def transform(self, Z, X=None):
"""Transform data.
Parameters
----------
Z : pd.Series
Series to transform.
X : pd.DataFrame, optional (default=None)
Exogenous data used in transformation.
Returns
-------
theta_lines: ndarray or pd.DataFrame
Transformed series: single Theta-line or a pd.DataFrame of
shape: len(Z)*len(self.theta).
"""
self.check_is_fitted()
z = check_series(Z, enforce_univariate=True)
theta = _check_theta(self.theta)
forecaster = PolynomialTrendForecaster()
forecaster.fit(z)
fh = ForecastingHorizon(z.index, is_relative=False)
trend = forecaster.predict(fh)
theta_lines = np.zeros((z.shape[0], len(theta)))
for i, theta in enumerate(theta):
theta_lines[:, i] = _theta_transform(z, trend, theta)
if isinstance(self.theta, (float, int)):
return pd.Series(theta_lines.flatten(), index=z.index)
else:
return pd.DataFrame(theta_lines, columns=self.theta, index=z.index)
def _transform(self, X, y=None):
"""Transform X and return a transformed version.
private _transform containing the core logic, called from transform
Parameters
----------
X : pd.Series or pd.DataFrame
Data to be transformed
y : ignored argument for interface compatibility
Additional data, e.g., labels for transformation
Returns
-------
theta_lines: pd.Series or pd.DataFrame
Transformed series
pd.Series, with single Theta-line, if self.theta is float
pd.DataFrame of shape: [len(X), len(self.theta)], if self.theta is tuple
"""
z = X
theta = _check_theta(self.theta)
forecaster = PolynomialTrendForecaster()
forecaster.fit(z)
fh = ForecastingHorizon(z.index, is_relative=False)
trend = forecaster.predict(fh)
theta_lines = np.zeros((z.shape[0], len(theta)))
for i, theta in enumerate(theta):
theta_lines[:, i] = _theta_transform(z, trend, theta)
if isinstance(self.theta, (float, int)):
return pd.Series(theta_lines.flatten(), index=z.index)
else:
return pd.DataFrame(theta_lines, columns=self.theta, index=z.index)
def transform(self, Z, X=None):
"""Transform data.
Returns a transformed version of Z.
Parameters
----------
Z : pd.Series, pd.DataFrame
Returns
-------
Z : pd.Series, pd.DataFrame
Transformed time series(es).
"""
self.check_is_fitted()
self._check_method()
Z = check_series(Z)
# replace missing_values with np.nan
if self.missing_values:
Z = Z.replace(to_replace=self.missing_values, value=np.nan)
if self.method == "random":
if isinstance(Z, pd.DataFrame):
for col in Z:
Z[col] = Z[col].apply(lambda i: self._get_random(Z[col])
if np.isnan(i) else i)
else:
Z = Z.apply(lambda i: self._get_random(Z)
if np.isnan(i) else i)
elif self.method == "constant":
Z = Z.fillna(value=self.value)
elif self.method in ["backfill", "bfill", "pad", "ffill"]:
Z = Z.fillna(method=self.method)
elif self.method in ["drift", "forecaster"]:
if self.method == "forecaster":
forecaster = self.forecaster
else:
forecaster = PolynomialTrendForecaster(degree=1)
# in-sample forecasting horizon
fh_ins = -np.arange(len(Z))
# fill NaN before fitting with ffill and backfill (heuristic)
Z = Z.fillna(method="ffill").fillna(method="backfill")
# multivariate
if isinstance(Z, pd.DataFrame):
for col in Z:
forecaster.fit(y=Z[col])
Z_pred = forecaster.predict(fh=fh_ins)
Z[col] = Z[col].fillna(value=Z_pred)
# univariate
else:
forecaster.fit(y=Z)
Z_pred = forecaster.predict(fh=fh_ins)
Z = Z.fillna(value=Z_pred)
elif self.method == "mean":
Z = Z.fillna(value=Z.mean())
elif self.method == "median":
Z = Z.fillna(value=Z.median())
elif self.method in ["nearest", "linear"]:
Z = Z.interpolate(method=self.method)
else:
raise ValueError(f"method {self.method} not available")
# fill first/last elements of series,
# as some methods (e.g. "linear") cant impute those
Z = Z.fillna(method="ffill").fillna(method="backfill")
return Z