Add classifier and FileDataStream unit tests to test_pipeline_combining.

src/python/nimbusml/tests/pipeline/test_pipeline_combining.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 import pandas as pd
-from nimbusml import Pipeline
+from nimbusml import Pipeline, FileDataStream
 from nimbusml.datasets import get_dataset
 from nimbusml.feature_extraction.categorical import OneHotVectorizer
 from nimbusml.linear_model import LogisticRegressionBinaryClassifier, OnlineGradientDescentRegressor
@@ -323,6 +323,89 @@ def test_combine_transform_and_pipeline(self):
         self.assertTrue(result_1.equals(result_2))
 
 
+    def test_combine_with_classifier_trained_with_y_arg(self):
+        """
+        Tests a sequence where the initial transform is computed
+        using both X and y input args. Note, any steps after the
+        initial transform will be operating on data where the X
+        and y have been combined in to one dataset.
+        """
+        np.random.seed(0)
+
+        df = get_dataset("infert").as_df()
+
+        X = df.loc[:, df.columns != 'case']
+        y = df['case']
+
+        transform = OneHotVectorizer() << 'education_str'
+
+        # Passing in both X and y
+        df = transform.fit_transform(X, y, as_binary_data_stream=True)
+
+        # NOTE: need to specify the label column here because the
+        # feature and label data was joined in the last step.
+        predictor = LogisticRegressionBinaryClassifier(label='case', feature=list(X.columns))
+        predictor.fit(df)
+
+        df = transform.transform(X, as_binary_data_stream=True)
+        result_1 = predictor.predict(df)
+
+        # Combine the models and perform a prediction
+        combined_pipeline = Pipeline.combine_models(transform, predictor)
+        result_2 = combined_pipeline.predict(X)
+
+        result_2 = result_2['PredictedLabel'].astype(np.float64)
+        self.assertTrue(result_1.equals(result_2))
+
+
+    def test_combine_with_classifier_trained_with_joined_X_and_y(self):
+        np.random.seed(0)
+
+        infert_df = get_dataset("infert").as_df()
+        feature_cols = [c for c in infert_df.columns if c != 'case']
+
+        transform = OneHotVectorizer() << 'education_str'
+        df = transform.fit_transform(infert_df, as_binary_data_stream=True)
+
+        predictor = LogisticRegressionBinaryClassifier(label='case', feature=feature_cols)
+        predictor.fit(df)
+
+        df = transform.transform(infert_df, as_binary_data_stream=True)
+        result_1 = predictor.predict(df)
+
+        # Combine the models and perform a prediction
+        combined_pipeline = Pipeline.combine_models(transform, predictor)
+        result_2 = combined_pipeline.predict(infert_df)
+
+        result_2 = result_2['PredictedLabel'].astype(np.float64)
+        self.assertTrue(result_1.equals(result_2))
+
+
+    def test_combine_with_classifier_trained_with_filedatastream(self):
+        path = get_dataset('infert').as_filepath()
+
+        data = FileDataStream.read_csv(path)
+
+        transform = OneHotVectorizer(columns={'edu': 'education'})
+        df = transform.fit_transform(data, as_binary_data_stream=True)
+
+        feature_cols = ['parity', 'edu', 'age', 'induced', 'spontaneous', 'stratum', 'pooled.stratum']
+        predictor = LogisticRegressionBinaryClassifier(feature=feature_cols, label='case')
+        predictor.fit(df)
+
+        data = FileDataStream.read_csv(path)
+        df = transform.transform(data, as_binary_data_stream=True)
+        result_1 = predictor.predict(df)
+
+        data = FileDataStream.read_csv(path)
+        combined_pipeline = Pipeline.combine_models(transform, predictor)
+        result_2 = combined_pipeline.predict(data)
+
+        result_1 = result_1.astype(np.int32)
+        result_2 = result_2['PredictedLabel'].astype(np.int32)
+        self.assertTrue(result_1.equals(result_2))
+
+
 if __name__ == '__main__':
     unittest.main()