def test_ensemble_supports_user_defined_transforms(self):
test2_df = test_df.copy(deep=True)
test2_df = test2_df.append(pd.DataFrame({'c1': [9, 11], 'c2': [1, 1]}))
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r1.fit(train_df)
result1 = r1.predict(test2_df)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r2.fit(train_df)
result2 = r2.predict(test2_df)
r3 = LightGbmRegressor(**lgbmArgs)
r3.fit(train_df)
result3 = r3.predict(test2_df)
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r3 = LightGbmRegressor(**lgbmArgs)
pipeline = Pipeline([
RangeFilter(min=0, max=10, columns='c1'),
VotingRegressor(estimators=[r1, r2, r3], combiner='Average')
])
pipeline.fit(train_df)
result4 = pipeline.predict(test2_df)
self.assertEqual(len(result4), 3)
average1 = (result1[0] + result2[0] + result3[0]) / 3
average2 = (result1[1] + result2[1] + result3[1]) / 3
average3 = (result1[2] + result2[2] + result3[2]) / 3
self.assertAlmostEqual(average1, result4.loc[0, 'Score'], places=5)
self.assertAlmostEqual(average2, result4.loc[1, 'Score'], places=5)
self.assertAlmostEqual(average3, result4.loc[2, 'Score'], places=5)
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_fit_predictor_with_idv(self):
train_data = {
'c0': ['a', 'b', 'a', 'b'],
'c1': [1, 2, 3, 4],
'c2': [2, 3, 4, 5]
}
train_df = pd.DataFrame(train_data).astype({
'c1': np.float64,
'c2': np.float64
})
test_data = {
'c0': ['a', 'b', 'b'],
'c1': [1.5, 2.3, 3.7],
'c2': [2.2, 4.9, 2.7]
}
test_df = pd.DataFrame(test_data).astype({
'c1': np.float64,
'c2': np.float64
})
# Fit a transform pipeline to the training data
transform_pipeline = Pipeline([OneHotVectorizer() << 'c0'])
transform_pipeline.fit(train_df)
df = transform_pipeline.transform(train_df, as_binary_data_stream=True)
# Fit a predictor pipeline given a transformed BinaryDataStream
predictor = OnlineGradientDescentRegressor(label='c2',
feature=['c0', 'c1'])
predictor_pipeline = Pipeline([predictor])
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)
# Create expected result
xf = OneHotVectorizer() << 'c0'
df = xf.fit_transform(train_df)
predictor = OnlineGradientDescentRegressor(
label='c2', feature=['c0.a', 'c0.b', 'c1'])
predictor.fit(df)
df = xf.transform(test_df)
expected_result = predictor.predict(df)
self.assertTrue(result_1.loc[:, 'Score'].equals(expected_result))
def test_ensemble_with_average_and_median_combiner(self):
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r1.fit(train_df)
result1 = r1.predict(test_df)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r2.fit(train_df)
result2 = r2.predict(test_df)
r3 = LightGbmRegressor(**lgbmArgs)
r3.fit(train_df)
result3 = r3.predict(test_df)
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r3 = LightGbmRegressor(**lgbmArgs)
pipeline = Pipeline([VotingRegressor(estimators=[r1, r2, r3], combiner='Average')])
pipeline.fit(train_df)
result4 = pipeline.predict(test_df)
average1 = (result1[0] + result2[0] + result3[0]) / 3
average2 = (result1[1] + result2[1] + result3[1]) / 3
self.assertAlmostEqual(average1, result4.loc[0, 'Score'], places=5)
self.assertAlmostEqual(average2, result4.loc[1, 'Score'], places=5)
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r3 = LightGbmRegressor(**lgbmArgs)
pipeline = Pipeline([VotingRegressor(estimators=[r1, r2, r3], combiner='Median')])
pipeline.fit(train_df)
result4 = pipeline.predict(test_df)
median1 = sorted([result1.loc[0], result2.loc[0], result3.loc[0]])[1]
median2 = sorted([result1.loc[1], result2.loc[1], result3.loc[1]])[1]
self.assertEqual(median1, result4.loc[0, 'Score'])
self.assertEqual(median2, result4.loc[1, 'Score'])
'label': 'c2',
'random_state': 1,
'number_of_leaves': 200,
'minimum_example_count_per_leaf': 1,
'normalize': 'Yes'
}
if show_individual_predictions:
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r1.fit(train_df)
result = r1.predict(test_df)
print(result)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r2.fit(train_df)
result = r2.predict(test_df)
print(result)
r3 = LightGbmRegressor(**lgbmArgs)
r3.fit(train_df)
result = r3.predict(test_df)
print(result)
# Perform a prediction using an ensemble
# of all three of the above predictors.
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r3 = LightGbmRegressor(**lgbmArgs)
pipeline = Pipeline(
[VotingRegressor(estimators=[r1, r2, r3], combiner='Average')])