def transform(self, X, y=None, copy=None):
"""Perform standardization by centering and scaling
Parameters
----------
X : array-like with shape [n_samples, n_features]
The data used to scale along the features axis.
"""
check_is_fitted(self, 'std_')
copy = copy if copy is not None else self.copy
X = check_array(X, copy=copy, accept_sparse="csc", ensure_2d=False)
if warn_if_not_float(X, estimator=self):
X = X.astype(np.float)
if sparse.issparse(X):
if self.center_sparse:
for i in range(X.shape[1]):
X.data[X.indptr[i]:X.indptr[i + 1]] -= self.mean_[i]
elif self.with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` "
"instead. See docstring for motivation and alternatives.")
else:
pass
if self.std_ is not None:
inplace_column_scale(X, 1 / self.std_)
else:
if self.with_mean:
X -= self.mean_
if self.with_std:
X /= self.std_
return X
def fit(self, X, y=None):
"""Don't trust the documentation of this module!
Compute the mean and std to be used for later scaling.
Parameters
----------
X : array-like or CSR matrix with shape [n_samples, n_features]
The data used to compute the mean and standard deviation
used for later scaling along the features axis.
"""
X = check_array(X, copy=self.copy, accept_sparse="csc",
ensure_2d=False)
if warn_if_not_float(X, estimator=self):
# Costly conversion, but otherwise the pipeline will break:
# https://github.com/scikit-learn/scikit-learn/issues/1709
X = X.astype(np.float32)
if sparse.issparse(X):
if self.center_sparse:
means = []
vars = []
# This only works for csc matrices...
for i in range(X.shape[1]):
if X.indptr[i] == X.indptr[i + 1]:
means.append(0)
vars.append(1)
else:
vars.append(
X.data[X.indptr[i]:X.indptr[i + 1]].var())
# If the variance is 0, set all occurences of this
# features to 1
means.append(
X.data[X.indptr[i]:X.indptr[i + 1]].mean())
if 0.0000001 >= vars[-1] >= -0.0000001:
means[-1] -= 1
self.std_ = np.sqrt(np.array(vars))
self.std_[np.array(vars) == 0.0] = 1.0
self.mean_ = np.array(means)
return self
elif self.with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` "
"instead. See docstring for motivation and alternatives.")
else:
self.mean_ = None
if self.with_std:
var = mean_variance_axis(X, axis=0)[1]
self.std_ = np.sqrt(var)
self.std_[var == 0.0] = 1.0
else:
self.std_ = None
return self
else:
self.mean_, self.std_ = _mean_and_std(
X, axis=0, with_mean=self.with_mean, with_std=self.with_std)
return self
def fit(self, X, y=None):
"""Compute the minimum and maximum to be used for later scaling.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data used to compute the per-feature minimum and maximum
used for later scaling along the features axis.
"""
X = check_array(X,
copy=self.copy,
ensure_2d=True,
accept_sparse="csc",
dtype=np.float32,
ensure_min_samples=2)
if warn_if_not_float(X, estimator=self):
# Costly conversion, but otherwise the pipeline will break:
# https://github.com/scikit-learn/scikit-learn/issues/1709
X = X.astype(np.float)
feature_range = self.feature_range
if feature_range[0] >= feature_range[1]:
raise ValueError("Minimum of desired feature range must be smaller"
" than maximum. Got %s." % str(feature_range))
if sparse.issparse(X):
data_min = []
data_max = []
data_range = []
for i in range(X.shape[1]):
if X.indptr[i] == X.indptr[i + 1]:
data_min.append(0)
data_max.append(0)
data_range.append(0)
else:
data_min.append(X.data[X.indptr[i]:X.indptr[i + 1]].min())
data_max.append(X.data[X.indptr[i]:X.indptr[i + 1]].max())
data_min = np.array(data_min, dtype=np.float32)
data_max = np.array(data_max, dtype=np.float32)
data_range = data_max - data_min
else:
data_min = np.min(X, axis=0)
data_range = np.max(X, axis=0) - data_min
# Do not scale constant features
if isinstance(data_range, np.ndarray):
# For a sparse matrix, constant features will be set to one!
if sparse.issparse(X):
for i in range(len(data_min)):
if data_range[i] == 0.0:
data_min[i] = data_min[i] - 1
data_range[data_range == 0.0] = 1.0
elif data_range == 0.:
data_range = 1.
self.scale_ = (feature_range[1] - feature_range[0]) / data_range
self.min_ = feature_range[0] - data_min * self.scale_
self.data_range = data_range
self.data_min = data_min
return self
def transform(self, X, y=None, copy=None):
"""Perform standardization by centering and scaling
Parameters
----------
X : array-like with shape [n_samples, n_features]
The data used to scale along the features axis.
"""
check_is_fitted(self, 'std_')
copy = copy if copy is not None else self.copy
X = check_array(X, copy=copy, accept_sparse="csc", ensure_2d=False)
if warn_if_not_float(X, estimator=self):
X = X.astype(np.float)
if sparse.issparse(X):
if self.center_sparse:
for i in range(X.shape[1]):
X.data[X.indptr[i]:X.indptr[i + 1]] -= self.mean_[i]
elif self.with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` "
"instead. See docstring for motivation and alternatives.")
else:
pass
if self.std_ is not None:
inplace_column_scale(X, 1 / self.std_)
else:
if self.with_mean:
X -= self.mean_
if self.with_std:
X /= self.std_
return X
def fit(self, X, y=None):
"""Compute the minimum and maximum to be used for later scaling.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data used to compute the per-feature minimum and maximum
used for later scaling along the features axis.
"""
X = check_array(X, copy=self.copy, ensure_2d=True, accept_sparse="csc", dtype=np.float32, ensure_min_samples=2)
if warn_if_not_float(X, estimator=self):
# Costly conversion, but otherwise the pipeline will break:
# https://github.com/scikit-learn/scikit-learn/issues/1709
X = X.astype(np.float)
feature_range = self.feature_range
if feature_range[0] >= feature_range[1]:
raise ValueError(
"Minimum of desired feature range must be smaller" " than maximum. Got %s." % str(feature_range)
)
if sparse.issparse(X):
data_min = []
data_max = []
data_range = []
for i in range(X.shape[1]):
if X.indptr[i] == X.indptr[i + 1]:
data_min.append(0)
data_max.append(0)
data_range.append(0)
else:
data_min.append(X.data[X.indptr[i] : X.indptr[i + 1]].min())
data_max.append(X.data[X.indptr[i] : X.indptr[i + 1]].max())
data_min = np.array(data_min, dtype=np.float32)
data_max = np.array(data_max, dtype=np.float32)
data_range = data_max - data_min
else:
data_min = np.min(X, axis=0)
data_range = np.max(X, axis=0) - data_min
# Do not scale constant features
if isinstance(data_range, np.ndarray):
# For a sparse matrix, constant features will be set to one!
if sparse.issparse(X):
for i in range(len(data_min)):
if data_range[i] == 0.0:
data_min[i] = data_min[i] - 1
data_range[data_range == 0.0] = 1.0
elif data_range == 0.0:
data_range = 1.0
self.scale_ = (feature_range[1] - feature_range[0]) / data_range
self.min_ = feature_range[0] - data_min * self.scale_
self.data_range = data_range
self.data_min = data_min
return self
def fit(self, X, y=None):
X = check_array(X,
copy=self.copy,
ensure_2d=True,
accept_sparse="csc",
dtype=np.float32,
ensure_min_samples=2)
if warn_if_not_float(X, estimator=self):
X = X.astype(np.float)
feature_range = self.feature_range
if feature_range[0] >= feature_range[1]:
raise ValueError("Minimum of desired feature range must be smaller"
" than maximum. Got %s." % str(feature_range))
if sparse.issparse(X):
data_min = []
data_max = []
data_range = []
for i in range(X.shape[1]):
if X.indptr[i] == X.indptr[i + 1]:
data_min.append(0)
data_max.append(0)
data_range.append(0)
else:
data_min.append(X.data[X.indptr[i]:X.indptr[i + 1]].min())
data_max.append(X.data[X.indptr[i]:X.indptr[i + 1]].max())
data_min = np.array(data_min, dtype=np.float32)
data_max = np.array(data_max, dtype=np.float32)
data_range = data_max - data_min
else:
data_min = np.min(X, axis=0)
data_range = np.max(X, axis=0) - data_min
# Do not scale constant features
if isinstance(data_range, np.ndarray):
# For a sparse matrix, constant features will be set to one!
if sparse.issparse(X):
for i in range(len(data_min)):
if data_range[i] == 0.0:
data_min[i] = data_min[i] - 1
data_range[data_range == 0.0] = 1.0
elif data_range == 0.:
data_range = 1.
self.scale_ = (feature_range[1] - feature_range[0]) / data_range
self.min_ = feature_range[0] - data_min * self.scale_
self.data_range = data_range
self.data_min = data_min
return self
def fit(self, X, y=None):
"""Don't trust the documentation of this module!
Compute the mean and std to be used for later scaling.
Parameters
----------
X : array-like or CSR matrix with shape [n_samples, n_features]
The data used to compute the mean and standard deviation
used for later scaling along the features axis.
"""
X = check_array(X,
copy=self.copy,
accept_sparse="csc",
ensure_2d=False)
if warn_if_not_float(X, estimator=self):
# Costly conversion, but otherwise the pipeline will break:
# https://github.com/scikit-learn/scikit-learn/issues/1709
X = X.astype(np.float32)
if sparse.issparse(X):
if self.center_sparse:
means = []
vars = []
# This only works for csc matrices...
for i in range(X.shape[1]):
if X.indptr[i] == X.indptr[i + 1]:
means.append(0)
vars.append(1)
else:
vars.append(X.data[X.indptr[i]:X.indptr[i + 1]].var())
# If the variance is 0, set all occurences of this
# features to 1
means.append(X.data[X.indptr[i]:X.indptr[i +
1]].mean())
if 0.0000001 >= vars[-1] >= -0.0000001:
means[-1] -= 1
self.std_ = np.sqrt(np.array(vars))
self.std_[np.array(vars) == 0.0] = 1.0
self.mean_ = np.array(means)
return self
elif self.with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` "
"instead. See docstring for motivation and alternatives.")
else:
self.mean_ = None
if self.with_std:
var = mean_variance_axis(X, axis=0)[1]
self.std_ = np.sqrt(var)
self.std_[var == 0.0] = 1.0
else:
self.std_ = None
return self
else:
self.mean_, self.std_ = _mean_and_std(X,
axis=0,
with_mean=self.with_mean,
with_std=self.with_std)
return self
