update samples from Release-53 as a part of 1.19.0 SDK stable release

2025-12-22 02:25:12 -05:00 · 2020-12-07 18:55:07 +00:00
parent 41366a4af0
commit 48e3e7b510
39 changed files with 371 additions and 279 deletions
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -3,7 +3,7 @@ dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
 - matplotlib==2.1.0
@@ -21,8 +21,8 @@ dependencies:

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.18.0
+  - azureml-widgets~=1.19.0
  - pytorch-transformers==1.0.0
  - spacy==2.1.8
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.18.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.19.0/validated_win32_requirements.txt [--no-deps]
--- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
@@ -3,7 +3,7 @@ dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
 - matplotlib==2.1.0
@@ -21,9 +21,9 @@ dependencies:

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.18.0
+  - azureml-widgets~=1.19.0
  - pytorch-transformers==1.0.0
  - spacy==2.1.8
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.18.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.19.0/validated_linux_requirements.txt [--no-deps]

--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -4,7 +4,7 @@ dependencies:
  # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
 - nomkl
- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
 - matplotlib==2.1.0
@@ -22,8 +22,8 @@ dependencies:

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.18.0
+  - azureml-widgets~=1.19.0
  - pytorch-transformers==1.0.0
  - spacy==2.1.8
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.18.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.19.0/validated_darwin_requirements.txt [--no-deps]
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
@@ -105,7 +105,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -93,7 +93,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
@@ -96,7 +96,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -81,7 +81,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
@@ -68,6 +68,7 @@
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
+        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        " \n",
@@ -92,7 +93,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -322,6 +323,24 @@
        "print(best_run)"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Show hyperparameters\n",
+        "Show the model pipeline used for the best run with its hyperparameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run_properties = json.loads(best_run.get_details()['properties']['pipeline_script'])\n",
+        "print(json.dumps(run_properties, indent = 1)) "
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {},
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
@@ -113,7 +113,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py
@@ -3,11 +3,11 @@ from azureml.core import Environment
 from azureml.core.conda_dependencies import CondaDependencies
 from azureml.train.estimator import Estimator
 from azureml.core.run import Run
+from azureml.automl.core.shared import constants


 def split_fraction_by_grain(df, fraction, time_column_name,
                            grain_column_names=None):
-
    if not grain_column_names:
        df['tmp_grain_column'] = 'grain'
        grain_column_names = ['tmp_grain_column']
@@ -17,10 +17,10 @@ def split_fraction_by_grain(df, fraction, time_column_name,
                  .groupby(grain_column_names, group_keys=False))

    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-int(len(dfg) *
-                               fraction)] if fraction > 0 else dfg)
+                                                          fraction)] if fraction > 0 else dfg)

    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-int(len(dfg) *
-                               fraction):] if fraction > 0 else dfg[:0])
+                                                         fraction):] if fraction > 0 else dfg[:0])

    if 'tmp_grain_column' in grain_column_names:
        for df2 in (df, df_head, df_tail):
@@ -59,11 +59,13 @@ def get_result_df(remote_run):
                                     'primary_metric', 'Score'])
    goal_minimize = False
    for run in children:
-        if('run_algorithm' in run.properties and 'score' in run.properties):
+        if run.get_status().lower() == constants.RunState.COMPLETE_RUN \
+                and 'run_algorithm' in run.properties and 'score' in run.properties:
+            # We only count in the completed child runs.
            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
                                  run.properties['primary_metric'],
                                  float(run.properties['score'])]
-            if('goal' in run.properties):
+            if ('goal' in run.properties):
                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'

    summary_df = summary_df.T.sort_values(
@@ -118,7 +120,6 @@ def run_multiple_inferences(summary_df, train_experiment, test_experiment,
                            compute_target, script_folder, test_dataset,
                            lookback_dataset, max_horizon, target_column_name,
                            time_column_name, freq):
-
    for run_name, run_summary in summary_df.iterrows():
        print(run_name)
        print(run_summary)
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -87,7 +87,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py
@@ -1,22 +1,24 @@
 import argparse
-import azureml.train.automl
-from azureml.core import Run
+from azureml.core import Dataset, Run
 from sklearn.externals import joblib

-
 parser = argparse.ArgumentParser()
 parser.add_argument(
    '--target_column_name', type=str, dest='target_column_name',
    help='Target Column Name')
+parser.add_argument(
+    '--test_dataset', type=str, dest='test_dataset',
+    help='Test Dataset')

 args = parser.parse_args()
 target_column_name = args.target_column_name
+test_dataset_id = args.test_dataset

 run = Run.get_context()
-# get input dataset by name
-test_dataset = run.input_datasets['test_data']
+ws = run.experiment.workspace

-df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
+# get the input dataset by id
+test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)

 X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
 y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py
@@ -1,29 +1,32 @@
-from azureml.train.estimator import Estimator
+from azureml.core import ScriptRunConfig


-def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
-                         target_column_name, inference_folder='./forecast'):
+def run_rolling_forecast(test_experiment, compute_target, train_run,
+                         test_dataset, target_column_name,
+                         inference_folder='./forecast'):
    train_run.download_file('outputs/model.pkl',
                            inference_folder + '/model.pkl')

    inference_env = train_run.get_environment()

-    est = Estimator(source_directory=inference_folder,
-                    entry_script='forecasting_script.py',
-                    script_params={
-                        '--target_column_name': target_column_name
-                    },
-                    inputs=[test_dataset.as_named_input('test_data')],
-                    compute_target=compute_target,
-                    environment_definition=inference_env)
+    config = ScriptRunConfig(source_directory=inference_folder,
+                             script='forecasting_script.py',
+                             arguments=['--target_column_name',
+                                        target_column_name,
+                                        '--test_dataset',
+                                        test_dataset.as_named_input(test_dataset.name)],
+                             compute_target=compute_target,
+                             environment=inference_env)

-    run = test_experiment.submit(est,
-                                 tags={
-                                     'training_run_id': train_run.id,
-                                     'run_algorithm': train_run.properties['run_algorithm'],
-                                     'valid_score': train_run.properties['score'],
-                                     'primary_metric': train_run.properties['primary_metric']
-                                 })
+    run = test_experiment.submit(config,
+                                 tags={'training_run_id':
+                                       train_run.id,
+                                       'run_algorithm':
+                                       train_run.properties['run_algorithm'],
+                                       'valid_score':
+                                       train_run.properties['score'],
+                                       'primary_metric':
+                                       train_run.properties['primary_metric']})

    run.log("run_algorithm", run.tags['run_algorithm'])
    return run
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -97,7 +97,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
@@ -94,7 +94,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -82,7 +82,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
@@ -96,7 +96,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
@@ -96,7 +96,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py
@@ -4,7 +4,7 @@ import os
 import joblib

 from interpret.ext.glassbox import LGBMExplainableModel
-from automl.client.core.common.constants import MODEL_PATH
+from azureml.automl.core.shared.constants import MODEL_PATH
 from azureml.core.experiment import Experiment
 from azureml.core.dataset import Dataset
 from azureml.core.run import Run
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -92,7 +92,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.18.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },