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# original author: Reza Hosseini
"""sklearn estimator for ``forecast_silverkite``"""
import pandas as pd
from sklearn.metrics import mean_squared_error
from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
from greykite.algo.forecast.silverkite.silverkite_diagnostics import SilverkiteDiagnostics
from greykite.common import constants as cst
from greykite.common.python_utils import update_dictionary
from greykite.sklearn.estimator.base_silverkite_estimator import BaseSilverkiteEstimator
[docs]class SilverkiteEstimator(BaseSilverkiteEstimator):
"""Wrapper for `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`.
Parameters
----------
score_func : callable, optional, default mean_squared_error
See `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`.
coverage : `float` between [0.0, 1.0] or None, optional
See `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`.
null_model_params : `dict` or None, optional
Dictionary with arguments to define ``DummyRegressor`` null model, default is `None`.
See `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`.
fit_algorithm_dict : `dict` or None, optional
How to fit the model. A dictionary with the following optional keys.
``"fit_algorithm"`` : `str`, optional, default "linear"
The type of predictive model used in fitting.
See `~greykite.algo.common.ml_models.fit_model_via_design_matrix`
for available options and their parameters.
``"fit_algorithm_params"`` : `dict` or None, optional, default None
Parameters passed to the requested fit_algorithm.
If None, uses the defaults in `~greykite.algo.common.ml_models.fit_model_via_design_matrix`.
uncertainty_dict : `dict` or `str` or None, optional
How to fit the uncertainty model.
See `~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`.
Note that this is allowed to be "auto". If None or "auto", will be set to
a default value by ``coverage`` before calling ``forecast_silverkite``.
See ``BaseForecastEstimator`` for details.
fs_components_df : `pandas.DataFrame` or None, optional
A dataframe with information about fourier series generation.
If provided, it must contain columns with following names:
- `"name"`: name of the timeseries feature (e.g. ``tod``, ``tow`` etc.).
- `"period"`: Period of the fourier series.
- `"order"`: Order of the fourier series.
`"seas_names"`: Label for the type of seasonality (e.g. ``daily``, ``weekly`` etc.)
and should be unique.
- `~greykite.sklearn.estimator.silverkite_estimator.SilverkiteEstimator.validate_fs_components_df`
checks for it, so that component plots don't have duplicate y-axis labels.
This differs from the expected input of `forecast_silverkite` where `"period"`, `"order"`
and `"seas_names"` are optional. This restriction is to facilitate appropriate computation
of component (e.g. trend, seasonalities and holidays) effects. See Notes section in this
docstring for a more detailed explanation with examples.
Other parameters are the same as in
`~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`.
If this Estimator is called from
:func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`,
``train_test_thresh`` and ``training_fraction`` should almost
always be `None`, because train/test is handled outside
this Estimator.
The attributes are the same as
`~greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator`.
See Also
--------
`~greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator`
For details on fit, predict, and component plots.
`~greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast.forecast`
Functions performing the fit and predict.
"""
def __init__(
self,
silverkite: SilverkiteForecast = SilverkiteForecast(),
silverkite_diagnostics: SilverkiteDiagnostics = SilverkiteDiagnostics(),
score_func=mean_squared_error,
coverage=None,
null_model_params=None,
origin_for_time_vars=None,
extra_pred_cols=None,
drop_pred_cols=None,
explicit_pred_cols=None,
train_test_thresh=None,
training_fraction=None,
fit_algorithm_dict=None,
daily_event_df_dict=None,
fs_components_df=pd.DataFrame({
"name": ["tod", "tow", "conti_year"],
"period": [24.0, 7.0, 1.0],
"order": [3, 3, 5],
"seas_names": ["daily", "weekly", "yearly"]}),
autoreg_dict=None,
past_df=None,
lagged_regressor_dict=None,
changepoints_dict=None,
seasonality_changepoints_dict=None,
changepoint_detector=None,
min_admissible_value=None,
max_admissible_value=None,
uncertainty_dict=None,
normalize_method=None,
adjust_anomalous_dict=None,
impute_dict=None,
regression_weight_col=None,
forecast_horizon=None,
simulation_based=False,
simulation_num=10):
# every subclass of BaseSilverkiteEstimator must call super().__init__
super().__init__(
silverkite=silverkite,
silverkite_diagnostics=silverkite_diagnostics,
score_func=score_func,
coverage=coverage,
null_model_params=null_model_params,
uncertainty_dict=uncertainty_dict)
# necessary to set parameters, to ensure get_params() works
# (used in grid search)
self.score_func = score_func
self.coverage = coverage
self.null_model_params = null_model_params
self.origin_for_time_vars = origin_for_time_vars
self.extra_pred_cols = extra_pred_cols
self.drop_pred_cols = drop_pred_cols
self.explicit_pred_cols = explicit_pred_cols
self.train_test_thresh = train_test_thresh
self.fit_algorithm_dict = fit_algorithm_dict
self.training_fraction = training_fraction
self.daily_event_df_dict = daily_event_df_dict
self.fs_components_df = fs_components_df
self.autoreg_dict = autoreg_dict
self.past_df = past_df
self.lagged_regressor_dict = lagged_regressor_dict
self.changepoints_dict = changepoints_dict
self.seasonality_changepoints_dict = seasonality_changepoints_dict
self.changepoint_detector = changepoint_detector
self.min_admissible_value = min_admissible_value
self.max_admissible_value = max_admissible_value
self.uncertainty_dict = uncertainty_dict
self.normalize_method = normalize_method
self.adjust_anomalous_dict = adjust_anomalous_dict
self.impute_dict = impute_dict
self.regression_weight_col = regression_weight_col
self.forecast_horizon = forecast_horizon
self.simulation_based = simulation_based
self.simulation_num = simulation_num
self.validate_inputs()
[docs] def fit(
self,
X,
y=None,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
**fit_params):
"""Fits ``Silverkite`` forecast model.
Parameters
----------
X: `pandas.DataFrame`
Input timeseries, with timestamp column,
value column, and any additional regressors.
The value column is the response, included in
``X`` to allow transformation by `sklearn.pipeline`.
y: ignored
The original timeseries values, ignored.
(The ``y`` for fitting is included in ``X``).
time_col: `str`
Time column name in ``X``.
value_col: `str`
Value column name in ``X``.
fit_params: `dict`
additional parameters for null model.
"""
# Initializes `fit_algorithm_dict` with default values.
# This cannot be done in __init__ to remain compatible
# with sklearn grid search.
default_fit_algorithm_dict = {
"fit_algorithm": "linear",
"fit_algorithm_params": None}
self.fit_algorithm_dict = update_dictionary(
default_fit_algorithm_dict,
overwrite_dict=self.fit_algorithm_dict)
# fits null model
super().fit(
X=X,
y=y,
time_col=time_col,
value_col=value_col,
**fit_params)
self.model_dict = self.silverkite.forecast(
df=X,
time_col=time_col,
value_col=value_col,
origin_for_time_vars=self.origin_for_time_vars,
extra_pred_cols=self.extra_pred_cols,
drop_pred_cols=self.drop_pred_cols,
explicit_pred_cols=self.explicit_pred_cols,
train_test_thresh=self.train_test_thresh,
training_fraction=self.training_fraction,
fit_algorithm=self.fit_algorithm_dict["fit_algorithm"],
fit_algorithm_params=self.fit_algorithm_dict["fit_algorithm_params"],
daily_event_df_dict=self.daily_event_df_dict,
fs_components_df=self.fs_components_df,
autoreg_dict=self.autoreg_dict,
past_df=self.past_df,
lagged_regressor_dict=self.lagged_regressor_dict,
changepoints_dict=self.changepoints_dict,
seasonality_changepoints_dict=self.seasonality_changepoints_dict,
changepoint_detector=self.changepoint_detector,
min_admissible_value=self.min_admissible_value,
max_admissible_value=self.max_admissible_value,
uncertainty_dict=self.uncertainty_dict,
normalize_method=self.normalize_method,
adjust_anomalous_dict=self.adjust_anomalous_dict,
impute_dict=self.impute_dict,
regression_weight_col=self.regression_weight_col,
forecast_horizon=self.forecast_horizon,
simulation_based=self.simulation_based,
simulation_num=self.simulation_num)
# sets attributes based on ``self.model_dict``
super().finish_fit()
return self
[docs] @staticmethod
def validate_fs_components_df(fs_components_df):
"""Validates the inputs of a fourier series components dataframe
called by ``SilverkiteEstimator`` to validate the input ``fs_components_df``.
Parameters
----------
fs_components_df : `pandas.DataFrame`
A DataFrame with information about fourier series generation.
Must contain columns with following names:
- "name": name of the timeseries feature (e.g. "tod", "tow" etc.)
- "period": Period of the fourier series
- "order": Order of the fourier series
- "seas_names": seas_name corresponding to the name (e.g. "daily", "weekly" etc.).
"""
fs_cols_expected = ["name", "period", "order", "seas_names"]
fs_cols_not_found = [col for col in fs_cols_expected if col not in fs_components_df.columns]
if fs_cols_not_found:
raise ValueError(f"fs_components_df is missing the following columns: {fs_cols_not_found}")
if any(fs_components_df.duplicated(subset=["name", "seas_names"])):
raise ValueError("Found multiple rows in fs_components_df with the same `names` and "
"`seas_names`. Make sure these are unique.")