print('Copying {} ...'.format(movie))\n",
|
||||
" shutil.copy2(path.join(mount_context.mount_point, movie[1:]), destination)\n",
|
||||
" mount_context.stop()\n",
|
||||
" except OSError as e:\n",
|
||||
" print(\"Mounting failed with error '{0}'. Going with dataset download.\".format(e))\n",
|
||||
" for i, artifact in enumerate(artifacts_ds.to_path()):\n",
|
||||
" if artifact in movies:\n",
|
||||
" print('Downloading {} ...'.format(artifact))\n",
|
||||
" artifacts_ds.skip(i).take(1).download(target_path=destination, overwrite=True)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" for i, artifact in enumerate(artifacts_ds.to_path()):\n",
|
||||
" if artifact in movies:\n",
|
||||
" print('Downloading {} ...'.format(artifact))\n",
|
||||
" artifacts_ds.skip(i).take(1).download(target_path=destination, overwrite=True)\n",
|
||||
"\n",
|
||||
" print('Downloading movies completed!')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -782,6 +772,7 @@
|
||||
" dockerfile=f.read()\n",
|
||||
"\n",
|
||||
"xvfb_env = Environment(name='xvfb-vdisplay')\n",
|
||||
"xvfb_env.docker.enabled = True\n",
|
||||
"xvfb_env.docker.base_image = None\n",
|
||||
"xvfb_env.docker.base_dockerfile = dockerfile\n",
|
||||
" \n",
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
FROM mcr.microsoft.com/azureml/base:openmpi3.1.2-ubuntu18.04
|
||||
FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20200423.v1
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
python-opengl \
|
||||
@@ -8,10 +8,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
rm -rf /var/lib/apt/lists/* && \
|
||||
rm -rf /usr/share/man/*
|
||||
|
||||
RUN conda install -y conda=4.7.12 python=3.6.2 && conda clean -ay && \
|
||||
RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
|
||||
pip install --no-cache-dir \
|
||||
azureml-defaults \
|
||||
azureml-dataprep[fuse,pandas] \
|
||||
azureml-dataset-runtime[fuse,pandas] \
|
||||
azureml-contrib-reinforcementlearning \
|
||||
gputil \
|
||||
cloudpickle==1.3.0 \
|
||||
@@ -26,4 +26,4 @@ RUN conda install -y conda=4.7.12 python=3.6.2 && conda clean -ay && \
|
||||
setproctitle \
|
||||
gym[atari] && \
|
||||
conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
|
||||
conda install opencv
|
||||
conda install -c anaconda opencv
|
||||
|
||||
@@ -1477,7 +1477,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
* Pause the old server. It's vital that we do this, otherwise it will
|
||||
* respond to the quit disconnect package straight away and kill the server
|
||||
* thread, which means there will be no server to respond to the loadWorld
|
||||
* code. (This was the cause of the infamous "Holder Lookups" hang.)
|
||||
* code.
|
||||
*/
|
||||
public class PauseOldServerEpisode extends ConfigAwareStateEpisode
|
||||
{
|
||||
@@ -1505,7 +1505,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
{
|
||||
if (!killPublicFlag(Minecraft.getMinecraft().getIntegratedServer()))
|
||||
{
|
||||
// Can't pause, don't want to risk the hang - so bail.
|
||||
episodeHasCompletedWithErrors(ClientState.ERROR_CANNOT_CREATE_WORLD, "Can not pause the old server since it's open to LAN; no way to safely create new world.");
|
||||
}
|
||||
}
|
||||
@@ -1555,8 +1554,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
if (inAbortState())
|
||||
episodeHasCompleted(ClientState.MISSION_ABORTED);
|
||||
|
||||
// We need to make sure that both the client and server have paused,
|
||||
// otherwise we are still susceptible to the "Holder Lookups" hang.
|
||||
// We need to make sure that both the client and server have paused.
|
||||
|
||||
// Since the server sets its pause state in response to the client's pause state,
|
||||
// and it only performs this check once, at the top of its tick method,
|
||||
@@ -1615,7 +1613,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
// If the Minecraft server isn't paused at this point,
|
||||
// then the following line will cause the server thread to exit...
|
||||
Minecraft.getMinecraft().world.sendQuittingDisconnectingPacket();
|
||||
// ...in which case the next line will hang.
|
||||
// ...in which case the next line will block.
|
||||
Minecraft.getMinecraft().loadWorld((WorldClient) null);
|
||||
// Must display the GUI or Minecraft will attempt to access a non-existent player in the client tick.
|
||||
Minecraft.getMinecraft().displayGuiScreen(new GuiMainMenu());
|
||||
@@ -2135,7 +2133,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
envServer.observation(data);
|
||||
}
|
||||
} else {
|
||||
// Bung the whole shebang off via TCP:
|
||||
if (this.observationSocket.sendTCPString(data)) {
|
||||
this.failedTCPObservationSendCount = 0;
|
||||
} else {
|
||||
|
||||
@@ -1477,7 +1477,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
* Pause the old server. It's vital that we do this, otherwise it will
|
||||
* respond to the quit disconnect package straight away and kill the server
|
||||
* thread, which means there will be no server to respond to the loadWorld
|
||||
* code. (This was the cause of the infamous "Holder Lookups" hang.)
|
||||
* code.
|
||||
*/
|
||||
public class PauseOldServerEpisode extends ConfigAwareStateEpisode
|
||||
{
|
||||
@@ -1505,7 +1505,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
{
|
||||
if (!killPublicFlag(Minecraft.getMinecraft().getIntegratedServer()))
|
||||
{
|
||||
// Can't pause, don't want to risk the hang - so bail.
|
||||
episodeHasCompletedWithErrors(ClientState.ERROR_CANNOT_CREATE_WORLD, "Can not pause the old server since it's open to LAN; no way to safely create new world.");
|
||||
}
|
||||
}
|
||||
@@ -1555,8 +1554,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
if (inAbortState())
|
||||
episodeHasCompleted(ClientState.MISSION_ABORTED);
|
||||
|
||||
// We need to make sure that both the client and server have paused,
|
||||
// otherwise we are still susceptible to the "Holder Lookups" hang.
|
||||
// We need to make sure that both the client and server have paused.
|
||||
|
||||
// Since the server sets its pause state in response to the client's pause state,
|
||||
// and it only performs this check once, at the top of its tick method,
|
||||
@@ -1615,7 +1613,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
// If the Minecraft server isn't paused at this point,
|
||||
// then the following line will cause the server thread to exit...
|
||||
Minecraft.getMinecraft().world.sendQuittingDisconnectingPacket();
|
||||
// ...in which case the next line will hang.
|
||||
// ...in which case the next line will block.
|
||||
Minecraft.getMinecraft().loadWorld((WorldClient) null);
|
||||
// Must display the GUI or Minecraft will attempt to access a non-existent player in the client tick.
|
||||
Minecraft.getMinecraft().displayGuiScreen(new GuiMainMenu());
|
||||
@@ -2135,7 +2133,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis
|
||||
envServer.observation(data);
|
||||
}
|
||||
} else {
|
||||
// Bung the whole shebang off via TCP:
|
||||
if (this.observationSocket.sendTCPString(data)) {
|
||||
this.failedTCPObservationSendCount = 0;
|
||||
} else {
|
||||
|
||||
@@ -0,0 +1,60 @@
|
||||
FROM mcr.microsoft.com/azureml/base:openmpi3.1.2-ubuntu18.04
|
||||
|
||||
# Install some basic utilities
|
||||
RUN apt-get update && apt-get install -y \
|
||||
curl \
|
||||
ca-certificates \
|
||||
sudo \
|
||||
cpio \
|
||||
git \
|
||||
bzip2 \
|
||||
libx11-6 \
|
||||
tmux \
|
||||
htop \
|
||||
gcc \
|
||||
xvfb \
|
||||
python-opengl \
|
||||
x11-xserver-utils \
|
||||
ffmpeg \
|
||||
mesa-utils \
|
||||
nano \
|
||||
vim \
|
||||
rsync \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install python 3.7
|
||||
RUN conda install python==3.7
|
||||
|
||||
# Create a working directory
|
||||
RUN mkdir /app
|
||||
WORKDIR /app
|
||||
|
||||
# Install required pip packages
|
||||
RUN pip install --upgrade pip setuptools && pip install --upgrade \
|
||||
pandas \
|
||||
matplotlib \
|
||||
psutil \
|
||||
numpy \
|
||||
scipy \
|
||||
gym \
|
||||
azureml-defaults \
|
||||
tensorboardX \
|
||||
tensorflow==1.15 \
|
||||
tensorflow-probability==0.8.0 \
|
||||
onnxruntime \
|
||||
tf2onnx \
|
||||
cloudpickle==1.2.0 \
|
||||
tabulate \
|
||||
dm_tree \
|
||||
lz4 \
|
||||
opencv-python \
|
||||
ray==0.8.3 \
|
||||
ray[rllib]==0.8.3 \
|
||||
ray[tune]==0.8.3
|
||||
|
||||
# Install particle
|
||||
RUN git clone https://github.com/openai/multiagent-particle-envs.git
|
||||
COPY patch_files/* multiagent-particle-envs/multiagent/
|
||||
RUN cd multiagent-particle-envs && \
|
||||
pip install -e . && \
|
||||
pip install --upgrade pyglet==1.3.2
|
||||
@@ -0,0 +1,70 @@
|
||||
# MIT License
|
||||
|
||||
# Copyright (c) 2018 OpenAI
|
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
# SOFTWARE.
|
||||
|
||||
import numpy as np
|
||||
import gym
|
||||
|
||||
|
||||
class MultiDiscrete(gym.Space):
|
||||
"""
|
||||
- The multi-discrete action space consists of a series of discrete action spaces with different
|
||||
parameters
|
||||
- It can be adapted to both a Discrete action space or a continuous (Box) action space
|
||||
- It is useful to represent game controllers or keyboards where each key can be represented as
|
||||
a discrete action space
|
||||
- It is parametrized by passing an array of arrays containing [min, max] for each discrete action
|
||||
space where the discrete action space can take any integers from `min` to `max` (both inclusive)
|
||||
Note: A value of 0 always need to represent the NOOP action.
|
||||
e.g. Nintendo Game Controller
|
||||
- Can be conceptualized as 3 discrete action spaces:
|
||||
1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4
|
||||
2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
|
||||
3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
|
||||
- Can be initialized as
|
||||
MultiDiscrete([ [0,4], [0,1], [0,1] ])
|
||||
"""
|
||||
def __init__(self, array_of_param_array):
|
||||
self.low = np.array([x[0] for x in array_of_param_array])
|
||||
self.high = np.array([x[1] for x in array_of_param_array])
|
||||
self.num_discrete_space = self.low.shape[0]
|
||||
|
||||
def sample(self):
|
||||
""" Returns a array with one sample from each discrete action space """
|
||||
# For each row: round(random .* (max - min) + min, 0)
|
||||
# random_array = prng.np_random.rand(self.num_discrete_space)
|
||||
random_array = np.random.RandomState().rand(self.num_discrete_space)
|
||||
return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]
|
||||
|
||||
def contains(self, x):
|
||||
return len(x) == self.num_discrete_space \
|
||||
and (np.array(x) >= self.low).all() \
|
||||
and (np.array(x) <= self.high).all()
|
||||
|
||||
@property
|
||||
def shape(self):
|
||||
return self.num_discrete_space
|
||||
|
||||
def __repr__(self):
|
||||
return "MultiDiscrete" + str(self.num_discrete_space)
|
||||
|
||||
def __eq__(self, other):
|
||||
return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high)
|
||||
@@ -0,0 +1,413 @@
|
||||
# MIT License
|
||||
|
||||
# Copyright (c) 2018 OpenAI
|
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
# SOFTWARE.
|
||||
|
||||
"""
|
||||
2D rendering framework
|
||||
"""
|
||||
from __future__ import division
|
||||
import os
|
||||
import six
|
||||
import sys
|
||||
from gym import error
|
||||
import math
|
||||
import numpy as np
|
||||
import pyglet
|
||||
|
||||
from pyglet.gl import glEnable, glHint, glLineWidth, glBlendFunc, glClearColor, glPushMatrix, \
|
||||
glTranslatef, glRotatef, glScalef, glPopMatrix, glColor4f, glBegin, glVertex3f, glEnd, glLineStipple, \
|
||||
glDisable, glVertex2f, GL_BLEND, GL_LINE_SMOOTH, GL_LINE_SMOOTH_HINT, GL_NICEST, GL_SRC_ALPHA, \
|
||||
GL_ONE_MINUS_SRC_ALPHA, GL_LINE_STIPPLE, GL_POINTS, GL_QUADS, GL_TRIANGLES, GL_POLYGON, GL_LINE_LOOP, \
|
||||
GL_LINE_STRIP, GL_LINES
|
||||
|
||||
|
||||
if "Apple" in sys.version:
|
||||
if 'DYLD_FALLBACK_LIBRARY_PATH' in os.environ:
|
||||
os.environ['DYLD_FALLBACK_LIBRARY_PATH'] += ':/usr/lib'
|
||||
# (JDS 2016/04/15): avoid bug on Anaconda 2.3.0 / Yosemite
|
||||
|
||||
|
||||
RAD2DEG = 57.29577951308232
|
||||
|
||||
|
||||
def get_display(spec):
|
||||
"""Convert a display specification (such as :0) into an actual Display
|
||||
object.
|
||||
|
||||
Pyglet only supports multiple Displays on Linux.
|
||||
"""
|
||||
if spec is None:
|
||||
return None
|
||||
elif isinstance(spec, six.string_types):
|
||||
return pyglet.canvas.Display(spec)
|
||||
else:
|
||||
raise error.Error('Invalid display specification: {}. (Must be a string like :0 or None.)'.format(spec))
|
||||
|
||||
|
||||
class Viewer(object):
|
||||
def __init__(self, width, height, display=None):
|
||||
display = get_display(display)
|
||||
|
||||
self.width = width
|
||||
self.height = height
|
||||
|
||||
self.window = pyglet.window.Window(width=width, height=height, display=display)
|
||||
self.window.on_close = self.window_closed_by_user
|
||||
self.geoms = []
|
||||
self.onetime_geoms = []
|
||||
self.transform = Transform()
|
||||
|
||||
glEnable(GL_BLEND)
|
||||
# glEnable(GL_MULTISAMPLE)
|
||||
glEnable(GL_LINE_SMOOTH)
|
||||
# glHint(GL_LINE_SMOOTH_HINT, GL_DONT_CARE)
|
||||
glHint(GL_LINE_SMOOTH_HINT, GL_NICEST)
|
||||
glLineWidth(2.0)
|
||||
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
|
||||
|
||||
def close(self):
|
||||
self.window.close()
|
||||
|
||||
def window_closed_by_user(self):
|
||||
self.close()
|
||||
|
||||
def set_bounds(self, left, right, bottom, top):
|
||||
assert right > left and top > bottom
|
||||
scalex = self.width / (right - left)
|
||||
scaley = self.height / (top - bottom)
|
||||
self.transform = Transform(
|
||||
translation=(-left * scalex, -bottom * scaley),
|
||||
scale=(scalex, scaley))
|
||||
|
||||
def add_geom(self, geom):
|
||||
self.geoms.append(geom)
|
||||
|
||||
def add_onetime(self, geom):
|
||||
self.onetime_geoms.append(geom)
|
||||
|
||||
def render(self, return_rgb_array=False):
|
||||
glClearColor(1, 1, 1, 1)
|
||||
self.window.clear()
|
||||
self.window.switch_to()
|
||||
self.window.dispatch_events()
|
||||
self.transform.enable()
|
||||
for geom in self.geoms:
|
||||
geom.render()
|
||||
for geom in self.onetime_geoms:
|
||||
geom.render()
|
||||
self.transform.disable()
|
||||
arr = None
|
||||
if return_rgb_array:
|
||||
buffer = pyglet.image.get_buffer_manager().get_color_buffer()
|
||||
image_data = buffer.get_image_data()
|
||||
arr = np.fromstring(image_data.data, dtype=np.uint8, sep='')
|
||||
# In https://github.com/openai/gym-http-api/issues/2, we
|
||||
# discovered that someone using Xmonad on Arch was having
|
||||
# a window of size 598 x 398, though a 600 x 400 window
|
||||
# was requested. (Guess Xmonad was preserving a pixel for
|
||||
# the boundary.) So we use the buffer height/width rather
|
||||
# than the requested one.
|
||||
arr = arr.reshape(buffer.height, buffer.width, 4)
|
||||
arr = arr[::-1, :, 0:3]
|
||||
self.window.flip()
|
||||
self.onetime_geoms = []
|
||||
return arr
|
||||
|
||||
# Convenience
|
||||
def draw_circle(self, radius=10, res=30, filled=True, **attrs):
|
||||
geom = make_circle(radius=radius, res=res, filled=filled)
|
||||
_add_attrs(geom, attrs)
|
||||
self.add_onetime(geom)
|
||||
return geom
|
||||
|
||||
def draw_polygon(self, v, filled=True, **attrs):
|
||||
geom = make_polygon(v=v, filled=filled)
|
||||
_add_attrs(geom, attrs)
|
||||
self.add_onetime(geom)
|
||||
return geom
|
||||
|
||||
def draw_polyline(self, v, **attrs):
|
||||
geom = make_polyline(v=v)
|
||||
_add_attrs(geom, attrs)
|
||||
self.add_onetime(geom)
|
||||
return geom
|
||||
|
||||
def draw_line(self, start, end, **attrs):
|
||||
geom = Line(start, end)
|
||||
_add_attrs(geom, attrs)
|
||||
self.add_onetime(geom)
|
||||
return geom
|
||||
|
||||
def get_array(self):
|
||||
self.window.flip()
|
||||
image_data = pyglet.image.get_buffer_manager().get_color_buffer().get_image_data()
|
||||
self.window.flip()
|
||||
arr = np.fromstring(image_data.data, dtype=np.uint8, sep='')
|
||||
arr = arr.reshape(self.height, self.width, 4)
|
||||
return arr[::-1, :, 0:3]
|
||||
|
||||
|
||||
def _add_attrs(geom, attrs):
|
||||
if "color" in attrs:
|
||||
geom.set_color(*attrs["color"])
|
||||
if "linewidth" in attrs:
|
||||
geom.set_linewidth(attrs["linewidth"])
|
||||
|
||||
|
||||
class Geom(object):
|
||||
def __init__(self):
|
||||
self._color = Color((0, 0, 0, 1.0))
|
||||
self.attrs = [self._color]
|
||||
|
||||
def render(self):
|
||||
for attr in reversed(self.attrs):
|
||||
attr.enable()
|
||||
self.render1()
|
||||
for attr in self.attrs:
|
||||
attr.disable()
|
||||
|
||||
def render1(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def add_attr(self, attr):
|
||||
self.attrs.append(attr)
|
||||
|
||||
def set_color(self, r, g, b, alpha=1):
|
||||
self._color.vec4 = (r, g, b, alpha)
|
||||
|
||||
|
||||
class Attr(object):
|
||||
def enable(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def disable(self):
|
||||
pass
|
||||
|
||||
|
||||
class Transform(Attr):
|
||||
def __init__(self, translation=(0.0, 0.0), rotation=0.0, scale=(1, 1)):
|
||||
self.set_translation(*translation)
|
||||
self.set_rotation(rotation)
|
||||
self.set_scale(*scale)
|
||||
|
||||
def enable(self):
|
||||
glPushMatrix()
|
||||
glTranslatef(self.translation[0], self.translation[1], 0) # translate to GL loc ppint
|
||||
glRotatef(RAD2DEG * self.rotation, 0, 0, 1.0)
|
||||
glScalef(self.scale[0], self.scale[1], 1)
|
||||
|
||||
def disable(self):
|
||||
glPopMatrix()
|
||||
|
||||
def set_translation(self, newx, newy):
|
||||
self.translation = (float(newx), float(newy))
|
||||
|
||||
def set_rotation(self, new):
|
||||
self.rotation = float(new)
|
||||
|
||||
def set_scale(self, newx, newy):
|
||||
self.scale = (float(newx), float(newy))
|
||||
|
||||
|
||||
class Color(Attr):
|
||||
def __init__(self, vec4):
|
||||
self.vec4 = vec4
|
||||
|
||||
def enable(self):
|
||||
glColor4f(*self.vec4)
|
||||
|
||||
|
||||
class LineStyle(Attr):
|
||||
def __init__(self, style):
|
||||
self.style = style
|
||||
|
||||
def enable(self):
|
||||
glEnable(GL_LINE_STIPPLE)
|
||||
glLineStipple(1, self.style)
|
||||
|
||||
def disable(self):
|
||||
glDisable(GL_LINE_STIPPLE)
|
||||
|
||||
|
||||
class LineWidth(Attr):
|
||||
def __init__(self, stroke):
|
||||
self.stroke = stroke
|
||||
|
||||
def enable(self):
|
||||
glLineWidth(self.stroke)
|
||||
|
||||
|
||||
class Point(Geom):
|
||||
def __init__(self):
|
||||
Geom.__init__(self)
|
||||
|
||||
def render1(self):
|
||||
glBegin(GL_POINTS) # draw point
|
||||
glVertex3f(0.0, 0.0, 0.0)
|
||||
glEnd()
|
||||
|
||||
|
||||
class FilledPolygon(Geom):
|
||||
def __init__(self, v):
|
||||
Geom.__init__(self)
|
||||
self.v = v
|
||||
|
||||
def render1(self):
|
||||
if len(self.v) == 4:
|
||||
glBegin(GL_QUADS)
|
||||
elif len(self.v) > 4:
|
||||
glBegin(GL_POLYGON)
|
||||
else:
|
||||
glBegin(GL_TRIANGLES)
|
||||
for p in self.v:
|
||||
glVertex3f(p[0], p[1], 0) # draw each vertex
|
||||
glEnd()
|
||||
|
||||
color = (
|
||||
self._color.vec4[0] * 0.5,
|
||||
self._color.vec4[1] * 0.5,
|
||||
self._color.vec4[2] * 0.5,
|
||||
self._color.vec4[3] * 0.5)
|
||||
glColor4f(*color)
|
||||
glBegin(GL_LINE_LOOP)
|
||||
for p in self.v:
|
||||
glVertex3f(p[0], p[1], 0) # draw each vertex
|
||||
glEnd()
|
||||
|
||||
|
||||
def make_circle(radius=10, res=30, filled=True):
|
||||
points = []
|
||||
for i in range(res):
|
||||
ang = 2 * math.pi * i / res
|
||||
points.append((math.cos(ang) * radius, math.sin(ang) * radius))
|
||||
if filled:
|
||||
return FilledPolygon(points)
|
||||
else:
|
||||
return PolyLine(points, True)
|
||||
|
||||
|
||||
def make_polygon(v, filled=True):
|
||||
if filled:
|
||||
return FilledPolygon(v)
|
||||
else:
|
||||
return PolyLine(v, True)
|
||||
|
||||
|
||||
def make_polyline(v):
|
||||
return PolyLine(v, False)
|
||||
|
||||
|
||||
def make_capsule(length, width):
|
||||
l, r, t, b = 0, length, width / 2, -width / 2
|
||||
box = make_polygon([(l, b), (l, t), (r, t), (r, b)])
|
||||
circ0 = make_circle(width / 2)
|
||||
circ1 = make_circle(width / 2)
|
||||
circ1.add_attr(Transform(translation=(length, 0)))
|
||||
geom = Compound([box, circ0, circ1])
|
||||
return geom
|
||||
|
||||
|
||||
class Compound(Geom):
|
||||
def __init__(self, gs):
|
||||
Geom.__init__(self)
|
||||
self.gs = gs
|
||||
for g in self.gs:
|
||||
g.attrs = [a for a in g.attrs if not isinstance(a, Color)]
|
||||
|
||||
def render1(self):
|
||||
for g in self.gs:
|
||||
g.render()
|
||||
|
||||
|
||||
class PolyLine(Geom):
|
||||
def __init__(self, v, close):
|
||||
Geom.__init__(self)
|
||||
self.v = v
|
||||
self.close = close
|
||||
self.linewidth = LineWidth(1)
|
||||
self.add_attr(self.linewidth)
|
||||
|
||||
def render1(self):
|
||||
glBegin(GL_LINE_LOOP if self.close else GL_LINE_STRIP)
|
||||
for p in self.v:
|
||||
glVertex3f(p[0], p[1], 0) # draw each vertex
|
||||
glEnd()
|
||||
|
||||
def set_linewidth(self, x):
|
||||
self.linewidth.stroke = x
|
||||
|
||||
|
||||
class Line(Geom):
|
||||
def __init__(self, start=(0.0, 0.0), end=(0.0, 0.0)):
|
||||
Geom.__init__(self)
|
||||
self.start = start
|
||||
self.end = end
|
||||
self.linewidth = LineWidth(1)
|
||||
self.add_attr(self.linewidth)
|
||||
|
||||
def render1(self):
|
||||
glBegin(GL_LINES)
|
||||
glVertex2f(*self.start)
|
||||
glVertex2f(*self.end)
|
||||
glEnd()
|
||||
|
||||
|
||||
class Image(Geom):
|
||||
def __init__(self, fname, width, height):
|
||||
Geom.__init__(self)
|
||||
self.width = width
|
||||
self.height = height
|
||||
img = pyglet.image.load(fname)
|
||||
self.img = img
|
||||
self.flip = False
|
||||
|
||||
def render1(self):
|
||||
self.img.blit(-self.width / 2, -self.height / 2, width=self.width, height=self.height)
|
||||
|
||||
|
||||
class SimpleImageViewer(object):
|
||||
def __init__(self, display=None):
|
||||
self.window = None
|
||||
self.isopen = False
|
||||
self.display = display
|
||||
|
||||
def imshow(self, arr):
|
||||
if self.window is None:
|
||||
height, width, channels = arr.shape
|
||||
self.window = pyglet.window.Window(width=width, height=height, display=self.display)
|
||||
self.width = width
|
||||
self.height = height
|
||||
self.isopen = True
|
||||
assert arr.shape == (self.height, self.width, 3), "You passed in an image with the wrong number shape"
|
||||
image = pyglet.image.ImageData(self.width, self.height, 'RGB', arr.tobytes(), pitch=self.width * -3)
|
||||
self.window.clear()
|
||||
self.window.switch_to()
|
||||
self.window.dispatch_events()
|
||||
image.blit(0, 0)
|
||||
self.window.flip()
|
||||
|
||||
def close(self):
|
||||
if self.isopen:
|
||||
self.window.close()
|
||||
self.isopen = False
|
||||
|
||||
def __del__(self):
|
||||
self.close()
|
||||
@@ -0,0 +1,124 @@
|
||||
import argparse
|
||||
import re
|
||||
import os
|
||||
|
||||
import ray
|
||||
from ray.tune import run_experiments
|
||||
from ray.tune.registry import register_trainable, register_env, get_trainable_cls
|
||||
import ray.rllib.contrib.maddpg.maddpg as maddpg
|
||||
|
||||
from rllib_multiagent_particle_env import env_creator
|
||||
from util import parse_args
|
||||
|
||||
|
||||
def setup_ray():
|
||||
ray.init(address='auto')
|
||||
|
||||
register_env('particle', env_creator)
|
||||
|
||||
|
||||
def gen_policy(args, env, id):
|
||||
use_local_critic = [
|
||||
args.adv_policy == 'ddpg' if id < args.num_adversaries else
|
||||
args.good_policy == 'ddpg' for id in range(env.num_agents)
|
||||
]
|
||||
return (
|
||||
None,
|
||||
env.observation_space_dict[id],
|
||||
env.action_space_dict[id],
|
||||
{
|
||||
'agent_id': id,
|
||||
'use_local_critic': use_local_critic[id],
|
||||
'obs_space_dict': env.observation_space_dict,
|
||||
'act_space_dict': env.action_space_dict,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def gen_policies(args, env_config):
|
||||
env = env_creator(env_config)
|
||||
return {'policy_%d' % i: gen_policy(args, env, i) for i in range(len(env.observation_space_dict))}
|
||||
|
||||
|
||||
def to_multiagent_config(policies):
|
||||
policy_ids = list(policies.keys())
|
||||
return {
|
||||
'policies': policies,
|
||||
'policy_mapping_fn': lambda index: policy_ids[index]
|
||||
}
|
||||
|
||||
|
||||
def train(args, env_config):
|
||||
def stop(trial_id, result):
|
||||
max_train_time = int(os.environ.get('AML_MAX_TRAIN_TIME_SECONDS', 2 * 60 * 60))
|
||||
|
||||
return result['episode_reward_mean'] >= args.final_reward \
|
||||
or result['time_total_s'] >= max_train_time
|
||||
|
||||
run_experiments({
|
||||
'MADDPG_RLLib': {
|
||||
'run': 'contrib/MADDPG',
|
||||
'env': 'particle',
|
||||
'stop': stop,
|
||||
# Uncomment to enable more frequent checkpoints:
|
||||
# 'checkpoint_freq': args.checkpoint_freq,
|
||||
'checkpoint_at_end': True,
|
||||
'local_dir': args.local_dir,
|
||||
'restore': args.restore,
|
||||
'config': {
|
||||
# === Log ===
|
||||
'log_level': 'ERROR',
|
||||
|
||||
# === Environment ===
|
||||
'env_config': env_config,
|
||||
'num_envs_per_worker': args.num_envs_per_worker,
|
||||
'horizon': args.max_episode_len,
|
||||
|
||||
# === Policy Config ===
|
||||
# --- Model ---
|
||||
'good_policy': args.good_policy,
|
||||
'adv_policy': args.adv_policy,
|
||||
'actor_hiddens': [args.num_units] * 2,
|
||||
'actor_hidden_activation': 'relu',
|
||||
'critic_hiddens': [args.num_units] * 2,
|
||||
'critic_hidden_activation': 'relu',
|
||||
'n_step': args.n_step,
|
||||
'gamma': args.gamma,
|
||||
|
||||
# --- Exploration ---
|
||||
'tau': 0.01,
|
||||
|
||||
# --- Replay buffer ---
|
||||
'buffer_size': int(1e6),
|
||||
|
||||
# --- Optimization ---
|
||||
'actor_lr': args.lr,
|
||||
'critic_lr': args.lr,
|
||||
'learning_starts': args.train_batch_size * args.max_episode_len,
|
||||
'sample_batch_size': args.sample_batch_size,
|
||||
'train_batch_size': args.train_batch_size,
|
||||
'batch_mode': 'truncate_episodes',
|
||||
|
||||
# --- Parallelism ---
|
||||
'num_workers': args.num_workers,
|
||||
'num_gpus': args.num_gpus,
|
||||
'num_gpus_per_worker': 0,
|
||||
|
||||
# === Multi-agent setting ===
|
||||
'multiagent': to_multiagent_config(gen_policies(args, env_config)),
|
||||
},
|
||||
},
|
||||
}, verbose=1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
setup_ray()
|
||||
|
||||
env_config = {
|
||||
'scenario_name': args.scenario,
|
||||
'horizon': args.max_episode_len,
|
||||
'video_frequency': args.checkpoint_freq,
|
||||
}
|
||||
|
||||
train(args, env_config)
|
||||
@@ -0,0 +1,113 @@
|
||||
# Some code taken from: https://github.com/wsjeon/maddpg-rllib/
|
||||
|
||||
import imp
|
||||
import os
|
||||
|
||||
import gym
|
||||
from gym import wrappers
|
||||
from ray import rllib
|
||||
|
||||
from multiagent.environment import MultiAgentEnv
|
||||
import multiagent.scenarios as scenarios
|
||||
|
||||
|
||||
CUSTOM_SCENARIOS = ['simple_switch']
|
||||
|
||||
|
||||
class ParticleEnvRenderWrapper(gym.Wrapper):
|
||||
def __init__(self, env, horizon):
|
||||
super().__init__(env)
|
||||
self.horizon = horizon
|
||||
|
||||
def reset(self):
|
||||
self._num_steps = 0
|
||||
|
||||
return self.env.reset()
|
||||
|
||||
def render(self, mode):
|
||||
if mode == 'human':
|
||||
self.env.render(mode=mode)
|
||||
else:
|
||||
return self.env.render(mode=mode)[0]
|
||||
|
||||
def step(self, actions):
|
||||
obs_list, rew_list, done_list, info_list = self.env.step(actions)
|
||||
|
||||
self._num_steps += 1
|
||||
done = (all(done_list) or self._num_steps >= self.horizon)
|
||||
|
||||
# Gym monitor expects reward to be an int. This is only used for its
|
||||
# stats reporter, which we're not interested in. To make video recording
|
||||
# work, we package the rewards in the info object and extract it below.
|
||||
return obs_list, 0, done, [rew_list, done_list, info_list]
|
||||
|
||||
|
||||
class RLlibMultiAgentParticleEnv(rllib.MultiAgentEnv):
|
||||
def __init__(self, scenario_name, horizon, monitor_enabled=False, video_frequency=500):
|
||||
self._env = _make_env(scenario_name, horizon, monitor_enabled, video_frequency)
|
||||
self.num_agents = self._env.n
|
||||
self.agent_ids = list(range(self.num_agents))
|
||||
|
||||
self.observation_space_dict = self._make_dict(self._env.observation_space)
|
||||
self.action_space_dict = self._make_dict(self._env.action_space)
|
||||
|
||||
def reset(self):
|
||||
obs_dict = self._make_dict(self._env.reset())
|
||||
return obs_dict
|
||||
|
||||
def step(self, action_dict):
|
||||
actions = list(action_dict.values())
|
||||
obs_list, _, _, infos = self._env.step(actions)
|
||||
rew_list, done_list, _ = infos
|
||||
|
||||
obs_dict = self._make_dict(obs_list)
|
||||
rew_dict = self._make_dict(rew_list)
|
||||
done_dict = self._make_dict(done_list)
|
||||
done_dict['__all__'] = all(done_list)
|
||||
info_dict = self._make_dict([{'done': done} for done in done_list])
|
||||
|
||||
return obs_dict, rew_dict, done_dict, info_dict
|
||||
|
||||
def render(self, mode='human'):
|
||||
self._env.render(mode=mode)
|
||||
|
||||
def _make_dict(self, values):
|
||||
return dict(zip(self.agent_ids, values))
|
||||
|
||||
|
||||
def _video_callable(video_frequency):
|
||||
def should_record_video(episode_id):
|
||||
if episode_id % video_frequency == 0:
|
||||
return True
|
||||
return False
|
||||
|
||||
return should_record_video
|
||||
|
||||
|
||||
def _make_env(scenario_name, horizon, monitor_enabled, video_frequency):
|
||||
if scenario_name in CUSTOM_SCENARIOS:
|
||||
# Scenario file must exist locally
|
||||
file_path = os.path.join(os.path.dirname(__file__), scenario_name + '.py')
|
||||
scenario = imp.load_source('', file_path).Scenario()
|
||||
else:
|
||||
scenario = scenarios.load(scenario_name + '.py').Scenario()
|
||||
|
||||
world = scenario.make_world()
|
||||
|
||||
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
|
||||
env.metadata['video.frames_per_second'] = 8
|
||||
|
||||
env = ParticleEnvRenderWrapper(env, horizon)
|
||||
|
||||
if not monitor_enabled:
|
||||
return env
|
||||
|
||||
return wrappers.Monitor(env, './logs/videos', resume=True, video_callable=_video_callable(video_frequency))
|
||||
|
||||
|
||||
def env_creator(config):
|
||||
monitor_enabled = False
|
||||
if hasattr(config, 'worker_index') and hasattr(config, 'vector_index'):
|
||||
monitor_enabled = (config.worker_index == 1 and config.vector_index == 0)
|
||||
|
||||
return RLlibMultiAgentParticleEnv(**config, monitor_enabled=monitor_enabled)
|
||||
@@ -0,0 +1,358 @@
|
||||
import numpy as np
|
||||
import random
|
||||
|
||||
from multiagent.core import World, Agent, Landmark
|
||||
from multiagent.scenario import BaseScenario
|
||||
|
||||
|
||||
class SwitchWorld(World):
|
||||
""" Extended World with hills and switches """
|
||||
def __init__(self, hills, switches):
|
||||
super().__init__()
|
||||
# add hills and switches
|
||||
self.hills = hills
|
||||
self.switches = switches
|
||||
self.landmarks.extend(self.hills)
|
||||
self.landmarks.extend(self.switches)
|
||||
|
||||
def step(self):
|
||||
|
||||
super().step()
|
||||
|
||||
# if all hills are activated, reset the switches and hills
|
||||
if all([hill.active for hill in self.hills]):
|
||||
self.reset_hills()
|
||||
self.reset_switches()
|
||||
else:
|
||||
# Update switches
|
||||
for switch in self.switches:
|
||||
switch.step(self)
|
||||
# Update hills
|
||||
for hill in self.hills:
|
||||
hill.step(self)
|
||||
|
||||
def reset_hills(self):
|
||||
possible_hill_positions = [np.array([-0.8, 0]), np.array([0, 0.8]), np.array([0.8, 0]), np.array([0, -0.8])]
|
||||
hill_positions = random.sample(possible_hill_positions, k=len(self.hills))
|
||||
for i, hill in enumerate(self.hills):
|
||||
hill.state.p_pos = hill_positions[i]
|
||||
hill.deactivate()
|
||||
|
||||
def reset_switches(self):
|
||||
possible_switch_positions = [
|
||||
np.array([-0.8, -0.8]),
|
||||
np.array([-0.8, 0.8]),
|
||||
np.array([0.8, -0.8]),
|
||||
np.array([0.8, 0.8])]
|
||||
switch_positions = random.sample(possible_switch_positions, k=len(self.switches))
|
||||
for i, switch in enumerate(self.switches):
|
||||
switch.state.p_pos = switch_positions[i]
|
||||
switch.deactivate()
|
||||
|
||||
|
||||
class Scenario(BaseScenario):
|
||||
def make_world(self):
|
||||
|
||||
# main configurations
|
||||
num_agents = 2
|
||||
num_hills = 2
|
||||
num_switches = 1
|
||||
self.max_episode_length = 100
|
||||
|
||||
# create hills (on edges)
|
||||
possible_hill_positions = [np.array([-0.8, 0]), np.array([0, 0.8]), np.array([0.8, 0]), np.array([0, -0.8])]
|
||||
hill_positions = random.sample(possible_hill_positions, k=num_hills)
|
||||
hills = [Hill(hill_positions[i]) for i in range(num_hills)]
|
||||
# create switches (in corners)
|
||||
possible_switch_positions = [
|
||||
np.array([-0.8, -0.8]),
|
||||
np.array([-0.8, 0.8]),
|
||||
np.array([0.8, -0.8]),
|
||||
np.array([0.8, 0.8])]
|
||||
switch_positions = random.sample(possible_switch_positions, k=num_switches)
|
||||
switches = [Switch(switch_positions[i]) for i in range(num_switches)]
|
||||
|
||||
# make world and set basic properties
|
||||
world = SwitchWorld(hills, switches)
|
||||
world.dim_c = 2
|
||||
world.collaborative = True
|
||||
|
||||
# add agents
|
||||
world.agents = [Agent() for i in range(num_agents)]
|
||||
for i, agent in enumerate(world.agents):
|
||||
agent.name = 'agent %d' % i
|
||||
agent.collide = True
|
||||
agent.silent = True
|
||||
agent.size = 0.1
|
||||
agent.accel = 5.0
|
||||
agent.max_speed = 5.0
|
||||
if i == 0:
|
||||
agent.color = np.array([0.35, 0.35, 0.85])
|
||||
else:
|
||||
agent.color = np.array([0.35, 0.85, 0.85])
|
||||
|
||||
# make initial conditions
|
||||
self.reset_world(world)
|
||||
|
||||
return world
|
||||
|
||||
def reset_world(self, world):
|
||||
# set random initial states
|
||||
for agent in world.agents:
|
||||
agent.state.p_pos = np.array([random.uniform(-1, +1) for _ in range(world.dim_p)])
|
||||
agent.state.p_vel = np.zeros(world.dim_p)
|
||||
agent.state.c = np.zeros(world.dim_c)
|
||||
# set hills randomly
|
||||
world.reset_hills()
|
||||
# set switches randomly
|
||||
world.reset_switches()
|
||||
|
||||
def is_collision(self, agent1, agent2):
|
||||
delta_pos = agent1.state.p_pos - agent2.state.p_pos
|
||||
dist = np.sqrt(np.sum(np.square(delta_pos)))
|
||||
dist_min = agent1.size + agent2.size
|
||||
return True if dist < dist_min else False
|
||||
|
||||
def reward(self, agent, world):
|
||||
# Agents are rewarded based on number of landmarks activated
|
||||
rew = 0
|
||||
if all([h.active for h in world.hills]):
|
||||
rew += 100
|
||||
else:
|
||||
# give bonus each time a hill is activated
|
||||
for hill in world.hills:
|
||||
if hill.activated_just_now:
|
||||
rew += 50
|
||||
# penalise timesteps where nothing is happening
|
||||
if rew == 0:
|
||||
rew -= 0.1
|
||||
# add collision penalty
|
||||
if agent.collide:
|
||||
for a in world.agents:
|
||||
# note: this also counts collision with "itself", so gives -1 at every timestep
|
||||
# would be good to tune the reward function and use (not a == agent) here
|
||||
if self.is_collision(a, agent):
|
||||
rew -= 1
|
||||
return rew
|
||||
|
||||
def observation(self, agent, world):
|
||||
# get positions of all entities in this agent's reference frame
|
||||
entity_pos = []
|
||||
for entity in world.landmarks: # world.entities:
|
||||
entity_pos.append(entity.state.p_pos - agent.state.p_pos)
|
||||
# entity colors
|
||||
entity_color = []
|
||||
for entity in world.landmarks: # world.entities:
|
||||
entity_color.append(entity.color)
|
||||
# communication of all other agents
|
||||
comm = []
|
||||
other_pos = []
|
||||
for other in world.agents:
|
||||
if other is agent:
|
||||
continue
|
||||
comm.append(other.state.c)
|
||||
other_pos.append(other.state.p_pos - agent.state.p_pos)
|
||||
return np.concatenate([agent.state.p_vel] + [agent.state.p_pos] + entity_pos + other_pos + comm)
|
||||
|
||||
|
||||
class Hill(Landmark):
|
||||
"""
|
||||
A hill that can be captured by an agent.
|
||||
To be captured, a team must occupy a hill for a fixed amount of time.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pos=None,
|
||||
size=0.08,
|
||||
capture_time=2
|
||||
):
|
||||
|
||||
# Initialize Landmark super class
|
||||
super().__init__()
|
||||
self.movable = False
|
||||
self.collide = False
|
||||
self.state.p_pos = pos
|
||||
self.size = size
|
||||
|
||||
# Set static configurations
|
||||
self.capture_time = capture_time
|
||||
|
||||
# Initialize all hills to be inactive
|
||||
self.active = False
|
||||
self.color = np.array([0.5, 0.5, 0.5])
|
||||
self.capture_timer = 0
|
||||
|
||||
self.activated_just_now = False
|
||||
|
||||
def activate(self):
|
||||
self.active = True
|
||||
self.color = np.array([0.1, 0.1, 0.9])
|
||||
|
||||
def deactivate(self):
|
||||
self.active = False
|
||||
self.color = np.array([0.5, 0.5, 0.5])
|
||||
|
||||
def _is_occupied(self, agents):
|
||||
# a hill is occupied if an agent stands on it
|
||||
for agent in agents:
|
||||
dist = np.sqrt(np.sum(np.square(agent.state.p_pos - self.state.p_pos)))
|
||||
if dist < agent.size + self.size:
|
||||
return True
|
||||
return False
|
||||
|
||||
def step(self, world):
|
||||
|
||||
self.activated_just_now = False
|
||||
|
||||
# If hill isn't activated yet, check if an agent activates it
|
||||
# if (not self.active) and (world.switch.is_active()):
|
||||
if (not self.active):
|
||||
|
||||
# Check if an agent is on the hill and all switches are active
|
||||
if (self._is_occupied(world.agents)) and all([switch.active for switch in world.switches]):
|
||||
self.capture_timer += 1
|
||||
|
||||
# activate hill (this is irreversible)
|
||||
if self.capture_timer > self.capture_time:
|
||||
self.activate()
|
||||
self.activated_just_now = True
|
||||
|
||||
# Reset capture timer if hill is not occupied
|
||||
else:
|
||||
self.capture_timer = 0
|
||||
|
||||
|
||||
class Switch(Landmark):
|
||||
"""
|
||||
A switch that can be activated by an agent.
|
||||
The agent has to stay on the switch for it to be active.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pos=None,
|
||||
size=0.03,
|
||||
):
|
||||
|
||||
# Initialize Landmark super class
|
||||
super().__init__()
|
||||
self.movable = False
|
||||
self.collide = False
|
||||
self.state.p_pos = pos
|
||||
self.size = size
|
||||
|
||||
# Initialize all hills to be inactive
|
||||
self.active = False
|
||||
self.color = np.array([0.8, 0.05, 0.3])
|
||||
self.capture_timer = 0
|
||||
|
||||
def activate(self):
|
||||
self.active = True
|
||||
self.color = np.array([0.1, 0.9, 0.4])
|
||||
|
||||
def deactivate(self):
|
||||
self.active = False
|
||||
self.color = np.array([0.8, 0.05, 0.3])
|
||||
|
||||
def _is_occupied(self, agents):
|
||||
# a switch is active if an agent stands on it
|
||||
for agent in agents:
|
||||
dist = np.sqrt(np.sum(np.square(agent.state.p_pos - self.state.p_pos)))
|
||||
if dist < agent.size + self.size:
|
||||
return True
|
||||
return False
|
||||
|
||||
def step(self, world):
|
||||
# check if an agent is on the switch and activate/deactive accordingly
|
||||
if self._is_occupied(world.agents):
|
||||
self.activate()
|
||||
else:
|
||||
self.deactivate()
|
||||
|
||||
|
||||
class SwitchExpertPolicy():
|
||||
"""
|
||||
Hand-coded expert policy for the simple switch environment.
|
||||
Types of possible experts:
|
||||
- always go to the switch
|
||||
- always go to the hills
|
||||
"""
|
||||
def __init__(self, dim_c, agent, world, expert_type=None, discrete_action_input=True):
|
||||
|
||||
self.dim_c = dim_c
|
||||
self.discrete_action_input = discrete_action_input
|
||||
# the agent we control and world we're in
|
||||
self.agent = agent
|
||||
self.world = world
|
||||
|
||||
if expert_type is None:
|
||||
self.expert_type = random.choice(['switch', 'hill'])
|
||||
else:
|
||||
self.expert_type = expert_type
|
||||
if self.expert_type == 'switch':
|
||||
self.target_switch = self.select_inital_target_switch()
|
||||
elif self.expert_type == 'hill':
|
||||
self.target_hill = self.select_inital_target_hill()
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
self.step_count = 0
|
||||
|
||||
def select_inital_target_switch(self):
|
||||
return random.choice(self.world.switches)
|
||||
|
||||
def select_inital_target_hill(self):
|
||||
return random.choice(self.world.hills)
|
||||
|
||||
def action(self):
|
||||
|
||||
# select a target!
|
||||
if self.expert_type == 'switch':
|
||||
# if agent is not already on a switch, choose target switch
|
||||
if not any([switch._is_occupied([self.agent]) for switch in self.world.switches]):
|
||||
# select a target switch if there's an inactive one
|
||||
inactive_switches = [switch for switch in self.world.switches if not switch.active]
|
||||
if len(inactive_switches) > 0 and (self.target_switch not in inactive_switches):
|
||||
self.target_switch = random.choice(inactive_switches)
|
||||
target = self.target_switch.state.p_pos
|
||||
elif self.expert_type == 'hill':
|
||||
# select a target hill if we haven't done so yet, or the current target switch is inactive
|
||||
inactive_hills = [hill for hill in self.world.hills if not hill.active]
|
||||
if len(inactive_hills) > 0 and (self.target_hill not in inactive_hills):
|
||||
self.target_hill = random.choice(inactive_hills)
|
||||
target = self.target_hill.state.p_pos
|
||||
|
||||
self.step_count += 1
|
||||
|
||||
impulse = np.clip(target - self.agent.state.p_pos, -self.agent.u_range, self.agent.u_range)
|
||||
|
||||
if self.discrete_action_input:
|
||||
u_idx = np.argmax(np.abs(impulse))
|
||||
if u_idx == 0 and impulse[u_idx] < 0:
|
||||
u = 1
|
||||
elif u_idx == 0 and impulse[u_idx] > 0:
|
||||
u = 2
|
||||
elif u_idx == 1 and impulse[u_idx] < 0:
|
||||
u = 3
|
||||
elif u_idx == 1 and impulse[u_idx] > 0:
|
||||
u = 4
|
||||
else:
|
||||
u = 0
|
||||
else:
|
||||
u = np.zeros(5)
|
||||
if (impulse[0] == impulse[1] == 0) \
|
||||
or (self.step_count < self.burn_in) \
|
||||
or (self.burn_step != 0 and self.step_count % self.burn_step != 0):
|
||||
u[0] = 0.1
|
||||
else:
|
||||
pass
|
||||
# u: noop (?), right, left, down, up
|
||||
if impulse[0] > 0: # x-direction (- left/right + )
|
||||
u[1] = impulse[0] # right
|
||||
elif impulse[0] < 0:
|
||||
u[2] = -impulse[0]
|
||||
if impulse[1] > 0: # y-direction (- up/down + )
|
||||
u[3] = impulse[1]
|
||||
elif impulse[1] < 0:
|
||||
u[4] = -impulse[1]
|
||||
|
||||
return u
|
||||
@@ -0,0 +1,82 @@
|
||||
import argparse
|
||||
import os
|
||||
import re
|
||||
|
||||
from rllib_multiagent_particle_env import CUSTOM_SCENARIOS
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser('MADDPG with OpenAI MPE')
|
||||
|
||||
# Environment
|
||||
parser.add_argument('--scenario', type=str, default='simple',
|
||||
choices=['simple', 'simple_speaker_listener',
|
||||
'simple_crypto', 'simple_push',
|
||||
'simple_tag', 'simple_spread', 'simple_adversary'
|
||||
] + CUSTOM_SCENARIOS,
|
||||
help='name of the scenario script')
|
||||
parser.add_argument('--max-episode-len', type=int, default=25,
|
||||
help='maximum episode length')
|
||||
parser.add_argument('--num-episodes', type=int, default=60000,
|
||||
help='number of episodes')
|
||||
parser.add_argument('--num-adversaries', type=int, default=0,
|
||||
help='number of adversaries')
|
||||
parser.add_argument('--good-policy', type=str, default='maddpg',
|
||||
help='policy for good agents')
|
||||
parser.add_argument('--adv-policy', type=str, default='maddpg',
|
||||
help='policy of adversaries')
|
||||
|
||||
# Core training parameters
|
||||
parser.add_argument('--lr', type=float, default=1e-2,
|
||||
help='learning rate for Adam optimizer')
|
||||
parser.add_argument('--gamma', type=float, default=0.95,
|
||||
help='discount factor')
|
||||
# NOTE: 1 iteration = sample_batch_size * num_workers timesteps * num_envs_per_worker
|
||||
parser.add_argument('--sample-batch-size', type=int, default=25,
|
||||
help='number of data points sampled /update /worker')
|
||||
parser.add_argument('--train-batch-size', type=int, default=1024,
|
||||
help='number of data points /update')
|
||||
parser.add_argument('--n-step', type=int, default=1,
|
||||
help='length of multistep value backup')
|
||||
parser.add_argument('--num-units', type=int, default=64,
|
||||
help='number of units in the mlp')
|
||||
parser.add_argument('--final-reward', type=int, default=-400,
|
||||
help='final reward after which to stop training')
|
||||
|
||||
# Checkpoint
|
||||
parser.add_argument('--checkpoint-freq', type=int, default=200,
|
||||
help='save model once every time this many iterations are completed')
|
||||
parser.add_argument('--local-dir', type=str, default='./logs',
|
||||
help='path to save checkpoints')
|
||||
parser.add_argument('--restore', type=str, default=None,
|
||||
help='directory in which training state and model are loaded')
|
||||
|
||||
# Parallelism
|
||||
parser.add_argument('--num-workers', type=int, default=1)
|
||||
parser.add_argument('--num-envs-per-worker', type=int, default=4)
|
||||
parser.add_argument('--num-gpus', type=int, default=0)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def find_final_checkpoint(start_dir):
|
||||
def find(pattern, path):
|
||||
result = []
|
||||
for root, _, files in os.walk(path):
|
||||
for name in files:
|
||||
if pattern.match(name):
|
||||
result.append(os.path.join(root, name))
|
||||
return result
|
||||
|
||||
cp_pattern = re.compile('.*checkpoint-\\d+$')
|
||||
checkpoint_files = find(cp_pattern, start_dir)
|
||||
|
||||
checkpoint_numbers = []
|
||||
for file in checkpoint_files:
|
||||
checkpoint_numbers.append(int(file.split('-')[-1]))
|
||||
|
||||
final_checkpoint_number = max(checkpoint_numbers)
|
||||
|
||||
return next(
|
||||
checkpoint_file for checkpoint_file in checkpoint_files
|
||||
if checkpoint_file.endswith(str(final_checkpoint_number)))
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 350 KiB |
@@ -0,0 +1,526 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Reinforcement Learning in Azure Machine Learning - Training multiple agents on collaborative ParticleEnv tasks\n",
|
||||
"\n",
|
||||
"This tutorial will show you how to train policies in a multi-agent scenario.\n",
|
||||
"We use OpenAI Gym's [Particle environments](https://github.com/openai/multiagent-particle-envs),\n",
|
||||
"which model agents and landmarks in a two-dimensional world. Particle comes with\n",
|
||||
"several predefined scenarios, both competitive and collaborative, and with or without communication.\n",
|
||||
"\n",
|
||||
"For this tutorial, we pick a cooperative navigation scenario where N agents are in a world with N\n",
|
||||
"landmarks. The agents' goal is to cover all the landmarks without collisions,\n",
|
||||
"so agents must learn to avoid each other (social distancing!). The video below shows training\n",
|
||||
"results for N=3 agents/landmarks:\n",
|
||||
"\n",
|
||||
"<table style=\"width:50%\">\n",
|
||||
" <tr>\n",
|
||||
" <th style=\"text-align: center;\">\n",
|
||||
" <img src=\"./images/particle_simple_spread.gif\" alt=\"Particle video\" align=\"middle\" margin-left=\"auto\" margin-right=\"auto\"/>\n",
|
||||
" </th>\n",
|
||||
" </tr>\n",
|
||||
" <tr style=\"text-align: center;\">\n",
|
||||
" <th>Fig 1. Video of 3 agents covering 3 landmarks in a multiagent Particle scenario.</th>\n",
|
||||
" </tr>\n",
|
||||
"</table>\n",
|
||||
"\n",
|
||||
"The tutorial will cover the following steps:\n",
|
||||
"- Initializing Azure Machine Learning resources for training\n",
|
||||
"- Training policies in a multi-agent environment with Azure Machine Learning service\n",
|
||||
"- Monitoring training progress\n",
|
||||
"\n",
|
||||
"## Prerequisites\n",
|
||||
"\n",
|
||||
"The user should have completed the Azure Machine Learning introductory tutorial. You will need to make sure that you have a valid subscription id, a resource group and a workspace. For detailed instructions see [Tutorial: Get started creating your first ML experiment](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup).\n",
|
||||
"\n",
|
||||
"Please ensure that you have a current version of IPython (>= 7.15) installed.\n",
|
||||
"\n",
|
||||
"While this is a standalone notebook, we highly recommend going over the introductory notebooks for RL first.\n",
|
||||
"- Getting started:\n",
|
||||
" - [RL using a compute instance with Azure Machine Learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb)\n",
|
||||
" - [RL using Azure Machine Learning compute](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb)\n",
|
||||
"- [Scaling RL training runs with Azure Machine Learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb)\n",
|
||||
"\n",
|
||||
"Advanced users might also be interested in [this notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/minecraft.ipynb) demonstrating how to train a Minecraft RL agent in Azure Machine Learning.\n",
|
||||
"\n",
|
||||
"## Initialize resources\n",
|
||||
"\n",
|
||||
"All required Azure Machine Learning service resources for this tutorial can be set up from Jupyter. This includes:\n",
|
||||
"\n",
|
||||
"- Connecting to your existing Azure Machine Learning workspace.\n",
|
||||
"- Creating an experiment to track runs.\n",
|
||||
"- Creating remote compute targets for [Ray](https://docs.ray.io/en/latest/index.html).\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"### Azure Machine Learning SDK\n",
|
||||
"\n",
|
||||
"Display the Azure Machine Learning SDK version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"print('Azure Machine Learning SDK Version: ', azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Connect to workspace\n",
|
||||
"\n",
|
||||
"Get a reference to an existing Azure Machine Learning workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.location, ws.resource_group, sep=' | ')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create an experiment\n",
|
||||
"\n",
|
||||
"Create an experiment to track the runs in your workspace. A\n",
|
||||
"workspace can have multiple experiments and each experiment\n",
|
||||
"can be used to track multiple runs (see [documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py)\n",
|
||||
"for details)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"exp = Experiment(workspace=ws, name='particle-multiagent')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create or attach an existing compute resource\n",
|
||||
"\n",
|
||||
"A compute target is a designated compute resource where you run your training script. For more information, see [What are compute targets in Azure Machine Learning service?](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target).\n",
|
||||
"\n",
|
||||
"#### CPU target for Ray head\n",
|
||||
"\n",
|
||||
"In the experiment setup for this tutorial, the Ray head node will\n",
|
||||
"run on a CPU node (D3 type). A maximum cluster size of 1 node is\n",
|
||||
"therefore sufficient. If you wish to run multiple experiments in\n",
|
||||
"parallel using the same CPU cluster, you may elect to increase this\n",
|
||||
"number. The cluster will automatically scale down to 0 nodes when\n",
|
||||
"no training jobs are scheduled (see min_nodes).\n",
|
||||
"\n",
|
||||
"The code below creates a compute cluster of D3 type nodes.\n",
|
||||
"If the cluster with the specified name is already in your workspace\n",
|
||||
"the code will skip the creation process.\n",
|
||||
"\n",
|
||||
"**Note: Creation of a compute resource can take several minutes**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import AmlCompute, ComputeTarget\n",
|
||||
"\n",
|
||||
"cpu_cluster_name = 'cpu-cl-d3'\n",
|
||||
"\n",
|
||||
"if cpu_cluster_name in ws.compute_targets:\n",
|
||||
" cpu_cluster = ws.compute_targets[cpu_cluster_name]\n",
|
||||
" if cpu_cluster and type(cpu_cluster) is AmlCompute:\n",
|
||||
" if cpu_cluster.provisioning_state == 'Succeeded':\n",
|
||||
" print('Found existing compute target for {}. Using it.'.format(cpu_cluster_name))\n",
|
||||
" else: \n",
|
||||
" raise Exception('Found existing compute target for {} '.format(cpu_cluster_name)\n",
|
||||
" + 'but it is in state {}'.format(cpu_cluster.provisioning_state))\n",
|
||||
"else:\n",
|
||||
" print('Creating a new compute target for {}...'.format(cpu_cluster_name))\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(\n",
|
||||
" vm_size='STANDARD_D3',\n",
|
||||
" min_nodes=0, \n",
|
||||
" max_nodes=1)\n",
|
||||
"\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
|
||||
" cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
|
||||
" \n",
|
||||
" print('Cluster created.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Training the policies\n",
|
||||
"\n",
|
||||
"### Training environment\n",
|
||||
"\n",
|
||||
"This tutorial uses a custom docker image\n",
|
||||
"with the necessary software installed. The [Environment](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments)\n",
|
||||
"class stores the configuration for the training environment. The\n",
|
||||
"docker image is set via `env.docker.base_image`.\n",
|
||||
"`user_managed_dependencies` is set so that\n",
|
||||
"the preinstalled Python packages in the image are preserved.\n",
|
||||
"\n",
|
||||
"Note that since we want to capture videos of the training runs requiring a display, we set the interpreter_path such that the Python process is started via **xvfb-run**."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from azureml.core import Environment\n",
|
||||
" \n",
|
||||
"cpu_particle_env = Environment(name='particle-cpu')\n",
|
||||
"\n",
|
||||
"cpu_particle_env.docker.enabled = True\n",
|
||||
"cpu_particle_env.docker.base_image = 'akdmsft/particle-cpu'\n",
|
||||
"cpu_particle_env.python.interpreter_path = 'xvfb-run -s \"-screen 0 640x480x16 -ac +extension GLX +render\" python'\n",
|
||||
"\n",
|
||||
"max_train_time = os.environ.get('AML_MAX_TRAIN_TIME_SECONDS', 2 * 60 * 60)\n",
|
||||
"cpu_particle_env.environment_variables['AML_MAX_TRAIN_TIME_SECONDS'] = str(max_train_time)\n",
|
||||
"cpu_particle_env.python.user_managed_dependencies = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Training script\n",
|
||||
"\n",
|
||||
"This tutorial uses the multiagent algorithm [Multi-Agent Deep Deterministic Policy Gradient (MADDPG)](https://docs.ray.io/en/latest/rllib-algorithms.html?highlight=maddpg#multi-agent-deep-deterministic-policy-gradient-contrib-maddpg).\n",
|
||||
"For training policies in a multiagent scenario, Ray's RLlib also\n",
|
||||
"requires the `multiagent` configuration section to be specified. You\n",
|
||||
"can find more information in the [common parameters](https://docs.ray.io/en/latest/rllib-training.html?highlight=multiagent#common-parameters)\n",
|
||||
"documentation.\n",
|
||||
"\n",
|
||||
"For monitoring and understanding the training progress, one\n",
|
||||
"of the training environments is wrapped in a [Gym monitor](https://github.com/openai/gym/blob/master/gym/wrappers/monitor.py)\n",
|
||||
"which periodically captures videos - by default every 200 training\n",
|
||||
"iterations.\n",
|
||||
"\n",
|
||||
"The stopping criteria are set such that the training run is\n",
|
||||
"terminated after either a mean reward of -400 is observed, or\n",
|
||||
"training has run for over 2 hours.\n",
|
||||
"\n",
|
||||
"### Submitting a training run\n",
|
||||
"\n",
|
||||
"Below, you create the training run using a `ReinforcementLearningEstimator`\n",
|
||||
"object, which contains all the configuration parameters for this experiment:\n",
|
||||
"\n",
|
||||
"- `source_directory`: Contains the training script and helper files to be\n",
|
||||
" copied onto the node.\n",
|
||||
"- `entry_script`: The training script, described in more detail above.\n",
|
||||
"- `script_params`: The command line arguments to pass to the entry script.\n",
|
||||
"- `compute_target`: The compute target for training script execution.\n",
|
||||
"- `environment`: The Azure Machine Learning environment definition for the node running the training.\n",
|
||||
"- `max_run_duration_seconds`: The time after which to abort the run if it is still running.\n",
|
||||
"\n",
|
||||
"For more details, please take a look at the [online documentation](https://docs.microsoft.com/en-us/python/api/azureml-contrib-reinforcementlearning/?view=azure-ml-py)\n",
|
||||
"for Azure Machine Learning service's reinforcement learning offering.\n",
|
||||
"\n",
|
||||
"Note that you can use the same notebook and scripts to experiment with\n",
|
||||
"different Particle environments. You can find a list of supported\n",
|
||||
"environments [here](https://github.com/openai/multiagent-particle-envs/tree/master#list-of-environments).\n",
|
||||
"Simply change the `--scenario` parameter to a supported scenario.\n",
|
||||
"\n",
|
||||
"In order to get the best training results, you can also adjust the\n",
|
||||
"`--final-reward` parameter to determine when to stop training. A greater\n",
|
||||
"reward means longer running time, but improved results. By default,\n",
|
||||
"the final reward will be -400, which should show good progress after\n",
|
||||
"about one hour of run time.\n",
|
||||
"\n",
|
||||
"For this notebook, we use a single D3 nodes, giving us a total of 4 CPUs and\n",
|
||||
"0 GPUs. One CPU is used by the MADDPG trainer, and an additional CPU is\n",
|
||||
"consumed by the RLlib rollout worker. The other 2 CPUs are not used, though\n",
|
||||
"smaller node types will run out of memory for this task.\n",
|
||||
"\n",
|
||||
"Lastly, the RunDetails widget displays information about the submitted RL\n",
|
||||
"experiment, including a link to the Azure portal with more details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.train.rl import ReinforcementLearningEstimator\n",
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"\n",
|
||||
"estimator = ReinforcementLearningEstimator(\n",
|
||||
" source_directory='files',\n",
|
||||
" entry_script='particle_train.py',\n",
|
||||
" script_params={\n",
|
||||
" '--scenario': 'simple_spread',\n",
|
||||
" '--final-reward': -400\n",
|
||||
" },\n",
|
||||
" compute_target=cpu_cluster,\n",
|
||||
" environment=cpu_particle_env,\n",
|
||||
" max_run_duration_seconds=3 * 60 * 60\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"train_run = exp.submit(config=estimator)\n",
|
||||
"\n",
|
||||
"RunDetails(train_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# If you wish to cancel the run before it completes, uncomment and execute:\n",
|
||||
"#train_run.cancel()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Monitoring training progress\n",
|
||||
"\n",
|
||||
"### View the Tensorboard\n",
|
||||
"\n",
|
||||
"The Tensorboard can be displayed via the Azure Machine Learning\n",
|
||||
"service's [Tensorboard API](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-tensorboard).\n",
|
||||
"When running locally, please make sure to follow the instructions\n",
|
||||
"in the link and install required packages. Running this cell will output a URL for the Tensorboard.\n",
|
||||
"\n",
|
||||
"Note that the training script sets the log directory when\n",
|
||||
"starting RLlib via the local_dir parameter. ./logs will automatically\n",
|
||||
"appear in the downloadable files for a run. Since this script is\n",
|
||||
"executed on the Ray head node run, we need to get a reference to it\n",
|
||||
"as shown below.\n",
|
||||
"\n",
|
||||
"The Tensorboard API will continuously stream logs from the run.\n",
|
||||
"\n",
|
||||
"**Note: It may take a couple of minutes after the run is in \"Running\"\n",
|
||||
"state before Tensorboard files are available and the board will refresh automatically**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import time\n",
|
||||
"from azureml.tensorboard import Tensorboard\n",
|
||||
"\n",
|
||||
"head_run = None\n",
|
||||
"\n",
|
||||
"timeout = 60\n",
|
||||
"while timeout > 0 and head_run is None:\n",
|
||||
" timeout -= 1\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" head_run = next(r for r in train_run.get_children() if r.id.endswith('head'))\n",
|
||||
" except StopIteration:\n",
|
||||
" time.sleep(1)\n",
|
||||
"\n",
|
||||
"tb = Tensorboard([head_run])\n",
|
||||
"tb.start()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### View training videos\n",
|
||||
"\n",
|
||||
"As mentioned above, we record videos of the agents interacting with the\n",
|
||||
"Particle world. These videos are often a crucial indicator for training\n",
|
||||
"success. The code below downloads the latest video as it becomes available\n",
|
||||
"and displays it in-line.\n",
|
||||
"\n",
|
||||
"Over time, the agents learn to cooperate and avoid collisions while\n",
|
||||
"traveling to all landmarks.\n",
|
||||
"\n",
|
||||
"**Note: It can take several minutes for a video to appear after the run\n",
|
||||
"was started.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import tempfile\n",
|
||||
"from azureml.core import Dataset\n",
|
||||
"from azureml.data.dataset_error_handling import DatasetValidationError\n",
|
||||
"\n",
|
||||
"from IPython.display import clear_output\n",
|
||||
"from IPython.core.display import display, Video\n",
|
||||
"\n",
|
||||
"datastore = ws.get_default_datastore()\n",
|
||||
"path_prefix = './tmp_videos'\n",
|
||||
"\n",
|
||||
"def download_latest_training_video(run, video_checkpoint_counter):\n",
|
||||
" run_artifacts_path = os.path.join('azureml', run.id)\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" run_artifacts_ds = Dataset.File.from_files(datastore.path(os.path.join(run_artifacts_path, '**')))\n",
|
||||
" except DatasetValidationError as e:\n",
|
||||
" # This happens at the start of the run when there is no data available\n",
|
||||
" # in the run's artifacts\n",
|
||||
" return None, video_checkpoint_counter\n",
|
||||
" \n",
|
||||
" video_files = [file for file in run_artifacts_ds.to_path() if file.endswith('.mp4')]\n",
|
||||
" if len(video_files) == video_checkpoint_counter:\n",
|
||||
" return None, video_checkpoint_counter\n",
|
||||
" \n",
|
||||
" iteration_numbers = [int(vf[vf.rindex('video') + len('video') : vf.index('.mp4')]) for vf in video_files]\n",
|
||||
" latest_video = next(vf for vf in video_files if vf.endswith('{num}.mp4'.format(num=max(iteration_numbers))))\n",
|
||||
" latest_video = os.path.join(run_artifacts_path, os.path.normpath(latest_video[1:]))\n",
|
||||
" \n",
|
||||
" datastore.download(\n",
|
||||
" target_path=path_prefix,\n",
|
||||
" prefix=latest_video.replace('\\\\', '/'),\n",
|
||||
" show_progress=False)\n",
|
||||
" \n",
|
||||
" return os.path.join(path_prefix, latest_video), len(video_files)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def render_video(vf):\n",
|
||||
" clear_output(wait=True)\n",
|
||||
" display(Video(data=vf, embed=True, html_attributes='loop autoplay width=50%'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import shutil\n",
|
||||
"\n",
|
||||
"terminal_statuses = ['Canceled', 'Completed', 'Failed']\n",
|
||||
"video_checkpoint_counter = 0\n",
|
||||
"\n",
|
||||
"while head_run.get_status() not in terminal_statuses:\n",
|
||||
" video_file, video_checkpoint_counter = download_latest_training_video(head_run, video_checkpoint_counter)\n",
|
||||
" if video_file is not None:\n",
|
||||
" render_video(video_file)\n",
|
||||
" \n",
|
||||
" print('Displaying video number {}'.format(video_checkpoint_counter))\n",
|
||||
" shutil.rmtree(path_prefix)\n",
|
||||
" \n",
|
||||
" # Interrupting the kernel can take up to 15 seconds\n",
|
||||
" # depending on when time.sleep started\n",
|
||||
" time.sleep(15)\n",
|
||||
" \n",
|
||||
"train_run.wait_for_completion()\n",
|
||||
"print('The training run has reached a terminal status.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Cleaning up\n",
|
||||
"\n",
|
||||
"Below, you can find code snippets for your convenience to clean up any resources created as part of this tutorial you don't wish to retain."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# to stop the Tensorboard, uncomment and run\n",
|
||||
"#tb.stop()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# to delete the cpu compute target, uncomment and run\n",
|
||||
"#cpu_cluster.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"We would love to hear your feedback! Please let us know what you think of Reinforcement Learning in Azure Machine Learning and what features you are looking forward to."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "andress"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
},
|
||||
"notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00afLicensed under the MIT License.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00af "
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
name: particle
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-contrib-reinforcementlearning
|
||||
- azureml-widgets
|
||||
- tensorboard
|
||||
- azureml-tensorboard
|
||||
- ipython
|
||||
@@ -58,7 +58,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Get Azure Machine Learning workspace\n",
|
||||
"Get a reference to an existing Azure Machine Learning workspace. Please make sure to change `STANDARD_NC6` and `STANDARD_D2_V2` to [the ones available in your region](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=virtual-machines).\n"
|
||||
"Get a reference to an existing Azure Machine Learning workspace.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -2,4 +2,3 @@ name: devenv_setup
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azure-mgmt-network
|
||||
|
||||
@@ -10,10 +10,10 @@
|
||||
[MLflow](https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.
|
||||
|
||||
Try out the sample notebooks:
|
||||
1. [Use MLflow with Azure Machine Learning for Local Training Run](./train-local/train-local.ipynb)
|
||||
1. [Use MLflow with Azure Machine Learning for Remote Training Run](./train-remote/train-remote.ipynb)
|
||||
1. [Deploy Model as Azure Machine Learning Web Service using MLflow](./deploy-model/deploy-model.ipynb)
|
||||
1. [Train and Deploy PyTorch Image Classifier](./train-deploy-pytorch/train-deploy-pytorch.ipynb)
|
||||
1. [Use MLflow with Azure Machine Learning for Local Training Run](./using-mlflow/train-local/train-local.ipynb)
|
||||
1. [Use MLflow with Azure Machine Learning for Remote Training Run](./using-mlflow/train-remote/train-remote.ipynb)
|
||||
1. [Train and Deploy PyTorch Image Classifier](./using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb)
|
||||
1. [Train and Deploy Keras Image Classifier with MLflow auto logging](./using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb)
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -100,7 +100,7 @@
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.9.0, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.12.0, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -215,6 +215,24 @@
|
||||
"!more hello.py"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Submitted runs take a snapshot of the *source_directory* to use when executing. You can control which files are available to the run by using an *.amlignore* file."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile .amlignore\n",
|
||||
"# Exclude the outputs directory automatically created by our earlier runs.\n",
|
||||
"/outputs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -373,7 +391,7 @@
|
||||
"source": [
|
||||
"## Query properties and tags\n",
|
||||
"\n",
|
||||
"You can quary runs within an experiment that match specific properties and tags. "
|
||||
"You can query runs within an experiment that match specific properties and tags."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -579,6 +597,22 @@
|
||||
"name": "roastala"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
"compute": [
|
||||
"Local"
|
||||
],
|
||||
"datasets": [
|
||||
"None"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"friendly_name": "Managing your training runs",
|
||||
"index_order": 2,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -596,26 +630,10 @@
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"friendly_name": "Managing your training runs",
|
||||
"exclude_from_index": false,
|
||||
"index_order": 2,
|
||||
"category": "training",
|
||||
"task": "Monitor and complete runs",
|
||||
"datasets": [
|
||||
"None"
|
||||
],
|
||||
"compute": [
|
||||
"Local"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"tags": [
|
||||
"None"
|
||||
]
|
||||
],
|
||||
"task": "Monitor and complete runs"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -0,0 +1,78 @@
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
import mlflow
|
||||
import mlflow.keras
|
||||
import numpy as np
|
||||
import warnings
|
||||
|
||||
import keras
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Dense
|
||||
from keras.optimizers import RMSprop
|
||||
|
||||
print("Keras version:", keras.__version__)
|
||||
|
||||
# Enable auto-logging to MLflow to capture Keras metrics.
|
||||
mlflow.keras.autolog()
|
||||
|
||||
# Model / data parameters
|
||||
n_inputs = 28 * 28
|
||||
n_h1 = 300
|
||||
n_h2 = 100
|
||||
n_outputs = 10
|
||||
learning_rate = 0.001
|
||||
|
||||
# the data, split between train and test sets
|
||||
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
|
||||
|
||||
# Scale images to the [0, 1] range
|
||||
x_train = x_train.astype("float32") / 255
|
||||
x_test = x_test.astype("float32") / 255
|
||||
|
||||
# Flatten image to be (n, 28 * 28)
|
||||
x_train = x_train.reshape(len(x_train), -1)
|
||||
x_test = x_test.reshape(len(x_test), -1)
|
||||
|
||||
print("x_train shape:", x_train.shape)
|
||||
print(x_train.shape[0], "train samples")
|
||||
print(x_test.shape[0], "test samples")
|
||||
|
||||
# convert class vectors to binary class matrices
|
||||
y_train = keras.utils.to_categorical(y_train, n_outputs)
|
||||
y_test = keras.utils.to_categorical(y_test, n_outputs)
|
||||
|
||||
|
||||
def driver():
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
with mlflow.start_run() as run:
|
||||
|
||||
# Build a simple MLP model
|
||||
model = Sequential()
|
||||
# first hidden layer
|
||||
model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,)))
|
||||
# second hidden layer
|
||||
model.add(Dense(n_h2, activation='relu'))
|
||||
# output layer
|
||||
model.add(Dense(n_outputs, activation='softmax'))
|
||||
model.summary()
|
||||
|
||||
batch_size = 128
|
||||
epochs = 5
|
||||
|
||||
model.compile(loss='categorical_crossentropy',
|
||||
optimizer=RMSprop(lr=learning_rate),
|
||||
metrics=['accuracy'])
|
||||
|
||||
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
|
||||
|
||||
score = model.evaluate(x_test, y_test, verbose=0)
|
||||
print('Test loss:', score[0])
|
||||
print('Test accuracy:', score[1])
|
||||
|
||||
return run
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
driver()
|
||||
@@ -0,0 +1,455 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier\n",
|
||||
"\n",
|
||||
"This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a Keras model to classify MNIST digit images and deploy the model as a web service. You'll learn how to:\n",
|
||||
"\n",
|
||||
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
|
||||
" 2. Create experiment\n",
|
||||
" 3. Instrument your model with MLflow tracking\n",
|
||||
" 4. Train a Keras model locally with MLflow auto logging\n",
|
||||
" 5. Train a model on GPU compute on Azure with MLflow auto logging\n",
|
||||
" 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
|
||||
" 7. Deploy the model as a web service on Azure Container Instance\n",
|
||||
" 8. Call the model to make predictions\n",
|
||||
" \n",
|
||||
"### Pre-requisites\n",
|
||||
" \n",
|
||||
"If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
|
||||
"\n",
|
||||
"Install TensorFlow and Keras, this notebook has been tested with TensorFlow version 2.1.0 and Keras version 2.3.1.\n",
|
||||
"\n",
|
||||
"Also, install azureml-mlflow package using ```pip install azureml-mlflow```. Note that azureml-mlflow installs mlflow package itself as a dependency if you haven't done so previously.\n",
|
||||
"\n",
|
||||
"### Set-up\n",
|
||||
"\n",
|
||||
"Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys, os\n",
|
||||
"import mlflow\n",
|
||||
"import mlflow.azureml\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)\n",
|
||||
"print(\"MLflow version:\", mlflow.version.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Set tracking URI\n",
|
||||
"\n",
|
||||
"Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create Experiment\n",
|
||||
"\n",
|
||||
"In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"experiment_name = \"keras-with-mlflow\"\n",
|
||||
"mlflow.set_experiment(experiment_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Train model locally while logging metrics and artifacts\n",
|
||||
"\n",
|
||||
"The ```scripts/train.py``` program contains the code to load the image dataset, train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
|
||||
"\n",
|
||||
"Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, MLflow's automatic logging is used to log metrics, parameters and model for the Keras run.\n",
|
||||
"\n",
|
||||
"Let's add the program to search path, import it as a module and invoke the driver function. Note that the training can take few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lib_path = os.path.abspath(\"scripts\")\n",
|
||||
"sys.path.append(lib_path)\n",
|
||||
"\n",
|
||||
"import train"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run = train.driver()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Train model on GPU compute on Azure\n",
|
||||
"\n",
|
||||
"Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
|
||||
"\n",
|
||||
"Clone an environment object from the Tensorflow 2.1 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"env = Environment.get(workspace=ws, name=\"AzureML-TensorFlow-2.1-GPU\").clone(\"mlflow-env\")\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"azureml-mlflow\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"keras==2.3.1\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"numpy\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Create a ScriptRunConfig to specify the training configuration: script, compute as well as environment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory=\"./scripts\", script=\"train.py\")\n",
|
||||
"src.run_config.environment = env\n",
|
||||
"src.run_config.target = \"gpu-cluster\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Get a reference to the experiment you created previously, but this time, as an Azure Machine Learning experiment object.\n",
|
||||
"\n",
|
||||
"Then, use the ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as the cached image is used."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"exp = Experiment(ws, experiment_name)\n",
|
||||
"run = exp.submit(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can monitor the run and its metrics on Azure Portal."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Also, you can wait for run to complete."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy model as web service\n",
|
||||
"\n",
|
||||
"The ```mlflow.azureml.deploy``` function registers the logged Keras+Tensorflow model and deploys the model in a framework-aware manner. It automatically creates the Tensorflow-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
|
||||
"\n",
|
||||
"In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
|
||||
"\n",
|
||||
"[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
|
||||
"\n",
|
||||
"Note that the service deployment can take several minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice, Webservice\n",
|
||||
"\n",
|
||||
"model_path = \"model\"\n",
|
||||
"\n",
|
||||
"aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
|
||||
" memory_gb=5, \n",
|
||||
" tags={\"data\": \"MNIST\", \"method\" : \"keras\"}, \n",
|
||||
" description=\"Predict using webservice\")\n",
|
||||
"\n",
|
||||
"webservice, azure_model = mlflow.azureml.deploy(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
|
||||
" workspace=ws,\n",
|
||||
" deployment_config=aci_config,\n",
|
||||
" service_name=\"keras-mnist-1\",\n",
|
||||
" model_name=\"keras_mnist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once the deployment has completed you can check the scoring URI of the web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Make predictions using a web service\n",
|
||||
"\n",
|
||||
"To make the web service, create a test data set as normalized NumPy array. \n",
|
||||
"\n",
|
||||
"Then, let's define a utility function that takes a random image and converts it into a format and shape suitable for input to the Keras inferencing end-point. The conversion is done by: \n",
|
||||
"\n",
|
||||
" 1. Select a random (image, label) tuple\n",
|
||||
" 2. Take the image and converting to to NumPy array \n",
|
||||
" 3. Reshape array into 1 x 1 x N array\n",
|
||||
" * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
|
||||
" * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
|
||||
" 4. Convert the NumPy array to list to make it into a built-in type.\n",
|
||||
" 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import keras\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"# the data, split between train and test sets\n",
|
||||
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
|
||||
"\n",
|
||||
"# Scale images to the [0, 1] range\n",
|
||||
"x_test = x_test.astype(\"float32\") / 255\n",
|
||||
"x_test = x_test.reshape(len(x_test), -1)\n",
|
||||
"\n",
|
||||
"# convert class vectors to binary class matrices\n",
|
||||
"y_test = keras.utils.to_categorical(y_test, 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"import json\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# send a random row from the test set to score\n",
|
||||
"random_index = np.random.randint(0, len(x_test)-1)\n",
|
||||
"input_data = \"{\\\"data\\\": [\" + str(list(x_test[random_index])) + \"]}\"\n",
|
||||
"\n",
|
||||
"response = webservice.run(input_data)\n",
|
||||
"\n",
|
||||
"response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
|
||||
"\n",
|
||||
"print(\"Predicted label:\", response[0][0])\n",
|
||||
"plt.imshow(x_test[random_index].reshape(28,28), cmap = \"gray\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also call the web service using a raw POST method against the web service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"response = requests.post(url=webservice.scoring_uri, data=input_data,headers={\"Content-type\": \"application/json\"})\n",
|
||||
"print(response.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Clean up\n",
|
||||
"You can delete the ACI deployment with a delete API call."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"webservice.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "hancwang"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"celltoolbar": "Edit Metadata",
|
||||
"compute": [
|
||||
"Local",
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"MNIST"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"Keras"
|
||||
],
|
||||
"friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
},
|
||||
"tags": [
|
||||
"mlflow",
|
||||
"keras"
|
||||
],
|
||||
"task": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier, leveraging MLflow auto logging"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
name: train-and-deploy-keras-auto-logging
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- azureml-mlflow
|
||||
- matplotlib
|
||||
- tensorflow==2.1
|
||||
- keras
|
||||
@@ -0,0 +1,150 @@
|
||||
# Copyright (c) 2017, PyTorch Team
|
||||
# All rights reserved
|
||||
# Licensed under BSD 3-Clause License.
|
||||
|
||||
# This example is based on PyTorch MNIST example:
|
||||
# https://github.com/pytorch/examples/blob/master/mnist/main.py
|
||||
|
||||
import mlflow
|
||||
import mlflow.pytorch
|
||||
from mlflow.utils.environment import _mlflow_conda_env
|
||||
import warnings
|
||||
import cloudpickle
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
import torchvision
|
||||
from torchvision import datasets, transforms
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(1, 20, 5, 1)
|
||||
self.conv2 = nn.Conv2d(20, 50, 5, 1)
|
||||
self.fc1 = nn.Linear(4 * 4 * 50, 500)
|
||||
self.fc2 = nn.Linear(500, 10)
|
||||
|
||||
def forward(self, x):
|
||||
# Added the view for reshaping score requests
|
||||
x = x.view(-1, 1, 28, 28)
|
||||
x = F.relu(self.conv1(x))
|
||||
x = F.max_pool2d(x, 2, 2)
|
||||
x = F.relu(self.conv2(x))
|
||||
x = F.max_pool2d(x, 2, 2)
|
||||
x = x.view(-1, 4 * 4 * 50)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = self.fc2(x)
|
||||
return F.log_softmax(x, dim=1)
|
||||
|
||||
|
||||
def train(args, model, device, train_loader, optimizer, epoch):
|
||||
model.train()
|
||||
for batch_idx, (data, target) in enumerate(train_loader):
|
||||
data, target = data.to(device), target.to(device)
|
||||
optimizer.zero_grad()
|
||||
output = model(data)
|
||||
loss = F.nll_loss(output, target)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if batch_idx % args.log_interval == 0:
|
||||
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
|
||||
epoch, batch_idx * len(data), len(train_loader.dataset),
|
||||
100. * batch_idx / len(train_loader), loss.item()))
|
||||
# Use MLflow logging
|
||||
mlflow.log_metric("epoch_loss", loss.item())
|
||||
|
||||
|
||||
def test(args, model, device, test_loader):
|
||||
model.eval()
|
||||
test_loss = 0
|
||||
correct = 0
|
||||
with torch.no_grad():
|
||||
for data, target in test_loader:
|
||||
data, target = data.to(device), target.to(device)
|
||||
output = model(data)
|
||||
# sum up batch loss
|
||||
test_loss += F.nll_loss(output, target, reduction="sum").item()
|
||||
# get the index of the max log-probability
|
||||
pred = output.argmax(dim=1, keepdim=True)
|
||||
correct += pred.eq(target.view_as(pred)).sum().item()
|
||||
|
||||
test_loss /= len(test_loader.dataset)
|
||||
print("\n")
|
||||
print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
|
||||
test_loss, correct, len(test_loader.dataset),
|
||||
100. * correct / len(test_loader.dataset)))
|
||||
# Use MLflow logging
|
||||
mlflow.log_metric("average_loss", test_loss)
|
||||
|
||||
|
||||
class Args(object):
|
||||
pass
|
||||
|
||||
|
||||
# Training settings
|
||||
args = Args()
|
||||
setattr(args, 'batch_size', 64)
|
||||
setattr(args, 'test_batch_size', 1000)
|
||||
setattr(args, 'epochs', 3) # Higher number for better convergence
|
||||
setattr(args, 'lr', 0.01)
|
||||
setattr(args, 'momentum', 0.5)
|
||||
setattr(args, 'no_cuda', True)
|
||||
setattr(args, 'seed', 1)
|
||||
setattr(args, 'log_interval', 10)
|
||||
setattr(args, 'save_model', True)
|
||||
|
||||
use_cuda = not args.no_cuda and torch.cuda.is_available()
|
||||
|
||||
torch.manual_seed(args.seed)
|
||||
|
||||
device = torch.device("cuda" if use_cuda else "cpu")
|
||||
|
||||
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
datasets.MNIST('../data', train=True, download=True,
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))
|
||||
])),
|
||||
batch_size=args.batch_size, shuffle=True, **kwargs)
|
||||
test_loader = torch.utils.data.DataLoader(
|
||||
datasets.MNIST(
|
||||
'../data',
|
||||
train=False,
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))])),
|
||||
batch_size=args.test_batch_size, shuffle=True, **kwargs)
|
||||
|
||||
|
||||
def driver():
|
||||
warnings.filterwarnings("ignore")
|
||||
# Dependencies for deploying the model
|
||||
pytorch_index = "https://download.pytorch.org/whl/"
|
||||
pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl"
|
||||
deps = [
|
||||
"cloudpickle=={}".format(cloudpickle.__version__),
|
||||
pytorch_index + pytorch_version,
|
||||
"torchvision=={}".format(torchvision.__version__),
|
||||
"Pillow=={}".format("6.0.0")
|
||||
]
|
||||
with mlflow.start_run() as run:
|
||||
model = Net().to(device)
|
||||
optimizer = optim.SGD(
|
||||
model.parameters(),
|
||||
lr=args.lr,
|
||||
momentum=args.momentum)
|
||||
for epoch in range(1, args.epochs + 1):
|
||||
train(args, model, device, train_loader, optimizer, epoch)
|
||||
test(args, model, device, test_loader)
|
||||
# Log model to run history using MLflow
|
||||
if args.save_model:
|
||||
model_env = _mlflow_conda_env(additional_pip_deps=deps)
|
||||
mlflow.pytorch.log_model(model, "model", conda_env=model_env)
|
||||
return run
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
driver()
|
||||
@@ -0,0 +1,464 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier\n",
|
||||
"\n",
|
||||
"This example shows you how to use MLflow together with Azure Machine Learning services for tracking the metrics and artifacts while training a PyTorch model to classify MNIST digit images and deploy the model as a web service. You'll learn how to:\n",
|
||||
"\n",
|
||||
" 1. Set up MLflow tracking URI so as to use Azure ML\n",
|
||||
" 2. Create experiment\n",
|
||||
" 3. Instrument your model with MLflow tracking\n",
|
||||
" 4. Train a PyTorch model locally\n",
|
||||
" 5. Train a model on GPU compute on Azure\n",
|
||||
" 6. View your experiment within your Azure ML Workspace in Azure Portal\n",
|
||||
" 7. Deploy the model as a web service on Azure Container Instance\n",
|
||||
" 8. Call the model to make predictions\n",
|
||||
" \n",
|
||||
"## Pre-requisites\n",
|
||||
" \n",
|
||||
"If you are using a Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.\n",
|
||||
"\n",
|
||||
"Install PyTorch, this notebook has been tested with torch==1.4\n",
|
||||
"\n",
|
||||
"Also, install azureml-mlflow package using ```pip install azureml-mlflow```. Note that azureml-mlflow installs mlflow package itself as a dependency if you haven't done so previously.\n",
|
||||
"\n",
|
||||
"## Set-up\n",
|
||||
"\n",
|
||||
"Import packages and check versions of Azure ML SDK and MLflow installed on your computer. Then connect to your Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys, os\n",
|
||||
"import mlflow\n",
|
||||
"import mlflow.azureml\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)\n",
|
||||
"print(\"MLflow version:\", mlflow.version.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Set tracking URI\n",
|
||||
"\n",
|
||||
"Set the MLflow tracking URI to point to your Azure ML Workspace. The subsequent logging calls from MLflow APIs will go to Azure ML services and will be tracked under your Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create Experiment\n",
|
||||
"\n",
|
||||
"In both MLflow and Azure ML, training runs are grouped into experiments. Let's create one for our experimentation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"experiment_name = \"pytorch-with-mlflow\"\n",
|
||||
"mlflow.set_experiment(experiment_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train model locally while logging metrics and artifacts\n",
|
||||
"\n",
|
||||
"The ```scripts/train.py``` program contains the code to load the image dataset, train and test the model. Within this program, the train.driver function wraps the end-to-end workflow.\n",
|
||||
"\n",
|
||||
"Within the driver, the ```mlflow.start_run``` starts MLflow tracking. Then, ```mlflow.log_metric``` functions are used to track the convergence of the neural network training iterations. Finally ```mlflow.pytorch.save_model``` is used to save the trained model in framework-aware manner.\n",
|
||||
"\n",
|
||||
"Let's add the program to search path, import it as a module and invoke the driver function. Note that the training can take few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lib_path = os.path.abspath(\"scripts\")\n",
|
||||
"sys.path.append(lib_path)\n",
|
||||
"\n",
|
||||
"import train"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run = train.driver()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Train model on GPU compute on Azure\n",
|
||||
"\n",
|
||||
"Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n",
|
||||
"\n",
|
||||
"Clone an environment object from the PyTorch 1.4 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"env = Environment.get(workspace=ws, name=\"AzureML-PyTorch-1.4-GPU\").clone(\"mlflow-env\")\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"azureml-mlflow\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"Pillow==6.0.0\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Create a ScriptRunConfig to specify the training configuration: script, compute as well as environment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory=\"./scripts\", script=\"train.py\")\n",
|
||||
"src.run_config.environment = env\n",
|
||||
"src.run_config.target = \"gpu-cluster\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Get a reference to the experiment you created previously, but this time, as an Azure Machine Learning experiment object.\n",
|
||||
"\n",
|
||||
"Then, use the ```Experiment.submit``` method to start the remote training run. Note that the first training run often takes longer as Azure Machine Learning service builds the Docker image for executing the script. Subsequent runs will be faster as the cached image is used."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"exp = Experiment(ws, experiment_name)\n",
|
||||
"run = exp.submit(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can monitor the run and its metrics on Azure Portal."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Also, you can wait for run to complete."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy model as web service\n",
|
||||
"\n",
|
||||
"The ```mlflow.azureml.deploy``` function registers the logged PyTorch model and deploys the model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
|
||||
"\n",
|
||||
"In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
|
||||
"\n",
|
||||
"[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n",
|
||||
"\n",
|
||||
"Note that the service deployment can take several minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice, Webservice\n",
|
||||
"\n",
|
||||
"model_path = \"model\"\n",
|
||||
"\n",
|
||||
"aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
|
||||
" memory_gb=5, \n",
|
||||
" tags={\"data\": \"MNIST\", \"method\" : \"pytorch\"}, \n",
|
||||
" description=\"Predict using webservice\")\n",
|
||||
"\n",
|
||||
"webservice, azure_model = mlflow.azureml.deploy(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
|
||||
" workspace=ws,\n",
|
||||
" deployment_config=aci_config,\n",
|
||||
" service_name=\"pytorch-mnist-1\",\n",
|
||||
" model_name=\"pytorch_mnist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once the deployment has completed you can check the scoring URI of the web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Make predictions using a web service\n",
|
||||
"\n",
|
||||
"To make the web service, create a test data set as normalized PyTorch tensors. \n",
|
||||
"\n",
|
||||
"Then, let's define a utility function that takes a random image and converts it into a format and shape suitable for input to the PyTorch inferencing end-point. The conversion is done by: \n",
|
||||
"\n",
|
||||
" 1. Select a random (image, label) tuple\n",
|
||||
" 2. Take the image and converting the tensor to NumPy array \n",
|
||||
" 3. Reshape array into 1 x 1 x N array\n",
|
||||
" * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
|
||||
" * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
|
||||
" 4. Convert the NumPy array to list to make it into a built-in type.\n",
|
||||
" 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torchvision import datasets, transforms\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
|
||||
" transforms.ToTensor(),\n",
|
||||
" transforms.Normalize((0.1307,), (0.3081,))]))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_random_image():\n",
|
||||
" image_idx = random.randint(0,len(test_data))\n",
|
||||
" image_as_tensor = test_data[image_idx][0]\n",
|
||||
" return {\"data\": elem for elem in image_as_tensor.numpy().reshape(1,1,-1).tolist()}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then, invoke the web service using a random test image. Convert the dictionary containing the image to JSON string before passing it to web service.\n",
|
||||
"\n",
|
||||
"The response contains the raw scores for each label, with greater value indicating higher probability. Sort the labels and select the one with greatest score to get the prediction. Let's also plot the image sent to web service for comparison purposes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"import json\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"test_image = get_random_image()\n",
|
||||
"\n",
|
||||
"response = webservice.run(json.dumps(test_image))\n",
|
||||
"\n",
|
||||
"response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(\"Predicted label:\", response[0][0])\n",
|
||||
"plt.imshow(np.array(test_image[\"data\"]).reshape(28,28), cmap = \"gray\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also call the web service using a raw POST method against the web service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"response = requests.post(url=webservice.scoring_uri, data=json.dumps(test_image),headers={\"Content-type\": \"application/json\"})\n",
|
||||
"print(response.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Clean up\n",
|
||||
"You can delete the ACI deployment with a delete API call."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"webservice.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "shipatel"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"celltoolbar": "Edit Metadata",
|
||||
"compute": [
|
||||
"Local",
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"MNIST"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"PyTorch"
|
||||
],
|
||||
"friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
},
|
||||
"name": "mlflow-sparksummit-pytorch",
|
||||
"notebookId": 2495374963457641,
|
||||
"tags": [
|
||||
"mlflow",
|
||||
"pytorch"
|
||||
],
|
||||
"task": "Use MLflow with Azure Machine Learning to train and deploy PyTorch image classifier model"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
name: train-and-deploy-pytorch
|
||||
dependencies:
|
||||
- pytorch==1.4.0 -c pytorch
|
||||
- torchvision -c pytorch
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- azureml-mlflow
|
||||
- matplotlib
|
||||
@@ -449,7 +449,7 @@
|
||||
" compute_target=compute_target, \n",
|
||||
" entry_script='keras_mnist.py',\n",
|
||||
" framework_version='2.0', \n",
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataprep[pandas,fuse]','matplotlib'])"
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataset-runtime[pandas,fuse]','matplotlib'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -604,21 +604,19 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Predict on the test set\n",
|
||||
"## Predict on the test set (Optional)\n",
|
||||
"Let's check the version of the local Keras. Make sure it matches with the version number printed out in the training script. Otherwise you might not be able to load the model properly."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import keras\n",
|
||||
"import tensorflow as tf\n",
|
||||
" import keras\n",
|
||||
" import tensorflow as tf\n",
|
||||
"\n",
|
||||
"print(\"Keras version:\", keras.__version__)\n",
|
||||
"print(\"Tensorflow version:\", tf.__version__)"
|
||||
" print(\"Keras version:\", keras.__version__)\n",
|
||||
" print(\"Tensorflow version:\", tf.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -629,21 +627,19 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from keras.models import model_from_json\n",
|
||||
" from keras.models import model_from_json\n",
|
||||
"\n",
|
||||
"# load json and create model\n",
|
||||
"json_file = open('model/model.json', 'r')\n",
|
||||
"loaded_model_json = json_file.read()\n",
|
||||
"json_file.close()\n",
|
||||
"loaded_model = model_from_json(loaded_model_json)\n",
|
||||
"# load weights into new model\n",
|
||||
"loaded_model.load_weights(\"model/model.h5\")\n",
|
||||
"print(\"Model loaded from disk.\")"
|
||||
" # load json and create model\n",
|
||||
" json_file = open('model/model.json', 'r')\n",
|
||||
" loaded_model_json = json_file.read()\n",
|
||||
" json_file.close()\n",
|
||||
" loaded_model = model_from_json(loaded_model_json)\n",
|
||||
" # load weights into new model\n",
|
||||
" loaded_model.load_weights(\"model/model.h5\")\n",
|
||||
" print(\"Model loaded from disk.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -654,19 +650,17 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# evaluate loaded model on test data\n",
|
||||
"loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
|
||||
"y_test_ohe = one_hot_encode(y_test, 10)\n",
|
||||
"y_hat = np.argmax(loaded_model.predict(X_test), axis=1)\n",
|
||||
" # evaluate loaded model on test data\n",
|
||||
" loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
|
||||
" y_test_ohe = one_hot_encode(y_test, 10)\n",
|
||||
" y_hat = np.argmax(loaded_model.predict(X_test), axis=1)\n",
|
||||
"\n",
|
||||
"# print the first 30 labels and predictions\n",
|
||||
"print('labels: \\t', y_test[:30])\n",
|
||||
"print('predictions:\\t', y_hat[:30])"
|
||||
" # print the first 30 labels and predictions\n",
|
||||
" print('labels: \\t', y_test[:30])\n",
|
||||
" print('predictions:\\t', y_hat[:30])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -677,12 +671,10 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
|
||||
" print(\"Accuracy on the test set:\", np.average(y_hat == y_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -730,7 +722,7 @@
|
||||
" compute_target=compute_target,\n",
|
||||
" entry_script='keras_mnist.py',\n",
|
||||
" framework_version='2.0',\n",
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataprep[pandas,fuse]','matplotlib'])"
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataset-runtime[pandas,fuse]','matplotlib'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1056,7 +1048,7 @@
|
||||
" font_color = 'red' if y_test[s] != result[i] else 'black'\n",
|
||||
" clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
|
||||
" \n",
|
||||
" plt.text(x=10, y=-10, s=y_test[s], fontsize=18, color=font_color)\n",
|
||||
" plt.text(x=10, y=-10, s=result[i], fontsize=18, color=font_color)\n",
|
||||
" plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)\n",
|
||||
" \n",
|
||||
" i = i + 1\n",
|
||||
|
||||
@@ -184,7 +184,7 @@
|
||||
"myenv = Environment(\"myenv\")\n",
|
||||
"\n",
|
||||
"myenv.docker.enabled = True\n",
|
||||
"myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
|
||||
"myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn', 'packaging'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -129,7 +129,8 @@
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your instance\n",
|
||||
"compute_name = \"compute-instance\"\n",
|
||||
"# Compute instance name should be unique across the azure region\n",
|
||||
"compute_name = \"ci{}\".format(ws._workspace_id)[:10]\n",
|
||||
"\n",
|
||||
"# Verify that instance does not exist already\n",
|
||||
"try:\n",
|
||||
@@ -182,7 +183,7 @@
|
||||
"source": [
|
||||
"### Create environment\n",
|
||||
"\n",
|
||||
"Create Docker based environment with scikit-learn installed."
|
||||
"Create an environment with scikit-learn installed."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -195,8 +196,6 @@
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"myenv = Environment(\"myenv\")\n",
|
||||
"\n",
|
||||
"myenv.docker.enabled = True\n",
|
||||
"myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
|
||||
]
|
||||
},
|
||||
@@ -240,9 +239,15 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"# Shows output of the run on stdout.\n",
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can use the get_active_runs() to get the currently running or queued jobs on the compute instance"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -251,6 +256,33 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# wait for the run to reach Queued or Running state if it is in Preparing state\n",
|
||||
"status = run.get_status()\n",
|
||||
"while status not in ['Queued', 'Running', 'Completed', 'Failed', 'Canceled']:\n",
|
||||
" state = run.get_status()\n",
|
||||
" print('Run status: {}'.format(status))\n",
|
||||
" time.sleep(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get active runs which are in Queued or Running state\n",
|
||||
"active_runs = instance.get_active_runs()\n",
|
||||
"for active_run in active_runs:\n",
|
||||
" print(active_run.run_id, ',', active_run.status)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion()\n",
|
||||
"print(run.get_metrics())"
|
||||
]
|
||||
},
|
||||
@@ -321,7 +353,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# delete() is used to delete the ComputeInstance target. Useful if you want to re-use the compute name \n",
|
||||
"# instance.delete()"
|
||||
"# instance.delete(wait_for_completion=True, show_output=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -309,7 +309,7 @@
|
||||
"conda_env = Environment(\"conda-env\")\n",
|
||||
"conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['scikit-learn',\n",
|
||||
" 'azureml-sdk',\n",
|
||||
" 'azureml-dataprep[pandas,fuse]>=1.1.21'])"
|
||||
" 'azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,33 +0,0 @@
|
||||
import pickle
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.linear_model import Ridge
|
||||
from azureml.core.model import Model
|
||||
# sklearn.externals.joblib is removed in 0.23
|
||||
try:
|
||||
from sklearn.externals import joblib
|
||||
except ImportError:
|
||||
import joblib
|
||||
|
||||
|
||||
def init():
|
||||
global model
|
||||
# note here "best_model" is the name of the model registered under the workspace
|
||||
# this call should return the path to the model.pkl file on the local disk.
|
||||
model_path = Model.get_model_path(model_name='best_model')
|
||||
# deserialize the model file back into a sklearn model
|
||||
model = joblib.load(model_path)
|
||||
|
||||
|
||||
# note you can pass in multiple rows for scoring
|
||||
def run(raw_data):
|
||||
try:
|
||||
data = json.loads(raw_data)['data']
|
||||
data = np.array(data)
|
||||
result = model.predict(data)
|
||||
|
||||
# you can return any data type as long as it is JSON-serializable
|
||||
return result.tolist()
|
||||
except Exception as e:
|
||||
result = str(e)
|
||||
return result
|
||||
@@ -1,721 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Train and deploy a model\n",
|
||||
"_**Create and deploy a model directly from a notebook**_\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"---\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
" 1. Viewing run results\n",
|
||||
" 1. Simple parameter sweep\n",
|
||||
" 1. Viewing experiment results\n",
|
||||
" 1. Select the best model\n",
|
||||
"1. [Deploy](#Deploy)\n",
|
||||
" 1. Register the model\n",
|
||||
" 1. Create a scoring file\n",
|
||||
" 1. Describe your environment\n",
|
||||
" 1. Descrice your target compute\n",
|
||||
" 1. Deploy your webservice\n",
|
||||
" 1. Test your webservice\n",
|
||||
" 1. Clean up\n",
|
||||
"1. [Next Steps](#nextsteps)\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"\n",
|
||||
"## Introduction\n",
|
||||
"Azure Machine Learning provides capabilities to control all aspects of model training and deployment directly from a notebook using the AML Python SDK. In this notebook we will\n",
|
||||
"* connect to our AML Workspace\n",
|
||||
"* create an experiment that contains multiple runs with tracked metrics\n",
|
||||
"* choose the best model created across all runs\n",
|
||||
"* deploy that model as a service\n",
|
||||
"\n",
|
||||
"In the end we will have a model deployed as a web service which we can call from an HTTP endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace. From the configuration, the important sections are the workspace configuration and ACI regristration.\n",
|
||||
"\n",
|
||||
"We will also need the following libraries install to our conda environment. If these are not installed, use the following command to do so and restart the notebook.\n",
|
||||
"```shell\n",
|
||||
"(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"For this notebook we need the Azure ML SDK and access to our workspace. The following cell imports the SDK, checks the version, and accesses our already configured AzureML workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"install"
|
||||
],
|
||||
"name": "load_ws",
|
||||
"msdoc": "how-to-track-experiments.md"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Experiment, Workspace\n",
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")\n",
|
||||
"print(\"\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"\n",
|
||||
"## Data\n",
|
||||
"We will use the diabetes dataset for this experiement, a well-known small dataset that comes with scikit-learn. This cell loads the dataset and splits it into random training and testing sets.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "load_data",
|
||||
"msdoc": "how-to-track-experiments.md"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"\n",
|
||||
"X, y = load_diabetes(return_X_y = True)\n",
|
||||
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
|
||||
"data = {\n",
|
||||
" \"train\":{\"X\": X_train, \"y\": y_train}, \n",
|
||||
" \"test\":{\"X\": X_test, \"y\": y_test}\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"print (\"Data contains\", len(data['train']['X']), \"training samples and\",len(data['test']['X']), \"test samples\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"Let's use scikit-learn to train a simple Ridge regression model. We use AML to record interesting information about the model in an Experiment. An Experiment contains a series of trials called Runs. During this trial we use AML in the following way:\n",
|
||||
"* We access an experiment from our AML workspace by name, which will be created if it doesn't exist\n",
|
||||
"* We use `start_logging` to create a new run in this experiment\n",
|
||||
"* We use `run.log()` to record a parameter, alpha, and an accuracy measure - the Mean Squared Error (MSE) to the run. We will be able to review and compare these measures in the Azure Portal at a later time.\n",
|
||||
"* We store the resulting model in the **outputs** directory, which is automatically captured by AML when the run is complete.\n",
|
||||
"* We use `run.complete()` to indicate that the run is over and results can be captured and finalized"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"local run",
|
||||
"outputs upload"
|
||||
],
|
||||
"name": "create_experiment",
|
||||
"msdoc": "how-to-track-experiments.md"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get an experiment object from Azure Machine Learning\n",
|
||||
"experiment = Experiment(workspace=ws, name=\"train-within-notebook\")\n",
|
||||
"\n",
|
||||
"# Create a run object in the experiment\n",
|
||||
"run = experiment.start_logging()\n",
|
||||
"# Log the algorithm parameter alpha to the run\n",
|
||||
"run.log('alpha', 0.03)\n",
|
||||
"\n",
|
||||
"# Create, fit, and test the scikit-learn Ridge regression model\n",
|
||||
"regression_model = Ridge(alpha=0.03)\n",
|
||||
"regression_model.fit(data['train']['X'], data['train']['y'])\n",
|
||||
"preds = regression_model.predict(data['test']['X'])\n",
|
||||
"\n",
|
||||
"# Output the Mean Squared Error to the notebook and to the run\n",
|
||||
"print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
|
||||
"run.log('mse', mean_squared_error(data['test']['y'], preds))\n",
|
||||
"\n",
|
||||
"# Save the model to the outputs directory for capture\n",
|
||||
"model_file_name = 'outputs/model.pkl'\n",
|
||||
"\n",
|
||||
"joblib.dump(value = regression_model, filename = model_file_name)\n",
|
||||
"\n",
|
||||
"# upload the model file explicitly into artifacts \n",
|
||||
"run.upload_file(name = model_file_name, path_or_stream = model_file_name)\n",
|
||||
"\n",
|
||||
"# Complete the run\n",
|
||||
"run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Viewing run results\n",
|
||||
"Azure Machine Learning stores all the details about the run in the Azure cloud. Let's access those details by retrieving a link to the run using the default run output. Clicking on the resulting link will take you to an interactive page presenting all run information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Simple parameter sweep\n",
|
||||
"Now let's take the same concept from above and modify the **alpha** parameter. For each value of alpha we will create a run that will store metrics and the resulting model. In the end we can use the captured run history to determine which model was the best for us to deploy. \n",
|
||||
"\n",
|
||||
"Note that by using `with experiment.start_logging() as run` AML will automatically call `run.complete()` at the end of each loop.\n",
|
||||
"\n",
|
||||
"This example also uses the **tqdm** library to provide a thermometer feedback"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"\n",
|
||||
"# list of numbers from 0 to 1.0 with a 0.05 interval\n",
|
||||
"alphas = np.arange(0.0, 1.0, 0.05)\n",
|
||||
"\n",
|
||||
"# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
|
||||
"for alpha in tqdm(alphas):\n",
|
||||
" # create a bunch of runs, each train a model with a different alpha value\n",
|
||||
" with experiment.start_logging() as run:\n",
|
||||
" # Use Ridge algorithm to build a regression model\n",
|
||||
" regression_model = Ridge(alpha=alpha)\n",
|
||||
" regression_model.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
|
||||
" preds = regression_model.predict(X=data[\"test\"][\"X\"])\n",
|
||||
" mse = mean_squared_error(y_true=data[\"test\"][\"y\"], y_pred=preds)\n",
|
||||
"\n",
|
||||
" # log alpha, mean_squared_error and feature names in run history\n",
|
||||
" run.log(name=\"alpha\", value=alpha)\n",
|
||||
" run.log(name=\"mse\", value=mse)\n",
|
||||
"\n",
|
||||
" # Save the model to the outputs directory for capture\n",
|
||||
" joblib.dump(value=regression_model, filename='outputs/model.pkl')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Viewing experiment results\n",
|
||||
"Similar to viewing the run, we can also view the entire experiment. The experiment report view in the Azure portal lets us view all the runs in a table, and also allows us to customize charts. This way, we can see how the alpha parameter impacts the quality of the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# now let's take a look at the experiment in Azure portal.\n",
|
||||
"experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Select the best model \n",
|
||||
"Now that we've created many runs with different parameters, we need to determine which model is the best for deployment. For this, we will iterate over the set of runs. From each run we will take the *run id* using the `id` property, and examine the metrics by calling `run.get_metrics()`. \n",
|
||||
"\n",
|
||||
"Since each run may be different, we do need to check if the run has the metric that we are looking for, in this case, **mse**. To find the best run, we create a dictionary mapping the run id's to the metrics.\n",
|
||||
"\n",
|
||||
"Finally, we use the `tag` method to mark the best run to make it easier to find later. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"runs = {}\n",
|
||||
"run_metrics = {}\n",
|
||||
"\n",
|
||||
"# Create dictionaries containing the runs and the metrics for all runs containing the 'mse' metric\n",
|
||||
"for r in tqdm(experiment.get_runs()):\n",
|
||||
" metrics = r.get_metrics()\n",
|
||||
" if 'mse' in metrics.keys():\n",
|
||||
" runs[r.id] = r\n",
|
||||
" run_metrics[r.id] = metrics\n",
|
||||
"\n",
|
||||
"# Find the run with the best (lowest) mean squared error and display the id and metrics\n",
|
||||
"best_run_id = min(run_metrics, key = lambda k: run_metrics[k]['mse'])\n",
|
||||
"best_run = runs[best_run_id]\n",
|
||||
"print('Best run is:', best_run_id)\n",
|
||||
"print('Metrics:', run_metrics[best_run_id])\n",
|
||||
"\n",
|
||||
"# Tag the best run for identification later\n",
|
||||
"best_run.tag(\"Best Run\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"## Deploy\n",
|
||||
"Now that we have trained a set of models and identified the run containing the best model, we want to deploy the model for real time inference. The process of deploying a model involves\n",
|
||||
"* registering a model in your workspace\n",
|
||||
"* creating a scoring file containing init and run methods\n",
|
||||
"* creating an environment dependency file describing packages necessary for your scoring file\n",
|
||||
"* deploying the model and packages as a web service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Register a model\n",
|
||||
"We have already identified which run contains the \"best model\" by our evaluation criteria. Each run has a file structure associated with it that contains various files collected during the run. Since a run can have many outputs we need to tell AML which file from those outputs represents the model that we want to use for our deployment. We can use the `run.get_file_names()` method to list the files associated with the run, and then use the `run.register_model()` method to place the model in the workspace's model registry.\n",
|
||||
"\n",
|
||||
"When using `run.register_model()` we supply a `model_name` that is meaningful for our scenario and the `model_path` of the model relative to the run. In this case, the model path is what is returned from `run.get_file_names()`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"query history"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# View the files in the run\n",
|
||||
"for f in best_run.get_file_names():\n",
|
||||
" print(f)\n",
|
||||
" \n",
|
||||
"# Register the model with the workspace\n",
|
||||
"model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once a model is registered, it is accessible from the list of models on the AML workspace. If you register models with the same name multiple times, AML keeps a version history of those models for you. The `Model.list()` lists all models in a workspace, and can be filtered by name, tags, or model properties. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"register model from history"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all models called \"best_model\" and display their version numbers\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"models = Model.list(ws, name='best_model')\n",
|
||||
"for m in models:\n",
|
||||
" print(m.name, m.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a scoring file\n",
|
||||
"\n",
|
||||
"Since your model file can essentially be anything you want it to be, you need to supply a scoring script that can load your model and then apply the model to new data. This script is your 'scoring file'. This scoring file is a python program containing, at a minimum, two methods `init()` and `run()`. The `init()` method is called once when your deployment is started so you can load your model and any other required objects. This method uses the `get_model_path` function to locate the registered model inside the docker container. The `run()` method is called interactively when the web service is called with one or more data samples to predict.\n",
|
||||
"\n",
|
||||
"The scoring file used for this exercise is [here](score.py). \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Describe your environment\n",
|
||||
"\n",
|
||||
"Each modelling process may require a unique set of packages. Therefore we need to create an environment object describing the dependencies. \n",
|
||||
"\n",
|
||||
"Next we create an inference configuration using this environment object and the scoring script that we created previously."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"env = Environment('deploytocloudenv')\n",
|
||||
"env.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],pip_packages=['azureml-defaults'])\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Describe your target compute\n",
|
||||
"In addition to the inference configuration, we also need to describe the type of compute we want to allocate for our webservice. In in this example we are using an [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/) which is a good choice for quick and cost-effective dev/test deployment scenarios. ACI instances require the number of cores you want to run and memory you need. Tags and descriptions are available for you to identify the instances in AML when viewing the Compute tab in the AML Portal.\n",
|
||||
"\n",
|
||||
"For production workloads, it is better to use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Try [this notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
|
||||
" memory_gb=1, \n",
|
||||
" tags={'sample name': 'AML 101'}, \n",
|
||||
" description='This is a great example.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy your webservice\n",
|
||||
"The final step to deploying your webservice is to call `Model.deploy()`. This function uses the deployment and inference configurations created above to perform the following:\n",
|
||||
"* Build a docker image\n",
|
||||
"* Deploy to the docker image to an Azure Container Instance\n",
|
||||
"* Copy your model files to the Azure Container Instance\n",
|
||||
"* Call the `init()` function in your scoring file\n",
|
||||
"* Provide an HTTP endpoint for scoring calls\n",
|
||||
"\n",
|
||||
"The `Model.deploy` method requires the following parameters\n",
|
||||
"* `workspace` - the workspace containing the service\n",
|
||||
"* `name` - a unique named used to identify the service in the workspace\n",
|
||||
"* `models` - an array of models to be deployed into the container\n",
|
||||
"* `inference_config` - a configuration object describing the image environment\n",
|
||||
"* `deployment_config` - a configuration object describing the compute type\n",
|
||||
" \n",
|
||||
"**Note:** The web service creation can take several minutes. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"\n",
|
||||
"# Create the webservice using all of the precreated configurations and our best model\n",
|
||||
"service = Model.deploy(workspace=ws,\n",
|
||||
" name='my-aci-svc',\n",
|
||||
" models=[model],\n",
|
||||
" inference_config=inference_config,\n",
|
||||
" deployment_config=aciconfig)\n",
|
||||
"\n",
|
||||
"# Wait for the service deployment to complete while displaying log output\n",
|
||||
"service.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"### Test your webservice"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that your web service is runing you can send JSON data directly to the service using the `run` method. This cell pulls the first test sample from the original dataset into JSON and then sends it to the service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"service = ws.webservices['my-aci-svc']\n",
|
||||
"\n",
|
||||
"# scrape the first row from the test set.\n",
|
||||
"test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
|
||||
"\n",
|
||||
"#score on our service\n",
|
||||
"service.run(input_data = test_samples)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This cell shows how you can send multiple rows to the webservice at once. It then calculates the residuals - that is, the errors - by subtracting out the actual values from the results. These residuals are used later to show a plotted result."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# score the entire test set.\n",
|
||||
"test_samples = json.dumps({'data': X_test.tolist()})\n",
|
||||
"\n",
|
||||
"result = service.run(input_data = test_samples)\n",
|
||||
"residual = result - y_test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This cell shows how you can use the `service.scoring_uri` property to access the HTTP endpoint of the service and call it using standard POST operations."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"# use the first row from the test set again\n",
|
||||
"test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
|
||||
"\n",
|
||||
"# create the required header\n",
|
||||
"headers = {'Content-Type':'application/json'}\n",
|
||||
"\n",
|
||||
"# post the request to the service and display the result\n",
|
||||
"resp = requests.post(service.scoring_uri, test_samples, headers = headers)\n",
|
||||
"print(resp.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Residual graph\n",
|
||||
"One way to understand the behavior of your model is to see how the data performs against data with known results. This cell uses matplotlib to create a histogram of the residual values, or errors, created from scoring the test samples.\n",
|
||||
"\n",
|
||||
"A good model should have residual values that cluster around 0 - that is, no error. Observing the resulting histogram can also show you if the model is skewed in any particular direction."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
|
||||
"f.suptitle('Residual Values', fontsize = 18)\n",
|
||||
"\n",
|
||||
"f.set_figheight(6)\n",
|
||||
"f.set_figwidth(14)\n",
|
||||
"\n",
|
||||
"a0.plot(residual, 'bo', alpha=0.4)\n",
|
||||
"a0.plot([0,90], [0,0], 'r', lw=2)\n",
|
||||
"a0.set_ylabel('residue values', fontsize=14)\n",
|
||||
"a0.set_xlabel('test data set', fontsize=14)\n",
|
||||
"\n",
|
||||
"a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step')\n",
|
||||
"a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10)\n",
|
||||
"a1.set_yticklabels([])\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Clean up"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Delete the ACI instance to stop the compute and any associated billing."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id='nextsteps'></a>\n",
|
||||
"## Next Steps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In this example, you created a series of models inside the notebook using local data, stored them inside an AML experiment, found the best one and deployed it as a live service! From here you can continue to use Azure Machine Learning in this regard to run your own experiments and deploy your own models, or you can expand into further capabilities of AML!\n",
|
||||
"\n",
|
||||
"If you have a model that is difficult to process locally, either because the data is remote or the model is large, try the [train-on-remote-vm](../train-on-remote-vm) notebook to learn about submitting remote jobs.\n",
|
||||
"\n",
|
||||
"If you want to take advantage of multiple cloud machines to perform large parameter sweeps try the [train-hyperparameter-tune-deploy-with-pytorch](../../training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch\n",
|
||||
") sample.\n",
|
||||
"\n",
|
||||
"If you want to deploy models to a production cluster try the [production-deploy-to-aks](../../deployment/production-deploy-to-aks\n",
|
||||
") notebook."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "roastala"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"compute": [
|
||||
"Local"
|
||||
],
|
||||
"datasets": [
|
||||
"Diabetes"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"friendly_name": "Train and deploy a model using Python SDK",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Training and deploying a model from a notebook"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: train-within-notebook
|
||||
dependencies:
|
||||
- tqdm
|
||||
- scikit-learn
|
||||
- matplotlib
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user