def test_combined_models_support_predict_proba_with_more_than_2_classes(
self):
path = get_dataset('infert').as_filepath()
data = FileDataStream.read_csv(path)
featurization_pipeline = Pipeline(
[OneHotVectorizer(columns={'education': 'education'})])
featurization_pipeline.fit(data)
featurized_data = featurization_pipeline.transform(data)
feature_cols = ['education', 'age']
training_pipeline = Pipeline([
DatasetTransformer(featurization_pipeline.model),
OneVsRestClassifier(LogisticRegressionBinaryClassifier(),
feature=feature_cols,
label='induced')
])
training_pipeline.fit(data, output_predictor_model=True)
concat_pipeline = Pipeline(
[PrefixColumnConcatenator({'education': 'education.'})])
concat_pipeline.fit(featurized_data)
predictor_pipeline = Pipeline()
predictor_pipeline.load_model(training_pipeline.predictor_model)
concat_and_predictor_pipeline = Pipeline.combine_models(
concat_pipeline, predictor_pipeline)
result = concat_and_predictor_pipeline.predict_proba(featurized_data)
self.assertEqual(result.shape[1], 3)
def test_combined_models_support_decision_function(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.decision_function(df)
data = FileDataStream.read_csv(path)
combined_pipeline = Pipeline.combine_models(transform, predictor)
result_2 = combined_pipeline.decision_function(data)
self.assertTrue(np.array_equal(result_1, 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_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))
def test_two_pipelines_created_using_idv_binary_data_can_be_combined_in_to_one_model(
self):
"""
This test verifies that two models can be combined
even if the transform increases the number of columns.
"""
# Create and fit a OneHotVectorizer transform using the
# training data and use it to transform the training data.
transform_pipeline = Pipeline([OneHotVectorizer() << 'c0'],
random_state=seed)
transform_pipeline.fit(train_df)
df = transform_pipeline.transform(train_df, as_binary_data_stream=True)
# Create and fit an OnlineGradientDescentRegressor using
# the transformed training data from the previous step.
predictor_pipeline = Pipeline(
[OnlineGradientDescentRegressor(label='c2', feature=['c0', 'c1'])],
random_state=seed)
predictor_pipeline.fit(df)
# Perform a prediction given the test data using
# the transform and predictor defined previously.
df = transform_pipeline.transform(test_df, as_binary_data_stream=True)
result_1 = predictor_pipeline.predict(df)
# Combine the above Pipelines in to one Pipeline and use
# the new Pipeline to get predictions given the test data.
combined_pipeline = Pipeline.combine_models(transform_pipeline,
predictor_pipeline)
result_2 = combined_pipeline.predict(test_df)
# Verify that the prediction from the combined Pipeline
# matches the prediction from the original two Pipelines.
self.assertEqual(result_1.loc[0, 'Score'], result_2.loc[0, 'Score'])
self.assertEqual(result_1.loc[1, 'Score'], result_2.loc[1, 'Score'])
def test_passing_in_a_single_transform_returns_new_pipeline(self):
transform = OneHotVectorizer() << 'c0'
transform.fit(train_df)
combined_pipeline = Pipeline.combine_models(transform,
contains_predictor=False)
result = combined_pipeline.transform(test_df)
self.assertEqual(len(result), 3)
self.assertEqual(len(result.columns), 4)
self.assertTrue(result.columns[0].startswith('c0.'))
self.assertTrue(result.columns[1].startswith('c0.'))
self.assertTrue(isinstance(combined_pipeline, Pipeline))
def test_passing_in_a_single_pipeline_returns_new_pipeline(self):
pipeline = Pipeline([
OneHotVectorizer() << 'c0',
OnlineGradientDescentRegressor(label='c2', feature=['c0', 'c1'])
])
pipeline.fit(train_df)
result_1 = pipeline.predict(test_df)
combined_pipeline = Pipeline.combine_models(pipeline)
result_2 = combined_pipeline.predict(test_df)
self.assertEqual(result_1.loc[0, 'Score'], result_2.loc[0, 'Score'])
self.assertEqual(result_1.loc[1, 'Score'], result_2.loc[1, 'Score'])
self.assertTrue(isinstance(combined_pipeline, Pipeline))
def test_passing_in_a_single_predictor_returns_new_pipeline(self):
train_dropped_df = train_df.drop(['c0'], axis=1)
test_dropped_df = test_df.drop(['c0'], axis=1)
predictor = OnlineGradientDescentRegressor(label='c2', feature=['c1'])
predictor.fit(train_dropped_df)
result_1 = predictor.predict(test_dropped_df)
combined_pipeline = Pipeline.combine_models(predictor)
result_2 = combined_pipeline.predict(test_dropped_df)
self.assertEqual(result_1[0], result_2.loc[0, 'Score'])
self.assertEqual(result_1[1], result_2.loc[1, 'Score'])
self.assertTrue(isinstance(combined_pipeline, Pipeline))
def test_combine_transform_and_predictor(self):
transform = OneHotVectorizer() << 'c0'
df = transform.fit_transform(train_df, as_binary_data_stream=True)
predictor = OnlineGradientDescentRegressor(label='c2',
feature=['c0', 'c1'])
predictor.fit(df)
df = transform.transform(test_df, as_binary_data_stream=True)
result_1 = predictor.predict(df)
combined_pipeline = Pipeline.combine_models(transform, predictor)
result_2 = combined_pipeline.predict(test_df)
self.assertEqual(result_1[0], result_2.loc[0, 'Score'])
self.assertEqual(result_1[1], result_2.loc[1, 'Score'])
def test_combine_transform_and_transform(self):
transform_1 = RangeFilter(min=0.0, max=4.5) << 'c2'
df = transform_1.fit_transform(train_df)
transform_2 = OneHotVectorizer() << 'c0'
transform_2.fit(df)
df = transform_1.transform(test_df)
result_1 = transform_2.transform(df)
combined_pipeline = Pipeline.combine_models(transform_1,
transform_2,
contains_predictor=False)
result_2 = combined_pipeline.transform(test_df)
self.assertTrue(result_1.equals(result_2))
def test_combine_transform_and_pipeline(self):
transform = RangeFilter(min=0.0, max=4.5) << 'c2'
df = transform.fit_transform(train_df, as_binary_data_stream=True)
pipeline = Pipeline([
OneHotVectorizer() << 'c0',
OnlineGradientDescentRegressor(label='c2', feature=['c0', 'c1'])
])
pipeline.fit(df)
df = transform.transform(test_df, as_binary_data_stream=True)
result_1 = pipeline.predict(df)
combined_pipeline = Pipeline.combine_models(transform, pipeline)
result_2 = combined_pipeline.predict(test_df)
self.assertTrue(result_1.equals(result_2))
def test_combine_two_pipelines_created_from_model_files(self):
"""
This test verifies that two models can be combined
after they are loaded from disk in to new Pipelines.
"""
# Create and fit a OneHotVectorizer transform using the
# training data and use it to transform the training data.
transform_pipeline_1 = Pipeline([OneHotVectorizer() << 'c0'],
random_state=seed)
transform_pipeline_1.fit(train_df)
df = transform_pipeline_1.transform(train_df,
as_binary_data_stream=True)
# Create and fit an OnlineGradientDescentRegressor using
# the transformed training data from the previous step.
predictor_pipeline_1 = Pipeline(
[OnlineGradientDescentRegressor(label='c2', feature=['c0', 'c1'])],
random_state=seed)
predictor_pipeline_1.fit(df)
# Perform a prediction given the test data using
# the transform and predictor defined previously.
df = transform_pipeline_1.transform(test_df,
as_binary_data_stream=True)
result_1 = predictor_pipeline_1.predict(df)
# Use the model files stored in the Pipelines
# to create new Pipelines (aka. create new Pipelines
# using the model files stored on disk).
transform_pipeline_2 = Pipeline()
transform_pipeline_2.load_model(transform_pipeline_1.model)
predictor_pipeline_2 = Pipeline()
predictor_pipeline_2.load_model(predictor_pipeline_1.model)
# Combine the newly created Pipelines in to one Pipeline
# and use it to get predictions given the test data.
combined_pipeline = Pipeline.combine_models(transform_pipeline_2,
predictor_pipeline_2)
result_2 = combined_pipeline.predict(test_df)
# Verify that the prediction from the combined Pipeline
# matches the prediction from the original two Pipelines.
self.assertEqual(result_1.loc[0, 'Score'], result_2.loc[0, 'Score'])
self.assertEqual(result_1.loc[1, 'Score'], result_2.loc[1, 'Score'])
def test_two_pipelines_created_using_dataframes_can_be_combined_when_the_schemas_are_the_same(
self):
"""
This test verifies that two models created using DataFrames
can be combined if the output schema of the first is the same
as the input schema of the second.
"""
df = train_df.drop(['c0'], axis=1)
# Create and fit a RangeFilter transform using the training
# data and use it to transform the training data.
transform_pipeline = Pipeline([RangeFilter(min=0.0, max=4.5) << 'c2'],
random_state=seed)
transform_pipeline.fit(df)
df = transform_pipeline.transform(df)
# Create and fit an OnlineGradientDescentRegressor using
# the transformed training data from the previous step.
predictor_pipeline = Pipeline(
[OnlineGradientDescentRegressor(label='c2')], random_state=seed)
predictor_pipeline.fit(df)
# Perform a prediction given the test data using
# the transform and predictor defined previously.
df = transform_pipeline.transform(test_df)
result_1 = predictor_pipeline.predict(df)
df = test_df.drop(['c0'], axis=1)
# Combine the above Pipelines in to one Pipeline and use
# the new Pipeline to get predictions given the test data.
combined_pipeline = Pipeline.combine_models(transform_pipeline,
predictor_pipeline)
result_2 = combined_pipeline.predict(df)
# Verify that the prediction from the combined Pipeline
# matches the prediction from the original two Pipelines.
self.assertEqual(result_1.loc[0, 'Score'], result_2.loc[0, 'Score'])
self.assertEqual(result_1.loc[1, 'Score'], result_2.loc[1, 'Score'])
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_vectorized_with_prefixconcat_output_predictor_model(self):
"""
This test shows how to prepend ColumnConcatenator transform
to outputted predictor model from combined (with featurizers) pipeline
so it successfully runs on featurized data with vectors.
"""
# Create and fit a OneHotVectorizer transform using the
# training data and use it to transform the training data.
transform_pipeline = Pipeline([OneHotVectorizer() << 'c0'],
random_state=seed)
transform_pipeline.fit(train_df)
df = transform_pipeline.transform(train_df)
# Create, fit and score with combined model.
# Output predictor model separately.
combined_pipeline = Pipeline([
OneHotVectorizer() << 'c0',
OnlineGradientDescentRegressor(label='c2')
],
random_state=seed)
combined_pipeline.fit(train_df, output_predictor_model=True)
result_1 = combined_pipeline.predict(train_df)
# train ColumnConcatenator on featurized data
concat_pipeline = Pipeline(
[PrefixColumnConcatenator(columns={'c0': 'c0.'})])
concat_pipeline.fit(df)
# Load predictor pipeline
predictor_pipeline = Pipeline()
predictor_pipeline.load_model(combined_pipeline.predictor_model)
# combine concat and predictor models and score
combined_predictor_pipeline = Pipeline.combine_models(
concat_pipeline, predictor_pipeline)
result_2 = combined_predictor_pipeline.predict(df)
self.assertEqual(result_1.loc[0, 'Score'], result_2.loc[0, 'Score'])
self.assertEqual(result_1.loc[1, 'Score'], result_2.loc[1, 'Score'])
def test_two_pipelines_created_using_dataframes_can_not_be_combined_when_the_schemas_are_different(
self):
"""
This test verifies that two models created using DataFrames
can not be combined if the output schema of the first is
different then the input schema of the second.
NOTE: This issue only happens with Pipelines created and fit
using dataframes. Pipelines created and fit using IDV binary
streams do not have this issue (see the tests below).
"""
# Create and fit a OneHotVectorizer transform using the
# training data and use it to transform the training data.
transform_pipeline = Pipeline([OneHotVectorizer() << 'c0'],
random_state=seed)
transform_pipeline.fit(train_df)
df = transform_pipeline.transform(train_df)
# Create and fit an OnlineGradientDescentRegressor using
# the transformed training data from the previous step.
predictor_pipeline = Pipeline(
[OnlineGradientDescentRegressor(label='c2')], random_state=seed)
predictor_pipeline.fit(df)
# Perform a prediction given the test data using
# the transform and predictor defined previously.
df = transform_pipeline.transform(test_df)
result_1 = predictor_pipeline.predict(df)
try:
# This does not work because the output schema of the
combined_pipeline = Pipeline.combine_models(
transform_pipeline, predictor_pipeline)
except Exception as e:
pass
else:
self.fail()
