def fit(self, X, y):
# Convert data
X, y = check_X_y(X,
y,
accept_sparse=("csr", "csc"),
multi_output=True,
y_numeric=True)
return self
def fit(self, X, y, sample_weight=None):
# Convert data
X, y = check_X_y(X,
y,
accept_sparse=("csr", "csc"),
multi_output=True,
y_numeric=True)
# Function is only called after we verify that pandas is installed
from pandas import Series
if isinstance(sample_weight, Series):
raise ValueError("Estimator does not accept 'sample_weight'"
"of type pandas.Series")
return self
def fit(self, X, y):
X, y = check_X_y(X,
y,
accept_sparse=("csr", "csc", "coo"),
accept_large_sparse=True,
multi_output=True,
y_numeric=True)
if sp.issparse(X):
if X.getformat() == "coo":
if X.row.dtype == "int64" or X.col.dtype == "int64":
raise ValueError(
"Estimator doesn't support 64-bit indices")
elif X.getformat() in ["csc", "csr"]:
if X.indices.dtype == "int64" or X.indptr.dtype == "int64":
raise ValueError(
"Estimator doesn't support 64-bit indices")
return self
def fit(self, X, y=None):
self._good_attribute = 1
X, y = check_X_y(X, y)
return self
def fit(self, X, y=None):
self.wrong_attribute = 0
X, y = check_X_y(X, y)
return self
def fit(self, X, y=None):
X, y = check_X_y(X, y)
return self
def fit(self, X, y):
X, y = check_X_y(X, y)
if (y <= 0).any():
raise ValueError('negative y values not supported!')
return super().fit(X, y)
def fit(self, X, y):
X, y = check_X_y(X, y)
self.coef_ = np.ones(X.shape[1])
return self
def fit(self, X, y):
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
if sp.issparse(X):
raise ValueError("Nonsensical Error")
return self