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55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
import numpy as np
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import argparse
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from azureml.core import Run
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from sklearn.externals import joblib
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from azureml.automl.core._vendor.automl.client.core.common import metrics
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from automl.client.core.common import constants
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from azureml.core.model import Model
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--target_column_name', type=str, dest='target_column_name',
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help='Target Column Name')
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parser.add_argument(
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'--model_name', type=str, dest='model_name',
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help='Name of registered model')
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args = parser.parse_args()
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target_column_name = args.target_column_name
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model_name = args.model_name
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print('args passed are: ')
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print('Target column name: ', target_column_name)
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print('Name of registered model: ', model_name)
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model_path = Model.get_model_path(model_name)
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# deserialize the model file back into a sklearn model
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model = joblib.load(model_path)
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run = Run.get_context()
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# get input dataset by name
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test_dataset = run.input_datasets['test_data']
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X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
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.to_pandas_dataframe()
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y_test_df = test_dataset.with_timestamp_columns(None) \
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.keep_columns(columns=[target_column_name]) \
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.to_pandas_dataframe()
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predicted = model.predict_proba(X_test_df)
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# use automl metrics module
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scores = metrics.compute_metrics_classification(
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np.array(predicted),
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np.array(y_test_df),
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class_labels=model.classes_,
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metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
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)
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print("scores:")
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print(scores)
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for key, value in scores.items():
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run.log(key, value)
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