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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 1.6.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.11.0 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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564
contrib/fairness/fairlearn-azureml-mitigation.ipynb
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contrib/fairness/fairlearn-azureml-mitigation.ipynb
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@@ -0,0 +1,564 @@
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{
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"cells": [
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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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"Copyright (c) Microsoft Corporation. All rights reserved. \n",
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"Licensed under the MIT License."
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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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""
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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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"# Unfairness Mitigation with Fairlearn and Azure Machine Learning\n",
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"**This notebook shows how to upload results from Fairlearn's GridSearch mitigation algorithm into a dashboard in Azure Machine Learning Studio**\n",
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"\n",
|
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"## Table of Contents\n",
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"\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Loading the Data](#LoadingData)\n",
|
||||
"1. [Training an Unmitigated Model](#UnmitigatedModel)\n",
|
||||
"1. [Mitigation with GridSearch](#Mitigation)\n",
|
||||
"1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
|
||||
" 1. Registering models\n",
|
||||
" 1. Computing Fairness Metrics\n",
|
||||
" 1. Uploading to Azure\n",
|
||||
"1. [Conclusion](#Conclusion)\n",
|
||||
"\n",
|
||||
"<a id=\"Introduction\"></a>\n",
|
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"## Introduction\n",
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"This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.github.io) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.github.io/).\n",
|
||||
"\n",
|
||||
"We will apply the [grid search algorithm](https://fairlearn.github.io/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
|
||||
"\n",
|
||||
"### Setup\n",
|
||||
"\n",
|
||||
"To use this notebook, an Azure Machine Learning workspace is required.\n",
|
||||
"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
|
||||
"This notebook also requires the following packages:\n",
|
||||
"* `azureml-contrib-fairness`\n",
|
||||
"* `fairlearn==0.4.6`\n",
|
||||
"* `joblib`\n",
|
||||
"* `shap`\n",
|
||||
"\n",
|
||||
"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:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# !pip install --upgrade scikit-learn>=0.22.1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LoadingData\"></a>\n",
|
||||
"## Loading the Data\n",
|
||||
"We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
|
||||
"from fairlearn.widget import FairlearnDashboard\n",
|
||||
"from sklearn import svm\n",
|
||||
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"import pandas as pd\n",
|
||||
"import shap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now load and inspect the data from the `shap` package:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_raw, Y = shap.datasets.adult()\n",
|
||||
"X_raw[\"Race\"].value_counts().to_dict()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). We also separate out the Race column, but we will not perform any mitigation based on it. Finally, we perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = X_raw[['Sex','Race']]\n",
|
||||
"X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
|
||||
"X = pd.get_dummies(X)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"Y = le.fit_transform(Y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"With our data prepared, we can make the conventional split in to 'test' and 'train' subsets:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_raw, \n",
|
||||
" Y, \n",
|
||||
" A,\n",
|
||||
" test_size = 0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=Y)\n",
|
||||
"\n",
|
||||
"# Work around indexing issue\n",
|
||||
"X_train = X_train.reset_index(drop=True)\n",
|
||||
"A_train = A_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"A_test = A_test.reset_index(drop=True)\n",
|
||||
"\n",
|
||||
"# Improve labels\n",
|
||||
"A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
|
||||
"A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"UnmitigatedModel\"></a>\n",
|
||||
"## Training an Unmitigated Model\n",
|
||||
"\n",
|
||||
"So we have a point of comparison, we first train a model (specifically, logistic regression from scikit-learn) on the raw data, without applying any mitigation algorithm:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
|
||||
"\n",
|
||||
"unmitigated_predictor.fit(X_train, Y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can view this model in the fairness dashboard, and see the disparities which appear:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
|
||||
" y_true=Y_test,\n",
|
||||
" y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Looking at the disparity in accuracy when we select 'Sex' as the sensitive feature, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n",
|
||||
"\n",
|
||||
"Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"Mitigation\"></a>\n",
|
||||
"## Mitigation with GridSearch\n",
|
||||
"\n",
|
||||
"The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox - for this simple example, we shall use the logistic regression estimator from scikit-learn. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n",
|
||||
"\n",
|
||||
"For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). *We are using this metric for the sake of simplicity* in this example; the appropriate fairness metric can only be selected after *careful examination of the broader context* in which the model is to be used."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),\n",
|
||||
" constraints=DemographicParity(),\n",
|
||||
" grid_size=71)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"With our estimator created, we can fit it to the data. After `fit()` completes, we extract the full set of predictors from the `GridSearch` object.\n",
|
||||
"\n",
|
||||
"The following cell trains a many copies of the underlying estimator, and may take a minute or two to run:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sweep.fit(X_train, Y_train,\n",
|
||||
" sensitive_features=A_train.Sex)\n",
|
||||
"\n",
|
||||
"predictors = sweep._predictors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the protected attribute; other potentially protected attributes will *not* be mitigated). In general, one might not want to do this, since there may be other considerations beyond the strict optimisation of error and disparity (of the given protected attribute)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"errors, disparities = [], []\n",
|
||||
"for m in predictors:\n",
|
||||
" classifier = lambda X: m.predict(X)\n",
|
||||
" \n",
|
||||
" error = ErrorRate()\n",
|
||||
" error.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.Sex)\n",
|
||||
" disparity = DemographicParity()\n",
|
||||
" disparity.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.Sex)\n",
|
||||
" \n",
|
||||
" errors.append(error.gamma(classifier)[0])\n",
|
||||
" disparities.append(disparity.gamma(classifier).max())\n",
|
||||
" \n",
|
||||
"all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
|
||||
"\n",
|
||||
"dominant_models_dict = dict()\n",
|
||||
"base_name_format = \"census_gs_model_{0}\"\n",
|
||||
"row_id = 0\n",
|
||||
"for row in all_results.itertuples():\n",
|
||||
" model_name = base_name_format.format(row_id)\n",
|
||||
" errors_for_lower_or_eq_disparity = all_results[\"error\"][all_results[\"disparity\"]<=row.disparity]\n",
|
||||
" if row.error <= errors_for_lower_or_eq_disparity.min():\n",
|
||||
" dominant_models_dict[model_name] = row.predictor\n",
|
||||
" row_id = row_id + 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can construct predictions for the dominant models (we include the unmitigated predictor as well, for comparison):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions_dominant = {\"census_unmitigated\": unmitigated_predictor.predict(X_test)}\n",
|
||||
"models_dominant = {\"census_unmitigated\": unmitigated_predictor}\n",
|
||||
"for name, predictor in dominant_models_dict.items():\n",
|
||||
" value = predictor.predict(X_test)\n",
|
||||
" predictions_dominant[name] = value\n",
|
||||
" models_dominant[name] = predictor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"These predictions may then be viewed in the fairness dashboard. We include the race column from the dataset, as an alternative basis for assessing the models. However, since we have not based our mitigation on it, the variation in the models with respect to race can be large."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"FairlearnDashboard(sensitive_features=A_test, \n",
|
||||
" sensitive_feature_names=['Sex', 'Race'],\n",
|
||||
" y_true=Y_test.tolist(),\n",
|
||||
" y_pred=predictions_dominant)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When using sex as the sensitive feature, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
|
||||
"\n",
|
||||
"By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"AzureUpload\"></a>\n",
|
||||
"## Uploading a Fairness Dashboard to Azure\n",
|
||||
"\n",
|
||||
"Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
|
||||
"1. Register the dominant models\n",
|
||||
"1. Precompute all the required metrics\n",
|
||||
"1. Upload to Azure\n",
|
||||
"\n",
|
||||
"Before that, we need to connect to Azure Machine Learning Studio:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Experiment, Model\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"RegisterModels\"></a>\n",
|
||||
"### Registering Models\n",
|
||||
"\n",
|
||||
"The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `<name>:<version>` pairs) of registered models in the workspace. We register each of the models in the `models_dominant` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.makedirs('models', exist_ok=True)\n",
|
||||
"def register_model(name, model):\n",
|
||||
" print(\"Registering \", name)\n",
|
||||
" model_path = \"models/{0}.pkl\".format(name)\n",
|
||||
" joblib.dump(value=model, filename=model_path)\n",
|
||||
" registered_model = Model.register(model_path=model_path,\n",
|
||||
" model_name=name,\n",
|
||||
" workspace=ws)\n",
|
||||
" print(\"Registered \", registered_model.id)\n",
|
||||
" return registered_model.id\n",
|
||||
"\n",
|
||||
"model_name_id_mapping = dict()\n",
|
||||
"for name, model in models_dominant.items():\n",
|
||||
" m_id = register_model(name, model)\n",
|
||||
" model_name_id_mapping[name] = m_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, produce new predictions dictionaries, with the updated names:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions_dominant_ids = dict()\n",
|
||||
"for name, y_pred in predictions_dominant.items():\n",
|
||||
" predictions_dominant_ids[model_name_id_mapping[name]] = y_pred"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"PrecomputeMetrics\"></a>\n",
|
||||
"### Precomputing Metrics\n",
|
||||
"\n",
|
||||
"We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `predictions_dominant_ids` dictionary we just created:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sf = { 'sex': A_test.Sex, 'race': A_test.Race }\n",
|
||||
"\n",
|
||||
"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"dash_dict = _create_group_metric_set(y_true=Y_test,\n",
|
||||
" predictions=predictions_dominant_ids,\n",
|
||||
" sensitive_features=sf,\n",
|
||||
" prediction_type='binary_classification')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"DashboardUpload\"></a>\n",
|
||||
"### Uploading the Dashboard\n",
|
||||
"\n",
|
||||
"Now, we import our `contrib` package which contains the routine to perform the upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we can create an Experiment, then a Run, and upload our dashboard to it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp = Experiment(ws, \"Test_Fairlearn_GridSearch_Census_Demo\")\n",
|
||||
"print(exp)\n",
|
||||
"\n",
|
||||
"run = exp.start_logging()\n",
|
||||
"try:\n",
|
||||
" dashboard_title = \"Dominant Models from GridSearch\"\n",
|
||||
" upload_id = upload_dashboard_dictionary(run,\n",
|
||||
" dash_dict,\n",
|
||||
" dashboard_name=dashboard_title)\n",
|
||||
" print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
|
||||
"\n",
|
||||
" downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
|
||||
"finally:\n",
|
||||
" run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The dashboard can be viewed in the Run Details page.\n",
|
||||
"\n",
|
||||
"Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(dash_dict == downloaded_dict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"Conclusion\"></a>\n",
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.github.io/) provides that discussion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "riedgar"
|
||||
}
|
||||
],
|
||||
"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": "3.6.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
507
contrib/fairness/upload-fairness-dashboard.ipynb
Normal file
507
contrib/fairness/upload-fairness-dashboard.ipynb
Normal file
@@ -0,0 +1,507 @@
|
||||
{
|
||||
"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": [
|
||||
"# Upload a Fairness Dashboard to Azure Machine Learning Studio\n",
|
||||
"**This notebook shows how to generate and upload a fairness assessment dashboard from Fairlearn to AzureML Studio**\n",
|
||||
"\n",
|
||||
"## Table of Contents\n",
|
||||
"\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Loading the Data](#LoadingData)\n",
|
||||
"1. [Processing the Data](#ProcessingData)\n",
|
||||
"1. [Training Models](#TrainingModels)\n",
|
||||
"1. [Logging in to AzureML](#LoginAzureML)\n",
|
||||
"1. [Registering the Models](#RegisterModels)\n",
|
||||
"1. [Using the Fairlearn Dashboard](#LocalDashboard)\n",
|
||||
"1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
|
||||
" 1. Computing Fairness Metrics\n",
|
||||
" 1. Uploading to Azure\n",
|
||||
"1. [Conclusion](#Conclusion)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"<a id=\"Introduction\"></a>\n",
|
||||
"## Introduction\n",
|
||||
"\n",
|
||||
"In this notebook, we walk through a simple example of using the `azureml-contrib-fairness` package to upload a collection of fairness statistics for a fairness dashboard. It is an example of integrating the [open source Fairlearn package](https://www.github.com/fairlearn/fairlearn) with Azure Machine Learning. This is not an example of fairness analysis or mitigation - this notebook simply shows how to get a fairness dashboard into the Azure Machine Learning portal. We will load the data and train a couple of simple models. We will then use Fairlearn to generate data for a Fairness dashboard, which we can upload to Azure Machine Learning portal and view there.\n",
|
||||
"\n",
|
||||
"### Setup\n",
|
||||
"\n",
|
||||
"To use this notebook, an Azure Machine Learning workspace is required.\n",
|
||||
"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
|
||||
"This notebook also requires the following packages:\n",
|
||||
"* `azureml-contrib-fairness`\n",
|
||||
"* `fairlearn==0.4.6`\n",
|
||||
"* `joblib`\n",
|
||||
"* `shap`\n",
|
||||
"\n",
|
||||
"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:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# !pip install --upgrade scikit-learn>=0.22.1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LoadingData\"></a>\n",
|
||||
"## Loading the Data\n",
|
||||
"We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import svm\n",
|
||||
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"import pandas as pd\n",
|
||||
"import shap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we can load the data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_raw, Y = shap.datasets.adult()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can take a look at some of the data. For example, the next cells shows the counts of the different races identified in the dataset:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(X_raw[\"Race\"].value_counts().to_dict())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"ProcessingData\"></a>\n",
|
||||
"## Processing the Data\n",
|
||||
"\n",
|
||||
"With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and put the rest of the feature data into `X`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = X_raw[['Sex','Race']]\n",
|
||||
"X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
|
||||
"X = pd.get_dummies(X)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Next, we apply a standard set of scalings:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sc = StandardScaler()\n",
|
||||
"X_scaled = sc.fit_transform(X)\n",
|
||||
"X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
|
||||
"\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"Y = le.fit_transform(Y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we can then split our data into training and test sets, and also make the labels on our test portion of `A` human-readable:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled, \n",
|
||||
" Y, \n",
|
||||
" A,\n",
|
||||
" test_size = 0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=Y)\n",
|
||||
"\n",
|
||||
"# Work around indexing issue\n",
|
||||
"X_train = X_train.reset_index(drop=True)\n",
|
||||
"A_train = A_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"A_test = A_test.reset_index(drop=True)\n",
|
||||
"\n",
|
||||
"# Improve labels\n",
|
||||
"A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
|
||||
"A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
|
||||
"A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"TrainingModels\"></a>\n",
|
||||
"## Training Models\n",
|
||||
"\n",
|
||||
"We now train a couple of different models on our data. The `adult` census dataset is a classification problem - the goal is to predict whether a particular individual exceeds an income threshold. For the purpose of generating a dashboard to upload, it is sufficient to train two basic classifiers. First, a logistic regression classifier:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
|
||||
"\n",
|
||||
"lr_predictor.fit(X_train, Y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And for comparison, a support vector classifier:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"svm_predictor = svm.SVC()\n",
|
||||
"\n",
|
||||
"svm_predictor.fit(X_train, Y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LoginAzureML\"></a>\n",
|
||||
"## Logging in to AzureML\n",
|
||||
"\n",
|
||||
"With our two classifiers trained, we can log into our AzureML workspace:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Experiment, Model\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"RegisterModels\"></a>\n",
|
||||
"## Registering the Models\n",
|
||||
"\n",
|
||||
"Next, we register our models. By default, the subroutine which uploads the models checks that the names provided correspond to registered models in the workspace. We define a utility routine to do the registering:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.makedirs('models', exist_ok=True)\n",
|
||||
"def register_model(name, model):\n",
|
||||
" print(\"Registering \", name)\n",
|
||||
" model_path = \"models/{0}.pkl\".format(name)\n",
|
||||
" joblib.dump(value=model, filename=model_path)\n",
|
||||
" registered_model = Model.register(model_path=model_path,\n",
|
||||
" model_name=name,\n",
|
||||
" workspace=ws)\n",
|
||||
" print(\"Registered \", registered_model.id)\n",
|
||||
" return registered_model.id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, we register the models. For convenience in subsequent method calls, we store the results in a dictionary, which maps the `id` of the registered model (a string in `name:version` format) to the predictor itself:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model_dict = {}\n",
|
||||
"\n",
|
||||
"lr_reg_id = register_model(\"fairness_linear_regression\", lr_predictor)\n",
|
||||
"model_dict[lr_reg_id] = lr_predictor\n",
|
||||
"svm_reg_id = register_model(\"fairness_svm\", svm_predictor)\n",
|
||||
"model_dict[svm_reg_id] = svm_predictor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LocalDashboard\"></a>\n",
|
||||
"## Using the Fairlearn Dashboard\n",
|
||||
"\n",
|
||||
"We can now examine the fairness of the two models we have training, both as a function of race and (binary) sex. Before uploading the dashboard to the AzureML portal, we will first instantiate a local instance of the Fairlearn dashboard.\n",
|
||||
"\n",
|
||||
"Regardless of the viewing location, the dashboard is based on three things - the true values, the model predictions and the sensitive feature values. The dashboard can use predictions from multiple models and multiple sensitive features if desired (as we are doing here).\n",
|
||||
"\n",
|
||||
"Our first step is to generate a dictionary mapping the `id` of the registered model to the corresponding array of predictions:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ys_pred = {}\n",
|
||||
"for n, p in model_dict.items():\n",
|
||||
" ys_pred[n] = p.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can examine these predictions in a locally invoked Fairlearn dashboard. This can be compared to the dashboard uploaded to the portal (in the next section):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairlearn.widget import FairlearnDashboard\n",
|
||||
"\n",
|
||||
"FairlearnDashboard(sensitive_features=A_test, \n",
|
||||
" sensitive_feature_names=['Sex', 'Race'],\n",
|
||||
" y_true=Y_test.tolist(),\n",
|
||||
" y_pred=ys_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"AzureUpload\"></a>\n",
|
||||
"## Uploading a Fairness Dashboard to Azure\n",
|
||||
"\n",
|
||||
"Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
|
||||
"1. Precompute all the required metrics\n",
|
||||
"1. Upload to Azure\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"### Computing Fairness Metrics\n",
|
||||
"We use Fairlearn to create a dictionary which contains all the data required to display a dashboard. This includes both the raw data (true values, predicted values and sensitive features), and also the fairness metrics. The API is similar to that used to invoke the Dashboard locally. However, there are a few minor changes to the API, and the type of problem being examined (binary classification, regression etc.) needs to be specified explicitly:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sf = { 'Race': A_test.Race, 'Sex': A_test.Sex }\n",
|
||||
"\n",
|
||||
"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
|
||||
"\n",
|
||||
"dash_dict = _create_group_metric_set(y_true=Y_test,\n",
|
||||
" predictions=ys_pred,\n",
|
||||
" sensitive_features=sf,\n",
|
||||
" prediction_type='binary_classification')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The `_create_group_metric_set()` method is currently underscored since its exact design is not yet final in Fairlearn."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Uploading to Azure\n",
|
||||
"\n",
|
||||
"We can now import the `azureml.contrib.fairness` package itself. We will round-trip the data, so there are two required subroutines:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we can upload the generated dictionary to AzureML. The upload method requires a run, so we first create an experiment and a run. The uploaded dashboard can be seen on the corresponding Run Details page in AzureML Studio. For completeness, we also download the dashboard dictionary which we uploaded."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exp = Experiment(ws, \"notebook-01\")\n",
|
||||
"print(exp)\n",
|
||||
"\n",
|
||||
"run = exp.start_logging()\n",
|
||||
"try:\n",
|
||||
" dashboard_title = \"Sample notebook upload\"\n",
|
||||
" upload_id = upload_dashboard_dictionary(run,\n",
|
||||
" dash_dict,\n",
|
||||
" dashboard_name=dashboard_title)\n",
|
||||
" print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
|
||||
"\n",
|
||||
" downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
|
||||
"finally:\n",
|
||||
" run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(dash_dict == downloaded_dict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"Conclusion\"></a>\n",
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"In this notebook we have demonstrated how to generate and upload a fairness dashboard to AzureML Studio. We have not discussed how to analyse the results and apply mitigations. Those topics will be covered elsewhere."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "riedgar"
|
||||
}
|
||||
],
|
||||
"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": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -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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|
||||
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
|
||||
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|
||||
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|
||||
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|
||||
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
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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
@@ -1,500 +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"
|
||||
]
|
||||
@@ -105,7 +105,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -675,10 +675,8 @@
|
||||
"model_name = best_run.properties['model_name']\n",
|
||||
"\n",
|
||||
"script_file_name = 'inference/score.py'\n",
|
||||
"conda_env_file_name = 'inference/env.yml'\n",
|
||||
"\n",
|
||||
"best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
|
||||
"best_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/env.yml')"
|
||||
"best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -721,8 +719,7 @@
|
||||
"from azureml.core.model import Model\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
|
||||
"inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
|
||||
"inference_config = InferenceConfig(entry_script=script_file_name)\n",
|
||||
"\n",
|
||||
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
|
||||
" memory_gb = 1, \n",
|
||||
|
||||
@@ -2,7 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- onnxruntime==1.0.0
|
||||
|
||||
@@ -93,7 +93,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 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."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -2,6 +2,3 @@ name: auto-ml-classification-credit-card-fraud
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -491,8 +491,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset,\n",
|
||||
" target_column_name, model_name)"
|
||||
"test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
|
||||
" train_dataset, test_dataset, target_column_name, model_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -2,11 +2,3 @@ name: auto-ml-classification-text-dnn
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
|
||||
- sentencepiece==0.1.82
|
||||
- pytorch-transformers==1.0
|
||||
- spacy==2.1.8
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
|
||||
@@ -6,7 +6,7 @@ from azureml.core.run import Run
|
||||
|
||||
|
||||
def run_inference(test_experiment, compute_target, script_folder, train_run,
|
||||
test_dataset, target_column_name, model_name):
|
||||
train_dataset, test_dataset, target_column_name, model_name):
|
||||
|
||||
train_run.download_file('outputs/conda_env_v_1_0_0.yml',
|
||||
'inference/condafile.yml')
|
||||
@@ -22,7 +22,10 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
|
||||
'--target_column_name': target_column_name,
|
||||
'--model_name': model_name
|
||||
},
|
||||
inputs=[test_dataset.as_named_input('test_data')],
|
||||
inputs=[
|
||||
train_dataset.as_named_input('train_data'),
|
||||
test_dataset.as_named_input('test_data')
|
||||
],
|
||||
compute_target=compute_target,
|
||||
environment_definition=inference_env)
|
||||
|
||||
|
||||
@@ -1,8 +1,11 @@
|
||||
import numpy as np
|
||||
import argparse
|
||||
from azureml.core import Run
|
||||
|
||||
import numpy as np
|
||||
|
||||
from sklearn.externals import joblib
|
||||
from azureml.automl.core.shared import constants, metrics
|
||||
|
||||
from azureml.automl.runtime.shared.score import scoring, constants
|
||||
from azureml.core import Run
|
||||
from azureml.core.model import Model
|
||||
|
||||
|
||||
@@ -29,22 +32,26 @@ model = joblib.load(model_path)
|
||||
run = Run.get_context()
|
||||
# get input dataset by name
|
||||
test_dataset = run.input_datasets['test_data']
|
||||
train_dataset = run.input_datasets['train_data']
|
||||
|
||||
X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
|
||||
.to_pandas_dataframe()
|
||||
y_test_df = test_dataset.with_timestamp_columns(None) \
|
||||
.keep_columns(columns=[target_column_name]) \
|
||||
.to_pandas_dataframe()
|
||||
y_train_df = test_dataset.with_timestamp_columns(None) \
|
||||
.keep_columns(columns=[target_column_name]) \
|
||||
.to_pandas_dataframe()
|
||||
|
||||
predicted = model.predict_proba(X_test_df)
|
||||
|
||||
# use automl metrics module
|
||||
scores = metrics.compute_metrics_classification(
|
||||
np.array(predicted),
|
||||
np.array(y_test_df),
|
||||
class_labels=model.classes_,
|
||||
metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
|
||||
)
|
||||
# Use the AutoML scoring module
|
||||
class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
|
||||
train_labels = model.classes_
|
||||
classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
|
||||
scores = scoring.score_classification(y_test_df.values, predicted,
|
||||
classification_metrics,
|
||||
class_labels, train_labels)
|
||||
|
||||
print("scores:")
|
||||
print(scores)
|
||||
|
||||
@@ -88,7 +88,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -201,7 +201,7 @@
|
||||
"conda_run_config.environment.docker.enabled = True\n",
|
||||
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets'], \n",
|
||||
"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",
|
||||
|
||||
@@ -2,7 +2,3 @@ name: auto-ml-continuous-retraining
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- azureml-pipeline
|
||||
|
||||
@@ -114,7 +114,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -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)."
|
||||
]
|
||||
},
|
||||
@@ -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."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,11 +1,4 @@
|
||||
name: auto-ml-forecasting-beer-remote
|
||||
dependencies:
|
||||
- py-xgboost<=0.90
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy==1.16.2
|
||||
- pandas==0.23.4
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- azureml-train
|
||||
|
||||
@@ -1,11 +1,14 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import argparse
|
||||
from azureml.core import Run
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from pandas.tseries.frequencies import to_offset
|
||||
from sklearn.externals import joblib
|
||||
from sklearn.metrics import mean_absolute_error, mean_squared_error
|
||||
from azureml.automl.core.shared import constants, metrics
|
||||
from pandas.tseries.frequencies import to_offset
|
||||
|
||||
from azureml.automl.runtime.shared.score import scoring, constants
|
||||
from azureml.core import Run
|
||||
|
||||
|
||||
def align_outputs(y_predicted, X_trans, X_test, y_test,
|
||||
@@ -299,12 +302,11 @@ print(df_all[target_column_name])
|
||||
print("predicted values:::")
|
||||
print(df_all['predicted'])
|
||||
|
||||
# use automl metrics module
|
||||
scores = metrics.compute_metrics_regression(
|
||||
df_all['predicted'],
|
||||
df_all[target_column_name],
|
||||
list(constants.Metric.SCALAR_REGRESSION_SET),
|
||||
None, None, None)
|
||||
# Use the AutoML scoring module
|
||||
regression_metrics = list(constants.REGRESSION_SCALAR_SET)
|
||||
y_test = np.array(df_all[target_column_name])
|
||||
y_pred = np.array(df_all['predicted'])
|
||||
scores = scoring.score_regression(y_test, y_pred, regression_metrics)
|
||||
|
||||
print("scores:")
|
||||
print(scores)
|
||||
|
||||
@@ -87,7 +87,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -250,7 +250,7 @@
|
||||
"|-|-|\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",
|
||||
@@ -263,7 +263,7 @@
|
||||
"|**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",
|
||||
"\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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -307,7 +307,7 @@
|
||||
"\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",
|
||||
@@ -461,7 +461,7 @@
|
||||
"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 the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,10 +1,4 @@
|
||||
name: auto-ml-forecasting-bike-share
|
||||
dependencies:
|
||||
- py-xgboost<=0.90
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy==1.16.2
|
||||
- pandas==0.23.4
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -303,7 +303,7 @@
|
||||
"|-|-|\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",
|
||||
@@ -318,7 +318,7 @@
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -334,7 +334,7 @@
|
||||
"\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",
|
||||
@@ -560,7 +560,7 @@
|
||||
"### 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",
|
||||
"\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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -578,7 +578,7 @@
|
||||
"\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",
|
||||
|
||||
@@ -2,8 +2,3 @@ name: auto-ml-forecasting-energy-demand
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy==1.16.2
|
||||
- pandas==0.23.4
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -94,7 +94,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 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",
|
||||
@@ -313,11 +312,11 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lags = [1,2,3]\n",
|
||||
"max_horizon = n_test_periods\n",
|
||||
"forecast_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",
|
||||
" 'time_series_id_column_names': [ TIME_SERIES_ID_COLUMN_NAME ],\n",
|
||||
" 'forecast_horizon': forecast_horizon ,\n",
|
||||
" 'target_lags': lags\n",
|
||||
"}"
|
||||
]
|
||||
@@ -326,7 +325,7 @@
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -482,7 +481,7 @@
|
||||
"# 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 +491,7 @@
|
||||
"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 +518,7 @@
|
||||
"\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 +535,13 @@
|
||||
" 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 +568,7 @@
|
||||
"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 +583,7 @@
|
||||
"\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 +653,8 @@
|
||||
"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 +684,7 @@
|
||||
"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 +700,7 @@
|
||||
"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 +715,7 @@
|
||||
"# 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 +723,14 @@
|
||||
"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 +744,17 @@
|
||||
"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 +775,8 @@
|
||||
"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 +785,7 @@
|
||||
"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 +801,7 @@
|
||||
"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. "
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -1,10 +1,4 @@
|
||||
name: auto-ml-forecasting-function
|
||||
dependencies:
|
||||
- py-xgboost<=0.90
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy==1.16.2
|
||||
- pandas==0.23.4
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
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@@ -82,7 +82,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -336,7 +336,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-featurizationconfig-remarks"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"featurization_config = FeaturizationConfig()\n",
|
||||
@@ -424,7 +428,7 @@
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,10 +1,4 @@
|
||||
name: auto-ml-forecasting-orange-juice-sales
|
||||
dependencies:
|
||||
- py-xgboost<=0.90
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy==1.16.2
|
||||
- pandas==0.23.4
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -28,7 +28,8 @@
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
"1. [Results](#Results)\n",
|
||||
"1. [Test](#Test)\n",
|
||||
"1. [Test](#Tests)\n",
|
||||
"1. [Explanation](#Explanation)\n",
|
||||
"1. [Acknowledgements](#Acknowledgements)"
|
||||
]
|
||||
},
|
||||
@@ -49,9 +50,9 @@
|
||||
"2. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"3. Train the model.\n",
|
||||
"4. Explore the results.\n",
|
||||
"5. Visualization model's feature importance in azure portal\n",
|
||||
"6. Explore any model's explanation and explore feature importance in azure portal\n",
|
||||
"7. Test the fitted model."
|
||||
"5. Test the fitted model.\n",
|
||||
"6. Explore any model's explanation and explore feature importance in azure portal.\n",
|
||||
"7. Create an AKS cluster, deploy the webservice of AutoML scoring model and the explainer model to the AKS and consume the web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -95,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -255,9 +256,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Analyze results\n",
|
||||
"### Analyze results\n",
|
||||
"\n",
|
||||
"### Retrieve the Best Model\n",
|
||||
"#### 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*."
|
||||
]
|
||||
@@ -284,9 +285,80 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Best Model 's explanation\n",
|
||||
"Retrieve the explanation from the best_run which includes explanations for engineered features and raw features.\n",
|
||||
"## Tests\n",
|
||||
"\n",
|
||||
"Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# convert the test data to dataframe\n",
|
||||
"X_test_df = validation_data.drop_columns(columns=[label_column_name]).to_pandas_dataframe()\n",
|
||||
"y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# call the predict functions on the model\n",
|
||||
"y_pred = fitted_model.predict(X_test_df)\n",
|
||||
"y_pred"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import confusion_matrix\n",
|
||||
"import numpy as np\n",
|
||||
"import itertools\n",
|
||||
"\n",
|
||||
"cf =confusion_matrix(y_test_df.values,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 = ['False','True']\n",
|
||||
"tick_marks = np.arange(len(class_labels))\n",
|
||||
"plt.xticks(tick_marks,class_labels)\n",
|
||||
"plt.yticks([-0.5,0,1,1.5],['','False','True',''])\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": [
|
||||
"## 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",
|
||||
"\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",
|
||||
"### Run the explanation\n",
|
||||
"#### Download engineered feature importance from artifact store\n",
|
||||
"You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
|
||||
]
|
||||
@@ -303,14 +375,6 @@
|
||||
"print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explanations\n",
|
||||
"In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. 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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -403,6 +467,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Compute the engineered explanations\n",
|
||||
"engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
|
||||
"print(engineered_explanations.get_feature_importance_dict())\n",
|
||||
"print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
|
||||
@@ -412,41 +477,37 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test the fitted model\n",
|
||||
"#### Initialize the scoring Explainer, save and upload it for later use in scoring explanation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer\n",
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# convert the test data to dataframe\n",
|
||||
"X_test_df = validation_data.drop_columns(columns=[label_column_name]).to_pandas_dataframe()\n",
|
||||
"y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# call the predict functions on the model\n",
|
||||
"y_pred = fitted_model.predict(X_test_df)\n",
|
||||
"y_pred"
|
||||
"# Initialize the ScoringExplainer\n",
|
||||
"scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
|
||||
"\n",
|
||||
"# Pickle scoring explainer locally to './scoring_explainer.pkl'\n",
|
||||
"scoring_explainer_file_name = 'scoring_explainer.pkl'\n",
|
||||
"with open(scoring_explainer_file_name, 'wb') as stream:\n",
|
||||
" joblib.dump(scoring_explainer, stream)\n",
|
||||
"\n",
|
||||
"# Upload the scoring explainer to the automl run\n",
|
||||
"automl_run.upload_file('outputs/scoring_explainer.pkl', scoring_explainer_file_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Calculate metrics for the prediction\n",
|
||||
"### Deploying the scoring and explainer models to a web service to Azure Kubernetes Service (AKS)\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."
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -455,25 +516,238 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import confusion_matrix\n",
|
||||
"import numpy as np\n",
|
||||
"import itertools\n",
|
||||
"# Register trained automl model present in the 'outputs' folder in the artifacts\n",
|
||||
"original_model = automl_run.register_model(model_name='automl_model', \n",
|
||||
" model_path='outputs/model.pkl')\n",
|
||||
"scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer',\n",
|
||||
" model_path='outputs/scoring_explainer.pkl')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create the conda dependencies for setting up the service\n",
|
||||
"\n",
|
||||
"cf =confusion_matrix(y_test_df.values,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 = ['False','True']\n",
|
||||
"tick_marks = np.arange(len(class_labels))\n",
|
||||
"plt.xticks(tick_marks,class_labels)\n",
|
||||
"plt.yticks([-0.5,0,1,1.5],['','False','True',''])\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()"
|
||||
"We need to create the conda dependencies comprising of the azureml-explain-model, azureml-train-automl and azureml-defaults packages."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.automl.core.shared import constants\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"automl_run.download_file(constants.CONDA_ENV_FILE_PATH, 'myenv.yml')\n",
|
||||
"myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
|
||||
"myenv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Write the Entry Script\n",
|
||||
"Write the script that will be used to predict on your model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import azureml.train.automl\n",
|
||||
"import azureml.explain.model\n",
|
||||
"from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \\\n",
|
||||
" automl_setup_model_explanations\n",
|
||||
"import joblib\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global automl_model\n",
|
||||
" global scoring_explainer\n",
|
||||
"\n",
|
||||
" # Retrieve the path to the model file using the model name\n",
|
||||
" # Assume original model is named original_prediction_model\n",
|
||||
" automl_model_path = Model.get_model_path('automl_model')\n",
|
||||
" scoring_explainer_path = Model.get_model_path('scoring_explainer')\n",
|
||||
"\n",
|
||||
" automl_model = joblib.load(automl_model_path)\n",
|
||||
" scoring_explainer = joblib.load(scoring_explainer_path)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def run(raw_data):\n",
|
||||
" data = pd.read_json(raw_data, orient='records') \n",
|
||||
" # Make prediction\n",
|
||||
" predictions = automl_model.predict(data)\n",
|
||||
" # Setup for inferencing explanations\n",
|
||||
" automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,\n",
|
||||
" X_test=data, task='classification')\n",
|
||||
" # Retrieve model explanations for engineered explanations\n",
|
||||
" engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform) \n",
|
||||
" # You can return any data type as long as it is JSON-serializable\n",
|
||||
" return {'predictions': predictions.tolist(),\n",
|
||||
" 'engineered_local_importance_values': engineered_local_importance_values}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create the InferenceConfig \n",
|
||||
"Create the inference config that will be used when deploying the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Provision the AKS Cluster\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AksCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your cluster.\n",
|
||||
"aks_name = 'scoring-explain'\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" prov_config = AksCompute.provisioning_configuration(vm_size='STANDARD_D3_V2')\n",
|
||||
" aks_target = ComputeTarget.create(workspace=ws, \n",
|
||||
" name=aks_name,\n",
|
||||
" provisioning_configuration=prov_config)\n",
|
||||
"aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Deploy web service to AKS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set the web service configuration (using default here)\n",
|
||||
"from azureml.core.webservice import AksWebservice\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service_name ='model-scoring-local-aks'\n",
|
||||
"\n",
|
||||
"aks_service = Model.deploy(workspace=ws,\n",
|
||||
" name=aks_service_name,\n",
|
||||
" models=[scoring_explainer_model, original_model],\n",
|
||||
" inference_config=inf_config,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View the service logs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service.get_logs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Consume the web service using run method to do the scoring and explanation of scoring.\n",
|
||||
"We test the web sevice by passing data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Serialize the first row of the test data into json\n",
|
||||
"X_test_json = X_test_df[:1].to_json(orient='records')\n",
|
||||
"print(X_test_json)\n",
|
||||
"\n",
|
||||
"# Call the service to get the predictions and the engineered and raw explanations\n",
|
||||
"output = aks_service.run(X_test_json)\n",
|
||||
"\n",
|
||||
"# Print the predicted value\n",
|
||||
"print('predictions:\\n{}\\n'.format(output['predictions']))\n",
|
||||
"# Print the engineered feature importances for the predicted value\n",
|
||||
"print('engineered_local_importance_values:\\n{}\\n'.format(output['engineered_local_importance_values']))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Clean up\n",
|
||||
"Delete the service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -2,6 +2,3 @@ name: auto-ml-classification-credit-card-fraud-local
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -98,7 +98,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -242,7 +242,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-featurizationconfig-remarks2"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"featurization_config = FeaturizationConfig()\n",
|
||||
@@ -260,7 +264,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-featurizationconfig-remarks3"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_settings = {\n",
|
||||
|
||||
@@ -2,6 +2,3 @@ name: auto-ml-regression-explanation-featurization
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -92,7 +92,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 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."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -2,7 +2,3 @@ name: auto-ml-regression
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- pandas==0.23.4
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -50,10 +50,12 @@ pip install azureml-accel-models[gpu]
|
||||
|
||||
### Step 4: Follow our notebooks
|
||||
|
||||
The notebooks in this repo walk through the following scenarios:
|
||||
* [Quickstart](accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
|
||||
* [Object Detection](accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
|
||||
* [Training models](accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
|
||||
We provide notebooks to walk through the following scenarios, linked below:
|
||||
* [Quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
|
||||
* [Object Detection](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
|
||||
* [Training models](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
|
||||
|
||||
**Note**: the above notebooks work only for tensorflow >= 1.6,<2.0.
|
||||
|
||||
<a name="model-classes"></a>
|
||||
## Model Classes
|
||||
|
||||
Binary file not shown.
@@ -86,7 +86,37 @@
|
||||
"source": [
|
||||
"In this example, we will be using and registering two models. \n",
|
||||
"\n",
|
||||
"You wil need to have a `first_model.pkl` file and `second_model.pkl` file in the current directory. The below call registers the files as Models with the names `my_first_model` and `my_second_model` in the workspace."
|
||||
"First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import BayesianRidge, Ridge\n",
|
||||
"\n",
|
||||
"x, y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"first_model = Ridge().fit(x, y)\n",
|
||||
"second_model = BayesianRidge().fit(x, y)\n",
|
||||
"\n",
|
||||
"joblib.dump(first_model, \"first_model.pkl\")\n",
|
||||
"joblib.dump(second_model, \"second_model.pkl\")\n",
|
||||
"\n",
|
||||
"print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -102,12 +132,12 @@
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"my_model_1 = Model.register(model_path=\"first_model.pkl\",\n",
|
||||
" model_name=\"my_first_model\",\n",
|
||||
" workspace=ws)\n",
|
||||
" model_name=\"my_first_model\",\n",
|
||||
" workspace=ws)\n",
|
||||
"\n",
|
||||
"my_model_2 = Model.register(model_path=\"second_model.pkl\",\n",
|
||||
" model_name=\"my_second_model\",\n",
|
||||
" workspace=ws)"
|
||||
" model_name=\"my_second_model\",\n",
|
||||
" workspace=ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -149,25 +179,24 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import pickle\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model_1, model_2\n",
|
||||
" # note here \"my_first_model\" is the name of the model registered under the workspace\n",
|
||||
" # this call should return the path to the model.pkl file on the local disk.\n",
|
||||
" # Here \"my_first_model\" is the name of the model registered under the workspace.\n",
|
||||
" # This call will return the path to the .pkl file on the local disk.\n",
|
||||
" model_1_path = Model.get_model_path(model_name='my_first_model')\n",
|
||||
" model_2_path = Model.get_model_path(model_name='my_second_model')\n",
|
||||
" \n",
|
||||
" # deserialize the model files back into a sklearn model\n",
|
||||
" # Deserialize the model files back into scikit-learn models.\n",
|
||||
" model_1 = joblib.load(model_1_path)\n",
|
||||
" model_2 = joblib.load(model_2_path)\n",
|
||||
"\n",
|
||||
"# note you can pass in multiple rows for scoring\n",
|
||||
"# Note you can pass in multiple rows for scoring.\n",
|
||||
"def run(raw_data):\n",
|
||||
" try:\n",
|
||||
" data = json.loads(raw_data)['data']\n",
|
||||
@@ -177,7 +206,7 @@
|
||||
" result_1 = model_1.predict(data)\n",
|
||||
" result_2 = model_2.predict(data)\n",
|
||||
"\n",
|
||||
" # you can return any data type as long as it is JSON-serializable\n",
|
||||
" # You can return any JSON-serializable value.\n",
|
||||
" return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n",
|
||||
" except Exception as e:\n",
|
||||
" result = str(e)\n",
|
||||
@@ -208,10 +237,10 @@
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
|
||||
"\n",
|
||||
"# This is optional at this point\n",
|
||||
"# env.register(workspace=ws)"
|
||||
"env = Environment(\"deploytocloudenv\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"joblib\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"numpy\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -281,25 +310,15 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice, Webservice\n",
|
||||
"from azureml.exceptions import WebserviceException\n",
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aci_service_name = \"aciservice-multimodel\"\n",
|
||||
"\n",
|
||||
"deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
|
||||
"aci_service_name = 'aciservice-multimodel'\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" # if you want to get existing service below is the command\n",
|
||||
" # since aci name needs to be unique in subscription deleting existing aci if any\n",
|
||||
" # we use aci_service_name to create azure aci\n",
|
||||
" service = Webservice(ws, name=aci_service_name)\n",
|
||||
" if service:\n",
|
||||
" service.delete()\n",
|
||||
"except WebserviceException as e:\n",
|
||||
" print()\n",
|
||||
"\n",
|
||||
"service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config)\n",
|
||||
"\n",
|
||||
"service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n",
|
||||
"service.wait_for_deployment(True)\n",
|
||||
"\n",
|
||||
"print(service.state)"
|
||||
]
|
||||
},
|
||||
@@ -317,13 +336,11 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"test_sample = json.dumps({'data': [\n",
|
||||
" [1,2,3,4,5,6,7,8,9,10], \n",
|
||||
" [10,9,8,7,6,5,4,3,2,1]\n",
|
||||
"]})\n",
|
||||
"\n",
|
||||
"test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
|
||||
"prediction = service.run(input_data=test_sample_encoded)\n",
|
||||
"test_sample = json.dumps({'data': x[0:2].tolist()})\n",
|
||||
"\n",
|
||||
"prediction = service.run(test_sample)\n",
|
||||
"\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -2,3 +2,5 @@ name: multi-model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- scikit-learn
|
||||
|
||||
@@ -1,8 +0,0 @@
|
||||
name: project_environment
|
||||
dependencies:
|
||||
- python=3.6.2
|
||||
- pip:
|
||||
- azureml-defaults
|
||||
- scikit-learn
|
||||
- numpy
|
||||
- inference-schema[numpy-support]
|
||||
Binary file not shown.
@@ -1,442 +0,0 @@
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||||
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|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register input and output datasets\n",
|
||||
"## Create trained model\n",
|
||||
"\n",
|
||||
"For this example, we have provided a small model (`sklearn_regression_model.pkl` in the notebook's directory) that was trained on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). Here, you will register the data used to create this model in your workspace."
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -91,9 +91,42 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"model = Ridge().fit(dataset_x, dataset_y)\n",
|
||||
"\n",
|
||||
"joblib.dump(model, 'sklearn_regression_model.pkl')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register input and output datasets\n",
|
||||
"\n",
|
||||
"Here, you will register the data used to create the model in your workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"from azureml.core import Dataset\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"np.savetxt('features.csv', dataset_x, delimiter=',')\n",
|
||||
"np.savetxt('labels.csv', dataset_y, delimiter=',')\n",
|
||||
"\n",
|
||||
"datastore = ws.get_default_datastore()\n",
|
||||
"datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
|
||||
" target_path='sklearn_regression/',\n",
|
||||
@@ -125,6 +158,8 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from azureml.core import Model\n",
|
||||
"from azureml.core.resource_configuration import ResourceConfiguration\n",
|
||||
"\n",
|
||||
@@ -133,7 +168,7 @@
|
||||
" model_name='my-sklearn-model', # Name of the registered model in your workspace.\n",
|
||||
" model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n",
|
||||
" model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n",
|
||||
" model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n",
|
||||
" model_framework_version=sklearn.__version__, # Version of scikit-learn used to create the model.\n",
|
||||
" sample_input_dataset=input_dataset,\n",
|
||||
" sample_output_dataset=output_dataset,\n",
|
||||
" resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
|
||||
@@ -174,19 +209,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Webservice\n",
|
||||
"from azureml.exceptions import WebserviceException\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"service_name = 'my-sklearn-service'\n",
|
||||
"\n",
|
||||
"# Remove any existing service under the same name.\n",
|
||||
"try:\n",
|
||||
" Webservice(ws, service_name).delete()\n",
|
||||
"except WebserviceException:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"service = Model.deploy(ws, service_name, [model])\n",
|
||||
"service = Model.deploy(ws, service_name, [model], overwrite=True)\n",
|
||||
"service.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
@@ -207,10 +232,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"input_payload = json.dumps({\n",
|
||||
" 'data': [\n",
|
||||
" [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n",
|
||||
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
|
||||
" ],\n",
|
||||
" 'data': dataset_x[0:2].tolist(),\n",
|
||||
" 'method': 'predict' # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
@@ -262,7 +284,7 @@
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn'\n",
|
||||
" 'scikit-learn=={}'.format(sklearn.__version__)\n",
|
||||
"])"
|
||||
]
|
||||
},
|
||||
@@ -303,20 +325,12 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Webservice\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"from azureml.exceptions import WebserviceException\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"service_name = 'my-custom-env-service'\n",
|
||||
"\n",
|
||||
"# Remove any existing service under the same name.\n",
|
||||
"try:\n",
|
||||
" Webservice(ws, service_name).delete()\n",
|
||||
"except WebserviceException:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
|
||||
"aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
|
||||
"\n",
|
||||
@@ -324,7 +338,8 @@
|
||||
" name=service_name,\n",
|
||||
" models=[model],\n",
|
||||
" inference_config=inference_config,\n",
|
||||
" deployment_config=aci_config)\n",
|
||||
" deployment_config=aci_config,\n",
|
||||
" overwrite=True)\n",
|
||||
"service.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
@@ -342,10 +357,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"input_payload = json.dumps({\n",
|
||||
" 'data': [\n",
|
||||
" [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n",
|
||||
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
|
||||
" ]\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"output = service.run(input_payload)\n",
|
||||
@@ -471,7 +483,7 @@
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn'\n",
|
||||
" 'scikit-learn=={}'.format(sklearn.__version__)\n",
|
||||
"])\n",
|
||||
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
|
||||
"# if cpu and memory_in_gb parameters are not provided\n",
|
||||
|
||||
@@ -2,3 +2,5 @@ name: model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- scikit-learn
|
||||
|
||||
Binary file not shown.
@@ -1,8 +0,0 @@
|
||||
name: project_environment
|
||||
dependencies:
|
||||
- python=3.6.2
|
||||
- pip:
|
||||
- azureml-defaults
|
||||
- scikit-learn==0.19.1
|
||||
- numpy
|
||||
- inference-schema[numpy-support]
|
||||
@@ -75,6 +75,33 @@
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create trained model\n",
|
||||
"\n",
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"sk_model = Ridge().fit(dataset_x, dataset_y)\n",
|
||||
"\n",
|
||||
"joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -148,13 +175,10 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile source_directory/x/y/score.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
@@ -165,16 +189,17 @@
|
||||
" # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
" global name\n",
|
||||
" # note here, entire source directory on inference config gets added into image\n",
|
||||
" # bellow is the example how you can use any extra files in image\n",
|
||||
" # Note here, the entire source directory from inference config gets added into image.\n",
|
||||
" # Below is an example of how you can use any extra files in image.\n",
|
||||
" with open('./source_directory/extradata.json') as json_file:\n",
|
||||
" data = json.load(json_file)\n",
|
||||
" name = data[\"people\"][0][\"name\"]\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
@@ -182,37 +207,13 @@
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # you can return any datatype as long as it is JSON-serializable\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return \"Hello \" + name + \" here is your result = \" + str(result)\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency for your environemnt. This package contains the functionality needed to host the model as a web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile source_directory/env/myenv.yml\n",
|
||||
"name: project_environment\n",
|
||||
"dependencies:\n",
|
||||
" - python=3.6.2\n",
|
||||
" - pip:\n",
|
||||
" - azureml-defaults\n",
|
||||
" - scikit-learn\n",
|
||||
" - numpy\n",
|
||||
" - inference-schema[numpy-support]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -249,11 +250,16 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"myenv = Environment.from_conda_specification(name='myenv', file_path='myenv.yml')\n",
|
||||
"myenv = Environment('myenv')\n",
|
||||
"myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
|
||||
"myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n",
|
||||
"myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n",
|
||||
"\n",
|
||||
"# explicitly set base_image to None when setting base_dockerfile\n",
|
||||
"myenv.docker.base_image = None\n",
|
||||
@@ -262,7 +268,7 @@
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(source_directory=source_directory,\n",
|
||||
" entry_script=\"x/y/score.py\",\n",
|
||||
" environment=myenv)\n"
|
||||
" environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -352,15 +358,10 @@
|
||||
"import json\n",
|
||||
"\n",
|
||||
"sample_input = json.dumps({\n",
|
||||
" 'data': [\n",
|
||||
" [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
|
||||
" [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
|
||||
" ]\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"sample_input = bytes(sample_input, encoding='utf-8')\n",
|
||||
"\n",
|
||||
"print(local_service.run(input_data=sample_input))"
|
||||
"print(local_service.run(sample_input))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -379,12 +380,10 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile source_directory/x/y/score.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
@@ -395,17 +394,18 @@
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
" global name, from_location\n",
|
||||
" # note here, entire source directory on inference config gets added into image\n",
|
||||
" # bellow is the example how you can use any extra files in image\n",
|
||||
" # Note here, the entire source directory from inference config gets added into image.\n",
|
||||
" # Below is an example of how you can use any extra files in image.\n",
|
||||
" with open('source_directory/extradata.json') as json_file: \n",
|
||||
" data = json.load(json_file)\n",
|
||||
" name = data[\"people\"][0][\"name\"]\n",
|
||||
" from_location = data[\"people\"][0][\"from\"]\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
@@ -413,8 +413,8 @@
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # you can return any datatype as long as it is JSON-serializable\n",
|
||||
" return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
@@ -430,7 +430,7 @@
|
||||
"print(\"--------------------------------------------------------------\")\n",
|
||||
"\n",
|
||||
"# After calling reload(), run() will return the updated message.\n",
|
||||
"local_service.run(input_data=sample_input)"
|
||||
"local_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -71,6 +71,33 @@
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create trained model\n",
|
||||
"\n",
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"sk_model = Ridge().fit(dataset_x, dataset_y)\n",
|
||||
"\n",
|
||||
"joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -82,9 +109,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
|
||||
"Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
|
||||
"\n",
|
||||
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
|
||||
"You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -119,11 +146,62 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"environment = Environment(\"LocalDeploy\")\n",
|
||||
"environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")"
|
||||
"environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
|
||||
"environment.python.conda_dependencies.add_pip_package(\"joblib\")\n",
|
||||
"environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Provide the Scoring Script\n",
|
||||
"\n",
|
||||
"This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
"@output_schema(NumpyParameterType(output_sample))\n",
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return result.tolist()\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -145,114 +223,6 @@
|
||||
" environment=environment)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Model Profiling\n",
|
||||
"\n",
|
||||
"Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
|
||||
"\n",
|
||||
"In order to profile your model you will need:\n",
|
||||
"- a registered model\n",
|
||||
"- an entry script\n",
|
||||
"- an inference configuration\n",
|
||||
"- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
|
||||
"\n",
|
||||
"Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
|
||||
"\n",
|
||||
"At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
|
||||
"\n",
|
||||
"Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"from azureml.core import Datastore\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.data import dataset_type_definitions\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# create a string that can be put in the body of the request\n",
|
||||
"serialized_input_json = json.dumps({\n",
|
||||
" 'data': [\n",
|
||||
" [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
|
||||
" [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
|
||||
" ]\n",
|
||||
"})\n",
|
||||
"dataset_content = []\n",
|
||||
"for i in range(100):\n",
|
||||
" dataset_content.append(serialized_input_json)\n",
|
||||
"dataset_content = '\\n'.join(dataset_content)\n",
|
||||
"file_name = 'sample_request_data_diabetes.txt'\n",
|
||||
"f = open(file_name, 'w')\n",
|
||||
"f.write(dataset_content)\n",
|
||||
"f.close()\n",
|
||||
"\n",
|
||||
"# upload the txt file created above to the Datastore and create a dataset from it\n",
|
||||
"data_store = Datastore.get_default(ws)\n",
|
||||
"data_store.upload_files(['./' + file_name], target_path='sample_request_data_diabetes')\n",
|
||||
"datastore_path = [(data_store, 'sample_request_data_diabetes' +'/' + file_name)]\n",
|
||||
"sample_request_data_diabetes = Dataset.Tabular.from_delimited_files(\n",
|
||||
" datastore_path,\n",
|
||||
" separator='\\n',\n",
|
||||
" infer_column_types=True,\n",
|
||||
" header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
|
||||
"sample_request_data_diabetes = sample_request_data_diabetes.register(workspace=ws,\n",
|
||||
" name='sample_request_data_diabetes',\n",
|
||||
" create_new_version=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime\n",
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.model import Model, InferenceConfig\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"environment = Environment('my-sklearn-environment')\n",
|
||||
"environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
|
||||
" 'azureml-defaults',\n",
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn==0.19.1',\n",
|
||||
" 'scipy'\n",
|
||||
"])\n",
|
||||
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
|
||||
"# if cpu and memory_in_gb parameters are not provided\n",
|
||||
"# the model will be profiled on default configuration of\n",
|
||||
"# 3.5CPU and 15GB memory\n",
|
||||
"profile = Model.profile(ws,\n",
|
||||
" 'profile-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
|
||||
" [model],\n",
|
||||
" inference_config,\n",
|
||||
" input_dataset=sample_request_data_diabetes,\n",
|
||||
" cpu=1.0,\n",
|
||||
" memory_in_gb=0.5)\n",
|
||||
"\n",
|
||||
"# profiling is a long running operation and may take up to 25 min\n",
|
||||
"profile.wait_for_completion(True)\n",
|
||||
"details = profile.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -339,15 +309,10 @@
|
||||
"import json\n",
|
||||
"\n",
|
||||
"sample_input = json.dumps({\n",
|
||||
" 'data': [\n",
|
||||
" [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
|
||||
" [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
|
||||
" ]\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"sample_input = bytes(sample_input, encoding='utf-8')\n",
|
||||
"\n",
|
||||
"local_service.run(input_data=sample_input)"
|
||||
"local_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -366,12 +331,10 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
@@ -382,10 +345,10 @@
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
@@ -393,8 +356,8 @@
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # you can return any datatype as long as it is JSON-serializable\n",
|
||||
" return 'hello from updated score.py'\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return 'Hello from the updated score.py: ' + str(result.tolist())\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
@@ -410,7 +373,7 @@
|
||||
"print(\"--------------------------------------------------------------\")\n",
|
||||
"\n",
|
||||
"# After calling reload(), run() will return the updated message.\n",
|
||||
"local_service.run(input_data=sample_input)"
|
||||
"local_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,35 +0,0 @@
|
||||
import os
|
||||
import pickle
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.externals import joblib
|
||||
from sklearn.linear_model import Ridge
|
||||
|
||||
from inference_schema.schema_decorators import input_schema, output_schema
|
||||
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
|
||||
|
||||
|
||||
def init():
|
||||
global model
|
||||
# AZUREML_MODEL_DIR is an environment variable created during deployment.
|
||||
# It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
|
||||
# For multiple models, it points to the folder containing all deployed models (./azureml-models)
|
||||
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')
|
||||
# deserialize the model file back into a sklearn model
|
||||
model = joblib.load(model_path)
|
||||
|
||||
|
||||
input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
|
||||
output_sample = np.array([3726.995])
|
||||
|
||||
|
||||
@input_schema('data', NumpyParameterType(input_sample))
|
||||
@output_schema(NumpyParameterType(output_sample))
|
||||
def run(data):
|
||||
try:
|
||||
result = model.predict(data)
|
||||
# you can return any datatype as long as it is JSON-serializable
|
||||
return result.tolist()
|
||||
except Exception as e:
|
||||
error = str(e)
|
||||
return error
|
||||
Binary file not shown.
@@ -108,9 +108,9 @@
|
||||
"environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
|
||||
" 'azureml-defaults',\n",
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn'\n",
|
||||
" 'scikit-learn==0.19.1',\n",
|
||||
" 'scipy'\n",
|
||||
"])"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -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": {
|
||||
|
||||
@@ -0,0 +1,354 @@
|
||||
{
|
||||
"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": [
|
||||
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
|
||||
"This notebook shows the steps for deploying a service: registering a model, provisioning a cluster with ssl (one time action), and deploying a service to it. \n",
|
||||
"We then test and delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"from azureml.core.webservice import Webservice, AksWebservice\n",
|
||||
"from azureml.core.model import Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"print(azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get workspace\n",
|
||||
"Load existing workspace from the config file info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register the model\n",
|
||||
"Register an existing trained model, add descirption and tags."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Register the model\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
|
||||
" model_name = \"sklearn_model\", # this is the name the model is registered as\n",
|
||||
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description = \"Ridge regression model to predict diabetes\",\n",
|
||||
" workspace = ws)\n",
|
||||
"\n",
|
||||
"print(model.name, model.description, model.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create the Environment\n",
|
||||
"Create an environment that the model will be deployed with"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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', 'inference-schema'])\n",
|
||||
"myenv = Environment(name='myenv')\n",
|
||||
"myenv.python.conda_dependencies = conda_deps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Use a custom Docker image\n",
|
||||
"\n",
|
||||
"You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
|
||||
"\n",
|
||||
"Only supported with `python` runtime.\n",
|
||||
"```python\n",
|
||||
"# use an image available in public Container Registry without authentication\n",
|
||||
"myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
|
||||
"\n",
|
||||
"# or, use an image available in a private Container Registry\n",
|
||||
"myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
|
||||
"myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
|
||||
"myenv.docker.base_image_registry.username = \"username\"\n",
|
||||
"myenv.docker.base_image_registry.password = \"password\"\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Write the Entry Script\n",
|
||||
"Write the script that will be used to predict on your model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score_ssl.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import json\n",
|
||||
"import numpy\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"standard_sample_input = {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }\n",
|
||||
"standard_sample_output = {'outcome': 1}\n",
|
||||
"\n",
|
||||
"@input_schema('param', StandardPythonParameterType(standard_sample_input))\n",
|
||||
"@output_schema(StandardPythonParameterType(standard_sample_output))\n",
|
||||
"def run(param):\n",
|
||||
" try:\n",
|
||||
" raw_data = [param['a'], param['b'], param['c'], param['d'], param['e'], param['f'], param['g'], param['h'], param['i'], param['j']]\n",
|
||||
" data = numpy.array([raw_data])\n",
|
||||
" result = model.predict(data)\n",
|
||||
" return { 'outcome' : result[0] }\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create the InferenceConfig\n",
|
||||
"Create the inference config that will be used when deploying the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Provision the AKS Cluster with SSL\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"\n",
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Use the default configuration (can also provide parameters to customize)\n",
|
||||
"\n",
|
||||
"provisioning_config = AksCompute.provisioning_configuration()\n",
|
||||
"# Leaf domain label generates a name using the formula\n",
|
||||
"# \"<leaf-domain-label>######.<azure-region>.cloudapp.azure.net\"\n",
|
||||
"# where \"######\" is a random series of characters\n",
|
||||
"provisioning_config.enable_ssl(leaf_domain_label = \"contoso\")\n",
|
||||
"\n",
|
||||
"aks_name = 'my-aks-ssl-1' \n",
|
||||
"# Create the cluster\n",
|
||||
"aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = provisioning_config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_target.wait_for_completion(show_output = True)\n",
|
||||
"print(aks_target.provisioning_state)\n",
|
||||
"print(aks_target.provisioning_errors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploy web service to AKS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-deploy-to-aks"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()\n",
|
||||
"\n",
|
||||
"aks_service_name ='aks-service-ssl-1'\n",
|
||||
"\n",
|
||||
"aks_service = Model.deploy(workspace=ws,\n",
|
||||
" name=aks_service_name,\n",
|
||||
" models=[model],\n",
|
||||
" inference_config=inf_config,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target,\n",
|
||||
" overwrite=True)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test the web service using run method\n",
|
||||
"We test the web sevice by passing data.\n",
|
||||
"Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"standard_sample_input = json.dumps({'param': {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }})\n",
|
||||
"\n",
|
||||
"aks_service.run(input_data=standard_sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Clean up\n",
|
||||
"Delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service.delete()\n",
|
||||
"model.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "vaidyas"
|
||||
}
|
||||
],
|
||||
"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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,8 @@
|
||||
name: production-deploy-to-aks-ssl
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- matplotlib
|
||||
- tqdm
|
||||
- scipy
|
||||
- sklearn
|
||||
@@ -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'], 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"
|
||||
]
|
||||
@@ -300,7 +300,8 @@
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn'\n",
|
||||
" 'scikit-learn==0.19.1',\n",
|
||||
" 'scipy'\n",
|
||||
"])\n",
|
||||
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
|
||||
"# if cpu and memory_in_gb parameters are not provided\n",
|
||||
|
||||
@@ -204,104 +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",
|
||||
"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"
|
||||
@@ -347,18 +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",
|
||||
@@ -378,7 +282,11 @@
|
||||
" 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",
|
||||
"\n",
|
||||
@@ -392,6 +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,
|
||||
@@ -416,7 +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",
|
||||
@@ -475,18 +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",
|
||||
@@ -508,7 +422,11 @@
|
||||
" 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",
|
||||
"\n",
|
||||
@@ -542,19 +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": {},
|
||||
@@ -670,7 +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",
|
||||
@@ -691,7 +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))"
|
||||
@@ -345,7 +345,11 @@
|
||||
" 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",
|
||||
" pin_sdk_version=False)\n",
|
||||
@@ -364,7 +368,7 @@
|
||||
"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')"
|
||||
]
|
||||
},
|
||||
@@ -412,11 +416,11 @@
|
||||
"\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",
|
||||
"print(\"POST to url\", service.scoring_uri)\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)"
|
||||
]
|
||||
@@ -427,7 +431,7 @@
|
||||
"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",
|
||||
@@ -263,7 +289,11 @@
|
||||
" 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",
|
||||
" pin_sdk_version=False)\n",
|
||||
@@ -323,7 +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",
|
||||
@@ -337,7 +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",
|
||||
@@ -351,7 +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",
|
||||
@@ -392,13 +422,6 @@
|
||||
"Deploy Model and ScoringExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -431,7 +454,11 @@
|
||||
" 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",
|
||||
" pin_sdk_version=False)\n",
|
||||
@@ -449,7 +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')"
|
||||
]
|
||||
},
|
||||
@@ -488,17 +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",
|
||||
"print(\"POST to url\", service.scoring_uri)\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)"
|
||||
]
|
||||
},
|
||||
@@ -528,13 +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",
|
||||
|
||||
@@ -716,7 +716,6 @@
|
||||
"\n",
|
||||
"trainWithAutomlStep = AutoMLStep(name='AutoML_Regression',\n",
|
||||
" automl_config=automl_config,\n",
|
||||
" passthru_automl_config=False,\n",
|
||||
" allow_reuse=True)\n",
|
||||
"print(\"trainWithAutomlStep created.\")"
|
||||
]
|
||||
|
||||
@@ -13,7 +13,7 @@ def init():
|
||||
global g_tf_sess
|
||||
|
||||
# pull down model from workspace
|
||||
model_path = Model.get_model_path("mnist")
|
||||
model_path = Model.get_model_path("mnist-prs")
|
||||
|
||||
# contruct graph to execute
|
||||
tf.reset_default_graph()
|
||||
|
||||
@@ -120,6 +120,6 @@ pipeline_run.wait_for_completion(show_output=True)
|
||||
|
||||
- [file-dataset-image-inference-mnist.ipynb](./file-dataset-image-inference-mnist.ipynb) demonstrates how to run batch inference on an MNIST dataset using FileDataset.
|
||||
- [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset using TabularDataset.
|
||||
- [pipeline-style-transfer.ipynb](../pipeline-style-transfer/pipeline-style-transfer.ipynb) demonstrates using ParallelRunStep in multi-step pipeline and using output from one step as input to ParallelRunStep.
|
||||
- [pipeline-style-transfer.ipynb](../pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb) demonstrates using ParallelRunStep in multi-step pipeline and using output from one step as input to ParallelRunStep.
|
||||
|
||||
|
||||
|
||||
@@ -138,7 +138,7 @@
|
||||
"\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 +181,13 @@
|
||||
"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 +206,7 @@
|
||||
"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 +217,7 @@
|
||||
"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 +270,11 @@
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"# register downloaded model \n",
|
||||
"model = Model.register(model_path = \"models/\",\n",
|
||||
" model_name = \"mnist\", # 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 +295,7 @@
|
||||
" \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 +325,10 @@
|
||||
"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 +341,7 @@
|
||||
"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",
|
||||
@@ -474,8 +470,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_on_datastore = mnist_data.path('mnist/0.png')\n",
|
||||
"single_image_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)\n",
|
||||
"single_image_ds._ensure_saved(ws)"
|
||||
"single_image_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -505,7 +500,7 @@
|
||||
"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"
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -515,19 +510,14 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import shutil\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"# remove previous run results, if present\n",
|
||||
"shutil.rmtree(\"mnist_results\", ignore_errors=True)\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",
|
||||
"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",
|
||||
"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",
|
||||
|
||||
@@ -168,15 +168,7 @@
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -227,7 +219,7 @@
|
||||
"\n",
|
||||
"# register downloaded model\n",
|
||||
"model = Model.register(model_path = \"iris_model.pkl/iris_model.pkl\",\n",
|
||||
" model_name = \"iris\", # this is the name the model is registered as\n",
|
||||
" model_name = \"iris-prs\", # this is the name the model is registered as\n",
|
||||
" tags = {'pretrained': \"iris\"},\n",
|
||||
" workspace = ws)"
|
||||
]
|
||||
@@ -250,11 +242,7 @@
|
||||
" \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 +274,11 @@
|
||||
"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 +291,7 @@
|
||||
"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",
|
||||
@@ -332,7 +324,7 @@
|
||||
" append_row_file_name=\"iris_outputs.txt\",\n",
|
||||
" environment=predict_env,\n",
|
||||
" compute_target=compute_target, \n",
|
||||
" node_count=3,\n",
|
||||
" node_count=2,\n",
|
||||
" run_invocation_timeout=600\n",
|
||||
")"
|
||||
]
|
||||
@@ -356,7 +348,7 @@
|
||||
" inputs=[named_iris_ds],\n",
|
||||
" output=output_folder,\n",
|
||||
" parallel_run_config=parallel_run_config,\n",
|
||||
" arguments=['--model_name', 'iris'],\n",
|
||||
" arguments=['--model_name', 'iris-prs'],\n",
|
||||
" allow_reuse=True\n",
|
||||
")"
|
||||
]
|
||||
@@ -380,7 +372,7 @@
|
||||
"\n",
|
||||
"pipeline = Pipeline(workspace=ws, steps=[distributed_csv_iris_step])\n",
|
||||
"\n",
|
||||
"pipeline_run = Experiment(ws, 'iris').submit(pipeline)"
|
||||
"pipeline_run = Experiment(ws, 'iris-prs').submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -438,19 +430,14 @@
|
||||
"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",
|
||||
|
||||
@@ -0,0 +1,185 @@
|
||||
# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import re
|
||||
|
||||
from PIL import Image
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
|
||||
|
||||
def load_image(filename, size=None, scale=None):
|
||||
img = Image.open(filename)
|
||||
if size is not None:
|
||||
img = img.resize((size, size), Image.ANTIALIAS)
|
||||
elif scale is not None:
|
||||
img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
|
||||
return img
|
||||
|
||||
|
||||
def save_image(filename, data):
|
||||
img = data.clone().clamp(0, 255).numpy()
|
||||
img = img.transpose(1, 2, 0).astype("uint8")
|
||||
img = Image.fromarray(img)
|
||||
img.save(filename)
|
||||
|
||||
|
||||
class TransformerNet(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(TransformerNet, self).__init__()
|
||||
# Initial convolution layers
|
||||
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
|
||||
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
|
||||
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
|
||||
self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
|
||||
self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
|
||||
self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
|
||||
# Residual layers
|
||||
self.res1 = ResidualBlock(128)
|
||||
self.res2 = ResidualBlock(128)
|
||||
self.res3 = ResidualBlock(128)
|
||||
self.res4 = ResidualBlock(128)
|
||||
self.res5 = ResidualBlock(128)
|
||||
# Upsampling Layers
|
||||
self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
|
||||
self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
|
||||
self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
|
||||
self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
|
||||
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
|
||||
# Non-linearities
|
||||
self.relu = torch.nn.ReLU()
|
||||
|
||||
def forward(self, X):
|
||||
y = self.relu(self.in1(self.conv1(X)))
|
||||
y = self.relu(self.in2(self.conv2(y)))
|
||||
y = self.relu(self.in3(self.conv3(y)))
|
||||
y = self.res1(y)
|
||||
y = self.res2(y)
|
||||
y = self.res3(y)
|
||||
y = self.res4(y)
|
||||
y = self.res5(y)
|
||||
y = self.relu(self.in4(self.deconv1(y)))
|
||||
y = self.relu(self.in5(self.deconv2(y)))
|
||||
y = self.deconv3(y)
|
||||
return y
|
||||
|
||||
|
||||
class ConvLayer(torch.nn.Module):
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride):
|
||||
super(ConvLayer, self).__init__()
|
||||
reflection_padding = kernel_size // 2
|
||||
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
|
||||
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
|
||||
|
||||
def forward(self, x):
|
||||
out = self.reflection_pad(x)
|
||||
out = self.conv2d(out)
|
||||
return out
|
||||
|
||||
|
||||
class ResidualBlock(torch.nn.Module):
|
||||
"""ResidualBlock
|
||||
introduced in: https://arxiv.org/abs/1512.03385
|
||||
recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
|
||||
"""
|
||||
|
||||
def __init__(self, channels):
|
||||
super(ResidualBlock, self).__init__()
|
||||
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
|
||||
self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
|
||||
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
|
||||
self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
|
||||
self.relu = torch.nn.ReLU()
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
out = self.relu(self.in1(self.conv1(x)))
|
||||
out = self.in2(self.conv2(out))
|
||||
out = out + residual
|
||||
return out
|
||||
|
||||
|
||||
class UpsampleConvLayer(torch.nn.Module):
|
||||
"""UpsampleConvLayer
|
||||
Upsamples the input and then does a convolution. This method gives better results
|
||||
compared to ConvTranspose2d.
|
||||
ref: http://distill.pub/2016/deconv-checkerboard/
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
|
||||
super(UpsampleConvLayer, self).__init__()
|
||||
self.upsample = upsample
|
||||
if upsample:
|
||||
self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
|
||||
reflection_padding = kernel_size // 2
|
||||
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
|
||||
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
|
||||
|
||||
def forward(self, x):
|
||||
x_in = x
|
||||
if self.upsample:
|
||||
x_in = self.upsample_layer(x_in)
|
||||
out = self.reflection_pad(x_in)
|
||||
out = self.conv2d(out)
|
||||
return out
|
||||
|
||||
|
||||
def stylize(args):
|
||||
device = torch.device("cuda" if args.cuda else "cpu")
|
||||
with torch.no_grad():
|
||||
style_model = TransformerNet()
|
||||
state_dict = torch.load(os.path.join(args.model_dir, args.style + ".pth"))
|
||||
# remove saved deprecated running_* keys in InstanceNorm from the checkpoint
|
||||
for k in list(state_dict.keys()):
|
||||
if re.search(r'in\d+\.running_(mean|var)$', k):
|
||||
del state_dict[k]
|
||||
style_model.load_state_dict(state_dict)
|
||||
style_model.to(device)
|
||||
|
||||
filenames = os.listdir(args.content_dir)
|
||||
|
||||
for filename in filenames:
|
||||
print("Processing {}".format(filename))
|
||||
full_path = os.path.join(args.content_dir, filename)
|
||||
content_image = load_image(full_path, scale=args.content_scale)
|
||||
content_transform = transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Lambda(lambda x: x.mul(255))
|
||||
])
|
||||
content_image = content_transform(content_image)
|
||||
content_image = content_image.unsqueeze(0).to(device)
|
||||
|
||||
output = style_model(content_image).cpu()
|
||||
|
||||
output_path = os.path.join(args.output_dir, filename)
|
||||
save_image(output_path, output[0])
|
||||
|
||||
|
||||
def main():
|
||||
arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
|
||||
|
||||
arg_parser.add_argument("--content-scale", type=float, default=None,
|
||||
help="factor for scaling down the content image")
|
||||
arg_parser.add_argument("--model-dir", type=str, required=True,
|
||||
help="saved model to be used for stylizing the image.")
|
||||
arg_parser.add_argument("--cuda", type=int, required=True,
|
||||
help="set it to 1 for running on GPU, 0 for CPU")
|
||||
arg_parser.add_argument("--style", type=str,
|
||||
help="style name")
|
||||
|
||||
arg_parser.add_argument("--content-dir", type=str, required=True,
|
||||
help="directory holding the images")
|
||||
arg_parser.add_argument("--output-dir", type=str, required=True,
|
||||
help="directory holding the output images")
|
||||
args = arg_parser.parse_args()
|
||||
|
||||
if args.cuda and not torch.cuda.is_available():
|
||||
print("ERROR: cuda is not available, try running on CPU")
|
||||
sys.exit(1)
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
stylize(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,207 @@
|
||||
# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import re
|
||||
|
||||
from PIL import Image
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
|
||||
from mpi4py import MPI
|
||||
|
||||
|
||||
def load_image(filename, size=None, scale=None):
|
||||
img = Image.open(filename)
|
||||
if size is not None:
|
||||
img = img.resize((size, size), Image.ANTIALIAS)
|
||||
elif scale is not None:
|
||||
img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
|
||||
return img
|
||||
|
||||
|
||||
def save_image(filename, data):
|
||||
img = data.clone().clamp(0, 255).numpy()
|
||||
img = img.transpose(1, 2, 0).astype("uint8")
|
||||
img = Image.fromarray(img)
|
||||
img.save(filename)
|
||||
|
||||
|
||||
class TransformerNet(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(TransformerNet, self).__init__()
|
||||
# Initial convolution layers
|
||||
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
|
||||
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
|
||||
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
|
||||
self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
|
||||
self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
|
||||
self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
|
||||
# Residual layers
|
||||
self.res1 = ResidualBlock(128)
|
||||
self.res2 = ResidualBlock(128)
|
||||
self.res3 = ResidualBlock(128)
|
||||
self.res4 = ResidualBlock(128)
|
||||
self.res5 = ResidualBlock(128)
|
||||
# Upsampling Layers
|
||||
self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
|
||||
self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
|
||||
self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
|
||||
self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
|
||||
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
|
||||
# Non-linearities
|
||||
self.relu = torch.nn.ReLU()
|
||||
|
||||
def forward(self, X):
|
||||
y = self.relu(self.in1(self.conv1(X)))
|
||||
y = self.relu(self.in2(self.conv2(y)))
|
||||
y = self.relu(self.in3(self.conv3(y)))
|
||||
y = self.res1(y)
|
||||
y = self.res2(y)
|
||||
y = self.res3(y)
|
||||
y = self.res4(y)
|
||||
y = self.res5(y)
|
||||
y = self.relu(self.in4(self.deconv1(y)))
|
||||
y = self.relu(self.in5(self.deconv2(y)))
|
||||
y = self.deconv3(y)
|
||||
return y
|
||||
|
||||
|
||||
class ConvLayer(torch.nn.Module):
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride):
|
||||
super(ConvLayer, self).__init__()
|
||||
reflection_padding = kernel_size // 2
|
||||
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
|
||||
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
|
||||
|
||||
def forward(self, x):
|
||||
out = self.reflection_pad(x)
|
||||
out = self.conv2d(out)
|
||||
return out
|
||||
|
||||
|
||||
class ResidualBlock(torch.nn.Module):
|
||||
"""ResidualBlock
|
||||
introduced in: https://arxiv.org/abs/1512.03385
|
||||
recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
|
||||
"""
|
||||
|
||||
def __init__(self, channels):
|
||||
super(ResidualBlock, self).__init__()
|
||||
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
|
||||
self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
|
||||
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
|
||||
self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
|
||||
self.relu = torch.nn.ReLU()
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
out = self.relu(self.in1(self.conv1(x)))
|
||||
out = self.in2(self.conv2(out))
|
||||
out = out + residual
|
||||
return out
|
||||
|
||||
|
||||
class UpsampleConvLayer(torch.nn.Module):
|
||||
"""UpsampleConvLayer
|
||||
Upsamples the input and then does a convolution. This method gives better results
|
||||
compared to ConvTranspose2d.
|
||||
ref: http://distill.pub/2016/deconv-checkerboard/
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
|
||||
super(UpsampleConvLayer, self).__init__()
|
||||
self.upsample = upsample
|
||||
if upsample:
|
||||
self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
|
||||
reflection_padding = kernel_size // 2
|
||||
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
|
||||
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
|
||||
|
||||
def forward(self, x):
|
||||
x_in = x
|
||||
if self.upsample:
|
||||
x_in = self.upsample_layer(x_in)
|
||||
out = self.reflection_pad(x_in)
|
||||
out = self.conv2d(out)
|
||||
return out
|
||||
|
||||
|
||||
def stylize(args, comm):
|
||||
|
||||
rank = comm.Get_rank()
|
||||
size = comm.Get_size()
|
||||
|
||||
device = torch.device("cuda" if args.cuda else "cpu")
|
||||
with torch.no_grad():
|
||||
style_model = TransformerNet()
|
||||
state_dict = torch.load(os.path.join(args.model_dir, args.style + ".pth"))
|
||||
# remove saved deprecated running_* keys in InstanceNorm from the checkpoint
|
||||
for k in list(state_dict.keys()):
|
||||
if re.search(r'in\d+\.running_(mean|var)$', k):
|
||||
del state_dict[k]
|
||||
style_model.load_state_dict(state_dict)
|
||||
style_model.to(device)
|
||||
|
||||
filenames = os.listdir(args.content_dir)
|
||||
filenames = sorted(filenames)
|
||||
partition_size = len(filenames) // size
|
||||
partitioned_filenames = filenames[rank * partition_size: (rank + 1) * partition_size]
|
||||
print("RANK {} - is processing {} images out of the total {}".format(rank, len(partitioned_filenames),
|
||||
len(filenames)))
|
||||
|
||||
output_paths = []
|
||||
for filename in partitioned_filenames:
|
||||
# print("Processing {}".format(filename))
|
||||
full_path = os.path.join(args.content_dir, filename)
|
||||
content_image = load_image(full_path, scale=args.content_scale)
|
||||
content_transform = transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Lambda(lambda x: x.mul(255))
|
||||
])
|
||||
content_image = content_transform(content_image)
|
||||
content_image = content_image.unsqueeze(0).to(device)
|
||||
|
||||
output = style_model(content_image).cpu()
|
||||
|
||||
output_path = os.path.join(args.output_dir, filename)
|
||||
save_image(output_path, output[0])
|
||||
|
||||
output_paths.append(output_path)
|
||||
|
||||
print("RANK {} - number of pre-aggregated output files {}".format(rank, len(output_paths)))
|
||||
|
||||
output_paths_list = comm.gather(output_paths, root=0)
|
||||
|
||||
if rank == 0:
|
||||
print("RANK {} - number of aggregated output files {}".format(rank, len(output_paths_list)))
|
||||
print("RANK {} - end".format(rank))
|
||||
|
||||
|
||||
def main():
|
||||
arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
|
||||
|
||||
arg_parser.add_argument("--content-scale", type=float, default=None,
|
||||
help="factor for scaling down the content image")
|
||||
arg_parser.add_argument("--model-dir", type=str, required=True,
|
||||
help="saved model to be used for stylizing the image.")
|
||||
arg_parser.add_argument("--cuda", type=int, required=True,
|
||||
help="set it to 1 for running on GPU, 0 for CPU")
|
||||
arg_parser.add_argument("--style", type=str, help="style name")
|
||||
arg_parser.add_argument("--content-dir", type=str, required=True,
|
||||
help="directory holding the images")
|
||||
arg_parser.add_argument("--output-dir", type=str, required=True,
|
||||
help="directory holding the output images")
|
||||
args = arg_parser.parse_args()
|
||||
|
||||
comm = MPI.COMM_WORLD
|
||||
|
||||
if args.cuda and not torch.cuda.is_available():
|
||||
print("ERROR: cuda is not available, try running on CPU")
|
||||
sys.exit(1)
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
stylize(args, comm)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,728 @@
|
||||
{
|
||||
"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": [
|
||||
"# Neural style transfer on video\n",
|
||||
"Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n",
|
||||
"1. Split a video into images\n",
|
||||
"2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n",
|
||||
"3. Stitch the image back into a video."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize Workspace\n",
|
||||
"\n",
|
||||
"Initialize a workspace object from persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from azureml.core import Workspace, Experiment\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')\n",
|
||||
"\n",
|
||||
"scripts_folder = \"mpi_scripts\"\n",
|
||||
"\n",
|
||||
"if not os.path.isdir(scripts_folder):\n",
|
||||
" os.mkdir(scripts_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import AmlCompute, ComputeTarget\n",
|
||||
"from azureml.core.datastore import Datastore\n",
|
||||
"from azureml.data.data_reference import DataReference\n",
|
||||
"from azureml.pipeline.core import Pipeline, PipelineData\n",
|
||||
"from azureml.pipeline.steps import PythonScriptStep, MpiStep\n",
|
||||
"from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create or use existing compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# AmlCompute\n",
|
||||
"cpu_cluster_name = \"cpu-cluster\"\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
|
||||
" print(\"found existing cluster.\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"creating new cluster\")\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n",
|
||||
" max_nodes = 1)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
|
||||
" cpu_cluster.wait_for_completion(show_output=True)\n",
|
||||
" \n",
|
||||
"# AmlCompute\n",
|
||||
"gpu_cluster_name = \"gpu-cluster\"\n",
|
||||
"try:\n",
|
||||
" gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n",
|
||||
" print(\"found existing cluster.\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"creating new cluster\")\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
|
||||
" max_nodes = 3)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
|
||||
" gpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Python Scripts\n",
|
||||
"We use an edited version of `neural_style_mpi.py` (original is [here](https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py)). Scripts to split and stitch the video are thin wrappers to calls to `ffmpeg`. These scripts are also located in the \"scripts_folder\".\n",
|
||||
"\n",
|
||||
"We install `ffmpeg` through conda dependencies."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile $scripts_folder/process_video.py\n",
|
||||
"import argparse\n",
|
||||
"import glob\n",
|
||||
"import os\n",
|
||||
"import subprocess\n",
|
||||
"\n",
|
||||
"parser = argparse.ArgumentParser(description=\"Process input video\")\n",
|
||||
"parser.add_argument('--input_video', required=True)\n",
|
||||
"parser.add_argument('--output_audio', required=True)\n",
|
||||
"parser.add_argument('--output_images', required=True)\n",
|
||||
"\n",
|
||||
"args = parser.parse_args()\n",
|
||||
"\n",
|
||||
"os.makedirs(args.output_audio, exist_ok=True)\n",
|
||||
"os.makedirs(args.output_images, exist_ok=True)\n",
|
||||
"\n",
|
||||
"subprocess.run(\"ffmpeg -i {} {}/video.aac\"\n",
|
||||
" .format(args.input_video, args.output_audio),\n",
|
||||
" shell=True, check=True\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"subprocess.run(\"ffmpeg -i {} {}/%05d_video.jpg -hide_banner\"\n",
|
||||
" .format(args.input_video, args.output_images),\n",
|
||||
" shell=True, check=True\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile $scripts_folder/stitch_video.py\n",
|
||||
"import argparse\n",
|
||||
"import os\n",
|
||||
"import subprocess\n",
|
||||
"\n",
|
||||
"parser = argparse.ArgumentParser(description=\"Process input video\")\n",
|
||||
"parser.add_argument('--images_dir', required=True)\n",
|
||||
"parser.add_argument('--input_audio', required=True)\n",
|
||||
"parser.add_argument('--output_dir', required=True)\n",
|
||||
"\n",
|
||||
"args = parser.parse_args()\n",
|
||||
"\n",
|
||||
"os.makedirs(args.output_dir, exist_ok=True)\n",
|
||||
"\n",
|
||||
"subprocess.run(\"ffmpeg -framerate 30 -i {}/%05d_video.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p \"\n",
|
||||
" \"-y {}/video_without_audio.mp4\"\n",
|
||||
" .format(args.images_dir, args.output_dir),\n",
|
||||
" shell=True, check=True\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"subprocess.run(\"ffmpeg -i {}/video_without_audio.mp4 -i {}/video.aac -map 0:0 -map 1:0 -vcodec \"\n",
|
||||
" \"copy -acodec copy -y {}/video_with_audio.mp4\"\n",
|
||||
" .format(args.output_dir, args.input_audio, args.output_dir),\n",
|
||||
" shell=True, check=True\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The sample video **organutan.mp4** is stored at a publicly shared datastore. We are registering the datastore below. If you want to take a look at the original video, click here. (https://pipelinedata.blob.core.windows.net/sample-videos/orangutan.mp4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# datastore for input video\n",
|
||||
"account_name = \"pipelinedata\"\n",
|
||||
"video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"sample-videos\",\n",
|
||||
" account_name=account_name, overwrite=True)\n",
|
||||
"\n",
|
||||
"# datastore for models\n",
|
||||
"models_ds = Datastore.register_azure_blob_container(ws, \"models\", \"styletransfer\", \n",
|
||||
" account_name=\"pipelinedata\", \n",
|
||||
" overwrite=True)\n",
|
||||
" \n",
|
||||
"# downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
|
||||
"models_dir = DataReference(data_reference_name=\"models\", datastore=models_ds, \n",
|
||||
" path_on_datastore=\"saved_models\", mode=\"download\")\n",
|
||||
"\n",
|
||||
"# the default blob store attached to a workspace\n",
|
||||
"default_datastore = ws.get_default_datastore()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Sample video"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"video_name=os.getenv(\"STYLE_TRANSFER_VIDEO_NAME\", \"orangutan.mp4\") \n",
|
||||
"orangutan_video = DataReference(datastore=video_ds,\n",
|
||||
" data_reference_name=\"video\",\n",
|
||||
" path_on_datastore=video_name, mode=\"download\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cd = CondaDependencies()\n",
|
||||
"\n",
|
||||
"cd.add_channel(\"conda-forge\")\n",
|
||||
"cd.add_conda_package(\"ffmpeg\")\n",
|
||||
"\n",
|
||||
"cd.add_channel(\"pytorch\")\n",
|
||||
"cd.add_conda_package(\"pytorch\")\n",
|
||||
"cd.add_conda_package(\"torchvision\")\n",
|
||||
"\n",
|
||||
"# Runconfig\n",
|
||||
"amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
|
||||
"amlcompute_run_config.environment.docker.enabled = True\n",
|
||||
"amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n",
|
||||
"amlcompute_run_config.environment.spark.precache_packages = False"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ffmpeg_audio = PipelineData(name=\"ffmpeg_audio\", datastore=default_datastore)\n",
|
||||
"ffmpeg_images = PipelineData(name=\"ffmpeg_images\", datastore=default_datastore)\n",
|
||||
"processed_images = PipelineData(name=\"processed_images\", datastore=default_datastore)\n",
|
||||
"output_video = PipelineData(name=\"output_video\", datastore=default_datastore)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Define tweakable parameters to pipeline\n",
|
||||
"These parameters can be changed when the pipeline is published and rerun from a REST call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core.graph import PipelineParameter\n",
|
||||
"# create a parameter for style (one of \"candy\", \"mosaic\", \"rain_princess\", \"udnie\") to transfer the images to\n",
|
||||
"style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n",
|
||||
"# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n",
|
||||
"nodecount_param = PipelineParameter(name=\"nodecount\", default_value=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"split_video_step = PythonScriptStep(\n",
|
||||
" name=\"split video\",\n",
|
||||
" script_name=\"process_video.py\",\n",
|
||||
" arguments=[\"--input_video\", orangutan_video,\n",
|
||||
" \"--output_audio\", ffmpeg_audio,\n",
|
||||
" \"--output_images\", ffmpeg_images,\n",
|
||||
" ],\n",
|
||||
" compute_target=cpu_cluster,\n",
|
||||
" inputs=[orangutan_video],\n",
|
||||
" outputs=[ffmpeg_images, ffmpeg_audio],\n",
|
||||
" runconfig=amlcompute_run_config,\n",
|
||||
" source_directory=scripts_folder\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# create a MPI step for distributing style transfer step across multiple nodes in AmlCompute \n",
|
||||
"# using 'nodecount_param' PipelineParameter\n",
|
||||
"distributed_style_transfer_step = MpiStep(\n",
|
||||
" name=\"mpi style transfer\",\n",
|
||||
" script_name=\"neural_style_mpi.py\",\n",
|
||||
" arguments=[\"--content-dir\", ffmpeg_images,\n",
|
||||
" \"--output-dir\", processed_images,\n",
|
||||
" \"--model-dir\", models_dir,\n",
|
||||
" \"--style\", style_param,\n",
|
||||
" \"--cuda\", 1\n",
|
||||
" ],\n",
|
||||
" compute_target=gpu_cluster,\n",
|
||||
" node_count=nodecount_param, \n",
|
||||
" process_count_per_node=1,\n",
|
||||
" inputs=[models_dir, ffmpeg_images],\n",
|
||||
" outputs=[processed_images],\n",
|
||||
" pip_packages=[\"mpi4py\", \"torch\", \"torchvision\"],\n",
|
||||
" use_gpu=True,\n",
|
||||
" source_directory=scripts_folder\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"stitch_video_step = PythonScriptStep(\n",
|
||||
" name=\"stitch\",\n",
|
||||
" script_name=\"stitch_video.py\",\n",
|
||||
" arguments=[\"--images_dir\", processed_images, \n",
|
||||
" \"--input_audio\", ffmpeg_audio, \n",
|
||||
" \"--output_dir\", output_video],\n",
|
||||
" compute_target=cpu_cluster,\n",
|
||||
" inputs=[processed_images, ffmpeg_audio],\n",
|
||||
" outputs=[output_video],\n",
|
||||
" runconfig=amlcompute_run_config,\n",
|
||||
" source_directory=scripts_folder\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Run the pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n",
|
||||
"# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n",
|
||||
"pipeline_run = Experiment(ws, 'style_transfer').submit(pipeline, pipeline_parameters={\"style\": \"mosaic\", \"nodecount\": 3})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Monitor using widget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Downloads the video in `output_video` folder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Download output video"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"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",
|
||||
" port_data.download(target_dir, show_progress=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_run.wait_for_completion()\n",
|
||||
"download_video(pipeline_run, \"output_video_mosaic\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Publish pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline = pipeline_run.publish_pipeline(\n",
|
||||
" name=\"batch score style transfer\", description=\"style transfer\", version=\"1.0\")\n",
|
||||
"\n",
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get published pipeline\n",
|
||||
"\n",
|
||||
"You can get the published pipeline using **pipeline id**.\n",
|
||||
"\n",
|
||||
"To get all the published pipelines for a given workspace(ws): \n",
|
||||
"```css\n",
|
||||
"all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PublishedPipeline\n",
|
||||
"\n",
|
||||
"pipeline_id = published_pipeline.id # use your published pipeline id\n",
|
||||
"published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
|
||||
"\n",
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Re-run pipeline through REST calls for other styles"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get AAD token\n",
|
||||
"[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.authentication import InteractiveLoginAuthentication\n",
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"auth = InteractiveLoginAuthentication()\n",
|
||||
"aad_token = auth.get_authentication_header()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get endpoint URL"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rest_endpoint = published_pipeline.endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Send request and monitor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the pipeline using PipelineParameter values style='candy' and nodecount=2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core.run import PipelineRun\n",
|
||||
"published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"RunDetails(published_pipeline_run_candy).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"RunDetails(published_pipeline_run_rain).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the pipeline using PipelineParameter values style='udnie' and nodecount=4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = requests.post(rest_endpoint, \n",
|
||||
" headers=aad_token,\n",
|
||||
" json={\"ExperimentName\": \"style_transfer\",\n",
|
||||
" \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 3}})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except Exception: \n",
|
||||
" raise Exception('Received bad response from the endpoint: {}\\n'\n",
|
||||
" 'Response Code: {}\\n'\n",
|
||||
" 'Headers: {}\\n'\n",
|
||||
" 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
|
||||
"\n",
|
||||
"run_id = response.json().get('Id')\n",
|
||||
"print('Submitted pipeline run: ', run_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
|
||||
"RunDetails(published_pipeline_run_udnie).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Download output from re-run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline_run_candy.wait_for_completion()\n",
|
||||
"published_pipeline_run_rain.wait_for_completion()\n",
|
||||
"published_pipeline_run_udnie.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")\n",
|
||||
"download_video(published_pipeline_run_rain, target_dir=\"output_video_rain_princess\")\n",
|
||||
"download_video(published_pipeline_run_udnie, target_dir=\"output_video_udnie\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "balapv mabables"
|
||||
}
|
||||
],
|
||||
"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": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,4 +1,4 @@
|
||||
name: pipeline-style-transfer
|
||||
name: pipeline-style-transfer-mpi
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -13,7 +13,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -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",
|
||||
@@ -541,8 +541,8 @@
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
@@ -0,0 +1,7 @@
|
||||
name: pipeline-style-transfer-parallel-run
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- requests
|
||||
@@ -381,7 +381,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.cancel()"
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -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]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -724,7 +724,7 @@
|
||||
" 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]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -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]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -456,6 +456,24 @@
|
||||
"monitor.enable_schedule()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Delete the DataDriftDetector\n",
|
||||
"\n",
|
||||
"Invoking the `delete()` method on the object deletes the the drift monitor permanently and cannot be undone. You will no longer be able to find it in the UI and the `list()` or `get()` methods. The object on which delete() was called will have its state set to deleted and name suffixed with deleted. The baseline and target datasets and model data that was collected, if any, are not deleted. The compute is not deleted. The DataDrift schedule pipeline is disabled and archived."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"monitor.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
@@ -7,4 +7,5 @@ dependencies:
|
||||
- azureml-monitoring
|
||||
- scikit-learn
|
||||
- numpy
|
||||
- packaging
|
||||
- inference-schema[numpy-support]
|
||||
|
||||
@@ -4,7 +4,13 @@ import numpy as np
|
||||
from azureml.monitoring import ModelDataCollector
|
||||
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
|
||||
from inference_schema.schema_decorators import input_schema, output_schema
|
||||
from sklearn.externals import joblib
|
||||
# sklearn.externals.joblib is removed in 0.23
|
||||
from sklearn import __version__ as sklearnver
|
||||
from packaging.version import Version
|
||||
if Version(sklearnver) < Version("0.23.0"):
|
||||
from sklearn.externals import joblib
|
||||
else:
|
||||
import joblib
|
||||
|
||||
|
||||
def init():
|
||||
|
||||
@@ -11,20 +11,31 @@ Taxonomies for products and languages: https://review.docs.microsoft.com/new-hop
|
||||
|
||||
This is an introduction to the [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/service/) Reinforcement Learning (Public Preview) using the [Ray](https://github.com/ray-project/ray/) framework.
|
||||
|
||||
Using these samples, you will be able to do the following.
|
||||
## What is reinforcement learning?
|
||||
|
||||
1. Use an Azure Machine Learning workspace, set up virtual network and create compute clusters for running Ray.
|
||||
2. Run some experiments to train a reinforcement learning agent using Ray and RLlib.
|
||||
Reinforcement learning is an approach to machine learning to train agents to make a sequence of decisions. This technique has gained popularity over the last few years as breakthroughs have been made to teach reinforcement learning agents to excel at complex tasks like playing video games. There are many practical real-world use cases as well, including robotics, chemistry, online recommendations, advertising and more.
|
||||
|
||||
In reinforcement learning, the goal is to train an agent *policy* that outputs actions based on the agent’s observations of its environment. Actions result in further observations and *rewards* for taking the actions. In reinforcement learning, the full reward for policy actions may take many steps to obtain. Learning a policy involves many trial-and-error runs of the agent interacting with the environment and improving its policy.
|
||||
|
||||
## Reinforcement learning on Azure Machine Learning
|
||||
|
||||
Reinforcement learning support in Azure Machine Learning service enables data scientists to scale training to many powerful CPU or GPU enabled VMs using [Azure Machine Learning compute clusters](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) which automatically provision, manage, and scale down these VMs to help manage your costs.
|
||||
|
||||
Using these samples, you will learn how to do the following.
|
||||
|
||||
1. Use an Azure Machine Learning workspace, set up virtual network and create compute clusters for distributed training.
|
||||
2. Train reinforcement learning agents using Ray RLlib.
|
||||
|
||||
## Contents
|
||||
|
||||
| File/folder | Description |
|
||||
|-------------------|--------------------------------------------|
|
||||
| [devenv_setup.ipynb](setup/devenv_setup.ipynb) | Notebook to setup development environment for Azure ML RL |
|
||||
| [cartpole_ci.ipynb](cartpole-on-compute-instance/cartpole_ci.ipynb) | Notebook to train a Cartpole playing agent on an Azure ML Compute Instance |
|
||||
| [cartpole_sc.ipynb](cartpole-on-single-compute/cartpole_sc.ipynb) | Notebook to train a Cartpole playing agent on an Azure ML Compute Cluster (single node) |
|
||||
| [pong_rllib.ipynb](atari-on-distributed-compute/pong_rllib.ipynb) | Notebook to train Pong agent using RLlib on multiple compute targets |
|
||||
| [devenv_setup.ipynb](setup/devenv_setup.ipynb) | Notebook to setup virtual network for using Azure Machine Learning. Needed for the Pong and Minecraft examples. |
|
||||
| [cartpole_ci.ipynb](cartpole-on-compute-instance/cartpole_ci.ipynb) | Notebook to train a Cartpole playing agent on an Azure Machine Learning Compute Instance |
|
||||
| [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
|
||||
|
||||
@@ -32,9 +43,10 @@ To make use of these samples, you need the following.
|
||||
|
||||
* A Microsoft Azure subscription.
|
||||
* A Microsoft Azure resource group.
|
||||
* An Azure Machine Learning Workspace in the resource group. Please make sure that the VM sizes `STANDARD_NC6` and `STANDARD_D2_V2` are supported in the workspace's region.
|
||||
* A virtual network set up in the resource group.
|
||||
* A virtual network is needed for the examples training on multiple compute targets.
|
||||
* An Azure Machine Learning Workspace in the resource group.
|
||||
* Azure Machine Learning training compute. These samples use the VM sizes `STANDARD_NC6` and `STANDARD_D2_V2`. If these are not available in your region,
|
||||
you can replace them with other sizes.
|
||||
* A virtual network set up in the resource group for samples that use multiple compute targets. The Cartpole examples do not need a virtual network.
|
||||
* The [devenv_setup.ipynb](setup/devenv_setup.ipynb) notebook shows you how to create a virtual network. You can alternatively use an existing virtual network, make sure it's in the same region as workspace is.
|
||||
* Any network security group defined on the virtual network must allow network traffic on ports used by Azure infrastructure services. This is described in more detail in the [devenv_setup.ipynb](setup/devenv_setup.ipynb) notebook.
|
||||
|
||||
@@ -43,10 +55,10 @@ To make use of these samples, you need the following.
|
||||
|
||||
You can run these samples in the following ways.
|
||||
|
||||
* On an Azure ML Compute Instance or Notebook VM.
|
||||
* On an Azure Machine Learning Compute Instance or Azure Data Science Virtual Machine (DSVM).
|
||||
* On a workstation with Python and the Azure ML Python SDK installed.
|
||||
|
||||
### Azure ML Compute Instance or Notebook VM
|
||||
### Compute Instance or DSVM
|
||||
#### Update packages
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,237 @@
|
||||
import sys
|
||||
import csv
|
||||
from azure.mgmt.network import NetworkManagementClient
|
||||
|
||||
|
||||
def check_port_in_port_range(expected_port: str,
|
||||
dest_port_range: str):
|
||||
"""
|
||||
Check if a port is within a port range
|
||||
Port range maybe like *, 8080 or 8888-8889
|
||||
"""
|
||||
|
||||
if dest_port_range == '*':
|
||||
return True
|
||||
|
||||
dest_ports = dest_port_range.split('-')
|
||||
|
||||
if len(dest_ports) == 1 and \
|
||||
int(dest_ports[0]) == int(expected_port):
|
||||
return True
|
||||
|
||||
if len(dest_ports) == 2 and \
|
||||
int(dest_ports[0]) <= int(expected_port) and \
|
||||
int(dest_ports[1]) >= int(expected_port):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_port_in_destination_port_ranges(expected_port: str,
|
||||
dest_port_ranges: list):
|
||||
"""
|
||||
Check if a port is within a given list of port ranges
|
||||
i.e. check if port 8080 is in port ranges of 22,80,8080-8090,443
|
||||
"""
|
||||
|
||||
for dest_port_range in dest_port_ranges:
|
||||
if check_port_in_port_range(expected_port, dest_port_range) is True:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_ports_in_destination_port_ranges(expected_ports: list,
|
||||
dest_port_ranges: list):
|
||||
"""
|
||||
Check if all ports in a given port list are within a given list
|
||||
of port ranges
|
||||
i.e. check if port 8080,8081 are in port ranges of 22,80,8080-8090,443
|
||||
"""
|
||||
|
||||
for expected_port in expected_ports:
|
||||
if check_port_in_destination_port_ranges(
|
||||
expected_port, dest_port_ranges) is False:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def check_source_address_prefix(source_address_prefix: str):
|
||||
"""Check if source address prefix is BatchNodeManagement or default"""
|
||||
|
||||
required_prefix = 'BatchNodeManagement'
|
||||
default_prefix = 'default'
|
||||
|
||||
if source_address_prefix.lower() == required_prefix.lower() or \
|
||||
source_address_prefix.lower() == default_prefix.lower():
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_protocol(protocol: str):
|
||||
"""Check if protocol is supported - Tcp/Any"""
|
||||
|
||||
required_protocol = 'Tcp'
|
||||
any_protocol = 'Any'
|
||||
|
||||
if required_protocol.lower() == protocol.lower() or \
|
||||
any_protocol.lower() == protocol.lower():
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_direction(direction: str):
|
||||
"""Check if port direction is inbound"""
|
||||
|
||||
required_direction = 'Inbound'
|
||||
|
||||
if required_direction.lower() == direction.lower():
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_provisioning_state(provisioning_state: str):
|
||||
"""Check if the provisioning state is succeeded"""
|
||||
|
||||
required_provisioning_state = 'Succeeded'
|
||||
|
||||
if required_provisioning_state.lower() == provisioning_state.lower():
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_rule_for_Azure_ML(rule):
|
||||
"""Check if the ports required for Azure Machine Learning are open"""
|
||||
|
||||
required_ports = ['29876', '29877']
|
||||
|
||||
if check_source_address_prefix(rule.source_address_prefix) is False:
|
||||
return False
|
||||
|
||||
if check_protocol(rule.protocol) is False:
|
||||
return False
|
||||
|
||||
if check_direction(rule.direction) is False:
|
||||
return False
|
||||
|
||||
if check_provisioning_state(rule.provisioning_state) is False:
|
||||
return False
|
||||
|
||||
if rule.destination_port_range is not None:
|
||||
if check_ports_in_destination_port_ranges(
|
||||
required_ports,
|
||||
[rule.destination_port_range]) is False:
|
||||
return False
|
||||
else:
|
||||
if check_ports_in_destination_port_ranges(
|
||||
required_ports,
|
||||
rule.destination_port_ranges) is False:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def check_vnet_security_rules(auth_object,
|
||||
vnet_subscription_id,
|
||||
vnet_resource_group,
|
||||
vnet_name,
|
||||
save_to_file=False):
|
||||
"""
|
||||
Check all the rules of virtual network if required ports for Azure Machine
|
||||
Learning are open
|
||||
"""
|
||||
|
||||
network_client = NetworkManagementClient(
|
||||
auth_object,
|
||||
vnet_subscription_id)
|
||||
|
||||
# get the vnet
|
||||
vnet = network_client.virtual_networks.get(
|
||||
resource_group_name=vnet_resource_group,
|
||||
virtual_network_name=vnet_name)
|
||||
|
||||
vnet_location = vnet.location
|
||||
vnet_info = []
|
||||
|
||||
if vnet.subnets is None or len(vnet.subnets) == 0:
|
||||
print('WARNING: No subnet found for VNet:', vnet_name)
|
||||
|
||||
# for each subnet of the vnet
|
||||
for subnet in vnet.subnets:
|
||||
if subnet.network_security_group is None:
|
||||
print('WARNING: No network security group found for subnet.',
|
||||
'Subnet',
|
||||
subnet.id.split("/")[-1])
|
||||
else:
|
||||
# get all the rules
|
||||
network_security_group_name = \
|
||||
subnet.network_security_group.id.split("/")[-1]
|
||||
network_security_group_resource_group_name = \
|
||||
subnet.network_security_group.id.split("/")[4]
|
||||
network_security_group_subscription_id = \
|
||||
subnet.network_security_group.id.split("/")[2]
|
||||
|
||||
security_rules = list(network_client.security_rules.list(
|
||||
network_security_group_resource_group_name,
|
||||
network_security_group_name))
|
||||
|
||||
rule_matched = None
|
||||
for rule in security_rules:
|
||||
rule_info = []
|
||||
# add vnet details
|
||||
rule_info.append(vnet_name)
|
||||
rule_info.append(vnet_subscription_id)
|
||||
rule_info.append(vnet_resource_group)
|
||||
rule_info.append(vnet_location)
|
||||
# add subnet details
|
||||
rule_info.append(subnet.id.split("/")[-1])
|
||||
rule_info.append(network_security_group_name)
|
||||
rule_info.append(network_security_group_subscription_id)
|
||||
rule_info.append(network_security_group_resource_group_name)
|
||||
# add rule details
|
||||
rule_info.append(rule.priority)
|
||||
rule_info.append(rule.name)
|
||||
rule_info.append(rule.source_address_prefix)
|
||||
if rule.destination_port_range is not None:
|
||||
rule_info.append(rule.destination_port_range)
|
||||
else:
|
||||
rule_info.append(rule.destination_port_ranges)
|
||||
rule_info.append(rule.direction)
|
||||
rule_info.append(rule.provisioning_state)
|
||||
vnet_info.append(rule_info)
|
||||
|
||||
if check_rule_for_Azure_ML(rule) is True:
|
||||
rule_matched = rule
|
||||
|
||||
if rule_matched is not None:
|
||||
print("INFORMATION: Rule matched with required ports. Subnet:",
|
||||
subnet.id.split("/")[-1], "Rule:", rule.name)
|
||||
else:
|
||||
print("WARNING: No rule matched with required ports. Subnet:",
|
||||
subnet.id.split("/")[-1])
|
||||
|
||||
if save_to_file is True:
|
||||
file_name = vnet_name + ".csv"
|
||||
with open(file_name, mode='w') as vnet_rule_file:
|
||||
vnet_rule_file_writer = csv.writer(
|
||||
vnet_rule_file,
|
||||
delimiter=',',
|
||||
quotechar='"',
|
||||
quoting=csv.QUOTE_MINIMAL)
|
||||
header = ['VNet_Name', 'VNet_Subscription_ID',
|
||||
'VNet_Resource_Group', 'VNet_Location',
|
||||
'Subnet_Name', 'NSG_Name',
|
||||
'NSG_Subscription_ID', 'NSG_Resource_Group',
|
||||
'Rule_Priority', 'Rule_Name', 'Rule_Source',
|
||||
'Rule_Destination_Ports', 'Rule_Direction',
|
||||
'Rule_Provisioning_State']
|
||||
vnet_rule_file_writer.writerow(header)
|
||||
vnet_rule_file_writer.writerows(vnet_info)
|
||||
|
||||
print("INFORMATION: Network security group rules for your virtual \
|
||||
network are saved in file", file_name)
|
||||
@@ -13,7 +13,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -155,6 +155,24 @@
|
||||
"vnet_name = 'your_vnet'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Ensure that the virtual network is configured correctly with required ports open. It is possible that you have configured rules with broader range of ports that allows ports 29876-29877 to be opened. Kindly review your network security group rules. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from files.networkutils import *\n",
|
||||
"\n",
|
||||
"check_vnet_security_rules(ws._auth_object, ws.subscription_id, ws.resource_group, vnet_name, True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user