update samples from Release-55 as a part of SDK release

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -1,8 +1,11 @@
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+
 from sklearn.externals import joblib
-from azureml.automl.core.shared import constants, metrics
+
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
 from azureml.core.model import Model
 
 
@@ -29,22 +32,26 @@ 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 automl metrics module
-scores = metrics.compute_metrics_classification(
-    np.array(predicted),
-    np.array(y_test_df),
-    class_labels=model.classes_,
-    metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
-)
+# 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)