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