def test_pipeline_with_params(self):
conv = SkBaseTransformLearner(LinearRegression(normalize=True))
pipe = make_pipeline(conv, DecisionTreeRegressor())
pars = pipe.get_params()
self.assertIn('skbasetransformlearner__model__fit_intercept', pars)
self.assertEqual(
pars['skbasetransformlearner__model__normalize'], True)
conv = SkBaseTransformLearner(LinearRegression(normalize=True))
pipe = make_pipeline(conv, DecisionTreeRegressor())
pipe.set_params(**pars)
pars = pipe.get_params()
self.assertIn('skbasetransformlearner__model__fit_intercept', pars)
self.assertEqual(
pars['skbasetransformlearner__model__normalize'], True)
def test_pickle(self):
df = pandas.DataFrame(dict(y=[0, 1, 0, 1, 0, 1, 0, 1],
X1=[0.5, 0.6, 0.52, 0.62,
0.5, 0.6, 0.51, 0.61],
X2=[0.5, 0.6, 0.7, 0.5, 1.5, 1.6, 1.7, 1.8]))
X = df.drop('y', axis=1)
y = df['y']
model = SkBaseTransformLearner(LinearRegression(normalize=True))
model.fit(X, y)
pred = model.transform(X)
st = BytesIO()
pickle.dump(model, st)
st = BytesIO(st.getvalue())
rec = pickle.load(st)
pred2 = rec.transform(X)
self.assertEqualArray(pred, pred2)
def test_pipeline_with_two_regressors(self):
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
conv = SkBaseTransformLearner(LinearRegression())
pipe = make_pipeline(conv, DecisionTreeRegressor())
pipe.fit(X_train, y_train)
pred = pipe.predict(X_test)
score = r2_score(y_test, pred)
self.assertLesser(score, 1.)
score2 = pipe.score(X_test, y_test)
self.assertEqualFloat(score, score2, precision=1e-5)
rp = repr(conv)
self.assertStartsWith(
'SkBaseTransformLearner(model=LinearRegression(copy_X=True,', rp)
def test_pipeline_with_two_classifiers(self):
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
conv = SkBaseTransformLearner(LogisticRegression())
pipe = make_pipeline(conv, DecisionTreeClassifier())
pipe.fit(X_train, y_train)
pred = pipe.predict(X_test)
score = accuracy_score(y_test, pred)
self.assertGreater(score, 0.8)
score2 = pipe.score(X_test, y_test)
self.assertEqual(score, score2)
rp = repr(conv)
self.assertStartsWith(
'SkBaseTransformLearner(model=LogisticRegression(C=1.0,', rp)
def test_pipeline_with_callable(self):
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
tmod = LogisticRegression()
conv = SkBaseTransformLearner(tmod, method=tmod.decision_function)
pipe = make_pipeline(conv, DecisionTreeClassifier())
pipe.fit(X_train, y_train)
pred = pipe.predict(X_test)
score = accuracy_score(y_test, pred)
self.assertGreater(score, 0.8)
score2 = pipe.score(X_test, y_test)
self.assertEqualFloat(score, score2, precision=1e-5)
rp = repr(conv)
self.assertStartsWith(
'SkBaseTransformLearner(model=LogisticRegression(C=1.0,', rp)
def test_grid(self):
df = pandas.DataFrame(dict(y=[0, 1, 0, 1, 0, 1, 0, 1],
X1=[0.5, 0.6, 0.52, 0.62,
0.5, 0.6, 0.51, 0.61],
X2=[0.5, 0.6, 0.7, 0.5, 1.5, 1.6, 1.7, 1.8]))
X = df.drop('y', axis=1)
y = df['y']
model = make_pipeline(SkBaseTransformLearner(LinearRegression(normalize=True)),
LogisticRegression())
res = model.get_params(True)
self.assertGreater(len(res), 0)
parameters = {
'skbasetransformlearner__model__fit_intercept': [False, True]}
clf = GridSearchCV(model, parameters)
clf.fit(X, y)
pred = clf.predict(X)
self.assertEqualArray(y, pred)
def test_pipeline_transform(self):
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
conv = SkBaseTransformLearner(PCA())
pipe = make_pipeline(conv, DecisionTreeClassifier())
try:
pipe.fit(X_train, y_train)
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
pred = pipe.predict(X_test)
score = accuracy_score(y_test, pred)
self.assertGreater(score, 0.75)
score2 = pipe.score(X_test, y_test)
self.assertEqual(score, score2)
rp = repr(conv)
self.assertStartsWith('SkBaseTransformLearner(model=PCA(', rp)
def test_pipeline_with_callable(self):
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
tmod = LogisticRegression(n_jobs=1)
conv = SkBaseTransformLearner(tmod, method=tmod.decision_function)
pipe = make_pipeline(conv, DecisionTreeClassifier())
try:
pipe.fit(X_train, y_train)
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
pred = pipe.predict(X_test)
score = accuracy_score(y_test, pred)
self.assertGreater(score, 0.8)
score2 = pipe.score(X_test, y_test)
self.assertEqualFloat(score, score2, precision=1e-5)
rp = repr(conv)
self.assertStartsWith(
'SkBaseTransformLearner(model=LogisticRegression(', rp)
