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update samples from Release-167 as a part of 1.0.83 SDK release
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Diff for: ‎configuration.ipynb

+1-1
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@@ -103,7 +103,7 @@
103103
"source": [
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"import azureml.core\n",
105105
"\n",
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"print(\"This notebook was created using version 1.0.81 of the Azure ML SDK\")\n",
106+
"print(\"This notebook was created using version 1.0.83 of the Azure ML SDK\")\n",
107107
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

Diff for: ‎how-to-use-azureml/automated-machine-learning/automl_env.yml

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@@ -13,7 +13,6 @@ dependencies:
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- scikit-learn>=0.19.0,<=0.20.3
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- pandas>=0.22.0,<=0.23.4
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- py-xgboost<=0.80
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- pyarrow>=0.11.0
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- fbprophet==0.5
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- pytorch=1.1.0
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- cudatoolkit=9.0

Diff for: ‎how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

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@@ -14,7 +14,6 @@ dependencies:
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- scikit-learn>=0.19.0,<=0.20.3
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- pandas>=0.22.0,<0.23.0
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- py-xgboost<=0.80
17-
- pyarrow>=0.11.0
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- fbprophet==0.5
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- pytorch=1.1.0
2019
- cudatoolkit=9.0

Diff for: ‎how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml

-9
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@@ -2,12 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features
22
dependencies:
33
- pip:
44
- azureml-sdk
5-
- interpret
6-
- azureml-defaults
7-
- azureml-train-automl
8-
- azureml-widgets
9-
- matplotlib
10-
- pandas_ml
11-
- onnxruntime==1.0.0
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- azureml-explain-model
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- azureml-contrib-interpret

Diff for: ‎how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml

-7
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@@ -2,10 +2,3 @@ name: auto-ml-classification-credit-card-fraud
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dependencies:
33
- pip:
44
- azureml-sdk
5-
- interpret
6-
- azureml-defaults
7-
- azureml-explain-model
8-
- azureml-train-automl
9-
- azureml-widgets
10-
- matplotlib
11-
- pandas_ml

Diff for: ‎how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

+3-3
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@@ -519,12 +519,12 @@
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"name": "anshirga"
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}
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],
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"datasets": [
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"None"
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],
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"compute": [
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"AML Compute"
527524
],
525+
"datasets": [
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"None"
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],
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"deployment": [
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"None"
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],

Diff for: ‎how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml

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@@ -2,9 +2,3 @@ name: auto-ml-classification-text-dnn
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dependencies:
33
- pip:
44
- azureml-sdk
5-
- azureml-train-automl
6-
- azureml-train
7-
- azureml-widgets
8-
- matplotlib
9-
- pandas_ml
10-
- statsmodels

Diff for: ‎how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

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@@ -347,7 +347,7 @@
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"\n",
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"automl_settings = {\n",
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" \"iteration_timeout_minutes\": 10,\n",
350-
" \"experiment_timeout_minutes\": 10,\n",
350+
" \"experiment_timeout_hours\": 0.2,\n",
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'r2_score',\n",
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" \"preprocess\": True,\n",
@@ -378,7 +378,7 @@
378378
"metrics_output_name = 'metrics_output'\n",
379379
"best_model_output_name = 'best_model_output'\n",
380380
"\n",
381-
"metirics_data = PipelineData(name='metrics_data',\n",
381+
"metrics_data = PipelineData(name='metrics_data',\n",
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" datastore=dstor,\n",
383383
" pipeline_output_name=metrics_output_name,\n",
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" training_output=TrainingOutput(type='Metrics'))\n",
@@ -397,7 +397,7 @@
397397
"automl_step = AutoMLStep(\n",
398398
" name='automl_module',\n",
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" automl_config=automl_config,\n",
400-
" outputs=[metirics_data, model_data],\n",
400+
" outputs=[metrics_data, model_data],\n",
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" allow_reuse=False)"
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]
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},

Diff for: ‎how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml

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@@ -2,8 +2,3 @@ name: auto-ml-continuous-retraining
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dependencies:
33
- pip:
44
- azureml-sdk
5-
- azureml-train-automl
6-
- azureml-pipeline
7-
- azureml-widgets
8-
- matplotlib
9-
- pandas_ml

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb

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@@ -358,7 +358,7 @@
358358
"\n",
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"automl_config = AutoMLConfig(task='forecasting', \n",
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" primary_metric='normalized_root_mean_squared_error',\n",
361-
" experiment_timeout_minutes = 60,\n",
361+
" experiment_timeout_hours = 1,\n",
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" training_data=train_dataset,\n",
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" label_column_name=target_column_name,\n",
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" validation_data=valid_dataset, \n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml

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@@ -1,12 +1,4 @@
11
name: auto-ml-forecasting-beer-remote
22
dependencies:
3-
- fbprophet==0.5
4-
- py-xgboost<=0.80
53
- pip:
64
- azureml-sdk
7-
- azureml-train-automl
8-
- azureml-train
9-
- azureml-widgets
10-
- matplotlib
11-
- pandas_ml
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- statsmodels

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

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@@ -248,7 +248,7 @@
248248
"|**task**|forecasting|\n",
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"|**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",
250250
"|**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",
251-
"|**experiment_timeout_minutes**|Experimentation timeout in minutes.|\n",
251+
"|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
252252
"|**training_data**|Input dataset, containing both features and label column.|\n",
253253
"|**label_column_name**|The name of the label column.|\n",
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"|**compute_target**|The remote compute for training.|\n",
@@ -260,7 +260,7 @@
260260
"|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
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"|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
262262
"\n",
263-
"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_minutes parameter value to get results."
263+
"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."
264264
]
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},
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{
@@ -305,7 +305,7 @@
305305
"automl_config = AutoMLConfig(task='forecasting', \n",
306306
" primary_metric='normalized_root_mean_squared_error',\n",
307307
" blacklist_models = ['ExtremeRandomTrees'], \n",
308-
" experiment_timeout_minutes=20,\n",
308+
" experiment_timeout_hours=0.3,\n",
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" training_data=train,\n",
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" label_column_name=target_column_name,\n",
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" compute_target=compute_target,\n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml

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@@ -1,11 +1,4 @@
11
name: auto-ml-forecasting-bike-share
22
dependencies:
3-
- fbprophet==0.5
4-
- py-xgboost<=0.80
53
- pip:
64
- azureml-sdk
7-
- azureml-train-automl
8-
- azureml-widgets
9-
- matplotlib
10-
- pandas_ml
11-
- statsmodels

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

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@@ -302,7 +302,7 @@
302302
"|**task**|forecasting|\n",
303303
"|**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",
304304
"|**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",
305-
"|**experiment_timeout_minutes**|Maximum amount of time in minutes that the experiment take before it terminates.|\n",
305+
"|**experiment_timeout_hours**|Maximum amount of time in hours that the experiment take before it terminates.|\n",
306306
"|**training_data**|The training data to be used within the experiment.|\n",
307307
"|**label_column_name**|The name of the label column.|\n",
308308
"|**compute_target**|The remote compute for training.|\n",
@@ -316,7 +316,7 @@
316316
"cell_type": "markdown",
317317
"metadata": {},
318318
"source": [
319-
"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_minutes parameter value to get results."
319+
"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."
320320
]
321321
},
322322
{
@@ -333,7 +333,7 @@
333333
"automl_config = AutoMLConfig(task='forecasting', \n",
334334
" primary_metric='normalized_root_mean_squared_error',\n",
335335
" blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'], \n",
336-
" experiment_timeout_minutes=20,\n",
336+
" experiment_timeout_hours=0.3,\n",
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" training_data=train,\n",
338338
" label_column_name=target_column_name,\n",
339339
" compute_target=compute_target,\n",
@@ -578,7 +578,7 @@
578578
"automl_config = AutoMLConfig(task='forecasting', \n",
579579
" primary_metric='normalized_root_mean_squared_error',\n",
580580
" blacklist_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blacklisted for tutorial purposes, remove this for real use cases. \n",
581-
" experiment_timeout_minutes=20,\n",
581+
" experiment_timeout_hours=0.3,\n",
582582
" training_data=train,\n",
583583
" label_column_name=target_column_name,\n",
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" compute_target=compute_target,\n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml

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@@ -2,11 +2,3 @@ name: auto-ml-forecasting-energy-demand
22
dependencies:
33
- pip:
44
- azureml-sdk
5-
- interpret
6-
- azureml-train-automl
7-
- azureml-widgets
8-
- matplotlib
9-
- pandas_ml
10-
- statsmodels
11-
- azureml-explain-model
12-
- azureml-contrib-interpret

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb

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251251
"source": [
252252
"automl_settings = {\n",
253253
" \"iteration_timeout_minutes\" : 5,\n",
254-
" \"experiment_timeout_minutes\" : 15,\n",
254+
" \"experiment_timeout_hours\" : 0.25,\n",
255255
" \"primary_metric\" : 'normalized_mean_absolute_error',\n",
256256
" \"time_column_name\": time_column_name,\n",
257257
" \"grain_column_names\": grain_column_names,\n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.yml

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@@ -2,9 +2,3 @@ name: auto-ml-forecasting-grouping
22
dependencies:
33
- pip:
44
- azureml-sdk
5-
- azureml-train-automl
6-
- azureml-pipeline
7-
- azureml-widgets
8-
- pandas_ml
9-
- statsmodels
10-
- matplotlib

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py

+2-2
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@@ -71,7 +71,7 @@ def build_pipeline_steps(automlconfig: AutoMLConfig,
7171
# create each automl step end-to-end (train, register)
7272
for group_name, conf in configs.items():
7373
# create automl metrics output
74-
metirics_data = PipelineData(
74+
metrics_data = PipelineData(
7575
name='metrics_data_{}'.format(group_name),
7676
pipeline_output_name=metrics_output_name.format(group_name),
7777
training_output=TrainingOutput(type='Metrics'))
@@ -84,7 +84,7 @@ def build_pipeline_steps(automlconfig: AutoMLConfig,
8484
automl_step = AutoMLStep(
8585
name='automl_{}'.format(group_name),
8686
automl_config=conf,
87-
outputs=[metirics_data, model_data],
87+
outputs=[metrics_data, model_data],
8888
allow_reuse=True)
8989
steps.append(automl_step)
9090

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb

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@@ -335,7 +335,7 @@
335335
"automl_config = AutoMLConfig(task='forecasting',\n",
336336
" debug_log='automl_forecasting_function.log',\n",
337337
" primary_metric='normalized_root_mean_squared_error',\n",
338-
" experiment_timeout_minutes=15,\n",
338+
" experiment_timeout_hours=0.25,\n",
339339
" enable_early_stopping=True,\n",
340340
" training_data=train_data,\n",
341341
" compute_target=compute_target,\n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml

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name: automl-forecasting-function
22
dependencies:
3-
- fbprophet==0.5
4-
- py-xgboost<=0.80
53
- pip:
64
- azureml-sdk
7-
- azureml-train-automl
8-
- azureml-widgets
9-
- pandas_ml
10-
- statsmodels
11-
- matplotlib

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

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@@ -335,7 +335,7 @@
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"|-|-|\n",
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"|**task**|forecasting|\n",
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"|**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",
338-
"|**experiment_timeout_minutes**|Experimentation timeout in minutes.|\n",
338+
"|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
339339
"|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
340340
"|**training_data**|Input dataset, containing both features and label column.|\n",
341341
"|**label_column_name**|The name of the label column.|\n",
@@ -366,7 +366,7 @@
366366
"automl_config = AutoMLConfig(task='forecasting',\n",
367367
" debug_log='automl_oj_sales_errors.log',\n",
368368
" primary_metric='normalized_mean_absolute_error',\n",
369-
" experiment_timeout_minutes=15,\n",
369+
" experiment_timeout_hours=0.25,\n",
370370
" training_data=train_dataset,\n",
371371
" label_column_name=target_column_name,\n",
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" compute_target=compute_target,\n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml

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11
name: auto-ml-forecasting-orange-juice-sales
22
dependencies:
3-
- fbprophet==0.5
4-
- py-xgboost<=0.80
53
- pip:
64
- azureml-sdk
7-
- azureml-train-automl
8-
- azureml-widgets
9-
- matplotlib
10-
- pandas_ml
11-
- statsmodels

Diff for: ‎how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

+2-12
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@@ -156,7 +156,7 @@
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'average_precision_score_weighted',\n",
158158
" \"preprocess\": True,\n",
159-
" \"experiment_timeout_minutes\": 10, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
159+
" \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n",
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" \"verbosity\": logging.INFO,\n",
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" \"enable_stack_ensemble\": False\n",
162162
"}\n",
@@ -260,17 +260,7 @@
260260
"metadata": {},
261261
"source": [
262262
"#### Print the properties of the model\n",
263-
"The fitted_model is a python object and you can read the different properties of the object.\n",
264-
"See *Print the properties of the model* section in [this sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb)."
265-
]
266-
},
267-
{
268-
"cell_type": "markdown",
269-
"metadata": {},
270-
"source": [
271-
"### Deploy\n",
272-
"\n",
273-
"To deploy the model into a web service endpoint, see _Deploy_ section in [this sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb)"
263+
"The fitted_model is a python object and you can read the different properties of the object.\n"
274264
]
275265
},
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{

Diff for: ‎how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml

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@@ -2,10 +2,3 @@ name: auto-ml-classification-credit-card-fraud-local
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dependencies:
33
- pip:
44
- azureml-sdk
5-
- interpret
6-
- azureml-defaults
7-
- azureml-explain-model
8-
- azureml-train-automl
9-
- azureml-widgets
10-
- matplotlib
11-
- pandas_ml

Diff for: ‎how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb

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@@ -206,7 +206,7 @@
206206
"|-|-|\n",
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"|**task**|classification, regression or forecasting|\n",
208208
"|**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",
209-
"|**experiment_timeout_minutes**| Maximum amount of time in minutes that all iterations combined can take before the experiment terminates.|\n",
209+
"|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
210210
"|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
211211
"|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Note: If the input data is sparse, featurization cannot be turned on.|\n",
212212
"|**n_cross_validations**|Number of cross validation splits.|\n",
@@ -262,7 +262,7 @@
262262
"source": [
263263
"automl_settings = {\n",
264264
" \"enable_early_stopping\": True, \n",
265-
" \"experiment_timeout_minutes\" : 10,\n",
265+
" \"experiment_timeout_hours\" : 0.2,\n",
266266
" \"max_concurrent_iterations\": 4,\n",
267267
" \"max_cores_per_iteration\": -1,\n",
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" \"n_cross_validations\": 5,\n",

Diff for: ‎how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.yml

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@@ -2,12 +2,3 @@ name: auto-ml-regression-hardware-performance-explanation-and-featurization
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dependencies:
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- pip:
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- azureml-sdk
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- interpret
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- azureml-defaults
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- azureml-explain-model
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- azureml-train-automl
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- azureml-widgets
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- matplotlib
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- pandas_ml
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- azureml-explain-model
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- azureml-contrib-interpret

Diff for: ‎how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

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@@ -188,15 +188,19 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": [
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"automlconfig-remarks-sample"
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]
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},
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"outputs": [],
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"source": [
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"automl_settings = {\n",
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" \"n_cross_validations\": 3,\n",
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" \"primary_metric\": 'r2_score',\n",
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" \"preprocess\": True,\n",
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" \"enable_early_stopping\": True, \n",
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" \"experiment_timeout_minutes\": 20, #for real scenarios we reccommend a timeout of at least one hour \n",
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" \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
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" \"max_concurrent_iterations\": 4,\n",
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" \"max_cores_per_iteration\": -1,\n",
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" \"verbosity\": logging.INFO,\n",

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