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update samples from Release-64 as a part of SDK release

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amlrelsa-ms
2020-09-09 22:30:45 +00:00
parent a2a33c70a6
commit 820c09734f
8 changed files with 4 additions and 727 deletions
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60
how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py
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import argparse
import numpy as np
from sklearn.externals import joblib
from azureml.automl.runtime.shared.score import scoring, constants
from azureml.core import Run
from azureml.core.model import Model
parser = argparse.ArgumentParser()
parser.add_argument(
'--target_column_name', type=str, dest='target_column_name',
help='Target Column Name')
parser.add_argument(
'--model_name', type=str, dest='model_name',
help='Name of registered model')
args = parser.parse_args()
target_column_name = args.target_column_name
model_name = args.model_name
print('args passed are: ')
print('Target column name: ', target_column_name)
print('Name of registered model: ', model_name)
model_path = Model.get_model_path(model_name)
# deserialize the model file back into a sklearn model
model = joblib.load(model_path)
run = Run.get_context()
# get input dataset by name
test_dataset = run.input_datasets['test_data']
train_dataset = run.input_datasets['train_data']
X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
.to_pandas_dataframe()
y_test_df = test_dataset.with_timestamp_columns(None) \
.keep_columns(columns=[target_column_name]) \
.to_pandas_dataframe()
y_train_df = test_dataset.with_timestamp_columns(None) \
.keep_columns(columns=[target_column_name]) \
.to_pandas_dataframe()
predicted = model.predict_proba(X_test_df)
# Use the AutoML scoring module
class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
train_labels = model.classes_
classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
scores = scoring.score_classification(y_test_df.values, predicted,
classification_metrics,
class_labels, train_labels)
print("scores:")
print(scores)
for key, value in scores.items():
run.log(key, value)