def test_hash_X_y():
rng = check_random_state(0)
X = rng.randn(2000, 20)
y = np.array([0] * 500 + [1] * 1500)
assert hash_X_y(X, y, 10, 10) == (joblib.hash(X[::200, ::2]),
joblib.hash(y[::200]))
X = rng.randn(5, 2)
y = np.array([0] * 2 + [1] * 3)
# all data will be used in this case
assert hash_X_y(X, y) == (joblib.hash(X), joblib.hash(y))
def test_hash_X_y():
rng = check_random_state(0)
X = rng.randn(2000, 20)
y = np.array([0] * 500 + [1] * 1500)
assert hash_X_y(X, y, 10,
10) == (joblib.hash(X[::200, ::2]), joblib.hash(y[::200]))
X = rng.randn(5, 2)
y = np.array([0] * 2 + [1] * 3)
# all data will be used in this case
assert hash_X_y(X, y) == (joblib.hash(X), joblib.hash(y))
def test_hash_X_y_pandas():
pd = pytest.importorskip("pandas")
rng = check_random_state(0)
X = pd.DataFrame(rng.randn(2000, 20))
y = pd.Series([0] * 500 + [1] * 1500)
assert hash_X_y(X, y, 10, 10) == (joblib.hash(X.iloc[::200, ::2]),
joblib.hash(y.iloc[::200]))
X = pd.DataFrame(rng.randn(5, 2))
y = pd.Series([0] * 2 + [1] * 3)
# all data will be used in this case
assert hash_X_y(X, y) == (joblib.hash(X), joblib.hash(y))
def fit(self, X, y):
"""Find the classes statistics before to perform sampling.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : array-like, shape (n_samples,)
Corresponding label for each sample in X.
Returns
-------
self : object,
Return self.
"""
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
y = check_target_type(y)
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
self.ratio_ = check_ratio(self.ratio, y, self._sampling_type)
# Cluster input space
self.clustering_labels_ = self.clusterer[0][1].fit_predict(X, y)
# Identify majority and minority
majority_label = [label for label, n_samples in self.ratio_.items() if n_samples == 0][0]
minority_labels = [label for label in self.ratio_.keys() if label != majority_label]
# Clusters imbalance ratios
weights = pd.DataFrame()
return self
def fit(self, X, y=None):
"""Save the initial input matrix and the number of samples
to be removed.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : array-like, shape (n_samples,)
Corresponding label for each sample in X.
Returns
-------
self : object,
Return self.
"""
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
if self.ratio is not None and self.ratio != 1.0:
self.ratio_ = self.ratio
self.n_samples_ = int(self.ratio_ * len(X))
else:
self.ratio_ = None
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
return self
def fit(self, X, y):
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
y = check_target_type(y)
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
self._fit(X, y)
return self
def fit(self, X, y):
"""
Find the classes statistics to perform sampling.
Parameters
----------
X : 2d ndarray or scipy sparse matrix, shape [n_samples, n_features]
Matrix containing the data which have to be sampled.
y : 1d ndarray, shape [n_samples]
Corresponding label for each sample in X.
Returns
-------
self
"""
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
y = check_target_type(y)
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
self.ratio_ = check_ratio(self.ratio, y)
return self
def fit(self, X, y):
"""Find the classes statistics before to perform sampling.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : array-like, shape (n_samples,)
Corresponding label for each sample in X.
Returns
-------
self : object,
Return self.
"""
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'], dtype=None)
y = check_target_type(y)
self.ratio_ = self.ratio
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
labels = np.unique(y)
counts = np.bincount(y)
under_dict = {}
over_dict = {}
for lbl in labels:
count = counts[lbl]
if count < self.min_freq:
under_dict[lbl] = count
over_dict[lbl] = self.min_freq
elif count > self.max_freq:
under_dict[lbl] = self.max_freq
over_dict[lbl] = self.max_freq
else:
under_dict[lbl] = count
over_dict[lbl] = count
self.under_sampler = RandomUnderSampler(ratio=under_dict,
random_state=self.random_state)
self.over_sampler = RandomOverSampler(ratio=over_dict,
random_state=self.random_state)
return self
def fit(self, X, y):
self.ratio_ = 1
self.X_hash_ = hash_X_y(X, y)
return self
def fit(self, X, y):
self.ratio_ = 1
self.X_hash_ = hash_X_y(X, y)
return self
