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_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_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_with_or_without_pipeline(self):
# Bug 227810
# data input (as a FileDataStream)
path = get_dataset('infert').as_filepath()
file_schema = 'sep=, col=education:TX:1 col=Features:R4:2-4,6-8 ' \
'col=case:R4:5 header=+'
data = FileDataStream(path, schema=file_schema)
# without pipeline -- fails
m = LogisticRegressionBinaryClassifier(feature=['Features'],
label='case')
m.fit(data)
scores1 = m.predict(data)
# with pipeline -- works
m = Pipeline([
LogisticRegressionBinaryClassifier(feature=['Features'],
label='case')
])
m.fit(data)
scores2 = m.predict(data)
diff = np.abs(scores1.values.ravel() -
scores2[['PredictedLabel']].values.ravel())
assert diff.sum() <= 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))