def test_pipeline_fit_internal_pd_stage(self): X = pd.DataFrame({'a': [1, 2, 3]}) y = pd.Series([1, 2, 3]) p = pd_pipeline.make_pipeline(pd_linear_model.LinearRegression()) self.assertTrue(isinstance(p, FrameMixin)) pd_p = frame(p) y_hat = pd_p.fit(X, y).predict(X) self.assertTrue(isinstance(y_hat, pd.Series))
def test_pipeline_fit(self): X = pd.DataFrame({'a': [1, 2, 3]}) y = pd.Series([1, 2, 3]) # Tmp Ami - make verify that are framemixins p = pd_pipeline.make_pipeline(pd_linear_model.LinearRegression()) self.assertTrue(isinstance(p, FrameMixin)) pd_p = frame(p) pd_p = pd_p.fit(X, y) y_hat = pd_p.fit(X, y).predict(X) self.assertTrue(isinstance(y_hat, pd.Series))
_estimators.append( (pipeline.make_pipeline(feature_selection.SelectKBest(k=2), decomposition.PCA(), linear_model.LinearRegression()), pd_feature_selection.SelectKBest(k=2) | pd_decomposition.PCA() | pd_linear_model.LinearRegression(), True)) _estimators.append( (pipeline.make_pipeline(feature_selection.SelectKBest(k=2), decomposition.PCA(), linear_model.LinearRegression()), pd_feature_selection.SelectKBest(k=2) | (pd_decomposition.PCA() | pd_linear_model.LinearRegression()), True)) _estimators.append( (pipeline.make_pipeline(decomposition.PCA(), linear_model.LinearRegression()), pd_pipeline.make_pipeline(pd_decomposition.PCA(), pd_linear_model.LinearRegression()), True)) _estimators.append((linear_model.LogisticRegression(), pd_linear_model.LogisticRegression(), True)) _estimators.append((cluster.KMeans(random_state=42), pd_cluster.KMeans(random_state=42), True)) _estimators.append( (cluster.KMeans(random_state=42), pickle.loads(pickle.dumps(pd_cluster.KMeans(random_state=42))), True)) _estimators.append((neighbors.KNeighborsClassifier(), pd_neighbors.KNeighborsClassifier(), True)) _estimators.append( (ensemble.GradientBoostingClassifier(random_state=42), pd_ensemble.GradientBoostingClassifier(random_state=42), True)) _estimators.append((pipeline.make_union(decomposition.PCA(n_components=2), feature_selection.SelectKBest(k=1)), pd_decomposition.PCA(n_components=2) +
def test_make_pipeline(self): p = pd_pipeline.make_pipeline(pd_preprocessing.StandardScaler(), pd_linear_model.LinearRegression())