update samples from Release-55 as a part of SDK release

Author: amlrelsa-ms

configuration.ipynb

@@ -103,7 +103,7 @@
  "source": [
  "import azureml.core\n",
  "\n",
- "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },

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 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },

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 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },

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

@@ -97,7 +97,7 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },
@@ -491,8 +491,8 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset,\n",
- " target_column_name, model_name)"
+ "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
+ "  train_dataset, test_dataset, target_column_name, model_name)"
  ]
  },
  {

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -6,7 +6,7 @@
 
 
 def run_inference(test_experiment, compute_target, script_folder, train_run,
- test_dataset, target_column_name, model_name):
+ train_dataset, test_dataset, target_column_name, model_name):
 
  train_run.download_file('outputs/conda_env_v_1_0_0.yml',
  'inference/condafile.yml')
@@ -22,7 +22,10 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
  '--target_column_name': target_column_name,
  '--model_name': model_name
  },
- inputs=[test_dataset.as_named_input('test_data')],
+ inputs=[
+ train_dataset.as_named_input('train_data'),
+ test_dataset.as_named_input('test_data')
+ ],
  compute_target=compute_target,
  environment_definition=inference_env)
 

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -1,8 +1,11 @@
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+
 from sklearn.externals import joblib
-from azureml.automl.core.shared import constants, metrics
+
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
 from azureml.core.model import Model
 
 
@@ -29,22 +32,26 @@
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
+train_dataset = run.input_datasets['train_data']
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
  .to_pandas_dataframe()
 y_test_df = test_dataset.with_timestamp_columns(None) \
  .keep_columns(columns=[target_column_name]) \
  .to_pandas_dataframe()
+y_train_df = test_dataset.with_timestamp_columns(None) \
+ .keep_columns(columns=[target_column_name]) \
+ .to_pandas_dataframe()
 
 predicted = model.predict_proba(X_test_df)
 
-# use automl metrics module
-scores = metrics.compute_metrics_classification(
- np.array(predicted),
- np.array(y_test_df),
- class_labels=model.classes_,
- metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
-)
+# Use the AutoML scoring module
+class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
+train_labels = model.classes_
+classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
+scores = scoring.score_classification(y_test_df.values, predicted,
+  classification_metrics,
+ class_labels, train_labels)
 
 print("scores:")
 print(scores)

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

@@ -88,7 +88,7 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },

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

@@ -114,7 +114,7 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py

@@ -1,11 +1,14 @@
-import pandas as pd
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+import pandas as pd
+
+from pandas.tseries.frequencies import to_offset
 from sklearn.externals import joblib
 from sklearn.metrics import mean_absolute_error, mean_squared_error
-from azureml.automl.core.shared import constants, metrics
-from pandas.tseries.frequencies import to_offset
+
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
 
 
 def align_outputs(y_predicted, X_trans, X_test, y_test,
@@ -299,12 +302,11 @@ def MAPE(actual, pred):
 print("predicted values:::")
 print(df_all['predicted'])
 
-# use automl metrics module
-scores = metrics.compute_metrics_regression(
- df_all['predicted'],
- df_all[target_column_name],
- list(constants.Metric.SCALAR_REGRESSION_SET),
- None, None, None)
+# Use the AutoML scoring module
+regression_metrics = list(constants.REGRESSION_SCALAR_SET)
+y_test = np.array(df_all[target_column_name])
+y_pred = np.array(df_all['predicted'])
+scores = scoring.score_regression(y_test, y_pred, regression_metrics)
 
 print("scores:")
 print(scores)

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 1.6.0 of the Azure ML SDK\")\n",
+ "print(\"This notebook was created using version 1.7.0 of the Azure ML SDK\")\n",
  "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
  ]
  },