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update samples from Release-108 as a part of 1.35.0 SDK stable release

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amlrelsa-ms
2021-10-11 15:36:08 +00:00
parent e2dddfde85
commit f9892966fd
57 changed files with 28923 additions and 192 deletions
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27
how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/forecasting_script.py
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@@ -27,20 +27,31 @@ ws = run.experiment.workspace
# 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)
X_test = 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()
# generate forecast
fitted_model = joblib.load('model.pkl')
y_pred, X_trans = fitted_model.forecast(X_test_df)
# rename target column
X_trans.reset_index(drop=False, inplace=True)
X_trans.rename(columns={TimeSeriesInternal.DUMMY_TARGET_COLUMN: 'predicted'}, inplace=True)
X_trans['actual'] = y_test_df[target_column_name].values
# We have default quantiles values set as below(95th percentile)
quantiles = [0.025, 0.5, 0.975]
predicted_column_name = 'predicted'
PI = 'prediction_interval'
fitted_model.quantiles = quantiles
pred_quantiles = fitted_model.forecast_quantiles(X_test)
pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply(lambda x: '[{}, {}]'.format(x[0],
x[1]), axis=1)
X_test[target_column_name] = y_test_df[target_column_name]
X_test[PI] = pred_quantiles[PI]
X_test[predicted_column_name] = pred_quantiles[0.5]
# drop rows where prediction or actuals are nan
# happens because of missing actuals
# or at edges of time due to lags/rolling windows
clean = X_test[X_test[[target_column_name,
predicted_column_name]].notnull().all(axis=1)]
clean.rename(columns={target_column_name: 'actual'}, inplace=True)
file_name = 'outputs/predictions.csv'
export_csv = X_trans.to_csv(file_name, header=True, index=False) # added Index
export_csv = clean.to_csv(file_name, header=True, index=False) # added Index
# Upload the predictions into artifacts
run.upload_file(name=file_name, path_or_stream=file_name)