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version 1.0.33
This commit is contained in:
Roope Astala
@@ -20,7 +20,9 @@
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"1. [Introduction](#Introduction)\n",
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"1. [Setup](#Setup)\n",
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"1. [Data](#Data)\n",
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"1. [Train](#Train)"
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"1. [Train](#Train)\n",
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"1. [Predict](#Predict)\n",
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"1. [Operationalize](#Operationalize)"
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]
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},
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{
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@@ -36,8 +38,7 @@
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"1. Create an Experiment in an existing Workspace\n",
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"2. Instantiate an AutoMLConfig \n",
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"3. Find and train a forecasting model using local compute\n",
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"4. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
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"5. Evaluate the performance of the model\n",
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"4. Evaluate the performance of the model\n",
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"\n",
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"The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
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]
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@@ -86,9 +87,9 @@
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"ws = Workspace.from_config()\n",
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"\n",
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"# choose a name for the run history container in the workspace\n",
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"experiment_name = 'automl-ojsalesforecasting'\n",
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"experiment_name = 'automl-ojforecasting'\n",
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"# project folder\n",
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"project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
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"project_folder = './sample_projects/automl-local-ojforecasting'\n",
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"\n",
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"experiment = Experiment(ws, experiment_name)\n",
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"\n",
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@@ -261,12 +262,12 @@
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" 'time_column_name': time_column_name,\n",
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" 'grain_column_names': grain_column_names,\n",
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" 'drop_column_names': ['logQuantity'],\n",
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" 'max_horizon': n_test_periods\n",
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" 'max_horizon': n_test_periods # optional\n",
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"}\n",
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"\n",
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"automl_config = AutoMLConfig(task='forecasting',\n",
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" debug_log='automl_oj_sales_errors.log',\n",
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" primary_metric='normalized_root_mean_squared_error',\n",
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" primary_metric='normalized_mean_absolute_error',\n",
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" iterations=10,\n",
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" X=X_train,\n",
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" y=y_train,\n",
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@@ -294,15 +295,6 @@
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"local_run = experiment.submit(automl_config, show_output=True)"
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]
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},
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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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"outputs": [],
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"source": [
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"local_run"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -325,46 +317,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### View the engineered names for featurized data\n",
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"Below we display the engineered feature names generated for the featurized data using the time-series featurization."
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]
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},
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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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"outputs": [],
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"source": [
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"fitted_pipeline.named_steps['timeseriestransformer'].get_engineered_feature_names()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### View the featurization summary\n",
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"Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
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"- Raw feature name\n",
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"- Number of engineered features formed out of this raw feature\n",
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"- Type detected\n",
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"- If feature was dropped\n",
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"- List of feature transformations for the raw feature"
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]
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},
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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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"outputs": [],
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"source": [
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"fitted_pipeline.named_steps['timeseriestransformer'].get_featurization_summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Make Predictions from the Best Fitted Model\n",
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"# Predict\n",
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"Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
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]
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},
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@@ -392,7 +345,7 @@
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"source": [
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"To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
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"\n",
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"The target predictions can be retrieved by calling the `predict` method on the best model:"
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"We will first create a query `y_query`, which is aligned index-for-index to `X_test`. This is a vector of target values where each `NaN` serves the function of the question mark to be replaced by forecast. Passing definite values in the `y` argument allows the `forecast` function to make predictions on data that does not immediately follow the train data which contains `y`. In each grain, the last time point where the model sees a definite value of `y` is that grain's _forecast origin_."
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]
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},
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{
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@@ -401,15 +354,76 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = fitted_pipeline.predict(X_test)"
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"# Replace ALL values in y_pred by NaN.\n",
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"# The forecast origin will be at the beginning of the first forecast period.\n",
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"# (Which is the same time as the end of the last training period.)\n",
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"y_query = y_test.copy().astype(np.float)\n",
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"y_query.fill(np.nan)\n",
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"# The featurized data, aligned to y, will also be returned.\n",
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"# This contains the assumptions that were made in the forecast\n",
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"# and helps align the forecast to the original data\n",
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"y_pred, X_trans = fitted_pipeline.forecast(X_test, y_query)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Calculate evaluation metrics for the prediction\n",
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"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
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"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",
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"\n",
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"The [energy demand forecasting notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) demonstrates the use of the forecast function in more detail in the context of using lags and rolling window features. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Evaluate\n",
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"\n",
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"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
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"\n",
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"It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
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]
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},
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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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"outputs": [],
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"source": [
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"def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
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" \"\"\"\n",
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" Demonstrates how to get the output aligned to the inputs\n",
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" using pandas indexes. Helps understand what happened if\n",
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" the output's shape differs from the input shape, or if\n",
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" the data got re-sorted by time and grain during forecasting.\n",
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" \n",
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" Typical causes of misalignment are:\n",
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" * we predicted some periods that were missing in actuals -> drop from eval\n",
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" * model was asked to predict past max_horizon -> increase max horizon\n",
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" * data at start of X_test was needed for lags -> provide previous periods in y\n",
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" \"\"\"\n",
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" \n",
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" df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
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" # y and X outputs are aligned by forecast() function contract\n",
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" df_fcst.index = X_trans.index\n",
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" \n",
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" # align original X_test to y_test \n",
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" X_test_full = X_test.copy()\n",
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" X_test_full[target_column_name] = y_test\n",
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"\n",
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" # X_test_full's index does not include origin, so reset for merge\n",
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" df_fcst.reset_index(inplace=True)\n",
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" X_test_full = X_test_full.reset_index().drop(columns='index')\n",
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" together = df_fcst.merge(X_test_full, how='right')\n",
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" \n",
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" # drop rows where prediction or actuals are nan \n",
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" # happens because of missing actuals \n",
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" # or at edges of time due to lags/rolling windows\n",
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" clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
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" return(clean)\n",
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"\n",
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"df_all = align_outputs(y_pred, X_trans, X_test, y_test)"
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]
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},
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{
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@@ -428,18 +442,392 @@
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" actual_safe = actual[not_na & not_zero]\n",
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" pred_safe = pred[not_na & not_zero]\n",
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" APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
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" return np.mean(APE)\n",
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" return np.mean(APE)"
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]
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},
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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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"outputs": [],
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"source": [
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"print(\"Simple forecasting model\")\n",
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"rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
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"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
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"mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
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"print('mean_absolute_error score: %.2f' % mae)\n",
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"print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
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"\n",
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"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
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"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
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"print('MAPE: %.2f' % MAPE(y_test, y_pred))"
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"# Plot outputs\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"%matplotlib notebook\n",
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"test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
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"test_test = plt.scatter(y_test, y_test, color='g')\n",
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"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Operationalize"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"_Operationalization_ means getting the model into the cloud so that other can run it after you close the notebook. We will create a docker running on Azure Container Instances with the model."
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]
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},
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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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"outputs": [],
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"source": [
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"description = 'AutoML OJ forecaster'\n",
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"tags = None\n",
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"model = local_run.register_model(description = description, tags = tags)\n",
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"\n",
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"print(local_run.model_id)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Develop the scoring script\n",
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"\n",
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"Serializing and deserializing complex data frames may be tricky. We first develop the `run()` function of the scoring script locally, then write it into a scoring script. It is much easier to debug any quirks of the scoring function without crossing two compute environments. For this exercise, we handle a common quirk of how pandas dataframes serialize time stamp values."
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]
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},
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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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"outputs": [],
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"source": [
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"# this is where we test the run function of the scoring script interactively\n",
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"# before putting it in the scoring script\n",
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"\n",
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"timestamp_columns = ['WeekStarting']\n",
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"\n",
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"def run(rawdata, test_model = None):\n",
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" \"\"\"\n",
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" Intended to process 'rawdata' string produced by\n",
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" \n",
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" {'X': X_test.to_json(), y' : y_test.to_json()}\n",
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" \n",
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" Don't convert the X payload to numpy.array, use it as pandas.DataFrame\n",
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" \"\"\"\n",
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" try:\n",
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" # unpack the data frame with timestamp \n",
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" rawobj = json.loads(rawdata) # rawobj is now a dict of strings \n",
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" X_pred = pd.read_json(rawobj['X'], convert_dates=False) # load the pandas DF from a json string\n",
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" for col in timestamp_columns: # fix timestamps\n",
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" X_pred[col] = pd.to_datetime(X_pred[col], unit='ms') \n",
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" \n",
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" y_pred = np.array(rawobj['y']) # reconstitute numpy array from serialized list\n",
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" \n",
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" if test_model is None:\n",
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" result = model.forecast(X_pred, y_pred) # use the global model from init function\n",
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" else:\n",
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" result = test_model.forecast(X_pred, y_pred) # use the model on which we are testing\n",
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" \n",
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" except Exception as e:\n",
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" result = str(e)\n",
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" return json.dumps({\"error\": result})\n",
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" \n",
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" forecast_as_list = result[0].tolist()\n",
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" index_as_df = result[1].index.to_frame().reset_index(drop=True)\n",
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" \n",
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" return json.dumps({\"forecast\": forecast_as_list, # return the minimum over the wire: \n",
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" \"index\": index_as_df.to_json() # no forecast and its featurized values\n",
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" })"
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]
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},
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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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"outputs": [],
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"source": [
|
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"# test the run function here before putting in the scoring script\n",
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"import json\n",
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"\n",
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"test_sample = json.dumps({'X': X_test.to_json(), 'y' : y_query.tolist()})\n",
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"response = run(test_sample, fitted_pipeline)\n",
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"\n",
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"# unpack the response, dealing with the timestamp serialization again\n",
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"res_dict = json.loads(response)\n",
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"y_fcst_all = pd.read_json(res_dict['index'])\n",
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"y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
|
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"y_fcst_all['forecast'] = res_dict['forecast']\n",
|
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"y_fcst_all.head()"
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]
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},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
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"Now that the function works locally in the notebook, let's write it down into the scoring script. The scoring script is authored by the data scientist. Adjust it to taste, adding inputs, outputs and processing as needed."
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score_fcast.py\n",
|
||||
"import pickle\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import azureml.train.automl\n",
|
||||
"from sklearn.externals import joblib\n",
|
||||
"from azureml.core.model import Model\n",
|
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"\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
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" model = joblib.load(model_path)\n",
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"\n",
|
||||
"timestamp_columns = ['WeekStarting']\n",
|
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"\n",
|
||||
"def run(rawdata, test_model = None):\n",
|
||||
" \"\"\"\n",
|
||||
" Intended to process 'rawdata' string produced by\n",
|
||||
" \n",
|
||||
" {'X': X_test.to_json(), y' : y_test.to_json()}\n",
|
||||
" \n",
|
||||
" Don't convert the X payload to numpy.array, use it as pandas.DataFrame\n",
|
||||
" \"\"\"\n",
|
||||
" try:\n",
|
||||
" # unpack the data frame with timestamp \n",
|
||||
" rawobj = json.loads(rawdata) # rawobj is now a dict of strings \n",
|
||||
" X_pred = pd.read_json(rawobj['X'], convert_dates=False) # load the pandas DF from a json string\n",
|
||||
" for col in timestamp_columns: # fix timestamps\n",
|
||||
" X_pred[col] = pd.to_datetime(X_pred[col], unit='ms') \n",
|
||||
" \n",
|
||||
" y_pred = np.array(rawobj['y']) # reconstitute numpy array from serialized list\n",
|
||||
" \n",
|
||||
" if test_model is None:\n",
|
||||
" result = model.forecast(X_pred, y_pred) # use the global model from init function\n",
|
||||
" else:\n",
|
||||
" result = test_model.forecast(X_pred, y_pred) # use the model on which we are testing\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" result = str(e)\n",
|
||||
" return json.dumps({\"error\": result})\n",
|
||||
" \n",
|
||||
" # prepare to send over wire as json\n",
|
||||
" forecast_as_list = result[0].tolist()\n",
|
||||
" index_as_df = result[1].index.to_frame().reset_index(drop=True)\n",
|
||||
" \n",
|
||||
" return json.dumps({\"forecast\": forecast_as_list, # return the minimum over the wire: \n",
|
||||
" \"index\": index_as_df.to_json() # no forecast and its featurized values\n",
|
||||
" })"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get the model\n",
|
||||
"from azureml.train.automl.run import AutoMLRun\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)\n",
|
||||
"best_iteration = int(str.split(best_run.id,'_')[-1]) # the iteration number is a postfix of the run ID."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get the best model's dependencies and write them into this file\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"conda_env_file_name = 'fcast_env.yml'\n",
|
||||
"\n",
|
||||
"dependencies = ml_run.get_run_sdk_dependencies(iteration = best_iteration)\n",
|
||||
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
|
||||
" print('{}\\t{}'.format(p, dependencies[p]))\n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
|
||||
"\n",
|
||||
"myenv.save_to_file('.', conda_env_file_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# this is the script file name we wrote a few cells above\n",
|
||||
"script_file_name = 'score_fcast.py'\n",
|
||||
"\n",
|
||||
"# Substitute the actual version number in the environment file.\n",
|
||||
"# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
|
||||
"# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
|
||||
"\n",
|
||||
"with open(conda_env_file_name, 'r') as cefr:\n",
|
||||
" content = cefr.read()\n",
|
||||
"\n",
|
||||
"with open(conda_env_file_name, 'w') as cefw:\n",
|
||||
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
|
||||
"\n",
|
||||
"# Substitute the actual model id in the script file.\n",
|
||||
"\n",
|
||||
"with open(script_file_name, 'r') as cefr:\n",
|
||||
" content = cefr.read()\n",
|
||||
"\n",
|
||||
"with open(script_file_name, 'w') as cefw:\n",
|
||||
" cefw.write(content.replace('<<modelid>>', local_run.model_id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a Container Image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.image import Image, ContainerImage\n",
|
||||
"\n",
|
||||
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
|
||||
" execution_script = script_file_name,\n",
|
||||
" conda_file = conda_env_file_name,\n",
|
||||
" tags = {'type': \"automl-forecasting\"},\n",
|
||||
" description = \"Image for automl forecasting sample\")\n",
|
||||
"\n",
|
||||
"image = Image.create(name = \"automl-fcast-image\",\n",
|
||||
" # this is the model object \n",
|
||||
" models = [model],\n",
|
||||
" image_config = image_config, \n",
|
||||
" workspace = ws)\n",
|
||||
"\n",
|
||||
"image.wait_for_creation(show_output = True)\n",
|
||||
"\n",
|
||||
"if image.creation_state == 'Failed':\n",
|
||||
" print(\"Image build log at: \" + image.image_build_log_uri)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy the Image as a Web Service on Azure Container Instance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
|
||||
" memory_gb = 2, \n",
|
||||
" tags = {'type': \"automl-forecasting\"},\n",
|
||||
" description = \"Automl forecasting sample service\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"\n",
|
||||
"aci_service_name = 'automl-forecast-01'\n",
|
||||
"print(aci_service_name)\n",
|
||||
"\n",
|
||||
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
|
||||
" image = image,\n",
|
||||
" name = aci_service_name,\n",
|
||||
" workspace = ws)\n",
|
||||
"aci_service.wait_for_deployment(True)\n",
|
||||
"print(aci_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Call the service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# we send the data to the service serialized into a json string\n",
|
||||
"test_sample = json.dumps({'X':X_test.to_json(), 'y' : y_query.tolist()})\n",
|
||||
"response = aci_service.run(input_data = test_sample)\n",
|
||||
"\n",
|
||||
"# translate from networkese to datascientese\n",
|
||||
"try: \n",
|
||||
" res_dict = json.loads(response)\n",
|
||||
" y_fcst_all = pd.read_json(res_dict['index'])\n",
|
||||
" y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
|
||||
" y_fcst_all['forecast'] = res_dict['forecast'] \n",
|
||||
"except:\n",
|
||||
" print(res_dict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_fcst_all.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Delete the web service if desired"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"serv = Webservice(ws, 'automl-forecast-01')\n",
|
||||
"# serv.delete() # don't do it accidentally"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "erwright"
|
||||
"name": "erwright, tosingli"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
@@ -457,7 +845,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
"version": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Reference in New Issue
Block a user