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Merged
BaptisteDE merged 1 commit into from
Mar 27, 2025
Merged

⚡️ SkProphet improved. SkSTLForecast and Regressor updated for sk API #31

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59 changes: 49 additions & 10 deletions tests/test_regressors.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,8 +23,16 @@ def toy_data(self):
)

toy_series = pd.Series(annual + daily + 5, index=index)

exo = 12 + 3 * np.arange(index.shape[0])

toy_df = pd.DataFrame(
{"Temp_1__°C": toy_series, "Temp_2__°C": toy_series * 1.25 + 2}
{
"Temp_1__°C": toy_series,
"Temp_2__°C": toy_series * 1.25 + 2,
"Temp_3__°C": toy_series + exo,
"Exo": exo,
}
)
return toy_df

Expand All @@ -36,40 +44,71 @@ def test_stl_forecaster(self, toy_data):
backcast=False,
)

forecaster.fit(toy_data["2009-01-24":"2009-07-24"])
forecaster.fit(
X=toy_data["2009-01-24":"2009-07-24"].index,
y=toy_data.loc["2009-01-24":"2009-07-24", ["Temp_1__°C", "Temp_2__°C"]],
)
reg_score = forecaster.score(
toy_data["2009-07-27":"2009-07-30"], toy_data["2009-07-27":"2009-07-30"]
toy_data["2009-07-27":"2009-07-30"],
toy_data.loc["2009-07-27":"2009-07-30", ["Temp_1__°C", "Temp_2__°C"]],
)
assert reg_score > 0.99

backcaster = SkSTLForecast(backcast=True)

backcaster.fit(toy_data["2009-01-24":"2009-07-24"])
backcaster.fit(
X=toy_data.loc["2009-01-24":"2009-07-24"],
y=toy_data.loc["2009-01-24":"2009-07-24", ["Temp_1__°C", "Temp_2__°C"]],
)

reg_score = backcaster.score(
toy_data["2009-01-20":"2009-01-22"], toy_data["2009-01-20":"2009-01-22"]
toy_data["2009-01-20":"2009-01-22"].index,
toy_data.loc["2009-01-20":"2009-01-22", ["Temp_1__°C", "Temp_2__°C"]],
)

assert reg_score > 0.99

def test_prophet_forecaster(self, toy_data):
forecaster = SkProphet()
forecaster.fit(toy_data["2009-01-24":"2009-07-24"])
forecaster.fit(
X=toy_data["2009-01-24":"2009-07-24"].index,
y=toy_data.loc["2009-01-24":"2009-07-24", ["Temp_1__°C", "Temp_2__°C"]],
)

reg_score = forecaster.score(
toy_data["2009-07-27":"2009-07-30"], toy_data["2009-07-27":"2009-07-30"]
toy_data["2009-07-27":"2009-07-30"].index,
toy_data.loc["2009-07-27":"2009-07-30", ["Temp_1__°C", "Temp_2__°C"]],
)
assert reg_score > 0.99

reg_score = forecaster.score(
toy_data["2009-01-20":"2009-01-22"], toy_data["2009-01-20":"2009-01-22"]
toy_data["2009-01-20":"2009-01-22"].index,
toy_data.loc["2009-01-20":"2009-01-22", ["Temp_1__°C", "Temp_2__°C"]],
)

assert reg_score > 0.99

forecaster = SkProphet(return_upper_lower_bounds=True)
forecaster.fit(toy_data["2009-01-24":"2009-07-24"])
forecaster.fit(
X=toy_data["2009-01-24":"2009-07-24"].index,
y=toy_data.loc["2009-01-24":"2009-07-24", ["Temp_1__°C", "Temp_2__°C"]],
)

feat_out = list(forecaster.get_feature_names_out())
predictions = forecaster.predict(toy_data["2009-07-27":"2009-07-30"])
predictions = forecaster.predict(toy_data["2009-07-27":"2009-07-30"].index)

assert np.all([feat in predictions.columns for feat in feat_out])

forecaster = SkProphet()
forecaster.fit(
X=toy_data.loc["2009-01-24":"2009-07-24", "Exo"],
y=toy_data.loc["2009-01-24":"2009-07-24", "Temp_3__°C"],
)

reg_score = forecaster.score(
toy_data.loc["2009-01-24":"2009-07-24", "Exo"],
toy_data.loc["2009-01-24":"2009-07-24", "Temp_3__°C"],
)

assert reg_score > 0.99
assert forecaster.get_feature_names_in() == ["Exo"]
7 changes: 3 additions & 4 deletions tide/processing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1912,10 +1912,9 @@ def fill_x(self, X, group, col, idx, backcast):
if self.resample_at_td:
self._check_forecast_horizon(idx)
x_fit, idx_pred = self._get_x_and_idx_at_freq(X.loc[group, col], idx, backcast)
bc_model.fit(x_fit)
to_predict = idx_pred.to_series()
to_predict.name = col
to_predict = to_predict[to_predict.index.isin(X.index)]
bc_model.fit(X=x_fit.index, y=x_fit)
to_predict = idx_pred
to_predict = to_predict[to_predict.isin(X.index)]
# Here a bit dirty. STL doesn't allow forecast on its fitting set
if self.model_name == "STL":
to_predict = to_predict[~to_predict.isin(x_fit.index)]
Expand Down
174 changes: 128 additions & 46 deletions tide/regressors.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,32 +1,43 @@
import datetime as dt
import warnings

import pandas as pd
import numpy as np
from pandas import DatetimeIndex

from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.forecasting.stl import STLForecast
from sklearn.base import RegressorMixin, BaseEstimator
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted

from prophet import Prophet

from tide.base import BaseSTL, TideBaseMixin
from tide.utils import check_and_return_dt_index_df
from tide.utils import check_and_return_dt_index_df, check_datetime_index

MODEL_MAP = {"ARIMA": ARIMA}
MODEL_DEFAULT_CONF = {"ARIMA": {"order": (1, 1, 0), "trend": "t"}}


def series_to_prophet_df(x: pd.Series | pd.DatetimeIndex) -> pd.DataFrame:
def format_prophet_df(
x: pd.Series | pd.DataFrame | pd.DatetimeIndex, y: pd.Series = None
) -> pd.DataFrame:
df = pd.DataFrame()
if isinstance(x, pd.Series):
idx = x.index
df["y"] = x.values
if y is not None:
if not x.shape[0] == y.shape[0]:
raise ValueError("x and y have incompatible shape")
df["y"] = y.values

if not isinstance(x, pd.DatetimeIndex):
x = check_and_return_dt_index_df(x)
df["ds"] = x.index.tz_localize(None)
df[x.columns] = x.values
elif isinstance(x, pd.DatetimeIndex):
df["ds"] = x.tz_localize(None)
else:
idx = x
df["ds"] = idx.tz_localize(None)
raise ValueError(
f"Invalid x. Was expecting an instance of DateTimeIndex"
f"DataFrame or Series, got {type(x)}"
)
return df


Expand Down Expand Up @@ -99,34 +110,37 @@ def __init__(
self.ar_model = ar_model
self.ar_kwargs = ar_kwargs

def fit(self, X: pd.Series | pd.DataFrame, y=None):
X = check_and_return_dt_index_df(X)
def fit(self, X: pd.Index | pd.Series | pd.DataFrame, y=pd.Series | pd.DataFrame):
if not isinstance(X, pd.DatetimeIndex):
X = check_and_return_dt_index_df(X)
y = check_and_return_dt_index_df(y)

ar_model = MODEL_MAP[self.ar_model]
if self.ar_kwargs is None:
ar_kwargs = MODEL_DEFAULT_CONF[self.ar_model]
else:
ar_kwargs = self.ar_kwargs

self._pre_fit(X)
self._pre_fit(y)
self.training_freq_ = (
X.index.freq if X.index.freq is not None else X.index.inferred_freq
y.index.freq if y.index.freq is not None else y.index.inferred_freq
)
if self.backcast:
X = X[::-1]
self.train_dat_end_ = X.index[-1]
y = y[::-1]
self.train_dat_end_ = y.index[-1]
self.forecaster_ = {}

for feat in X:
for feat in y:
self.forecaster_[feat] = STLForecast(
endog=X[feat].to_numpy(),
endog=y[feat].to_numpy(),
model=ar_model,
model_kwargs=ar_kwargs,
**self.stl_kwargs,
).fit()

return self

def predict(self, X: pd.Series | pd.DataFrame):
def predict(self, X: pd.DatetimeIndex | pd.Series | pd.DataFrame):
check_is_fitted(
self,
attributes=[
Expand All @@ -135,9 +149,11 @@ def predict(self, X: pd.Series | pd.DataFrame):
"training_freq_",
],
)

X = check_and_return_dt_index_df(X)
check_array(X)
if isinstance(X, DatetimeIndex):
check_datetime_index(X)
X = X.to_frame()
else:
X = check_and_return_dt_index_df(X)

if X.index.shape[0] == 2:
X.index.freq = pd.tseries.frequencies.to_offset(
Expand All @@ -162,13 +178,6 @@ def predict(self, X: pd.Series | pd.DataFrame):

output_index = X.index[::-1] if self.backcast else X.index

if set(self.forecaster_.keys()) != set(X.columns):
warnings.warn(
"Columns in X differs from columns in the training DataSet. "
"Forecast will be performed for the trained data",
UserWarning,
)

casting_steps = int(
len(output_index)
+ abs(output_index[0] - self.train_dat_end_) / self.training_freq_
Expand Down Expand Up @@ -220,6 +229,64 @@ class SkProphet(RegressorMixin, BaseEstimator, TideBaseMixin):
Fit the Prophet model to the input data.
predict(X)
Make predictions using the fitted Prophet model.

Examples
--------
>>> import pandas as pd
>>> import numpy as np
>>> import datetime as dt
>>> from tide.regressors import SkProphet

>>> index = pd.date_range("2009-01-01", "2009-12-31 23:00:00", freq="h", tz="UTC")
>>> cumsum_second = np.arange(
... 0, (index[-1] - index[0]).total_seconds() + 1, step=3600
... )

>>> annual = 5 * -np.cos(
... 2 * np.pi / dt.timedelta(days=360).total_seconds() * cumsum_second
...)

>>> daily = 5 * np.sin(
... 2 * np.pi / dt.timedelta(days=1).total_seconds() * cumsum_second
...)

>>> toy_series = pd.Series(annual + daily + 5, index=index)

>>> exo = 12 + 3 * np.arange(index.shape[0])

>>> toy_df = pd.DataFrame(
... {
... "Temp_3__°C": toy_series + exo,
... "Exo": exo,
... }
...)

>>> forecaster = SkProphet()
>>> forecaster.fit(
... X=toy_df.loc["2009-01-24":"2009-07-24", "Exo"],
... y=toy_df.loc["2009-01-24":"2009-07-24", "Temp_3__°C"],
...)

>>> result = forecaster.predict(X=toy_df.loc["2009-07-25":"2009-07-30", "Exo"])
>>> print(result.head())
Temp_3__°C
2009-07-25 00:00:00+00:00 14781.715143
2009-07-25 01:00:00+00:00 14786.009401
2009-07-25 02:00:00+00:00 14790.215280
2009-07-25 03:00:00+00:00 14794.250621
2009-07-25 04:00:00+00:00 14798.044970

Notes
-----
- Additional regressors are passed in X during fitting operation
- Holidays cannot be configured in this regressor. We recommend to pass it
as a feature during the fitting process. It will be treated as an additional
regressor

Returns
-------
pd.DataFrame
A DataFrame with DateTime index. Columns are the y targets
"""

def __init__(
Expand All @@ -237,43 +304,58 @@ def __init__(
self.return_upper_lower_bounds = return_upper_lower_bounds
self.backcast = backcast

def fit(self, X: pd.Series | pd.DataFrame, y=None):
X = check_and_return_dt_index_df(X)
def fit(self, X: pd.Index | pd.Series | pd.DataFrame, y=pd.Series | pd.DataFrame):
y = check_and_return_dt_index_df(y)
self.feature_names_out_ = list(y.columns)
if isinstance(X, pd.Series) or isinstance(X, pd.DataFrame):
X = check_and_return_dt_index_df(X)
self.feature_names_in_ = list(X.columns)
else:
check_datetime_index(X)
self.feature_names_in_ = []

self.forecaster_ = {}
self.fit_check_features(X)
if self.return_upper_lower_bounds:
self.added_columns = []
for bound in ["upper", "lower"]:
for feat in self.feature_names_in_:
for feat in self.feature_names_out_:
parts = feat.split("__")
parts[0] = f"{parts[0]}_{bound}"
self.added_columns.append("__".join(parts))

for feat in X:
x = series_to_prophet_df(X[feat])
self.forecaster_[feat] = Prophet(
for target in y:
prophet_df = format_prophet_df(X, y[target])
model = Prophet(
seasonality_prior_scale=self.seasonality_prior_scale,
changepoint_prior_scale=self.changepoint_prior_scale,
**self.prophet_kwargs,
).fit(x)
)
if isinstance(X, pd.DataFrame):
for feat in X:
model.add_regressor(feat)
self.forecaster_[target] = model.fit(prophet_df)
return self

def predict(self, X: pd.Series | pd.DataFrame):
X = check_and_return_dt_index_df(X)
def predict(self, X: pd.Index | pd.Series | pd.DataFrame):
check_is_fitted(
self,
attributes=["forecaster_", "feature_names_in_"],
)
if not np.all([f in self.feature_names_in_ for f in X.columns]):
raise ValueError(
"One of the requested feature was not present during fitting"
)

X = check_and_return_dt_index_df(X)
inferred_df = pd.DataFrame(index=X.index)
if isinstance(X, pd.Series) or isinstance(X, pd.DataFrame):
X = check_and_return_dt_index_df(X)
if not np.all([f in self.feature_names_in_ for f in X.columns]):
raise ValueError(
"One of the requested feature was not present during fitting"
)
else:
check_datetime_index(X)

out_idx = X if isinstance(X, pd.DatetimeIndex) else X.index
inferred_df = pd.DataFrame(index=out_idx)
for feat in self.forecaster_.keys():
x = series_to_prophet_df(X.index)
prediction = self.forecaster_[feat].predict(x)
df_prophet = format_prophet_df(X)
prediction = self.forecaster_[feat].predict(df_prophet)
inferred_df[feat] = prediction["yhat"].values
if self.return_upper_lower_bounds:
for bound in ["upper", "lower"]:
Expand Down
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