def test_check_pandas_dataframe_fit_backward():
for floating in [True, False]:
iris = load_iris()
X = iris.data
y = iris.target
lr = SoftmaxRegression(random_seed=1)
sfs1 = SFS(lr,
k_features=2,
forward=False,
floating=floating,
scoring='accuracy',
cv=0,
verbose=0,
n_jobs=1)
df = pd.DataFrame(
X,
columns=['sepal len', 'sepal width', 'petal len', 'petal width'])
sfs1 = sfs1.fit(X, y)
assert sfs1.k_feature_idx_ == (1, 2)
assert sfs1.k_feature_names_ == ('1', '2')
assert sfs1.subsets_[2]['feature_names'] == ('1', '2')
sfs1 = sfs1.fit(df, y)
assert sfs1.subsets_[3]['feature_names'] == ('sepal len',
'sepal width',
'petal len')
assert sfs1.subsets_[2]['feature_names'] == ('sepal width',
'petal len')
assert sfs1.subsets_[3]['feature_idx'] == (0, 1, 2)
assert sfs1.subsets_[2]['feature_idx'] == (1, 2)
assert sfs1.k_feature_idx_ == (1, 2)
assert sfs1.k_feature_names_ == ('sepal width', 'petal len')
sfs1._TESTING_INTERRUPT_MODE = True
out = sfs1.fit(df, y)
assert len(out.subsets_.keys()) > 0
assert sfs1.interrupted_
assert sfs1.subsets_[3]['feature_names'] == ('sepal len',
'sepal width',
'petal len')
assert sfs1.k_feature_idx_ == (0, 1, 2)
assert sfs1.k_feature_names_ == ('sepal len', 'sepal width',
'petal len')
def test_check_pandas_dataframe_fit_backward():
for floating in [True, False]:
iris = load_iris()
X = iris.data
y = iris.target
lr = SoftmaxRegression(random_seed=1)
sfs1 = SFS(lr,
k_features=2,
forward=False,
floating=floating,
scoring='accuracy',
cv=0,
verbose=0,
n_jobs=1)
df = pd.DataFrame(X, columns=['sepal len', 'sepal width',
'petal len', 'petal width'])
sfs1 = sfs1.fit(X, y)
assert sfs1.k_feature_idx_ == (1, 2)
assert sfs1.k_feature_names_ == ('1', '2')
assert sfs1.subsets_[2]['feature_names'] == ('1', '2')
sfs1 = sfs1.fit(df, y)
assert sfs1.subsets_[3]['feature_names'] == ('sepal len',
'sepal width',
'petal len')
assert sfs1.subsets_[2]['feature_names'] == ('sepal width',
'petal len')
assert sfs1.subsets_[3]['feature_idx'] == (0, 1, 2)
assert sfs1.subsets_[2]['feature_idx'] == (1, 2)
assert sfs1.k_feature_idx_ == (1, 2)
assert sfs1.k_feature_names_ == ('sepal width', 'petal len')
sfs1._TESTING_INTERRUPT_MODE = True
out = sfs1.fit(df, y)
assert len(out.subsets_.keys()) > 0
assert sfs1.interrupted_
assert sfs1.subsets_[3]['feature_names'] == ('sepal len',
'sepal width',
'petal len')
assert sfs1.k_feature_idx_ == (0, 1, 2)
assert sfs1.k_feature_names_ == ('sepal len', 'sepal width',
'petal len')
def test_keyboard_interrupt():
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)
sfs1 = SFS(knn,
k_features=3,
forward=True,
floating=False,
cv=3,
clone_estimator=False,
verbose=5,
n_jobs=1)
sfs1._TESTING_INTERRUPT_MODE = True
out = sfs1.fit(X, y)
assert len(out.subsets_.keys()) > 0
assert sfs1.interrupted_
def test_keyboard_interrupt():
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)
sfs1 = SFS(
knn,
k_features=3,
forward=True,
floating=False,
cv=3,
clone_estimator=False,
verbose=5,
n_jobs=1
)
sfs1._TESTING_INTERRUPT_MODE = True
out = sfs1.fit(X, y)
assert len(out.subsets_.keys()) > 0
assert sfs1.interrupted_
