class HorizonTransformer(BaseEstimator, TransformerMixin):
needs_refit = True
y_only = True
def __init__(self, horizon=2):
super().__init__()
if horizon < 2:
raise ValueError("horizon must be greater than 1")
self.horizon = horizon
self.autoregressive_transformer = AutoregressiveTransformer(
num_lags=self.horizon, pred_stride=1)
def fit(self, X, y=None):
self.autoregressive_transformer.fit(expand_dim_if_needed(X))
return self
def transform(self, X, y=None, refit=False):
X = expand_dim_if_needed(X)
if refit:
self.autoregressive_transformer.fit(X)
Xt = self.autoregressive_transformer.transform(X)
# The autoregressive transformer won't build lags _with_ the last element of X.
# So, we have to manually tack it on here.
last_non_nan_piece = np.hstack((Xt[-1, 1:], X[-1]))
Xt = np.vstack(
(Xt[self.horizon:, :], last_non_nan_piece, Xt[1:self.horizon, :]))
return Xt
def inverse_transform(self, X, y=None):
Xt = np.vstack((X[-self.horizon:, :], X[:-self.horizon, :]))
return self.autoregressive_transformer.inverse_transform(Xt)
def fit_transform(self, X, y=None):
return self.fit(X).transform(X)
class HorizonTransformer(BaseEstimator, TransformerMixin):
needs_refit = True
y_only = True
def __init__(self, horizon=2):
super().__init__()
if horizon < 2:
raise ValueError('horizon must be greater than 1')
self.horizon = horizon
self.autoregressive_transformer = AutoregressiveTransformer(
num_lags=self.horizon, pred_stride=1)
def fit(self, X, y=None):
self.autoregressive_transformer.fit(expand_dim_if_needed(X))
return self
def transform(self, X, y=None, refit=False):
X = expand_dim_if_needed(X)
if refit:
self.autoregressive_transformer.fit(X)
Xt = self.autoregressive_transformer.transform(X)
# Need to move beginning of Xt to the end.
Xt = np.vstack((Xt[self.horizon:, :], Xt[:self.horizon, :]))
# TODO: replace beginning with nans?
return Xt
def inverse_transform(self, X, y=None):
Xt = np.vstack((X[-self.horizon:, :], X[:-self.horizon, :]))
return self.autoregressive_transformer.inverse_transform(Xt)
def fit_transform(self, X, y=None):
return self.fit(X).transform(X)