def fit(self, X, y=None):
""" A reference implementation of a fitting function.
Parameters
----------
X : array-like, pandas DataFrame or Series, shape (n_samples, ...)
The training input samples.
y : None, as it is transformer on X
Returns
-------
self : object
Returns self.
"""
# check the validity of input
X = check_ts_array(X)
if not X.shape[1] == len(self.funcs):
raise ValueError(
"No. of columns and No. of functions supplied to transformer dont match"
)
# fitting - this transformer needs no fitting
pass
# let the model know that it is fitted
self.is_fitted_ = True
# `fit` should always return `self`
return self
def fit(self, X, y=None):
""" A reference implementation of a fitting function.
Parameters
----------
X : array-like, pandas DataFrame or Series, shape (n_samples, ...)
The training input samples.
y : None, as it is transformer on X
Returns
-------
self : object
Returns self.
"""
# check the validity of input
X = check_ts_array(X)
# fitting - this transformer needs no fitting
pass
# let the model know that it is fitted
self.is_fitted_ = True
# `fit` should always return `self`
return self
def predict(self, X):
""" A reference implementation of a predicting function.
Parameters
----------
X : array-like, pandas DataFrame or Series, shape (n_samples, ...)
The training input samples.
Returns
-------
y : ndarray, shape (n_samples,)
Returns the dummy predictions
"""
X = check_ts_array(X)
check_is_fitted(self, 'is_fitted_')
return np.ones(X.shape[0], dtype=np.int64) * self.theta_
def predict(self, X):
""" A reference implementation of a predicting function.
Parameters
----------
X : array-like, pandas DataFrame or Series, shape (n_samples, ...)
The training input samples.
Returns
-------
y : ndarray, shape (n_samples,)
Returns the dummy predictions
"""
X = pd.DataFrame([X[col].apply(self.func) for col in self.columns]).T
X = check_ts_array(X)
check_is_fitted(self, 'is_fitted_')
return self.estimator.predict(X)
def transform(self, X):
""" A reference implementation of a predicting function.
Parameters
----------
X : array-like, pandas DataFrame or Series, shape (n_samples, ...)
The training input samples.
Returns
-------
T : array-like, pandas DataFrame or Series, shape (n_samples, ...)
The transformed data
"""
# check validity of input
X = check_ts_array(X)
check_is_fitted(self, 'is_fitted_')
T = X
return T