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71 lines
1.8 KiB
Python
71 lines
1.8 KiB
Python
import argparse
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import pandas as pd
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import numpy as np
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from sklearn.externals import joblib
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from azureml.automl.runtime.shared.score import scoring, constants
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from azureml.core import Run, Dataset
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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",
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type=str,
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dest="target_column_name",
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help="Target Column Name",
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)
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parser.add_argument(
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"--model_name", type=str, dest="model_name", help="Name of registered model"
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)
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parser.add_argument("--input-data", type=str, dest="input_data", help="Dataset")
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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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test_dataset = Dataset.get_by_id(run.experiment.workspace, id=args.input_data)
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X_test_df = test_dataset.drop_columns(
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columns=[target_column_name]
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).to_pandas_dataframe()
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y_test_df = (
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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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)
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predicted = model.predict_proba(X_test_df)
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if isinstance(predicted, pd.DataFrame):
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predicted = predicted.values
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# Use the AutoML scoring module
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train_labels = model.classes_
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class_labels = np.unique(
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np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1))))
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)
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classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
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scores = scoring.score_classification(
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y_test_df.values, predicted, classification_metrics, class_labels, train_labels
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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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