Azure · harneetvirk · May 27, 2020 · May 27, 2020
diff --git a/README.md b/README.md
@@ -40,6 +40,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
 - [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
+- [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
 
 ---
 ## Documentation

diff --git a/configuration.ipynb b/configuration.ipynb
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/how-to-use-azureml/automated-machine-learning/README.md b/how-to-use-azureml/automated-machine-learning/README.md
@@ -144,7 +144,7 @@ jupyter notebook
     - Dataset: forecasting for a bike-sharing
     - Example of training an automated ML forecasting model on multiple time-series
 
-- [auto-ml-forecasting-function.ipynb](forecasting-high-frequency/auto-ml-forecasting-function.ipynb)
+- [auto-ml-forecasting-function.ipynb](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)

diff --git a/...tion-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/...tion-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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb b/...-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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/.../automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb b/.../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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...reml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/...reml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -88,7 +88,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/.../automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb b/.../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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...ml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb b/...ml/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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -510,16 +510,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core.shared import constants, metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
         "from matplotlib import pyplot as plt\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

diff --git a/...omated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb b/...omated-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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -465,7 +465,7 @@
       "metadata": {},
       "source": [
         "### Forecast Function\n",
-        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb)."
+        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see the [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -507,15 +507,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core.shared import constants, metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",
@@ -667,15 +667,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core.shared import constants, metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

diff --git a/...quency/auto-ml-forecasting-function.ipynb → ...nction/auto-ml-forecasting-function.ipynb b/...quency/auto-ml-forecasting-function.ipynb → ...nction/auto-ml-forecasting-function.ipynb
@@ -35,7 +35,6 @@
         "Terminology:\n",
         "* forecast origin: the last period when the target value is known\n",
         "* forecast periods(s): the period(s) for which the value of the target is desired.\n",
-        "* forecast horizon: the number of forecast periods\n",
         "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n",
         "* prediction context: `lookback` periods immediately preceding the forecast origin\n",
         "\n",
@@ -95,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -720,6 +719,90 @@
         "X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
         "# prediction is in _automl_target_col"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting farther than the maximum horizon <a id=\"recursive forecasting\"></a>\n",
+        "When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified maximum horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
+        "\n",
+        "To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the maximum horizon given at training time.\n",
+        "\n",
+        "![Recursive_forecast_overview](recursive_forecast_overview_small.png)\n",
+        "\n",
+        "Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first max-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next max-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
+        "\n",
+        "A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n",
+        "\n",
+        "![Recursive_forecast_iter1](recursive_forecast_iter1.png)\n",
+        "![Recursive_forecast_iter2](recursive_forecast_iter2.png)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# generate the same kind of test data we trained on, but with a single grain/time-series and test period twice as long as the max_horizon\n",
+        "_, _, X_test_long, y_test_long = get_timeseries(train_len=n_train_periods,\n",
+        "                                                  test_len=max_horizon*2,\n",
+        "                                                  time_column_name=TIME_COLUMN_NAME,\n",
+        "                                                  target_column_name=TARGET_COLUMN_NAME,\n",
+        "                                                  grain_column_name=GRAIN_COLUMN_NAME,\n",
+        "                                                  grains=1)\n",
+        "\n",
+        "print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
+        "print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# forecast() function will invoke the recursive forecast method internally.\n",
+        "y_pred_long, X_trans_long = fitted_model.forecast(X_test_long)\n",
+        "y_pred_long"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following. \n",
+        "y_pred1, _ = fitted_model.forecast(X_test_long[:max_horizon])\n",
+        "y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(max_horizon, np.nan))))\n",
+        "np.array_equal(y_pred_all, y_pred_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Confidence interval and distributional forecasts\n",
+        "AutoML cannot currently estimate forecast errors beyond the maximum horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the maximum horizon. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fitted_model.forecast_quantiles(X_test_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Similarly with the simple senarios illustrated above, forecasting farther than the max horizon in other senarios like 'multiple grain', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
+      ]
     }
   ],
   "metadata": {

diff --git a/...requency/auto-ml-forecasting-function.yml → ...function/auto-ml-forecasting-function.yml b/...requency/auto-ml-forecasting-function.yml → ...function/auto-ml-forecasting-function.yml
diff --git a/...-frequency/forecast_function_at_train.png → ...t-function/forecast_function_at_train.png b/...-frequency/forecast_function_at_train.png → ...t-function/forecast_function_at_train.png
diff --git a/...ncy/forecast_function_away_from_train.png → ...ion/forecast_function_away_from_train.png b/...ncy/forecast_function_away_from_train.png → ...ion/forecast_function_away_from_train.png
diff --git a/...ted-machine-learning/forecasting-forecast-function/recursive_forecast_iter1.png b/...ted-machine-learning/forecasting-forecast-function/recursive_forecast_iter1.png
diff --git a/...ted-machine-learning/forecasting-forecast-function/recursive_forecast_iter2.png b/...ted-machine-learning/forecasting-forecast-function/recursive_forecast_iter2.png
diff --git a/...ne-learning/forecasting-forecast-function/recursive_forecast_overview_small.png b/...ne-learning/forecasting-forecast-function/recursive_forecast_overview_small.png
diff --git a/...hine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb b/...hine-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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -545,7 +545,7 @@
       "source": [
         "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
         "\n",
-        "The [forecast function notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb) demonstrates the use of the forecast function for a variety of use cases. Also, please see the [API documentation for the forecast function](https://docs.microsoft.com/en-us/python/api/azureml-automl-runtime/azureml.automl.runtime.shared.model_wrappers.forecastingpipelinewrapper?view=azure-ml-py#forecast-x-pred--typing-union-pandas-core-frame-dataframe--nonetype----none--y-pred--typing-union-pandas-core-frame-dataframe--numpy-ndarray--nonetype----none--forecast-destination--typing-union-pandas--libs-tslibs-timestamps-timestamp--nonetype----none--ignore-data-errors--bool---false-----typing-tuple-numpy-ndarray--pandas-core-frame-dataframe-)."
+        "The [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -576,15 +576,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core.shared import constants, metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
         "\n",
-        "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "# use automl scoring module\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

diff --git a/...run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb b/...run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
@@ -95,7 +95,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...g/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb b/...g/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
@@ -98,7 +98,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py b/...-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py
@@ -7,7 +7,7 @@
 import azureml.explain.model
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
     automl_setup_model_explanations
-from sklearn.externals import joblib
+import joblib
 from azureml.core.model import Model
 
 

diff --git 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 1.5.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.6.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/how-to-use-azureml/deployment/deploy-to-local/myenv.yml b/how-to-use-azureml/deployment/deploy-to-local/myenv.yml
@@ -3,6 +3,6 @@ dependencies:
   - python=3.6.2
   - pip:
     - azureml-defaults
-    - scikit-learn
+    - scikit-learn==0.19.1
     - numpy
     - inference-schema[numpy-support]
diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb
@@ -233,7 +233,8 @@
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",
         "    'numpy',\n",
-        "    'scikit-learn'\n",
+        "    'scikit-learn==0.19.1',\n",
+        "    'scipy'\n",
         "])\n",
         "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
         "# if cpu and memory_in_gb parameters are not provided\n",