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Merge pull request #1700 from Azure/release_update/Release-123

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update samples from Release-123 as a part of  SDK release
This commit is contained in:
Harneet Virk
2022-03-01 16:33:02 -08:00
committed by GitHub
parent 9094da4085 6d8bf32243
commit c42ba64b15
58 changed files with 2924 additions and 480 deletions
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2
configuration.ipynb
View File

@@ -103,7 +103,7 @@
"source": [
"import azureml.core\n",
"\n",
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

9
contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
View File

@@ -188,13 +188,6 @@
"### Script to process data and train model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The _process_data.py_ script used in the step below is a slightly modified implementation of [RAPIDS Mortgage E2E example](https://github.com/rapidsai/notebooks-contrib/blob/master/intermediate_notebooks/E2E/mortgage/mortgage_e2e.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -373,7 +366,7 @@
"run_config.target = gpu_cluster_name\n",
"run_config.environment.docker.enabled = True\n",
"run_config.environment.docker.gpu_support = True\n",
"run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/base-gpu:intelmpi2018.3-cuda10.0-cudnn7-ubuntu16.04\"\n",
"run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04\"\n",
"run_config.environment.spark.precache_packages = False\n",
"run_config.data_references={'data':data_ref.to_config()}"
]

2
contrib/fairness/fairlearn-azureml-mitigation.ipynb
View File

@@ -49,7 +49,7 @@
"* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
"* `joblib`\n",
"* `liac-arff`\n",
"* `raiwidgets~=0.7.0`\n",
"* `raiwidgets`\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:"
]

2
contrib/fairness/fairlearn-azureml-mitigation.yml
View File

@@ -6,4 +6,4 @@ dependencies:
- fairlearn>=0.6.2
- joblib
- liac-arff
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0

2
contrib/fairness/upload-fairness-dashboard.ipynb
View File

@@ -51,7 +51,7 @@
"* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
"* `joblib`\n",
"* `liac-arff`\n",
"* `raiwidgets~=0.7.0`\n",
"* `raiwidgets`\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:"
]

2
contrib/fairness/upload-fairness-dashboard.yml
View File

@@ -6,4 +6,4 @@ dependencies:
- fairlearn>=0.6.2
- joblib
- liac-arff
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0

32
how-to-use-azureml/automated-machine-learning/automl_env.yml
View File

@@ -1,29 +1,33 @@
name: azure_automl
channels:
- conda-forge
- pytorch
- main
dependencies:
# The python interpreter version.
# Currently Azure ML only supports 3.5.2 and later.
- pip==21.1.2
- python>=3.5.2,<3.8
- boto3==1.15.18
- matplotlib==2.1.0
# Currently Azure ML only supports 3.6.0 and later.
- pip==20.2.4
- python>=3.6,<3.8
- boto3==1.20.19
- botocore<=1.23.19
- matplotlib==3.3.4
- numpy==1.18.5
- cython
- urllib3<1.24
- cython==0.29.14
- urllib3==1.26.7
- scipy>=1.4.1,<=1.5.2
- scikit-learn==0.22.1
- pandas==0.25.1
- py-xgboost<=0.90
- conda-forge::fbprophet==0.5
- holidays==0.9.11
- py-xgboost<=1.3.3
- holidays==0.10.3
- conda-forge::fbprophet==0.7.1
- pytorch::pytorch=1.4.0
- cudatoolkit=10.1.243
- tornado==6.1.0
- pip:
# Required packages for AzureML execution, history, and data preparation.
- azureml-widgets~=1.38.0
- azureml-widgets~=1.39.0
- pytorch-transformers==1.0.0
- spacy==2.1.8
- pystan==2.19.1.1
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.38.0/validated_win32_requirements.txt [--no-deps]
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.39.0/validated_win32_requirements.txt [--no-deps]
- arch==4.14

32
how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
View File

@@ -1,29 +1,33 @@
name: azure_automl
channels:
- conda-forge
- pytorch
- main
dependencies:
# The python interpreter version.
# Currently Azure ML only supports 3.5.2 and later.
- pip==21.1.2
- python>=3.5.2,<3.8
- boto3==1.15.18
- matplotlib==2.1.0
# Currently Azure ML only supports 3.6.0 and later.
- pip==20.2.4
- python>=3.6,<3.8
- boto3==1.20.19
- botocore<=1.23.19
- matplotlib==3.3.4
- numpy==1.18.5
- cython
- urllib3<1.24
- cython==0.29.14
- urllib3==1.26.7
- scipy>=1.4.1,<=1.5.2
- scikit-learn==0.22.1
- pandas==0.25.1
- py-xgboost<=0.90
- conda-forge::fbprophet==0.5
- holidays==0.9.11
- py-xgboost<=1.3.3
- holidays==0.10.3
- conda-forge::fbprophet==0.7.1
- pytorch::pytorch=1.4.0
- cudatoolkit=10.1.243
- tornado==6.1.0
- pip:
# Required packages for AzureML execution, history, and data preparation.
- azureml-widgets~=1.38.0
- azureml-widgets~=1.39.0
- pytorch-transformers==1.0.0
- spacy==2.1.8
- pystan==2.19.1.1
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.38.0/validated_linux_requirements.txt [--no-deps]
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.39.0/validated_linux_requirements.txt [--no-deps]
- arch==4.14

32
how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
View File

@@ -1,30 +1,34 @@
name: azure_automl
channels:
- conda-forge
- pytorch
- main
dependencies:
# The python interpreter version.
# Currently Azure ML only supports 3.5.2 and later.
- pip==21.1.2
# Currently Azure ML only supports 3.6.0 and later.
- pip==20.2.4
- nomkl
- python>=3.5.2,<3.8
- boto3==1.15.18
- matplotlib==2.1.0
- python>=3.6,<3.8
- boto3==1.20.19
- botocore<=1.23.19
- matplotlib==3.3.4
- numpy==1.18.5
- cython
- urllib3<1.24
- cython==0.29.14
- urllib3==1.26.7
- scipy>=1.4.1,<=1.5.2
- scikit-learn==0.22.1
- pandas==0.25.1
- py-xgboost<=0.90
- conda-forge::fbprophet==0.5
- holidays==0.9.11
- py-xgboost<=1.3.3
- holidays==0.10.3
- conda-forge::fbprophet==0.7.1
- pytorch::pytorch=1.4.0
- cudatoolkit=9.0
- tornado==6.1.0
- pip:
# Required packages for AzureML execution, history, and data preparation.
- azureml-widgets~=1.38.0
- azureml-widgets~=1.39.0
- pytorch-transformers==1.0.0
- spacy==2.1.8
- pystan==2.19.1.1
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.38.0/validated_darwin_requirements.txt [--no-deps]
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.39.0/validated_darwin_requirements.txt [--no-deps]
- arch==4.14

4
how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
View File

@@ -105,7 +105,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -154,7 +154,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]

4
how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
View File

@@ -93,7 +93,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -116,7 +116,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]

4
how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
View File

@@ -97,7 +97,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -127,7 +127,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]

4
how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
View File

@@ -81,7 +81,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -127,7 +127,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]

10
how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml
View File

@@ -1,11 +1,11 @@
name: azure_automl_experimental
dependencies:
# The python interpreter version.
# Currently Azure ML only supports 3.5.2 and later.
- pip<=19.3.1
- python>=3.5.2,<3.8
- cython
- urllib3<1.24
# Currently Azure ML only supports 3.6.0 and later.
- pip<=20.2.4
- python>=3.6.0,<3.9
- cython==0.29.14
- urllib3==1.26.7
- PyJWT < 2.0.0
- numpy==1.18.5

14
how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml
View File

@@ -1,14 +1,16 @@
name: azure_automl_experimental
channels:
- conda-forge
- main
dependencies:
# The python interpreter version.
# Currently Azure ML only supports 3.5.2 and later.
- pip<=19.3.1
# Currently Azure ML only supports 3.6.0 and later.
- pip<=20.2.4
- nomkl
- python>=3.5.2,<3.8
- cython
- urllib3<1.24
- python>=3.6.0,<3.9
- urllib3==1.26.7
- PyJWT < 2.0.0
- numpy==1.18.5
- numpy==1.19.5
- pip:
# Required packages for AzureML execution, history, and data preparation.

2
how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb
View File

@@ -92,7 +92,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

2
how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
View File

@@ -91,7 +91,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

2
how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb
View File

@@ -86,7 +86,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Default datastore name\"] = dstore.name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]

2
how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb
View File

@@ -100,7 +100,7 @@
"output[\"SKU\"] = ws.sku\n",
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]

20
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_test.csv
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@@ -1,20 +0,0 @@
DATE,grain,BeerProduction
2017-01-01,grain,9049
2017-02-01,grain,10458
2017-03-01,grain,12489
2017-04-01,grain,11499
2017-05-01,grain,13553
2017-06-01,grain,14740
2017-07-01,grain,11424
2017-08-01,grain,13412
2017-09-01,grain,11917
2017-10-01,grain,12721
2017-11-01,grain,13272
2017-12-01,grain,14278
2018-01-01,grain,9572
2018-02-01,grain,10423
2018-03-01,grain,12667
2018-04-01,grain,11904
2018-05-01,grain,14120
2018-06-01,grain,14565
2018-07-01,grain,12622
1 DATE grain BeerProduction
2 2017-01-01 grain 9049
3 2017-02-01 grain 10458
4 2017-03-01 grain 12489
5 2017-04-01 grain 11499
6 2017-05-01 grain 13553
7 2017-06-01 grain 14740
8 2017-07-01 grain 11424
9 2017-08-01 grain 13412
10 2017-09-01 grain 11917
11 2017-10-01 grain 12721
12 2017-11-01 grain 13272
13 2017-12-01 grain 14278
14 2018-01-01 grain 9572
15 2018-02-01 grain 10423
16 2018-03-01 grain 12667
17 2018-04-01 grain 11904
18 2018-05-01 grain 14120
19 2018-06-01 grain 14565
20 2018-07-01 grain 12622

301
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_train.csv
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@@ -1,301 +0,0 @@
DATE,grain,BeerProduction
1992-01-01,grain,3459
1992-02-01,grain,3458
1992-03-01,grain,4002
1992-04-01,grain,4564
1992-05-01,grain,4221
1992-06-01,grain,4529
1992-07-01,grain,4466
1992-08-01,grain,4137
1992-09-01,grain,4126
1992-10-01,grain,4259
1992-11-01,grain,4240
1992-12-01,grain,4936
1993-01-01,grain,3031
1993-02-01,grain,3261
1993-03-01,grain,4160
1993-04-01,grain,4377
1993-05-01,grain,4307
1993-06-01,grain,4696
1993-07-01,grain,4458
1993-08-01,grain,4457
1993-09-01,grain,4364
1993-10-01,grain,4236
1993-11-01,grain,4500
1993-12-01,grain,4974
1994-01-01,grain,3075
1994-02-01,grain,3377
1994-03-01,grain,4443
1994-04-01,grain,4261
1994-05-01,grain,4460
1994-06-01,grain,4985
1994-07-01,grain,4324
1994-08-01,grain,4719
1994-09-01,grain,4374
1994-10-01,grain,4248
1994-11-01,grain,4784
1994-12-01,grain,4971
1995-01-01,grain,3370
1995-02-01,grain,3484
1995-03-01,grain,4269
1995-04-01,grain,3994
1995-05-01,grain,4715
1995-06-01,grain,4974
1995-07-01,grain,4223
1995-08-01,grain,5000
1995-09-01,grain,4235
1995-10-01,grain,4554
1995-11-01,grain,4851
1995-12-01,grain,4826
1996-01-01,grain,3699
1996-02-01,grain,3983
1996-03-01,grain,4262
1996-04-01,grain,4619
1996-05-01,grain,5219
1996-06-01,grain,4836
1996-07-01,grain,4941
1996-08-01,grain,5062
1996-09-01,grain,4365
1996-10-01,grain,5012
1996-11-01,grain,4850
1996-12-01,grain,5097
1997-01-01,grain,3758
1997-02-01,grain,3825
1997-03-01,grain,4454
1997-04-01,grain,4635
1997-05-01,grain,5210
1997-06-01,grain,5057
1997-07-01,grain,5231
1997-08-01,grain,5034
1997-09-01,grain,4970
1997-10-01,grain,5342
1997-11-01,grain,4831
1997-12-01,grain,5965
1998-01-01,grain,3796
1998-02-01,grain,4019
1998-03-01,grain,4898
1998-04-01,grain,5090
1998-05-01,grain,5237
1998-06-01,grain,5447
1998-07-01,grain,5435
1998-08-01,grain,5107
1998-09-01,grain,5515
1998-10-01,grain,5583
1998-11-01,grain,5346
1998-12-01,grain,6286
1999-01-01,grain,4032
1999-02-01,grain,4435
1999-03-01,grain,5479
1999-04-01,grain,5483
1999-05-01,grain,5587
1999-06-01,grain,6176
1999-07-01,grain,5621
1999-08-01,grain,5889
1999-09-01,grain,5828
1999-10-01,grain,5849
1999-11-01,grain,6180
1999-12-01,grain,6771
2000-01-01,grain,4243
2000-02-01,grain,4952
2000-03-01,grain,6008
2000-04-01,grain,5353
2000-05-01,grain,6435
2000-06-01,grain,6673
2000-07-01,grain,5636
2000-08-01,grain,6630
2000-09-01,grain,5887
2000-10-01,grain,6322
2000-11-01,grain,6520
2000-12-01,grain,6678
2001-01-01,grain,5082
2001-02-01,grain,5216
2001-03-01,grain,5893
2001-04-01,grain,5894
2001-05-01,grain,6799
2001-06-01,grain,6667
2001-07-01,grain,6374
2001-08-01,grain,6840
2001-09-01,grain,5575
2001-10-01,grain,6545
2001-11-01,grain,6789
2001-12-01,grain,7180
2002-01-01,grain,5117
2002-02-01,grain,5442
2002-03-01,grain,6337
2002-04-01,grain,6525
2002-05-01,grain,7216
2002-06-01,grain,6761
2002-07-01,grain,6958
2002-08-01,grain,7070
2002-09-01,grain,6148
2002-10-01,grain,6924
2002-11-01,grain,6716
2002-12-01,grain,7975
2003-01-01,grain,5326
2003-02-01,grain,5609
2003-03-01,grain,6414
2003-04-01,grain,6741
2003-05-01,grain,7144
2003-06-01,grain,7133
2003-07-01,grain,7568
2003-08-01,grain,7266
2003-09-01,grain,6634
2003-10-01,grain,7626
2003-11-01,grain,6843
2003-12-01,grain,8540
2004-01-01,grain,5629
2004-02-01,grain,5898
2004-03-01,grain,7045
2004-04-01,grain,7094
2004-05-01,grain,7333
2004-06-01,grain,7918
2004-07-01,grain,7289
2004-08-01,grain,7396
2004-09-01,grain,7259
2004-10-01,grain,7268
2004-11-01,grain,7731
2004-12-01,grain,9058
2005-01-01,grain,5557
2005-02-01,grain,6237
2005-03-01,grain,7723
2005-04-01,grain,7262
2005-05-01,grain,8241
2005-06-01,grain,8757
2005-07-01,grain,7352
2005-08-01,grain,8496
2005-09-01,grain,7741
2005-10-01,grain,7710
2005-11-01,grain,8247
2005-12-01,grain,8902
2006-01-01,grain,6066
2006-02-01,grain,6590
2006-03-01,grain,7923
2006-04-01,grain,7335
2006-05-01,grain,8843
2006-06-01,grain,9327
2006-07-01,grain,7792
2006-08-01,grain,9156
2006-09-01,grain,8037
2006-10-01,grain,8640
2006-11-01,grain,9128
2006-12-01,grain,9545
2007-01-01,grain,6627
2007-02-01,grain,6743
2007-03-01,grain,8195
2007-04-01,grain,7828
2007-05-01,grain,9570
2007-06-01,grain,9484
2007-07-01,grain,8608
2007-08-01,grain,9543
2007-09-01,grain,8123
2007-10-01,grain,9649
2007-11-01,grain,9390
2007-12-01,grain,10065
2008-01-01,grain,7093
2008-02-01,grain,7483
2008-03-01,grain,8365
2008-04-01,grain,8895
2008-05-01,grain,9794
2008-06-01,grain,9977
2008-07-01,grain,9553
2008-08-01,grain,9375
2008-09-01,grain,9225
2008-10-01,grain,9948
2008-11-01,grain,8758
2008-12-01,grain,10839
2009-01-01,grain,7266
2009-02-01,grain,7578
2009-03-01,grain,8688
2009-04-01,grain,9162
2009-05-01,grain,9369
2009-06-01,grain,10167
2009-07-01,grain,9507
2009-08-01,grain,8923
2009-09-01,grain,9272
2009-10-01,grain,9075
2009-11-01,grain,8949
2009-12-01,grain,10843
2010-01-01,grain,6558
2010-02-01,grain,7481
2010-03-01,grain,9475
2010-04-01,grain,9424
2010-05-01,grain,9351
2010-06-01,grain,10552
2010-07-01,grain,9077
2010-08-01,grain,9273
2010-09-01,grain,9420
2010-10-01,grain,9413
2010-11-01,grain,9866
2010-12-01,grain,11455
2011-01-01,grain,6901
2011-02-01,grain,8014
2011-03-01,grain,9832
2011-04-01,grain,9281
2011-05-01,grain,9967
2011-06-01,grain,11344
2011-07-01,grain,9106
2011-08-01,grain,10469
2011-09-01,grain,10085
2011-10-01,grain,9612
2011-11-01,grain,10328
2011-12-01,grain,11483
2012-01-01,grain,7486
2012-02-01,grain,8641
2012-03-01,grain,9709
2012-04-01,grain,9423
2012-05-01,grain,11342
2012-06-01,grain,11274
2012-07-01,grain,9845
2012-08-01,grain,11163
2012-09-01,grain,9532
2012-10-01,grain,10754
2012-11-01,grain,10953
2012-12-01,grain,11922
2013-01-01,grain,8395
2013-02-01,grain,8888
2013-03-01,grain,10110
2013-04-01,grain,10493
2013-05-01,grain,12218
2013-06-01,grain,11385
2013-07-01,grain,11186
2013-08-01,grain,11462
2013-09-01,grain,10494
2013-10-01,grain,11540
2013-11-01,grain,11138
2013-12-01,grain,12709
2014-01-01,grain,8557
2014-02-01,grain,9059
2014-03-01,grain,10055
2014-04-01,grain,10977
2014-05-01,grain,11792
2014-06-01,grain,11904
2014-07-01,grain,10965
2014-08-01,grain,10981
2014-09-01,grain,10828
2014-10-01,grain,11817
2014-11-01,grain,10470
2014-12-01,grain,13310
2015-01-01,grain,8400
2015-02-01,grain,9062
2015-03-01,grain,10722
2015-04-01,grain,11107
2015-05-01,grain,11508
2015-06-01,grain,12904
2015-07-01,grain,11869
2015-08-01,grain,11224
2015-09-01,grain,12022
2015-10-01,grain,11983
2015-11-01,grain,11506
2015-12-01,grain,14183
2016-01-01,grain,8650
2016-02-01,grain,10323
2016-03-01,grain,12110
2016-04-01,grain,11424
2016-05-01,grain,12243
2016-06-01,grain,13686
2016-07-01,grain,10956
2016-08-01,grain,12706
2016-09-01,grain,12279
2016-10-01,grain,11914
2016-11-01,grain,13025
2016-12-01,grain,14431
1 DATE grain BeerProduction
2 1992-01-01 grain 3459
3 1992-02-01 grain 3458
4 1992-03-01 grain 4002
5 1992-04-01 grain 4564
6 1992-05-01 grain 4221
7 1992-06-01 grain 4529
8 1992-07-01 grain 4466
9 1992-08-01 grain 4137
10 1992-09-01 grain 4126
11 1992-10-01 grain 4259
12 1992-11-01 grain 4240
13 1992-12-01 grain 4936
14 1993-01-01 grain 3031
15 1993-02-01 grain 3261
16 1993-03-01 grain 4160
17 1993-04-01 grain 4377
18 1993-05-01 grain 4307
19 1993-06-01 grain 4696
20 1993-07-01 grain 4458
21 1993-08-01 grain 4457
22 1993-09-01 grain 4364
23 1993-10-01 grain 4236
24 1993-11-01 grain 4500
25 1993-12-01 grain 4974
26 1994-01-01 grain 3075
27 1994-02-01 grain 3377
28 1994-03-01 grain 4443
29 1994-04-01 grain 4261
30 1994-05-01 grain 4460
31 1994-06-01 grain 4985
32 1994-07-01 grain 4324
33 1994-08-01 grain 4719
34 1994-09-01 grain 4374
35 1994-10-01 grain 4248
36 1994-11-01 grain 4784
37 1994-12-01 grain 4971
38 1995-01-01 grain 3370
39 1995-02-01 grain 3484
40 1995-03-01 grain 4269
41 1995-04-01 grain 3994
42 1995-05-01 grain 4715
43 1995-06-01 grain 4974
44 1995-07-01 grain 4223
45 1995-08-01 grain 5000
46 1995-09-01 grain 4235
47 1995-10-01 grain 4554
48 1995-11-01 grain 4851
49 1995-12-01 grain 4826
50 1996-01-01 grain 3699
51 1996-02-01 grain 3983
52 1996-03-01 grain 4262
53 1996-04-01 grain 4619
54 1996-05-01 grain 5219
55 1996-06-01 grain 4836
56 1996-07-01 grain 4941
57 1996-08-01 grain 5062
58 1996-09-01 grain 4365
59 1996-10-01 grain 5012
60 1996-11-01 grain 4850
61 1996-12-01 grain 5097
62 1997-01-01 grain 3758
63 1997-02-01 grain 3825
64 1997-03-01 grain 4454
65 1997-04-01 grain 4635
66 1997-05-01 grain 5210
67 1997-06-01 grain 5057
68 1997-07-01 grain 5231
69 1997-08-01 grain 5034
70 1997-09-01 grain 4970
71 1997-10-01 grain 5342
72 1997-11-01 grain 4831
73 1997-12-01 grain 5965
74 1998-01-01 grain 3796
75 1998-02-01 grain 4019
76 1998-03-01 grain 4898
77 1998-04-01 grain 5090
78 1998-05-01 grain 5237
79 1998-06-01 grain 5447
80 1998-07-01 grain 5435
81 1998-08-01 grain 5107
82 1998-09-01 grain 5515
83 1998-10-01 grain 5583
84 1998-11-01 grain 5346
85 1998-12-01 grain 6286
86 1999-01-01 grain 4032
87 1999-02-01 grain 4435
88 1999-03-01 grain 5479
89 1999-04-01 grain 5483
90 1999-05-01 grain 5587
91 1999-06-01 grain 6176
92 1999-07-01 grain 5621
93 1999-08-01 grain 5889
94 1999-09-01 grain 5828
95 1999-10-01 grain 5849
96 1999-11-01 grain 6180
97 1999-12-01 grain 6771
98 2000-01-01 grain 4243
99 2000-02-01 grain 4952
100 2000-03-01 grain 6008
101 2000-04-01 grain 5353
102 2000-05-01 grain 6435
103 2000-06-01 grain 6673
104 2000-07-01 grain 5636
105 2000-08-01 grain 6630
106 2000-09-01 grain 5887
107 2000-10-01 grain 6322
108 2000-11-01 grain 6520
109 2000-12-01 grain 6678
110 2001-01-01 grain 5082
111 2001-02-01 grain 5216
112 2001-03-01 grain 5893
113 2001-04-01 grain 5894
114 2001-05-01 grain 6799
115 2001-06-01 grain 6667
116 2001-07-01 grain 6374
117 2001-08-01 grain 6840
118 2001-09-01 grain 5575
119 2001-10-01 grain 6545
120 2001-11-01 grain 6789
121 2001-12-01 grain 7180
122 2002-01-01 grain 5117
123 2002-02-01 grain 5442
124 2002-03-01 grain 6337
125 2002-04-01 grain 6525
126 2002-05-01 grain 7216
127 2002-06-01 grain 6761
128 2002-07-01 grain 6958
129 2002-08-01 grain 7070
130 2002-09-01 grain 6148
131 2002-10-01 grain 6924
132 2002-11-01 grain 6716
133 2002-12-01 grain 7975
134 2003-01-01 grain 5326
135 2003-02-01 grain 5609
136 2003-03-01 grain 6414
137 2003-04-01 grain 6741
138 2003-05-01 grain 7144
139 2003-06-01 grain 7133
140 2003-07-01 grain 7568
141 2003-08-01 grain 7266
142 2003-09-01 grain 6634
143 2003-10-01 grain 7626
144 2003-11-01 grain 6843
145 2003-12-01 grain 8540
146 2004-01-01 grain 5629
147 2004-02-01 grain 5898
148 2004-03-01 grain 7045
149 2004-04-01 grain 7094
150 2004-05-01 grain 7333
151 2004-06-01 grain 7918
152 2004-07-01 grain 7289
153 2004-08-01 grain 7396
154 2004-09-01 grain 7259
155 2004-10-01 grain 7268
156 2004-11-01 grain 7731
157 2004-12-01 grain 9058
158 2005-01-01 grain 5557
159 2005-02-01 grain 6237
160 2005-03-01 grain 7723
161 2005-04-01 grain 7262
162 2005-05-01 grain 8241
163 2005-06-01 grain 8757
164 2005-07-01 grain 7352
165 2005-08-01 grain 8496
166 2005-09-01 grain 7741
167 2005-10-01 grain 7710
168 2005-11-01 grain 8247
169 2005-12-01 grain 8902
170 2006-01-01 grain 6066
171 2006-02-01 grain 6590
172 2006-03-01 grain 7923
173 2006-04-01 grain 7335
174 2006-05-01 grain 8843
175 2006-06-01 grain 9327
176 2006-07-01 grain 7792
177 2006-08-01 grain 9156
178 2006-09-01 grain 8037
179 2006-10-01 grain 8640
180 2006-11-01 grain 9128
181 2006-12-01 grain 9545
182 2007-01-01 grain 6627
183 2007-02-01 grain 6743
184 2007-03-01 grain 8195
185 2007-04-01 grain 7828
186 2007-05-01 grain 9570
187 2007-06-01 grain 9484
188 2007-07-01 grain 8608
189 2007-08-01 grain 9543
190 2007-09-01 grain 8123
191 2007-10-01 grain 9649
192 2007-11-01 grain 9390
193 2007-12-01 grain 10065
194 2008-01-01 grain 7093
195 2008-02-01 grain 7483
196 2008-03-01 grain 8365
197 2008-04-01 grain 8895
198 2008-05-01 grain 9794
199 2008-06-01 grain 9977
200 2008-07-01 grain 9553
201 2008-08-01 grain 9375
202 2008-09-01 grain 9225
203 2008-10-01 grain 9948
204 2008-11-01 grain 8758
205 2008-12-01 grain 10839
206 2009-01-01 grain 7266
207 2009-02-01 grain 7578
208 2009-03-01 grain 8688
209 2009-04-01 grain 9162
210 2009-05-01 grain 9369
211 2009-06-01 grain 10167
212 2009-07-01 grain 9507
213 2009-08-01 grain 8923
214 2009-09-01 grain 9272
215 2009-10-01 grain 9075
216 2009-11-01 grain 8949
217 2009-12-01 grain 10843
218 2010-01-01 grain 6558
219 2010-02-01 grain 7481
220 2010-03-01 grain 9475
221 2010-04-01 grain 9424
222 2010-05-01 grain 9351
223 2010-06-01 grain 10552
224 2010-07-01 grain 9077
225 2010-08-01 grain 9273
226 2010-09-01 grain 9420
227 2010-10-01 grain 9413
228 2010-11-01 grain 9866
229 2010-12-01 grain 11455
230 2011-01-01 grain 6901
231 2011-02-01 grain 8014
232 2011-03-01 grain 9832
233 2011-04-01 grain 9281
234 2011-05-01 grain 9967
235 2011-06-01 grain 11344
236 2011-07-01 grain 9106
237 2011-08-01 grain 10469
238 2011-09-01 grain 10085
239 2011-10-01 grain 9612
240 2011-11-01 grain 10328
241 2011-12-01 grain 11483
242 2012-01-01 grain 7486
243 2012-02-01 grain 8641
244 2012-03-01 grain 9709
245 2012-04-01 grain 9423
246 2012-05-01 grain 11342
247 2012-06-01 grain 11274
248 2012-07-01 grain 9845
249 2012-08-01 grain 11163
250 2012-09-01 grain 9532
251 2012-10-01 grain 10754
252 2012-11-01 grain 10953
253 2012-12-01 grain 11922
254 2013-01-01 grain 8395
255 2013-02-01 grain 8888
256 2013-03-01 grain 10110
257 2013-04-01 grain 10493
258 2013-05-01 grain 12218
259 2013-06-01 grain 11385
260 2013-07-01 grain 11186
261 2013-08-01 grain 11462
262 2013-09-01 grain 10494
263 2013-10-01 grain 11540
264 2013-11-01 grain 11138
265 2013-12-01 grain 12709
266 2014-01-01 grain 8557
267 2014-02-01 grain 9059
268 2014-03-01 grain 10055
269 2014-04-01 grain 10977
270 2014-05-01 grain 11792
271 2014-06-01 grain 11904
272 2014-07-01 grain 10965
273 2014-08-01 grain 10981
274 2014-09-01 grain 10828
275 2014-10-01 grain 11817
276 2014-11-01 grain 10470
277 2014-12-01 grain 13310
278 2015-01-01 grain 8400
279 2015-02-01 grain 9062
280 2015-03-01 grain 10722
281 2015-04-01 grain 11107
282 2015-05-01 grain 11508
283 2015-06-01 grain 12904
284 2015-07-01 grain 11869
285 2015-08-01 grain 11224
286 2015-09-01 grain 12022
287 2015-10-01 grain 11983
288 2015-11-01 grain 11506
289 2015-12-01 grain 14183
290 2016-01-01 grain 8650
291 2016-02-01 grain 10323
292 2016-03-01 grain 12110
293 2016-04-01 grain 11424
294 2016-05-01 grain 12243
295 2016-06-01 grain 13686
296 2016-07-01 grain 10956
297 2016-08-01 grain 12706
298 2016-09-01 grain 12279
299 2016-10-01 grain 11914
300 2016-11-01 grain 13025
301 2016-12-01 grain 14431

4
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml
View File

@@ -1,4 +0,0 @@
name: auto-ml-forecasting-beer-remote
dependencies:
- pip:
- azureml-sdk

4
how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
View File

@@ -89,7 +89,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -120,7 +120,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Run History Name\"] = experiment_name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]

2
how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
View File

@@ -100,7 +100,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

2
how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
View File

@@ -94,7 +94,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

53
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb → how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb
View File

@@ -30,7 +30,7 @@
},
"source": [
"# Automated Machine Learning\n",
"**Beer Production Forecasting**\n",
"**Github DAU Forecasting**\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
@@ -48,7 +48,7 @@
},
"source": [
"## Introduction\n",
"This notebook demonstrates demand forecasting for Beer Production Dataset using AutoML.\n",
"This notebook demonstrates demand forecasting for Github Daily Active Users Dataset using AutoML.\n",
"\n",
"AutoML highlights here include using Deep Learning forecasts, Arima, Prophet, Remote Execution and Remote Inferencing, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
"\n",
@@ -104,7 +104,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
"This notebook is compatible with Azure ML SDK version 1.35.0 or later."
]
},
{
@@ -113,7 +113,6 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -139,7 +138,7 @@
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = \"beer-remote-cpu\"\n",
"experiment_name = \"github-remote-cpu\"\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -149,7 +148,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Run History Name\"] = experiment_name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]
@@ -180,7 +179,7 @@
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# Choose a name for your CPU cluster\n",
"cpu_cluster_name = \"beer-cluster\"\n",
"cpu_cluster_name = \"github-cluster\"\n",
"\n",
"# Verify that cluster does not exist already\n",
"try:\n",
@@ -203,7 +202,7 @@
},
"source": [
"## Data\n",
"Read Beer demand data from file, and preview data."
"Read Github DAU data from file, and preview data."
]
},
{
@@ -246,21 +245,19 @@
"plt.tight_layout()\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.title(\"Beer Production By Year\")\n",
"df = pd.read_csv(\n",
" \"Beer_no_valid_split_train.csv\", parse_dates=True, index_col=\"DATE\"\n",
").drop(columns=\"grain\")\n",
"plt.title(\"Github Daily Active User By Year\")\n",
"df = pd.read_csv(\"github_dau_2011-2018_train.csv\", parse_dates=True, index_col=\"date\")\n",
"test_df = pd.read_csv(\n",
" \"Beer_no_valid_split_test.csv\", parse_dates=True, index_col=\"DATE\"\n",
").drop(columns=\"grain\")\n",
" \"github_dau_2011-2018_test.csv\", parse_dates=True, index_col=\"date\"\n",
")\n",
"plt.plot(df)\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.title(\"Beer Production By Month\")\n",
"plt.title(\"Github Daily Active User By Month\")\n",
"groups = df.groupby(df.index.month)\n",
"months = concat([DataFrame(x[1].values) for x in groups], axis=1)\n",
"months = DataFrame(months)\n",
"months.columns = range(1, 13)\n",
"months.columns = range(1, 49)\n",
"months.boxplot()\n",
"\n",
"plt.show()"
@@ -275,10 +272,10 @@
},
"outputs": [],
"source": [
"target_column_name = \"BeerProduction\"\n",
"time_column_name = \"DATE\"\n",
"target_column_name = \"count\"\n",
"time_column_name = \"date\"\n",
"time_series_id_column_names = []\n",
"freq = \"M\" # Monthly data"
"freq = \"D\" # Daily data"
]
},
{
@@ -308,19 +305,19 @@
"datastore = ws.get_default_datastore()\n",
"datastore.upload_files(\n",
" files=[\"./train.csv\"],\n",
" target_path=\"beer-dataset/tabular/\",\n",
" target_path=\"github-dataset/tabular/\",\n",
" overwrite=True,\n",
" show_progress=True,\n",
")\n",
"datastore.upload_files(\n",
" files=[\"./valid.csv\"],\n",
" target_path=\"beer-dataset/tabular/\",\n",
" target_path=\"github-dataset/tabular/\",\n",
" overwrite=True,\n",
" show_progress=True,\n",
")\n",
"datastore.upload_files(\n",
" files=[\"./test.csv\"],\n",
" target_path=\"beer-dataset/tabular/\",\n",
" target_path=\"github-dataset/tabular/\",\n",
" overwrite=True,\n",
" show_progress=True,\n",
")\n",
@@ -328,13 +325,13 @@
"from azureml.core import Dataset\n",
"\n",
"train_dataset = Dataset.Tabular.from_delimited_files(\n",
" path=[(datastore, \"beer-dataset/tabular/train.csv\")]\n",
" path=[(datastore, \"github-dataset/tabular/train.csv\")]\n",
")\n",
"valid_dataset = Dataset.Tabular.from_delimited_files(\n",
" path=[(datastore, \"beer-dataset/tabular/valid.csv\")]\n",
" path=[(datastore, \"github-dataset/tabular/valid.csv\")]\n",
")\n",
"test_dataset = Dataset.Tabular.from_delimited_files(\n",
" path=[(datastore, \"beer-dataset/tabular/test.csv\")]\n",
" path=[(datastore, \"github-dataset/tabular/test.csv\")]\n",
")"
]
},
@@ -397,7 +394,7 @@
"forecasting_parameters = ForecastingParameters(\n",
" time_column_name=time_column_name,\n",
" forecast_horizon=forecast_horizon,\n",
" freq=\"MS\", # Set the forecast frequency to be monthly (start of the month)\n",
" freq=\"D\", # Set the forecast frequency to be daily\n",
")\n",
"\n",
"# We will disable the enable_early_stopping flag to ensure the DNN model is recommended for demonstration purpose.\n",
@@ -570,7 +567,7 @@
"from azureml.core import Dataset\n",
"\n",
"test_dataset = Dataset.Tabular.from_delimited_files(\n",
" path=[(datastore, \"beer-dataset/tabular/test.csv\")]\n",
" path=[(datastore, \"github-dataset/tabular/test.csv\")]\n",
")\n",
"# preview the first 3 rows of the dataset\n",
"test_dataset.take(5).to_pandas_dataframe()"
@@ -582,7 +579,7 @@
"metadata": {},
"outputs": [],
"source": [
"compute_target = ws.compute_targets[\"beer-cluster\"]\n",
"compute_target = ws.compute_targets[\"github-cluster\"]\n",
"test_experiment = Experiment(ws, experiment_name + \"_test\")"
]
},

4
how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml Normal file
View File

@@ -0,0 +1,4 @@
name: auto-ml-forecasting-github-dau
dependencies:
- pip:
- azureml-sdk

455
how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv Normal file
View File

@@ -0,0 +1,455 @@
date,count,day_of_week,month_of_year,holiday
2017-06-04,104663,6.0,5.0,0.0
2017-06-05,155824,0.0,5.0,0.0
2017-06-06,164908,1.0,5.0,0.0
2017-06-07,170309,2.0,5.0,0.0
2017-06-08,164256,3.0,5.0,0.0
2017-06-09,153406,4.0,5.0,0.0
2017-06-10,97024,5.0,5.0,0.0
2017-06-11,103442,6.0,5.0,0.0
2017-06-12,160768,0.0,5.0,0.0
2017-06-13,166288,1.0,5.0,0.0
2017-06-14,163819,2.0,5.0,0.0
2017-06-15,157593,3.0,5.0,0.0
2017-06-16,149259,4.0,5.0,0.0
2017-06-17,95579,5.0,5.0,0.0
2017-06-18,98723,6.0,5.0,0.0
2017-06-19,159076,0.0,5.0,0.0
2017-06-20,163340,1.0,5.0,0.0
2017-06-21,163344,2.0,5.0,0.0
2017-06-22,159528,3.0,5.0,0.0
2017-06-23,146563,4.0,5.0,0.0
2017-06-24,92631,5.0,5.0,0.0
2017-06-25,96549,6.0,5.0,0.0
2017-06-26,153249,0.0,5.0,0.0
2017-06-27,160357,1.0,5.0,0.0
2017-06-28,159941,2.0,5.0,0.0
2017-06-29,156781,3.0,5.0,0.0
2017-06-30,144709,4.0,5.0,0.0
2017-07-01,89101,5.0,6.0,0.0
2017-07-02,93046,6.0,6.0,0.0
2017-07-03,144113,0.0,6.0,0.0
2017-07-04,143061,1.0,6.0,1.0
2017-07-05,154603,2.0,6.0,0.0
2017-07-06,157200,3.0,6.0,0.0
2017-07-07,147213,4.0,6.0,0.0
2017-07-08,92348,5.0,6.0,0.0
2017-07-09,97018,6.0,6.0,0.0
2017-07-10,157192,0.0,6.0,0.0
2017-07-11,161819,1.0,6.0,0.0
2017-07-12,161998,2.0,6.0,0.0
2017-07-13,160280,3.0,6.0,0.0
2017-07-14,146818,4.0,6.0,0.0
2017-07-15,93041,5.0,6.0,0.0
2017-07-16,97505,6.0,6.0,0.0
2017-07-17,156167,0.0,6.0,0.0
2017-07-18,162855,1.0,6.0,0.0
2017-07-19,162519,2.0,6.0,0.0
2017-07-20,159941,3.0,6.0,0.0
2017-07-21,148460,4.0,6.0,0.0
2017-07-22,93431,5.0,6.0,0.0
2017-07-23,98553,6.0,6.0,0.0
2017-07-24,156202,0.0,6.0,0.0
2017-07-25,162503,1.0,6.0,0.0
2017-07-26,158479,2.0,6.0,0.0
2017-07-27,158192,3.0,6.0,0.0
2017-07-28,147108,4.0,6.0,0.0
2017-07-29,93799,5.0,6.0,0.0
2017-07-30,97920,6.0,6.0,0.0
2017-07-31,152197,0.0,6.0,0.0
2017-08-01,158477,1.0,7.0,0.0
2017-08-02,159089,2.0,7.0,0.0
2017-08-03,157182,3.0,7.0,0.0
2017-08-04,146345,4.0,7.0,0.0
2017-08-05,92534,5.0,7.0,0.0
2017-08-06,97128,6.0,7.0,0.0
2017-08-07,151359,0.0,7.0,0.0
2017-08-08,159895,1.0,7.0,0.0
2017-08-09,158329,2.0,7.0,0.0
2017-08-10,155468,3.0,7.0,0.0
2017-08-11,144914,4.0,7.0,0.0
2017-08-12,92258,5.0,7.0,0.0
2017-08-13,95933,6.0,7.0,0.0
2017-08-14,147706,0.0,7.0,0.0
2017-08-15,151115,1.0,7.0,0.0
2017-08-16,157640,2.0,7.0,0.0
2017-08-17,156600,3.0,7.0,0.0
2017-08-18,146980,4.0,7.0,0.0
2017-08-19,94592,5.0,7.0,0.0
2017-08-20,99320,6.0,7.0,0.0
2017-08-21,145727,0.0,7.0,0.0
2017-08-22,160260,1.0,7.0,0.0
2017-08-23,160440,2.0,7.0,0.0
2017-08-24,157830,3.0,7.0,0.0
2017-08-25,145822,4.0,7.0,0.0
2017-08-26,94706,5.0,7.0,0.0
2017-08-27,99047,6.0,7.0,0.0
2017-08-28,152112,0.0,7.0,0.0
2017-08-29,162440,1.0,7.0,0.0
2017-08-30,162902,2.0,7.0,0.0
2017-08-31,159498,3.0,7.0,0.0
2017-09-01,145689,4.0,8.0,0.0
2017-09-02,93589,5.0,8.0,0.0
2017-09-03,100058,6.0,8.0,0.0
2017-09-04,140865,0.0,8.0,1.0
2017-09-05,165715,1.0,8.0,0.0
2017-09-06,167463,2.0,8.0,0.0
2017-09-07,164811,3.0,8.0,0.0
2017-09-08,156157,4.0,8.0,0.0
2017-09-09,101358,5.0,8.0,0.0
2017-09-10,107915,6.0,8.0,0.0
2017-09-11,167845,0.0,8.0,0.0
2017-09-12,172756,1.0,8.0,0.0
2017-09-13,172851,2.0,8.0,0.0
2017-09-14,171675,3.0,8.0,0.0
2017-09-15,159266,4.0,8.0,0.0
2017-09-16,103547,5.0,8.0,0.0
2017-09-17,110964,6.0,8.0,0.0
2017-09-18,170976,0.0,8.0,0.0
2017-09-19,177864,1.0,8.0,0.0
2017-09-20,173567,2.0,8.0,0.0
2017-09-21,172017,3.0,8.0,0.0
2017-09-22,161357,4.0,8.0,0.0
2017-09-23,104681,5.0,8.0,0.0
2017-09-24,111711,6.0,8.0,0.0
2017-09-25,173517,0.0,8.0,0.0
2017-09-26,180049,1.0,8.0,0.0
2017-09-27,178307,2.0,8.0,0.0
2017-09-28,174157,3.0,8.0,0.0
2017-09-29,161707,4.0,8.0,0.0
2017-09-30,110536,5.0,8.0,0.0
2017-10-01,106505,6.0,9.0,0.0
2017-10-02,157565,0.0,9.0,0.0
2017-10-03,164764,1.0,9.0,0.0
2017-10-04,163383,2.0,9.0,0.0
2017-10-05,162847,3.0,9.0,0.0
2017-10-06,153575,4.0,9.0,0.0
2017-10-07,107472,5.0,9.0,0.0
2017-10-08,116127,6.0,9.0,0.0
2017-10-09,174457,0.0,9.0,1.0
2017-10-10,185217,1.0,9.0,0.0
2017-10-11,185120,2.0,9.0,0.0
2017-10-12,180844,3.0,9.0,0.0
2017-10-13,170178,4.0,9.0,0.0
2017-10-14,112754,5.0,9.0,0.0
2017-10-15,121251,6.0,9.0,0.0
2017-10-16,183906,0.0,9.0,0.0
2017-10-17,188945,1.0,9.0,0.0
2017-10-18,187297,2.0,9.0,0.0
2017-10-19,183867,3.0,9.0,0.0
2017-10-20,173021,4.0,9.0,0.0
2017-10-21,115851,5.0,9.0,0.0
2017-10-22,126088,6.0,9.0,0.0
2017-10-23,189452,0.0,9.0,0.0
2017-10-24,194412,1.0,9.0,0.0
2017-10-25,192293,2.0,9.0,0.0
2017-10-26,190163,3.0,9.0,0.0
2017-10-27,177053,4.0,9.0,0.0
2017-10-28,114934,5.0,9.0,0.0
2017-10-29,125289,6.0,9.0,0.0
2017-10-30,189245,0.0,9.0,0.0
2017-10-31,191480,1.0,9.0,0.0
2017-11-01,182281,2.0,10.0,0.0
2017-11-02,186351,3.0,10.0,0.0
2017-11-03,175422,4.0,10.0,0.0
2017-11-04,118160,5.0,10.0,0.0
2017-11-05,127602,6.0,10.0,0.0
2017-11-06,191067,0.0,10.0,0.0
2017-11-07,197083,1.0,10.0,0.0
2017-11-08,194333,2.0,10.0,0.0
2017-11-09,193914,3.0,10.0,0.0
2017-11-10,179933,4.0,10.0,1.0
2017-11-11,121346,5.0,10.0,0.0
2017-11-12,131900,6.0,10.0,0.0
2017-11-13,196969,0.0,10.0,0.0
2017-11-14,201949,1.0,10.0,0.0
2017-11-15,198424,2.0,10.0,0.0
2017-11-16,196902,3.0,10.0,0.0
2017-11-17,183893,4.0,10.0,0.0
2017-11-18,122767,5.0,10.0,0.0
2017-11-19,130890,6.0,10.0,0.0
2017-11-20,194515,0.0,10.0,0.0
2017-11-21,198601,1.0,10.0,0.0
2017-11-22,191041,2.0,10.0,0.0
2017-11-23,170321,3.0,10.0,1.0
2017-11-24,155623,4.0,10.0,0.0
2017-11-25,115759,5.0,10.0,0.0
2017-11-26,128771,6.0,10.0,0.0
2017-11-27,199419,0.0,10.0,0.0
2017-11-28,207253,1.0,10.0,0.0
2017-11-29,205406,2.0,10.0,0.0
2017-11-30,200674,3.0,10.0,0.0
2017-12-01,187017,4.0,11.0,0.0
2017-12-02,129735,5.0,11.0,0.0
2017-12-03,139120,6.0,11.0,0.0
2017-12-04,205505,0.0,11.0,0.0
2017-12-05,208218,1.0,11.0,0.0
2017-12-06,202480,2.0,11.0,0.0
2017-12-07,197822,3.0,11.0,0.0
2017-12-08,180686,4.0,11.0,0.0
2017-12-09,123667,5.0,11.0,0.0
2017-12-10,130987,6.0,11.0,0.0
2017-12-11,193901,0.0,11.0,0.0
2017-12-12,194997,1.0,11.0,0.0
2017-12-13,192063,2.0,11.0,0.0
2017-12-14,186496,3.0,11.0,0.0
2017-12-15,170812,4.0,11.0,0.0
2017-12-16,110474,5.0,11.0,0.0
2017-12-17,118165,6.0,11.0,0.0
2017-12-18,176843,0.0,11.0,0.0
2017-12-19,179550,1.0,11.0,0.0
2017-12-20,173506,2.0,11.0,0.0
2017-12-21,165910,3.0,11.0,0.0
2017-12-22,145886,4.0,11.0,0.0
2017-12-23,95246,5.0,11.0,0.0
2017-12-24,88781,6.0,11.0,0.0
2017-12-25,98189,0.0,11.0,1.0
2017-12-26,121383,1.0,11.0,0.0
2017-12-27,135300,2.0,11.0,0.0
2017-12-28,136827,3.0,11.0,0.0
2017-12-29,127700,4.0,11.0,0.0
2017-12-30,93014,5.0,11.0,0.0
2017-12-31,82878,6.0,11.0,0.0
2018-01-01,86419,0.0,0.0,1.0
2018-01-02,147428,1.0,0.0,0.0
2018-01-03,162193,2.0,0.0,0.0
2018-01-04,163784,3.0,0.0,0.0
2018-01-05,158606,4.0,0.0,0.0
2018-01-06,113467,5.0,0.0,0.0
2018-01-07,118313,6.0,0.0,0.0
2018-01-08,175623,0.0,0.0,0.0
2018-01-09,183880,1.0,0.0,0.0
2018-01-10,183945,2.0,0.0,0.0
2018-01-11,181769,3.0,0.0,0.0
2018-01-12,170552,4.0,0.0,0.0
2018-01-13,115707,5.0,0.0,0.0
2018-01-14,121191,6.0,0.0,0.0
2018-01-15,176127,0.0,0.0,1.0
2018-01-16,188032,1.0,0.0,0.0
2018-01-17,189871,2.0,0.0,0.0
2018-01-18,189348,3.0,0.0,0.0
2018-01-19,177456,4.0,0.0,0.0
2018-01-20,123321,5.0,0.0,0.0
2018-01-21,128306,6.0,0.0,0.0
2018-01-22,186132,0.0,0.0,0.0
2018-01-23,197618,1.0,0.0,0.0
2018-01-24,196402,2.0,0.0,0.0
2018-01-25,192722,3.0,0.0,0.0
2018-01-26,179415,4.0,0.0,0.0
2018-01-27,125769,5.0,0.0,0.0
2018-01-28,133306,6.0,0.0,0.0
2018-01-29,194151,0.0,0.0,0.0
2018-01-30,198680,1.0,0.0,0.0
2018-01-31,198652,2.0,0.0,0.0
2018-02-01,195472,3.0,1.0,0.0
2018-02-02,183173,4.0,1.0,0.0
2018-02-03,124276,5.0,1.0,0.0
2018-02-04,129054,6.0,1.0,0.0
2018-02-05,190024,0.0,1.0,0.0
2018-02-06,198658,1.0,1.0,0.0
2018-02-07,198272,2.0,1.0,0.0
2018-02-08,195339,3.0,1.0,0.0
2018-02-09,183086,4.0,1.0,0.0
2018-02-10,122536,5.0,1.0,0.0
2018-02-11,133033,6.0,1.0,0.0
2018-02-12,185386,0.0,1.0,0.0
2018-02-13,184789,1.0,1.0,0.0
2018-02-14,176089,2.0,1.0,0.0
2018-02-15,171317,3.0,1.0,0.0
2018-02-16,162693,4.0,1.0,0.0
2018-02-17,116342,5.0,1.0,0.0
2018-02-18,122466,6.0,1.0,0.0
2018-02-19,172364,0.0,1.0,1.0
2018-02-20,185896,1.0,1.0,0.0
2018-02-21,188166,2.0,1.0,0.0
2018-02-22,189427,3.0,1.0,0.0
2018-02-23,178732,4.0,1.0,0.0
2018-02-24,132664,5.0,1.0,0.0
2018-02-25,134008,6.0,1.0,0.0
2018-02-26,200075,0.0,1.0,0.0
2018-02-27,207996,1.0,1.0,0.0
2018-02-28,204416,2.0,1.0,0.0
2018-03-01,201320,3.0,2.0,0.0
2018-03-02,188205,4.0,2.0,0.0
2018-03-03,131162,5.0,2.0,0.0
2018-03-04,138320,6.0,2.0,0.0
2018-03-05,207326,0.0,2.0,0.0
2018-03-06,212462,1.0,2.0,0.0
2018-03-07,209357,2.0,2.0,0.0
2018-03-08,194876,3.0,2.0,0.0
2018-03-09,193761,4.0,2.0,0.0
2018-03-10,133449,5.0,2.0,0.0
2018-03-11,142258,6.0,2.0,0.0
2018-03-12,208753,0.0,2.0,0.0
2018-03-13,210602,1.0,2.0,0.0
2018-03-14,214236,2.0,2.0,0.0
2018-03-15,210761,3.0,2.0,0.0
2018-03-16,196619,4.0,2.0,0.0
2018-03-17,133056,5.0,2.0,0.0
2018-03-18,141335,6.0,2.0,0.0
2018-03-19,211580,0.0,2.0,0.0
2018-03-20,219051,1.0,2.0,0.0
2018-03-21,215435,2.0,2.0,0.0
2018-03-22,211961,3.0,2.0,0.0
2018-03-23,196009,4.0,2.0,0.0
2018-03-24,132390,5.0,2.0,0.0
2018-03-25,140021,6.0,2.0,0.0
2018-03-26,205273,0.0,2.0,0.0
2018-03-27,212686,1.0,2.0,0.0
2018-03-28,210683,2.0,2.0,0.0
2018-03-29,189044,3.0,2.0,0.0
2018-03-30,170256,4.0,2.0,0.0
2018-03-31,125999,5.0,2.0,0.0
2018-04-01,126749,6.0,3.0,0.0
2018-04-02,186546,0.0,3.0,0.0
2018-04-03,207905,1.0,3.0,0.0
2018-04-04,201528,2.0,3.0,0.0
2018-04-05,188580,3.0,3.0,0.0
2018-04-06,173714,4.0,3.0,0.0
2018-04-07,125723,5.0,3.0,0.0
2018-04-08,142545,6.0,3.0,0.0
2018-04-09,204767,0.0,3.0,0.0
2018-04-10,212048,1.0,3.0,0.0
2018-04-11,210517,2.0,3.0,0.0
2018-04-12,206924,3.0,3.0,0.0
2018-04-13,191679,4.0,3.0,0.0
2018-04-14,126394,5.0,3.0,0.0
2018-04-15,137279,6.0,3.0,0.0
2018-04-16,208085,0.0,3.0,0.0
2018-04-17,213273,1.0,3.0,0.0
2018-04-18,211580,2.0,3.0,0.0
2018-04-19,206037,3.0,3.0,0.0
2018-04-20,191211,4.0,3.0,0.0
2018-04-21,125564,5.0,3.0,0.0
2018-04-22,136469,6.0,3.0,0.0
2018-04-23,206288,0.0,3.0,0.0
2018-04-24,212115,1.0,3.0,0.0
2018-04-25,207948,2.0,3.0,0.0
2018-04-26,205759,3.0,3.0,0.0
2018-04-27,181330,4.0,3.0,0.0
2018-04-28,130046,5.0,3.0,0.0
2018-04-29,120802,6.0,3.0,0.0
2018-04-30,170390,0.0,3.0,0.0
2018-05-01,169054,1.0,4.0,0.0
2018-05-02,197891,2.0,4.0,0.0
2018-05-03,199820,3.0,4.0,0.0
2018-05-04,186783,4.0,4.0,0.0
2018-05-05,124420,5.0,4.0,0.0
2018-05-06,130666,6.0,4.0,0.0
2018-05-07,196014,0.0,4.0,0.0
2018-05-08,203058,1.0,4.0,0.0
2018-05-09,198582,2.0,4.0,0.0
2018-05-10,191321,3.0,4.0,0.0
2018-05-11,183639,4.0,4.0,0.0
2018-05-12,122023,5.0,4.0,0.0
2018-05-13,128775,6.0,4.0,0.0
2018-05-14,199104,0.0,4.0,0.0
2018-05-15,200658,1.0,4.0,0.0
2018-05-16,201541,2.0,4.0,0.0
2018-05-17,196886,3.0,4.0,0.0
2018-05-18,188597,4.0,4.0,0.0
2018-05-19,121392,5.0,4.0,0.0
2018-05-20,126981,6.0,4.0,0.0
2018-05-21,189291,0.0,4.0,0.0
2018-05-22,203038,1.0,4.0,0.0
2018-05-23,205330,2.0,4.0,0.0
2018-05-24,199208,3.0,4.0,0.0
2018-05-25,187768,4.0,4.0,0.0
2018-05-26,117635,5.0,4.0,0.0
2018-05-27,124352,6.0,4.0,0.0
2018-05-28,180398,0.0,4.0,1.0
2018-05-29,194170,1.0,4.0,0.0
2018-05-30,200281,2.0,4.0,0.0
2018-05-31,197244,3.0,4.0,0.0
2018-06-01,184037,4.0,5.0,0.0
2018-06-02,121135,5.0,5.0,0.0
2018-06-03,129389,6.0,5.0,0.0
2018-06-04,200331,0.0,5.0,0.0
2018-06-05,207735,1.0,5.0,0.0
2018-06-06,203354,2.0,5.0,0.0
2018-06-07,200520,3.0,5.0,0.0
2018-06-08,182038,4.0,5.0,0.0
2018-06-09,120164,5.0,5.0,0.0
2018-06-10,125256,6.0,5.0,0.0
2018-06-11,194786,0.0,5.0,0.0
2018-06-12,200815,1.0,5.0,0.0
2018-06-13,197740,2.0,5.0,0.0
2018-06-14,192294,3.0,5.0,0.0
2018-06-15,173587,4.0,5.0,0.0
2018-06-16,105955,5.0,5.0,0.0
2018-06-17,110780,6.0,5.0,0.0
2018-06-18,174582,0.0,5.0,0.0
2018-06-19,193310,1.0,5.0,0.0
2018-06-20,193062,2.0,5.0,0.0
2018-06-21,187986,3.0,5.0,0.0
2018-06-22,173606,4.0,5.0,0.0
2018-06-23,111795,5.0,5.0,0.0
2018-06-24,116134,6.0,5.0,0.0
2018-06-25,185919,0.0,5.0,0.0
2018-06-26,193142,1.0,5.0,0.0
2018-06-27,188114,2.0,5.0,0.0
2018-06-28,183737,3.0,5.0,0.0
2018-06-29,171496,4.0,5.0,0.0
2018-06-30,107210,5.0,5.0,0.0
2018-07-01,111053,6.0,6.0,0.0
2018-07-02,176198,0.0,6.0,0.0
2018-07-03,184040,1.0,6.0,0.0
2018-07-04,169783,2.0,6.0,1.0
2018-07-05,177996,3.0,6.0,0.0
2018-07-06,167378,4.0,6.0,0.0
2018-07-07,106401,5.0,6.0,0.0
2018-07-08,112327,6.0,6.0,0.0
2018-07-09,182835,0.0,6.0,0.0
2018-07-10,187694,1.0,6.0,0.0
2018-07-11,185762,2.0,6.0,0.0
2018-07-12,184099,3.0,6.0,0.0
2018-07-13,170860,4.0,6.0,0.0
2018-07-14,106799,5.0,6.0,0.0
2018-07-15,108475,6.0,6.0,0.0
2018-07-16,175704,0.0,6.0,0.0
2018-07-17,183596,1.0,6.0,0.0
2018-07-18,179897,2.0,6.0,0.0
2018-07-19,183373,3.0,6.0,0.0
2018-07-20,169626,4.0,6.0,0.0
2018-07-21,106785,5.0,6.0,0.0
2018-07-22,112387,6.0,6.0,0.0
2018-07-23,180572,0.0,6.0,0.0
2018-07-24,186943,1.0,6.0,0.0
2018-07-25,185744,2.0,6.0,0.0
2018-07-26,183117,3.0,6.0,0.0
2018-07-27,168526,4.0,6.0,0.0
2018-07-28,105936,5.0,6.0,0.0
2018-07-29,111708,6.0,6.0,0.0
2018-07-30,179950,0.0,6.0,0.0
2018-07-31,185930,1.0,6.0,0.0
2018-08-01,183366,2.0,7.0,0.0
2018-08-02,182412,3.0,7.0,0.0
2018-08-03,173429,4.0,7.0,0.0
2018-08-04,106108,5.0,7.0,0.0
2018-08-05,110059,6.0,7.0,0.0
2018-08-06,178355,0.0,7.0,0.0
2018-08-07,185518,1.0,7.0,0.0
2018-08-08,183204,2.0,7.0,0.0
2018-08-09,181276,3.0,7.0,0.0
2018-08-10,168297,4.0,7.0,0.0
2018-08-11,106488,5.0,7.0,0.0
2018-08-12,111786,6.0,7.0,0.0
2018-08-13,178620,0.0,7.0,0.0
2018-08-14,181922,1.0,7.0,0.0
2018-08-15,172198,2.0,7.0,0.0
2018-08-16,177367,3.0,7.0,0.0
2018-08-17,166550,4.0,7.0,0.0
2018-08-18,107011,5.0,7.0,0.0
2018-08-19,112299,6.0,7.0,0.0
2018-08-20,176718,0.0,7.0,0.0
2018-08-21,182562,1.0,7.0,0.0
2018-08-22,181484,2.0,7.0,0.0
2018-08-23,180317,3.0,7.0,0.0
2018-08-24,170197,4.0,7.0,0.0
2018-08-25,109383,5.0,7.0,0.0
2018-08-26,113373,6.0,7.0,0.0
2018-08-27,180142,0.0,7.0,0.0
2018-08-28,191628,1.0,7.0,0.0
2018-08-29,191149,2.0,7.0,0.0
2018-08-30,187503,3.0,7.0,0.0
2018-08-31,172280,4.0,7.0,0.0
1 date count day_of_week month_of_year holiday
2 2017-06-04 104663 6.0 5.0 0.0
3 2017-06-05 155824 0.0 5.0 0.0
4 2017-06-06 164908 1.0 5.0 0.0
5 2017-06-07 170309 2.0 5.0 0.0
6 2017-06-08 164256 3.0 5.0 0.0
7 2017-06-09 153406 4.0 5.0 0.0
8 2017-06-10 97024 5.0 5.0 0.0
9 2017-06-11 103442 6.0 5.0 0.0
10 2017-06-12 160768 0.0 5.0 0.0
11 2017-06-13 166288 1.0 5.0 0.0
12 2017-06-14 163819 2.0 5.0 0.0
13 2017-06-15 157593 3.0 5.0 0.0
14 2017-06-16 149259 4.0 5.0 0.0
15 2017-06-17 95579 5.0 5.0 0.0
16 2017-06-18 98723 6.0 5.0 0.0
17 2017-06-19 159076 0.0 5.0 0.0
18 2017-06-20 163340 1.0 5.0 0.0
19 2017-06-21 163344 2.0 5.0 0.0
20 2017-06-22 159528 3.0 5.0 0.0
21 2017-06-23 146563 4.0 5.0 0.0
22 2017-06-24 92631 5.0 5.0 0.0
23 2017-06-25 96549 6.0 5.0 0.0
24 2017-06-26 153249 0.0 5.0 0.0
25 2017-06-27 160357 1.0 5.0 0.0
26 2017-06-28 159941 2.0 5.0 0.0
27 2017-06-29 156781 3.0 5.0 0.0
28 2017-06-30 144709 4.0 5.0 0.0
29 2017-07-01 89101 5.0 6.0 0.0
30 2017-07-02 93046 6.0 6.0 0.0
31 2017-07-03 144113 0.0 6.0 0.0
32 2017-07-04 143061 1.0 6.0 1.0
33 2017-07-05 154603 2.0 6.0 0.0
34 2017-07-06 157200 3.0 6.0 0.0
35 2017-07-07 147213 4.0 6.0 0.0
36 2017-07-08 92348 5.0 6.0 0.0
37 2017-07-09 97018 6.0 6.0 0.0
38 2017-07-10 157192 0.0 6.0 0.0
39 2017-07-11 161819 1.0 6.0 0.0
40 2017-07-12 161998 2.0 6.0 0.0
41 2017-07-13 160280 3.0 6.0 0.0
42 2017-07-14 146818 4.0 6.0 0.0
43 2017-07-15 93041 5.0 6.0 0.0
44 2017-07-16 97505 6.0 6.0 0.0
45 2017-07-17 156167 0.0 6.0 0.0
46 2017-07-18 162855 1.0 6.0 0.0
47 2017-07-19 162519 2.0 6.0 0.0
48 2017-07-20 159941 3.0 6.0 0.0
49 2017-07-21 148460 4.0 6.0 0.0
50 2017-07-22 93431 5.0 6.0 0.0
51 2017-07-23 98553 6.0 6.0 0.0
52 2017-07-24 156202 0.0 6.0 0.0
53 2017-07-25 162503 1.0 6.0 0.0
54 2017-07-26 158479 2.0 6.0 0.0
55 2017-07-27 158192 3.0 6.0 0.0
56 2017-07-28 147108 4.0 6.0 0.0
57 2017-07-29 93799 5.0 6.0 0.0
58 2017-07-30 97920 6.0 6.0 0.0
59 2017-07-31 152197 0.0 6.0 0.0
60 2017-08-01 158477 1.0 7.0 0.0
61 2017-08-02 159089 2.0 7.0 0.0
62 2017-08-03 157182 3.0 7.0 0.0
63 2017-08-04 146345 4.0 7.0 0.0
64 2017-08-05 92534 5.0 7.0 0.0
65 2017-08-06 97128 6.0 7.0 0.0
66 2017-08-07 151359 0.0 7.0 0.0
67 2017-08-08 159895 1.0 7.0 0.0
68 2017-08-09 158329 2.0 7.0 0.0
69 2017-08-10 155468 3.0 7.0 0.0
70 2017-08-11 144914 4.0 7.0 0.0
71 2017-08-12 92258 5.0 7.0 0.0
72 2017-08-13 95933 6.0 7.0 0.0
73 2017-08-14 147706 0.0 7.0 0.0
74 2017-08-15 151115 1.0 7.0 0.0
75 2017-08-16 157640 2.0 7.0 0.0
76 2017-08-17 156600 3.0 7.0 0.0
77 2017-08-18 146980 4.0 7.0 0.0
78 2017-08-19 94592 5.0 7.0 0.0
79 2017-08-20 99320 6.0 7.0 0.0
80 2017-08-21 145727 0.0 7.0 0.0
81 2017-08-22 160260 1.0 7.0 0.0
82 2017-08-23 160440 2.0 7.0 0.0
83 2017-08-24 157830 3.0 7.0 0.0
84 2017-08-25 145822 4.0 7.0 0.0
85 2017-08-26 94706 5.0 7.0 0.0
86 2017-08-27 99047 6.0 7.0 0.0
87 2017-08-28 152112 0.0 7.0 0.0
88 2017-08-29 162440 1.0 7.0 0.0
89 2017-08-30 162902 2.0 7.0 0.0
90 2017-08-31 159498 3.0 7.0 0.0
91 2017-09-01 145689 4.0 8.0 0.0
92 2017-09-02 93589 5.0 8.0 0.0
93 2017-09-03 100058 6.0 8.0 0.0
94 2017-09-04 140865 0.0 8.0 1.0
95 2017-09-05 165715 1.0 8.0 0.0
96 2017-09-06 167463 2.0 8.0 0.0
97 2017-09-07 164811 3.0 8.0 0.0
98 2017-09-08 156157 4.0 8.0 0.0
99 2017-09-09 101358 5.0 8.0 0.0
100 2017-09-10 107915 6.0 8.0 0.0
101 2017-09-11 167845 0.0 8.0 0.0
102 2017-09-12 172756 1.0 8.0 0.0
103 2017-09-13 172851 2.0 8.0 0.0
104 2017-09-14 171675 3.0 8.0 0.0
105 2017-09-15 159266 4.0 8.0 0.0
106 2017-09-16 103547 5.0 8.0 0.0
107 2017-09-17 110964 6.0 8.0 0.0
108 2017-09-18 170976 0.0 8.0 0.0
109 2017-09-19 177864 1.0 8.0 0.0
110 2017-09-20 173567 2.0 8.0 0.0
111 2017-09-21 172017 3.0 8.0 0.0
112 2017-09-22 161357 4.0 8.0 0.0
113 2017-09-23 104681 5.0 8.0 0.0
114 2017-09-24 111711 6.0 8.0 0.0
115 2017-09-25 173517 0.0 8.0 0.0
116 2017-09-26 180049 1.0 8.0 0.0
117 2017-09-27 178307 2.0 8.0 0.0
118 2017-09-28 174157 3.0 8.0 0.0
119 2017-09-29 161707 4.0 8.0 0.0
120 2017-09-30 110536 5.0 8.0 0.0
121 2017-10-01 106505 6.0 9.0 0.0
122 2017-10-02 157565 0.0 9.0 0.0
123 2017-10-03 164764 1.0 9.0 0.0
124 2017-10-04 163383 2.0 9.0 0.0
125 2017-10-05 162847 3.0 9.0 0.0
126 2017-10-06 153575 4.0 9.0 0.0
127 2017-10-07 107472 5.0 9.0 0.0
128 2017-10-08 116127 6.0 9.0 0.0
129 2017-10-09 174457 0.0 9.0 1.0
130 2017-10-10 185217 1.0 9.0 0.0
131 2017-10-11 185120 2.0 9.0 0.0
132 2017-10-12 180844 3.0 9.0 0.0
133 2017-10-13 170178 4.0 9.0 0.0
134 2017-10-14 112754 5.0 9.0 0.0
135 2017-10-15 121251 6.0 9.0 0.0
136 2017-10-16 183906 0.0 9.0 0.0
137 2017-10-17 188945 1.0 9.0 0.0
138 2017-10-18 187297 2.0 9.0 0.0
139 2017-10-19 183867 3.0 9.0 0.0
140 2017-10-20 173021 4.0 9.0 0.0
141 2017-10-21 115851 5.0 9.0 0.0
142 2017-10-22 126088 6.0 9.0 0.0
143 2017-10-23 189452 0.0 9.0 0.0
144 2017-10-24 194412 1.0 9.0 0.0
145 2017-10-25 192293 2.0 9.0 0.0
146 2017-10-26 190163 3.0 9.0 0.0
147 2017-10-27 177053 4.0 9.0 0.0
148 2017-10-28 114934 5.0 9.0 0.0
149 2017-10-29 125289 6.0 9.0 0.0
150 2017-10-30 189245 0.0 9.0 0.0
151 2017-10-31 191480 1.0 9.0 0.0
152 2017-11-01 182281 2.0 10.0 0.0
153 2017-11-02 186351 3.0 10.0 0.0
154 2017-11-03 175422 4.0 10.0 0.0
155 2017-11-04 118160 5.0 10.0 0.0
156 2017-11-05 127602 6.0 10.0 0.0
157 2017-11-06 191067 0.0 10.0 0.0
158 2017-11-07 197083 1.0 10.0 0.0
159 2017-11-08 194333 2.0 10.0 0.0
160 2017-11-09 193914 3.0 10.0 0.0
161 2017-11-10 179933 4.0 10.0 1.0
162 2017-11-11 121346 5.0 10.0 0.0
163 2017-11-12 131900 6.0 10.0 0.0
164 2017-11-13 196969 0.0 10.0 0.0
165 2017-11-14 201949 1.0 10.0 0.0
166 2017-11-15 198424 2.0 10.0 0.0
167 2017-11-16 196902 3.0 10.0 0.0
168 2017-11-17 183893 4.0 10.0 0.0
169 2017-11-18 122767 5.0 10.0 0.0
170 2017-11-19 130890 6.0 10.0 0.0
171 2017-11-20 194515 0.0 10.0 0.0
172 2017-11-21 198601 1.0 10.0 0.0
173 2017-11-22 191041 2.0 10.0 0.0
174 2017-11-23 170321 3.0 10.0 1.0
175 2017-11-24 155623 4.0 10.0 0.0
176 2017-11-25 115759 5.0 10.0 0.0
177 2017-11-26 128771 6.0 10.0 0.0
178 2017-11-27 199419 0.0 10.0 0.0
179 2017-11-28 207253 1.0 10.0 0.0
180 2017-11-29 205406 2.0 10.0 0.0
181 2017-11-30 200674 3.0 10.0 0.0
182 2017-12-01 187017 4.0 11.0 0.0
183 2017-12-02 129735 5.0 11.0 0.0
184 2017-12-03 139120 6.0 11.0 0.0
185 2017-12-04 205505 0.0 11.0 0.0
186 2017-12-05 208218 1.0 11.0 0.0
187 2017-12-06 202480 2.0 11.0 0.0
188 2017-12-07 197822 3.0 11.0 0.0
189 2017-12-08 180686 4.0 11.0 0.0
190 2017-12-09 123667 5.0 11.0 0.0
191 2017-12-10 130987 6.0 11.0 0.0
192 2017-12-11 193901 0.0 11.0 0.0
193 2017-12-12 194997 1.0 11.0 0.0
194 2017-12-13 192063 2.0 11.0 0.0
195 2017-12-14 186496 3.0 11.0 0.0
196 2017-12-15 170812 4.0 11.0 0.0
197 2017-12-16 110474 5.0 11.0 0.0
198 2017-12-17 118165 6.0 11.0 0.0
199 2017-12-18 176843 0.0 11.0 0.0
200 2017-12-19 179550 1.0 11.0 0.0
201 2017-12-20 173506 2.0 11.0 0.0
202 2017-12-21 165910 3.0 11.0 0.0
203 2017-12-22 145886 4.0 11.0 0.0
204 2017-12-23 95246 5.0 11.0 0.0
205 2017-12-24 88781 6.0 11.0 0.0
206 2017-12-25 98189 0.0 11.0 1.0
207 2017-12-26 121383 1.0 11.0 0.0
208 2017-12-27 135300 2.0 11.0 0.0
209 2017-12-28 136827 3.0 11.0 0.0
210 2017-12-29 127700 4.0 11.0 0.0
211 2017-12-30 93014 5.0 11.0 0.0
212 2017-12-31 82878 6.0 11.0 0.0
213 2018-01-01 86419 0.0 0.0 1.0
214 2018-01-02 147428 1.0 0.0 0.0
215 2018-01-03 162193 2.0 0.0 0.0
216 2018-01-04 163784 3.0 0.0 0.0
217 2018-01-05 158606 4.0 0.0 0.0
218 2018-01-06 113467 5.0 0.0 0.0
219 2018-01-07 118313 6.0 0.0 0.0
220 2018-01-08 175623 0.0 0.0 0.0
221 2018-01-09 183880 1.0 0.0 0.0
222 2018-01-10 183945 2.0 0.0 0.0
223 2018-01-11 181769 3.0 0.0 0.0
224 2018-01-12 170552 4.0 0.0 0.0
225 2018-01-13 115707 5.0 0.0 0.0
226 2018-01-14 121191 6.0 0.0 0.0
227 2018-01-15 176127 0.0 0.0 1.0
228 2018-01-16 188032 1.0 0.0 0.0
229 2018-01-17 189871 2.0 0.0 0.0
230 2018-01-18 189348 3.0 0.0 0.0
231 2018-01-19 177456 4.0 0.0 0.0
232 2018-01-20 123321 5.0 0.0 0.0
233 2018-01-21 128306 6.0 0.0 0.0
234 2018-01-22 186132 0.0 0.0 0.0
235 2018-01-23 197618 1.0 0.0 0.0
236 2018-01-24 196402 2.0 0.0 0.0
237 2018-01-25 192722 3.0 0.0 0.0
238 2018-01-26 179415 4.0 0.0 0.0
239 2018-01-27 125769 5.0 0.0 0.0
240 2018-01-28 133306 6.0 0.0 0.0
241 2018-01-29 194151 0.0 0.0 0.0
242 2018-01-30 198680 1.0 0.0 0.0
243 2018-01-31 198652 2.0 0.0 0.0
244 2018-02-01 195472 3.0 1.0 0.0
245 2018-02-02 183173 4.0 1.0 0.0
246 2018-02-03 124276 5.0 1.0 0.0
247 2018-02-04 129054 6.0 1.0 0.0
248 2018-02-05 190024 0.0 1.0 0.0
249 2018-02-06 198658 1.0 1.0 0.0
250 2018-02-07 198272 2.0 1.0 0.0
251 2018-02-08 195339 3.0 1.0 0.0
252 2018-02-09 183086 4.0 1.0 0.0
253 2018-02-10 122536 5.0 1.0 0.0
254 2018-02-11 133033 6.0 1.0 0.0
255 2018-02-12 185386 0.0 1.0 0.0
256 2018-02-13 184789 1.0 1.0 0.0
257 2018-02-14 176089 2.0 1.0 0.0
258 2018-02-15 171317 3.0 1.0 0.0
259 2018-02-16 162693 4.0 1.0 0.0
260 2018-02-17 116342 5.0 1.0 0.0
261 2018-02-18 122466 6.0 1.0 0.0
262 2018-02-19 172364 0.0 1.0 1.0
263 2018-02-20 185896 1.0 1.0 0.0
264 2018-02-21 188166 2.0 1.0 0.0
265 2018-02-22 189427 3.0 1.0 0.0
266 2018-02-23 178732 4.0 1.0 0.0
267 2018-02-24 132664 5.0 1.0 0.0
268 2018-02-25 134008 6.0 1.0 0.0
269 2018-02-26 200075 0.0 1.0 0.0
270 2018-02-27 207996 1.0 1.0 0.0
271 2018-02-28 204416 2.0 1.0 0.0
272 2018-03-01 201320 3.0 2.0 0.0
273 2018-03-02 188205 4.0 2.0 0.0
274 2018-03-03 131162 5.0 2.0 0.0
275 2018-03-04 138320 6.0 2.0 0.0
276 2018-03-05 207326 0.0 2.0 0.0
277 2018-03-06 212462 1.0 2.0 0.0
278 2018-03-07 209357 2.0 2.0 0.0
279 2018-03-08 194876 3.0 2.0 0.0
280 2018-03-09 193761 4.0 2.0 0.0
281 2018-03-10 133449 5.0 2.0 0.0
282 2018-03-11 142258 6.0 2.0 0.0
283 2018-03-12 208753 0.0 2.0 0.0
284 2018-03-13 210602 1.0 2.0 0.0
285 2018-03-14 214236 2.0 2.0 0.0
286 2018-03-15 210761 3.0 2.0 0.0
287 2018-03-16 196619 4.0 2.0 0.0
288 2018-03-17 133056 5.0 2.0 0.0
289 2018-03-18 141335 6.0 2.0 0.0
290 2018-03-19 211580 0.0 2.0 0.0
291 2018-03-20 219051 1.0 2.0 0.0
292 2018-03-21 215435 2.0 2.0 0.0
293 2018-03-22 211961 3.0 2.0 0.0
294 2018-03-23 196009 4.0 2.0 0.0
295 2018-03-24 132390 5.0 2.0 0.0
296 2018-03-25 140021 6.0 2.0 0.0
297 2018-03-26 205273 0.0 2.0 0.0
298 2018-03-27 212686 1.0 2.0 0.0
299 2018-03-28 210683 2.0 2.0 0.0
300 2018-03-29 189044 3.0 2.0 0.0
301 2018-03-30 170256 4.0 2.0 0.0
302 2018-03-31 125999 5.0 2.0 0.0
303 2018-04-01 126749 6.0 3.0 0.0
304 2018-04-02 186546 0.0 3.0 0.0
305 2018-04-03 207905 1.0 3.0 0.0
306 2018-04-04 201528 2.0 3.0 0.0
307 2018-04-05 188580 3.0 3.0 0.0
308 2018-04-06 173714 4.0 3.0 0.0
309 2018-04-07 125723 5.0 3.0 0.0
310 2018-04-08 142545 6.0 3.0 0.0
311 2018-04-09 204767 0.0 3.0 0.0
312 2018-04-10 212048 1.0 3.0 0.0
313 2018-04-11 210517 2.0 3.0 0.0
314 2018-04-12 206924 3.0 3.0 0.0
315 2018-04-13 191679 4.0 3.0 0.0
316 2018-04-14 126394 5.0 3.0 0.0
317 2018-04-15 137279 6.0 3.0 0.0
318 2018-04-16 208085 0.0 3.0 0.0
319 2018-04-17 213273 1.0 3.0 0.0
320 2018-04-18 211580 2.0 3.0 0.0
321 2018-04-19 206037 3.0 3.0 0.0
322 2018-04-20 191211 4.0 3.0 0.0
323 2018-04-21 125564 5.0 3.0 0.0
324 2018-04-22 136469 6.0 3.0 0.0
325 2018-04-23 206288 0.0 3.0 0.0
326 2018-04-24 212115 1.0 3.0 0.0
327 2018-04-25 207948 2.0 3.0 0.0
328 2018-04-26 205759 3.0 3.0 0.0
329 2018-04-27 181330 4.0 3.0 0.0
330 2018-04-28 130046 5.0 3.0 0.0
331 2018-04-29 120802 6.0 3.0 0.0
332 2018-04-30 170390 0.0 3.0 0.0
333 2018-05-01 169054 1.0 4.0 0.0
334 2018-05-02 197891 2.0 4.0 0.0
335 2018-05-03 199820 3.0 4.0 0.0
336 2018-05-04 186783 4.0 4.0 0.0
337 2018-05-05 124420 5.0 4.0 0.0
338 2018-05-06 130666 6.0 4.0 0.0
339 2018-05-07 196014 0.0 4.0 0.0
340 2018-05-08 203058 1.0 4.0 0.0
341 2018-05-09 198582 2.0 4.0 0.0
342 2018-05-10 191321 3.0 4.0 0.0
343 2018-05-11 183639 4.0 4.0 0.0
344 2018-05-12 122023 5.0 4.0 0.0
345 2018-05-13 128775 6.0 4.0 0.0
346 2018-05-14 199104 0.0 4.0 0.0
347 2018-05-15 200658 1.0 4.0 0.0
348 2018-05-16 201541 2.0 4.0 0.0
349 2018-05-17 196886 3.0 4.0 0.0
350 2018-05-18 188597 4.0 4.0 0.0
351 2018-05-19 121392 5.0 4.0 0.0
352 2018-05-20 126981 6.0 4.0 0.0
353 2018-05-21 189291 0.0 4.0 0.0
354 2018-05-22 203038 1.0 4.0 0.0
355 2018-05-23 205330 2.0 4.0 0.0
356 2018-05-24 199208 3.0 4.0 0.0
357 2018-05-25 187768 4.0 4.0 0.0
358 2018-05-26 117635 5.0 4.0 0.0
359 2018-05-27 124352 6.0 4.0 0.0
360 2018-05-28 180398 0.0 4.0 1.0
361 2018-05-29 194170 1.0 4.0 0.0
362 2018-05-30 200281 2.0 4.0 0.0
363 2018-05-31 197244 3.0 4.0 0.0
364 2018-06-01 184037 4.0 5.0 0.0
365 2018-06-02 121135 5.0 5.0 0.0
366 2018-06-03 129389 6.0 5.0 0.0
367 2018-06-04 200331 0.0 5.0 0.0
368 2018-06-05 207735 1.0 5.0 0.0
369 2018-06-06 203354 2.0 5.0 0.0
370 2018-06-07 200520 3.0 5.0 0.0
371 2018-06-08 182038 4.0 5.0 0.0
372 2018-06-09 120164 5.0 5.0 0.0
373 2018-06-10 125256 6.0 5.0 0.0
374 2018-06-11 194786 0.0 5.0 0.0
375 2018-06-12 200815 1.0 5.0 0.0
376 2018-06-13 197740 2.0 5.0 0.0
377 2018-06-14 192294 3.0 5.0 0.0
378 2018-06-15 173587 4.0 5.0 0.0
379 2018-06-16 105955 5.0 5.0 0.0
380 2018-06-17 110780 6.0 5.0 0.0
381 2018-06-18 174582 0.0 5.0 0.0
382 2018-06-19 193310 1.0 5.0 0.0
383 2018-06-20 193062 2.0 5.0 0.0
384 2018-06-21 187986 3.0 5.0 0.0
385 2018-06-22 173606 4.0 5.0 0.0
386 2018-06-23 111795 5.0 5.0 0.0
387 2018-06-24 116134 6.0 5.0 0.0
388 2018-06-25 185919 0.0 5.0 0.0
389 2018-06-26 193142 1.0 5.0 0.0
390 2018-06-27 188114 2.0 5.0 0.0
391 2018-06-28 183737 3.0 5.0 0.0
392 2018-06-29 171496 4.0 5.0 0.0
393 2018-06-30 107210 5.0 5.0 0.0
394 2018-07-01 111053 6.0 6.0 0.0
395 2018-07-02 176198 0.0 6.0 0.0
396 2018-07-03 184040 1.0 6.0 0.0
397 2018-07-04 169783 2.0 6.0 1.0
398 2018-07-05 177996 3.0 6.0 0.0
399 2018-07-06 167378 4.0 6.0 0.0
400 2018-07-07 106401 5.0 6.0 0.0
401 2018-07-08 112327 6.0 6.0 0.0
402 2018-07-09 182835 0.0 6.0 0.0
403 2018-07-10 187694 1.0 6.0 0.0
404 2018-07-11 185762 2.0 6.0 0.0
405 2018-07-12 184099 3.0 6.0 0.0
406 2018-07-13 170860 4.0 6.0 0.0
407 2018-07-14 106799 5.0 6.0 0.0
408 2018-07-15 108475 6.0 6.0 0.0
409 2018-07-16 175704 0.0 6.0 0.0
410 2018-07-17 183596 1.0 6.0 0.0
411 2018-07-18 179897 2.0 6.0 0.0
412 2018-07-19 183373 3.0 6.0 0.0
413 2018-07-20 169626 4.0 6.0 0.0
414 2018-07-21 106785 5.0 6.0 0.0
415 2018-07-22 112387 6.0 6.0 0.0
416 2018-07-23 180572 0.0 6.0 0.0
417 2018-07-24 186943 1.0 6.0 0.0
418 2018-07-25 185744 2.0 6.0 0.0
419 2018-07-26 183117 3.0 6.0 0.0
420 2018-07-27 168526 4.0 6.0 0.0
421 2018-07-28 105936 5.0 6.0 0.0
422 2018-07-29 111708 6.0 6.0 0.0
423 2018-07-30 179950 0.0 6.0 0.0
424 2018-07-31 185930 1.0 6.0 0.0
425 2018-08-01 183366 2.0 7.0 0.0
426 2018-08-02 182412 3.0 7.0 0.0
427 2018-08-03 173429 4.0 7.0 0.0
428 2018-08-04 106108 5.0 7.0 0.0
429 2018-08-05 110059 6.0 7.0 0.0
430 2018-08-06 178355 0.0 7.0 0.0
431 2018-08-07 185518 1.0 7.0 0.0
432 2018-08-08 183204 2.0 7.0 0.0
433 2018-08-09 181276 3.0 7.0 0.0
434 2018-08-10 168297 4.0 7.0 0.0
435 2018-08-11 106488 5.0 7.0 0.0
436 2018-08-12 111786 6.0 7.0 0.0
437 2018-08-13 178620 0.0 7.0 0.0
438 2018-08-14 181922 1.0 7.0 0.0
439 2018-08-15 172198 2.0 7.0 0.0
440 2018-08-16 177367 3.0 7.0 0.0
441 2018-08-17 166550 4.0 7.0 0.0
442 2018-08-18 107011 5.0 7.0 0.0
443 2018-08-19 112299 6.0 7.0 0.0
444 2018-08-20 176718 0.0 7.0 0.0
445 2018-08-21 182562 1.0 7.0 0.0
446 2018-08-22 181484 2.0 7.0 0.0
447 2018-08-23 180317 3.0 7.0 0.0
448 2018-08-24 170197 4.0 7.0 0.0
449 2018-08-25 109383 5.0 7.0 0.0
450 2018-08-26 113373 6.0 7.0 0.0
451 2018-08-27 180142 0.0 7.0 0.0
452 2018-08-28 191628 1.0 7.0 0.0
453 2018-08-29 191149 2.0 7.0 0.0
454 2018-08-30 187503 3.0 7.0 0.0
455 2018-08-31 172280 4.0 7.0 0.0

2286
how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_train.csv Normal file
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7
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py → how-to-use-azureml/automated-machine-learning/forecasting-github-dau/helper.py
View File

@@ -105,13 +105,8 @@ def run_inference(
train_run.download_file(
"outputs/{}".format(model_base_name), "inference/{}".format(model_base_name)
)
train_run.download_file("outputs/conda_env_v_1_0_0.yml", "inference/condafile.yml")
inference_env = Environment("myenv")
inference_env.docker.enabled = True
inference_env.python.conda_dependencies = CondaDependencies(
conda_dependencies_file_path="inference/condafile.yml"
)
inference_env = train_run.get_environment()
est = Estimator(
source_directory=script_folder,

0
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py → how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py
View File

2
how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
View File

@@ -78,7 +78,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Default datastore name\"] = dstore.name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]

9
how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb
View File

@@ -78,7 +78,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Default datastore name\"] = dstore.name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]
@@ -234,11 +234,14 @@
"input_ds_small = Dataset.Tabular.from_delimited_files(\n",
" path=oj_datastore.path(ds_name_small + \"/\"), validate=False\n",
")\n",
"# Drop the columns 'Revenue' as this column contains leak feature.\n",
"input_ds_small = input_ds_small.drop_columns(columns=[\"Revenue\"])\n",
"\n",
"inference_name_small = \"oj-inference-small-tabular\"\n",
"inference_ds_small = Dataset.Tabular.from_delimited_files(\n",
" path=oj_datastore.path(inference_name_small + \"/\"), validate=False\n",
")"
")\n",
"inference_ds_small = inference_ds_small.drop_columns(columns=[\"Revenue\"])"
]
},
{
@@ -354,7 +357,6 @@
" \"label_column_name\": \"Quantity\",\n",
" \"n_cross_validations\": 3,\n",
" \"time_column_name\": \"WeekStarting\",\n",
" \"drop_column_names\": \"Revenue\",\n",
" \"max_horizon\": 6,\n",
" \"grain_column_names\": partition_column_names,\n",
" \"track_child_runs\": False,\n",
@@ -649,7 +651,6 @@
" \"Quantity\",\n",
" \"Advert\",\n",
" \"Price\",\n",
" \"Revenue\",\n",
" \"Predicted\",\n",
"]\n",
"print(\n",

4
how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
View File

@@ -82,7 +82,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -113,7 +113,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Run History Name\"] = experiment_name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"outputDf.T"
]

2
how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb
View File

@@ -229,7 +229,7 @@
"output[\"Resource Group\"] = ws.resource_group\n",
"output[\"Location\"] = ws.location\n",
"output[\"Run History Name\"] = experiment_name\n",
"pd.set_option(\"display.max_colwidth\", -1)\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
"print(outputDf.T)"
]

4
how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/helper_functions.py
View File

@@ -46,11 +46,11 @@ def kpss_test(series, **kw):
"""
if kw["store"]:
statistic, p_value, critical_values, rstore = stattools.kpss(
series, regression=kw["reg_type"], lags=kw["lags"], store=kw["store"]
series, regression=kw["reg_type"], nlags=kw["lags"], store=kw["store"]
)
else:
statistic, p_value, lags, critical_values = stattools.kpss(
series, regression=kw["reg_type"], lags=kw["lags"]
series, regression=kw["reg_type"], nlags=kw["lags"]
)
output = {
"statistic": statistic,

4
how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
View File

@@ -96,7 +96,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -119,7 +119,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]

6
how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
View File

@@ -96,7 +96,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -118,7 +118,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
@@ -847,7 +847,7 @@
"outputs": [],
"source": [
"%matplotlib inline\n",
"test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
"test_pred = plt.scatter(y_test, y_pred_test, color=None)\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"

21
how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py
View File

@@ -2,6 +2,7 @@ import pandas as pd
import joblib
from azureml.core.model import Model
from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations
import scipy as sp
def init():
@@ -18,6 +19,22 @@ def init():
scoring_explainer = joblib.load(scoring_explainer_path)
def is_multi_dimensional(matrix):
if hasattr(matrix, 'ndim') and matrix.ndim > 1:
return True
if hasattr(matrix, 'shape') and matrix.shape[1]:
return True
return False
def convert_matrix(matrix):
if sp.sparse.issparse(matrix):
matrix = matrix.todense()
if is_multi_dimensional(matrix):
matrix = matrix.tolist()
return matrix
def run(raw_data):
# Get predictions and explanations for each data point
data = pd.read_json(raw_data, orient='records')
@@ -28,8 +45,12 @@ def run(raw_data):
X_test=data, task='regression')
# Retrieve model explanations for engineered explanations
engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)
engineered_local_importance_values = convert_matrix(engineered_local_importance_values)
# Retrieve model explanations for raw explanations
raw_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform, get_raw=True)
raw_local_importance_values = convert_matrix(raw_local_importance_values)
# You can return any data type as long as it is JSON-serializable
return {'predictions': predictions.tolist(),
'engineered_local_importance_values': engineered_local_importance_values,

6
how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
View File

@@ -92,7 +92,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},
@@ -115,7 +115,7 @@
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_colwidth', None)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
@@ -430,7 +430,7 @@
"outputs": [],
"source": [
"%matplotlib inline\n",
"test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
"test_pred = plt.scatter(y_test, y_pred_test, color=None)\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"

4
how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
View File

@@ -81,7 +81,7 @@
"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). "
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
]
},
{
@@ -263,7 +263,7 @@
"\n",
"# explicitly set base_image to None when setting base_dockerfile\n",
"myenv.docker.base_image = None\n",
"myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\\nRUN echo \\\"this is test\\\"\"\n",
"myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\\nRUN echo \\\"this is test\\\"\"\n",
"myenv.inferencing_stack_version = \"latest\"\n",
"\n",
"inference_config = InferenceConfig(source_directory=source_directory,\n",

2
how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb
View File

@@ -106,7 +106,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

2
how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml
View File

@@ -11,4 +11,4 @@ dependencies:
- matplotlib
- azureml-dataset-runtime
- ipywidgets
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0

3
how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
View File

@@ -10,4 +10,5 @@ dependencies:
- ipython
- matplotlib
- ipywidgets
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0
- packaging>=20.9

2
how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
View File

@@ -391,7 +391,7 @@
"\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" memory_gb=2, \n",
" tags={\"data\": \"IBM_Attrition\", \n",
" \"method\" : \"local_explanation\"}, \n",
" description='Get local explanations for IBM Employee Attrition data')\n",

3
how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
View File

@@ -10,4 +10,5 @@ dependencies:
- ipython
- matplotlib
- ipywidgets
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0
- packaging>=20.9

2
how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml
View File

@@ -12,4 +12,4 @@ dependencies:
- azureml-dataset-runtime
- azureml-core
- ipywidgets
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0

2
how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb
View File

@@ -199,7 +199,7 @@
"Specify docker steps as a string:\n",
"```python \n",
"dockerfile = r\"\"\" \\\n",
"FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\n",
"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\n",
"RUN echo \"Hello from custom container!\" \\\n",
"\"\"\"\n",
"```\n",

4
how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
View File

@@ -261,7 +261,7 @@
" \n",
" # 2. Execute the Python process via the xvfb-run command to set up the headless display driver.\n",
" xvfb_env.python.user_managed_dependencies = True\n",
" xvfb_env.python.interpreter_path = \"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' python\"\n",
" xvfb_env.python.interpreter_path = \"xvfb-run -s '-screen 0 640x480x24 -ac +extension GLX +render' python\"\n",
"\n",
"\n",
"training_estimator = ReinforcementLearningEstimator(\n",
@@ -718,7 +718,7 @@
"# 2. Execute the Python process via the xvfb-run command to set up the headless display driver.\n",
"xvfb_env.python.user_managed_dependencies = True\n",
"if video_capture:\n",
" xvfb_env.python.interpreter_path = \"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' python\"\n",
" xvfb_env.python.interpreter_path = \"xvfb-run -s '-screen 0 640x480x24 -ac +extension GLX +render' python\"\n",
"\n",
"\n",
"rollout_estimator = ReinforcementLearningEstimator(\n",

2
how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
View File

@@ -26,6 +26,6 @@ RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
ray[rllib,dashboard,tune]==0.8.3 \
psutil \
setproctitle \
gym[atari] && \
gym[classic_control] && \
conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
conda install -c anaconda opencv

2
how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb
View File

@@ -95,7 +95,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

3
how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml
View File

@@ -8,5 +8,6 @@ dependencies:
- matplotlib
- azureml-dataset-runtime
- ipywidgets
- raiwidgets~=0.16.0
- raiwidgets~=0.17.0
- liac-arff
- packaging>=20.9

2
how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
View File

@@ -100,7 +100,7 @@
"\n",
"# Check core SDK version number\n",
"\n",
"print(\"This notebook was created using SDK version 1.38.0, you are currently running version\", azureml.core.VERSION)"
"print(\"This notebook was created using SDK version 1.39.0, you are currently running version\", azureml.core.VERSION)"
]
},
{

2
index.md
View File

@@ -108,8 +108,8 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
| [auto-ml-continuous-retraining](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) | | | | | | |
| [auto-ml-regression-model-proxy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb) | | | | | | |
| [auto-ml-forecasting-backtest-many-models](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb) | | | | | | |
| [auto-ml-forecasting-beer-remote](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb) | | | | | | |
| [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) | | | | | | |
| [auto-ml-forecasting-github-dau](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb) | | | | | | |
| [auto-ml-forecasting-hierarchical-timeseries](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb) | | | | | | |
| [auto-ml-forecasting-many-models](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) | | | | | | |
| [auto-ml-forecasting-univariate-recipe-experiment-settings](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb) | | | | | | |

2
setup-environment/configuration.ipynb
View File

@@ -102,7 +102,7 @@
"source": [
"import azureml.core\n",
"\n",
"print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.39.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

4
tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb
View File

@@ -19,7 +19,7 @@
"source": [
"# Quickstart: Train and deploy a model in Azure Machine Learning in 10 minutes\n",
"\n",
"In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset.\n",
"In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset.\n",
"\n",
"You'll learn how to:\n",
"\n",
@@ -280,7 +280,7 @@
"# get a curated environment\n",
"env = Environment.get(\n",
" workspace=ws, \n",
" name=\"AzureML-sklearn-0.24.1-ubuntu18.04-py37-cpu-inference\",\n",
" name=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu\",\n",
" version=1\n",
")\n",
"env.inferencing_stack_version='latest'\n",

2
tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb
View File

@@ -21,7 +21,7 @@
"\n",
"In this quickstart, you learn how to submit a batch training job using the Python SDK. In this example, we submit the job to the 'local' machine (the compute instance you are running this notebook on). However, you can use exactly the same method to submit the job to different compute targets (for example, AKS, Azure Machine Learning Compute Cluster, Synapse, etc) by changing a single line of code. A full list of support compute targets can be viewed [here](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target). \n",
"\n",
"This quickstart trains a simple logistic regression using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"This quickstart trains a simple logistic regression using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"\n",
"You will learn how to:\n",
"\n",

2
tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
View File

@@ -17,7 +17,7 @@
"\n",
"In this tutorial, you train a machine learning model on remote compute resources. You'll use the training and deployment workflow for Azure Machine Learning service (preview) in a Python Jupyter notebook. You can then use the notebook as a template to train your own machine learning model with your own data. This tutorial is **part one of a two-part tutorial series**. \n",
"\n",
"This tutorial trains a simple logistic regression using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"This tutorial trains a simple logistic regression using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"\n",
"Learn how to:\n",
"\n",