def test_set_params():
model = OneByOneEstimator(estimator="SimpleSilverkiteEstimator",
forecast_horizon=3,
estimator_map=1,
estimator_params={
"autoreg_dict": "auto",
"yearly_seasonality": 2,
"feature_sets_enabled": False
})
assert model.estimator_map == 1
assert model.estimator_params["yearly_seasonality"] == 2
assert model.estimator_params.get("daily_seasonality") is None
model.set_params(**{
"estimator_map": 2,
"yearly_seasonality": 4,
"daily_seasonality": 2
})
assert model.estimator_map == 2
assert model.estimator_params["yearly_seasonality"] == 4
assert model.estimator_params["daily_seasonality"] == 2
assert set(model.estimator_param_names) == set(
SimpleSilverkiteEstimator().get_params().keys())
with pytest.raises(
ValueError,
match=
r"Invalid parameter some_param for estimator OneByOneEstimator. "
r"Check the list of available parameters with "
r"`estimator.get\_params\(\).keys\(\)`."):
model.set_params(**{"some_param": 5})
def test_setup(params):
"""Tests __init__ and attributes set during fit"""
coverage = 0.90
silverkite = SimpleSilverkiteForecast()
model = SimpleSilverkiteEstimator(silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params)
assert model.silverkite == silverkite
assert model.score_func == mean_squared_error
assert model.coverage == coverage
assert model.null_model_params is None
# set_params must be able to replicate the init
model2 = SimpleSilverkiteEstimator()
model2.set_params(**dict(silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params))
assert model2.__dict__ == model.__dict__
initialized_params = model.__dict__
initialized_params_subset = {
k: v
for k, v in initialized_params.items() if k in params.keys()
}
assert_equal(initialized_params_subset, params)
assert model.model_dict is None
assert model.pred_cols is None
assert model.feature_cols is None
assert model.coef_ is None
train_df = daily_data_reg().get("train_df").copy()
model.fit(train_df)
assert model.fit_algorithm_dict == {
"fit_algorithm": "sgd",
"fit_algorithm_params": {
"alpha": 0.1
}
}
assert model.model_dict is not None
assert type(model.model_dict["ml_model"]) == SGDRegressor
assert model.model_dict["ml_model"].alpha == (
params["fit_algorithm_dict"]["fit_algorithm_params"]["alpha"])
assert model.model_dict["training_evaluation"] is not None
assert model.model_dict["test_evaluation"] is None
assert model.pred_cols is not None
assert model.feature_cols is not None
assert_frame_equal(model.df, train_df)
assert model.coef_ is not None
def test_uncertainty(daily_data):
"""Runs a basic model with uncertainty intervals
and checks coverage"""
uncertainty_dict = {
"uncertainty_method": "simple_conditional_residuals",
"params": {
"conditional_cols": ["dow_hr"],
"quantiles": [0.025, 0.975],
"quantile_estimation_method": "normal_fit",
"sample_size_thresh": 10,
"small_sample_size_method": "std_quantiles",
"small_sample_size_quantile": 0.98
}
}
model = SimpleSilverkiteEstimator(
uncertainty_dict=uncertainty_dict,
fit_algorithm_dict={"fit_algorithm": "linear"},
holidays_to_model_separately=[],
yearly_seasonality=5,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=3,
feature_sets_enabled=False)
train_df = daily_data["train_df"]
test_df = daily_data["test_df"]
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.forecast is None
predictions = model.predict(test_df)
expected_forecast_cols = \
{"ts", "y", "y_quantile_summary", "err_std", "forecast_lower", "forecast_upper"}
assert expected_forecast_cols.issubset(list(model.forecast.columns))
actual = daily_data["test_df"][cst.VALUE_COL]
forecast_lower = predictions[cst.PREDICTED_LOWER_COL]
forecast_upper = predictions[cst.PREDICTED_UPPER_COL]
calc_pred_coverage = 100 * ((actual <= forecast_upper)
& (actual >= forecast_lower)).mean()
assert round(calc_pred_coverage) == 95, "forecast coverage is incorrect"
def test_score_function_null(daily_data):
"""Tests fit and its compatibility with predict/score.
Checks score function accuracy with null model
"""
model = SimpleSilverkiteEstimator(
null_model_params={"strategy": "mean"},
fit_algorithm_dict={"fit_algorithm": "linear"},
holidays_to_model_separately=[],
yearly_seasonality=5,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=3,
feature_sets_enabled=False)
train_df = daily_data["train_df"]
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.fit_algorithm_dict == {
"fit_algorithm": "linear",
"fit_algorithm_params": None
}
score = model.score(daily_data["test_df"],
daily_data["test_df"][cst.VALUE_COL])
assert score == pytest.approx(0.90, rel=1e-2)
def test_summary(daily_data):
"""Checks summary function returns without error"""
model = SimpleSilverkiteEstimator(
fit_algorithm_dict={"fit_algorithm_params": {
"cv": 3
}})
train_df = daily_data["train_df"]
model.summary()
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.fit_algorithm_dict == {
"fit_algorithm": "ridge",
"fit_algorithm_params": {
"cv": 3
}
}
model.summary()
def test_pipeline_union(X, fs):
"""Tests PandasFeatureUnion on a pipeline of transformers and estimator, and shows
that null model extracted from estimator in pipeline is equivalent to null model trained
directly"""
model_estimator = Pipeline([
("input", fs),
("estimator", SimpleSilverkiteEstimator(score_func=mean_squared_error,
coverage=0.80,
null_model_params={"strategy": "mean"}))
])
# fits pipeline with estimator, and extract dummy null model
z_cutoff = 2.0
model_estimator.set_params(input__response__outlier__z_cutoff=z_cutoff)
model_estimator.fit(X)
output_estimator_null = model_estimator.steps[-1][-1].null_model.predict(X)
# fits pipeline with dummy estimator
model_dummy = Pipeline([
("input", fs),
("dummy", DummyEstimator(score_func=mean_squared_error, strategy="mean"))
])
model_dummy.fit(X)
output_dummy = model_dummy.predict(X)
# fits dummy estimator by hand, without Pipeline
X_after_column_select = ColumnSelector([VALUE_COL]).fit_transform(X)
X_after_z_score = ZscoreOutlierTransformer(z_cutoff=z_cutoff).fit_transform(X_after_column_select)
X_after_null = NullTransformer().fit_transform(X_after_z_score)
X_after_union = pd.concat([X[TIME_COL], X_after_null], axis=1)
model_hand = DummyEstimator(strategy="mean")
model_hand.fit(X_after_union)
output_by_hand = model_hand.predict(X_after_union)
assert output_estimator_null.equals(output_by_hand)
assert output_dummy.equals(output_by_hand)
def test_score_function(daily_data_with_reg):
"""Tests fit and its compatibility with predict/score.
Checks score function accuracy without null model
"""
model = SimpleSilverkiteEstimator(
extra_pred_cols=["ct1", "regressor1", "regressor2"],
fit_algorithm_dict={"fit_algorithm": "linear"},
holidays_to_model_separately=[],
yearly_seasonality=5,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=3,
feature_sets_enabled=False)
train_df = daily_data_with_reg["train_df"]
test_df = daily_data_with_reg["test_df"]
model.fit(X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
score = model.score(test_df, test_df[cst.VALUE_COL])
pred_df = model.predict(test_df)
assert list(pred_df.columns) == [cst.TIME_COL, cst.PREDICTED_COL]
assert score == pytest.approx(
mean_squared_error(pred_df[cst.PREDICTED_COL], test_df[cst.VALUE_COL]))
assert score == pytest.approx(4.39, rel=1e-2)
def pipeline_wrapper(
# The arguments to this wrapper must be identical to forecast_pipeline() function.
# We don't use **kwargs
# because it's easier to check parameters directly.
# input
df: pd.DataFrame,
time_col=TIME_COL,
value_col=VALUE_COL,
date_format=None,
tz=None,
freq=None,
train_end_date=None,
anomaly_info=None,
# model
pipeline=None,
regressor_cols=None,
lagged_regressor_cols=None,
estimator=SimpleSilverkiteEstimator(),
hyperparameter_grid=None,
hyperparameter_budget=None,
n_jobs=COMPUTATION_N_JOBS,
verbose=1,
# forecast
forecast_horizon=None,
coverage=0.95,
test_horizon=None,
periods_between_train_test=None,
agg_periods=None,
agg_func=None,
# evaluation
score_func=EvaluationMetricEnum.MeanAbsolutePercentError.name,
score_func_greater_is_better=False,
cv_report_metrics=None,
null_model_params=None,
relative_error_tolerance=None,
# CV
cv_horizon=None,
cv_min_train_periods=None,
cv_expanding_window=False,
cv_use_most_recent_splits=False,
cv_periods_between_splits=None,
cv_periods_between_train_test=0,
cv_max_splits=3):
if coverage is not None and (coverage < 0 or coverage > 1):
raise ValueError(f"coverage must be between 0 and 1, found {coverage}")
if relative_error_tolerance is not None and relative_error_tolerance < 0:
raise ValueError(f"relative_error_tolerance must non-negative, found {relative_error_tolerance}")
# default values for forecast horizon, test, and cross-validation parameters
period = min_gap_in_seconds(df=df, time_col=time_col)
num_observations = df.shape[0]
default_time_params = get_default_time_parameters(
period=period,
num_observations=num_observations,
forecast_horizon=forecast_horizon,
test_horizon=test_horizon,
periods_between_train_test=periods_between_train_test,
cv_horizon=cv_horizon,
cv_min_train_periods=cv_min_train_periods,
cv_periods_between_train_test=cv_periods_between_train_test)
forecast_horizon = default_time_params.get("forecast_horizon")
test_horizon = default_time_params.get("test_horizon")
periods_between_train_test = default_time_params.get("periods_between_train_test")
cv_horizon = default_time_params.get("cv_horizon")
cv_min_train_periods = default_time_params.get("cv_min_train_periods")
cv_periods_between_train_test = default_time_params.get("cv_periods_between_train_test")
# ensures the values are integers in the proper domain
if hyperparameter_budget is not None:
hyperparameter_budget = get_integer(
hyperparameter_budget,
"hyperparameter_budget",
min_value=1)
if (cv_horizon == 0 or cv_max_splits == 0) and test_horizon == 0:
raise ValueError("Either CV or backtest must be enabled."
" Set cv_horizon and cv_max_splits to nonzero values to enable CV."
" Set test_horizon to nonzero value to enable backtest."
" It's important to check model"
" performance on historical data.")
if test_horizon == 0:
warnings.warn("No data selected for test (test_horizon=0). "
"It is important to check out of sample performance")
# checks horizon against data size
if num_observations < forecast_horizon * 2:
warnings.warn(f"Not enough training data to forecast the full forecast_horizon."
" Exercise extra caution with"
f" forecasted values after {num_observations // 2} periods.")
if test_horizon > num_observations:
raise ValueError(f"test_horizon ({test_horizon}) is too large."
" Must be less than the number "
f"of input data points: {num_observations})")
if test_horizon > forecast_horizon:
warnings.warn(f"test_horizon should never be larger than forecast_horizon.")
if test_horizon > num_observations // 3:
warnings.warn(f"test_horizon should be <= than 1/3 of the data set size to allow enough data to train"
f" a backtest model. Consider reducing to {num_observations // 3}. If this is smaller"
f" than the forecast_horizon, you will need to make a trade-off between setting"
f" test_horizon=forecast_horizon and having enough data left over to properly"
f" train a realistic backtest model.")
log_message(f"forecast_horizon: {forecast_horizon}", LoggingLevelEnum.INFO)
log_message(f"test_horizon: {test_horizon}", LoggingLevelEnum.INFO)
log_message(f"cv_horizon: {cv_horizon}", LoggingLevelEnum.INFO)
return pipeline_function(
df,
time_col=time_col,
value_col=value_col,
date_format=date_format,
tz=tz,
freq=freq,
train_end_date=train_end_date,
anomaly_info=anomaly_info,
pipeline=pipeline,
regressor_cols=regressor_cols,
lagged_regressor_cols=lagged_regressor_cols,
estimator=estimator,
hyperparameter_grid=hyperparameter_grid,
hyperparameter_budget=hyperparameter_budget,
n_jobs=n_jobs,
verbose=verbose,
forecast_horizon=forecast_horizon,
coverage=coverage,
test_horizon=test_horizon,
periods_between_train_test=periods_between_train_test,
agg_periods=agg_periods,
agg_func=agg_func,
score_func=score_func,
score_func_greater_is_better=score_func_greater_is_better,
cv_report_metrics=cv_report_metrics,
null_model_params=null_model_params,
relative_error_tolerance=relative_error_tolerance,
cv_horizon=cv_horizon,
cv_min_train_periods=cv_min_train_periods,
cv_expanding_window=cv_expanding_window,
cv_use_most_recent_splits=cv_use_most_recent_splits,
cv_periods_between_splits=cv_periods_between_splits,
cv_periods_between_train_test=cv_periods_between_train_test,
cv_max_splits=cv_max_splits
)
def forecast_pipeline(
# input
df: pd.DataFrame,
time_col=TIME_COL,
value_col=VALUE_COL,
date_format=None,
tz=None,
freq=None,
train_end_date=None,
anomaly_info=None,
# model
pipeline=None,
regressor_cols=None,
lagged_regressor_cols=None,
estimator=SimpleSilverkiteEstimator(),
hyperparameter_grid=None,
hyperparameter_budget=None,
n_jobs=COMPUTATION_N_JOBS,
verbose=1,
# forecast
forecast_horizon=None,
coverage=0.95,
test_horizon=None,
periods_between_train_test=None,
agg_periods=None,
agg_func=None,
# evaluation
score_func=EvaluationMetricEnum.MeanAbsolutePercentError.name,
score_func_greater_is_better=False,
cv_report_metrics=CV_REPORT_METRICS_ALL,
null_model_params=None,
relative_error_tolerance=None,
# CV
cv_horizon=None,
cv_min_train_periods=None,
cv_expanding_window=False,
cv_use_most_recent_splits=False,
cv_periods_between_splits=None,
cv_periods_between_train_test=None,
cv_max_splits=3):
"""Computation pipeline for end-to-end forecasting.
Trains a forecast model end-to-end:
1. checks input data
2. runs cross-validation to select optimal hyperparameters e.g. best model
3. evaluates best model on test set
4. provides forecast of best model (re-trained on all data) into the future
Returns forecasts with methods to plot and see diagnostics.
Also returns the fitted pipeline and CV results.
Provides a high degree of customization over training and evaluation parameters:
1. model
2. cross validation
3. evaluation
4. forecast horizon
See test cases for examples.
Parameters
----------
df : `pandas.DataFrame`
Timeseries data to forecast.
Contains columns [`time_col`, `value_col`], and optional regressor columns
Regressor columns should include future values for prediction
time_col : `str`, default TIME_COL in constants.py
name of timestamp column in df
value_col : `str`, default VALUE_COL in constants.py
name of value column in df (the values to forecast)
date_format : `str` or None, default None
strftime format to parse time column, eg ``%m/%d/%Y``.
Note that ``%f`` will parse all the way up to nanoseconds.
If None (recommended), inferred by `pandas.to_datetime`.
tz : `str` or None, default None
Passed to `pandas.tz_localize` to localize the timestamp
freq : `str` or None, default None
Frequency of input data. Used to generate future dates for prediction.
Frequency strings can have multiples, e.g. '5H'.
See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
for a list of frequency aliases.
If None, inferred by `pandas.infer_freq`.
Provide this parameter if ``df`` has missing timepoints.
train_end_date : `datetime.datetime`, optional, default None
Last date to use for fitting the model. Forecasts are generated after this date.
If None, it is set to the last date with a non-null value in
``value_col`` of ``df``.
anomaly_info : `dict` or `list` [`dict`] or None, default None
Anomaly adjustment info. Anomalies in ``df``
are corrected before any forecasting is done.
If None, no adjustments are made.
A dictionary containing the parameters to
`~greykite.common.features.adjust_anomalous_data.adjust_anomalous_data`.
See that function for details.
The possible keys are:
``"value_col"`` : `str`
The name of the column in ``df`` to adjust. You may adjust the value
to forecast as well as any numeric regressors.
``"anomaly_df"`` : `pandas.DataFrame`
Adjustments to correct the anomalies.
``"start_date_col"``: `str`, default START_DATE_COL
Start date column in ``anomaly_df``.
``"end_date_col"``: `str`, default END_DATE_COL
End date column in ``anomaly_df``.
``"adjustment_delta_col"``: `str` or None, default None
Impact column in ``anomaly_df``.
``"filter_by_dict"``: `dict` or None, default None
Used to filter ``anomaly_df`` to the relevant anomalies for
the ``value_col`` in this dictionary.
Key specifies the column name, value specifies the filter value.
``"filter_by_value_col""``: `str` or None, default None
Adds ``{filter_by_value_col: value_col}`` to ``filter_by_dict``
if not None, for the ``value_col`` in this dictionary.
``"adjustment_method"`` : `str` ("add" or "subtract"), default "add"
How to make the adjustment, if ``adjustment_delta_col`` is provided.
Accepts a list of such dictionaries to adjust multiple columns in ``df``.
pipeline : `sklearn.pipeline.Pipeline` or None, default None
Pipeline to fit. The final named step must be called "estimator".
If None, will use the default Pipeline from
`~greykite.framework.pipeline.utils.get_basic_pipeline`.
regressor_cols : `list` [`str`] or None, default None
A list of regressor columns used in the training and prediction DataFrames.
It should contain only the regressors that are being used in the grid search.
If None, no regressor columns are used.
Regressor columns that are unavailable in ``df`` are dropped.
lagged_regressor_cols : `list` [`str`] or None, default None
A list of additional columns needed for lagged regressors in the training and prediction DataFrames.
This list can have overlap with ``regressor_cols``.
If None, no additional columns are added to the DataFrame.
Lagged regressor columns that are unavailable in ``df`` are dropped.
estimator : instance of an estimator that implements `greykite.algo.models.base_forecast_estimator.BaseForecastEstimator`
Estimator to use as the final step in the pipeline.
Ignored if ``pipeline`` is provided.
forecast_horizon : `int` or None, default None
Number of periods to forecast into the future. Must be > 0.
If None, default is determined from input data frequency
coverage : `float` or None, default=0.95
Intended coverage of the prediction bands (0.0 to 1.0)
If None, the upper/lower predictions are not returned
Ignored if `pipeline` is provided. Uses coverage of the ``pipeline`` estimator instead.
test_horizon : `int` or None, default None
Numbers of periods held back from end of df for test.
The rest is used for cross validation.
If None, default is forecast_horizon. Set to 0 to skip backtest.
periods_between_train_test : `int` or None, default None
Number of periods for the gap between train and test data.
If None, default is 0.
agg_periods : `int` or None, default None
Number of periods to aggregate before evaluation.
Model is fit and forecasted on the dataset's original frequency.
Before evaluation, the actual and forecasted values are aggregated,
using rolling windows of size ``agg_periods`` and the function
``agg_func``. (e.g. if the dataset is hourly, use ``agg_periods=24, agg_func=np.sum``,
to evaluate performance on the daily totals).
If None, does not aggregate before evaluation.
Currently, this is only used when calculating CV metrics and
the R2_null_model_score metric in backtest/forecast. No pre-aggregation
is applied for the other backtest/forecast evaluation metrics.
agg_func : callable or None, default None
Takes an array and returns a number, e.g. np.max, np.sum.
Defines how to aggregate rolling windows of actual and predicted values
before evaluation.
Ignored if ``agg_periods`` is None.
Currently, this is only used when calculating CV metrics and
the R2_null_model_score metric in backtest/forecast. No pre-aggregation
is applied for the other backtest/forecast evaluation metrics.
score_func : `str` or callable, default ``EvaluationMetricEnum.MeanAbsolutePercentError.name``
Score function used to select optimal model in CV.
If a callable, takes arrays ``y_true``, ``y_pred`` and returns a float.
If a string, must be either a
`~greykite.common.evaluation.EvaluationMetricEnum` member name
or `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
score_func_greater_is_better : `bool`, default False
True if ``score_func`` is a score function, meaning higher is better,
and False if it is a loss function, meaning lower is better.
Must be provided if ``score_func`` is a callable (custom function).
Ignored if ``score_func`` is a string, because the direction is known.
cv_report_metrics : `str`, or `list` [`str`], or None, default `~greykite.common.constants.CV_REPORT_METRICS_ALL`
Additional metrics to compute during CV, besides the one specified by ``score_func``.
- If the string constant `greykite.framework.constants.CV_REPORT_METRICS_ALL`,
computes all metrics in ``EvaluationMetricEnum``. Also computes
``FRACTION_OUTSIDE_TOLERANCE`` if ``relative_error_tolerance`` is not None.
The results are reported by the short name (``.get_metric_name()``) for ``EvaluationMetricEnum``
members and ``FRACTION_OUTSIDE_TOLERANCE_NAME`` for ``FRACTION_OUTSIDE_TOLERANCE``.
These names appear in the keys of ``forecast_result.grid_search.cv_results_``
returned by this function.
- If a list of strings, each of the listed metrics is computed. Valid strings are
`~greykite.common.evaluation.EvaluationMetricEnum` member names
and `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
For example::
["MeanSquaredError", "MeanAbsoluteError", "MeanAbsolutePercentError", "MedianAbsolutePercentError", "FractionOutsideTolerance2"]
- If None, no additional metrics are computed.
null_model_params : `dict` or None, default None
Defines baseline model to compute ``R2_null_model_score`` evaluation metric.
``R2_null_model_score`` is the improvement in the loss function relative
to a null model. It can be used to evaluate model quality with respect to
a simple baseline. For details, see
`~greykite.common.evaluation.r2_null_model_score`.
The null model is a `~sklearn.dummy.DummyRegressor`,
which returns constant predictions.
Valid keys are "strategy", "constant", "quantile".
See `~sklearn.dummy.DummyRegressor`. For example::
null_model_params = {
"strategy": "mean",
}
null_model_params = {
"strategy": "median",
}
null_model_params = {
"strategy": "quantile",
"quantile": 0.8,
}
null_model_params = {
"strategy": "constant",
"constant": 2.0,
}
If None, ``R2_null_model_score`` is not calculated.
Note: CV model selection always optimizes ``score_func`, not
the ``R2_null_model_score``.
relative_error_tolerance : `float` or None, default None
Threshold to compute the ``Outside Tolerance`` metric,
defined as the fraction of forecasted values whose relative
error is strictly greater than ``relative_error_tolerance``.
For example, 0.05 allows for 5% relative error.
If `None`, the metric is not computed.
hyperparameter_grid : `dict`, `list` [`dict`] or None, default None
Sets properties of the steps in the pipeline,
and specifies combinations to search over.
Should be valid input to `sklearn.model_selection.GridSearchCV` (param_grid)
or `sklearn.model_selection.RandomizedSearchCV` (param_distributions).
Prefix transform/estimator attributes by the name of the step in the pipeline.
See details at: https://scikit-learn.org/stable/modules/compose.html#nested-parameters
If None, uses the default pipeline parameters.
hyperparameter_budget : `int` or None, default None
Max number of hyperparameter sets to try within the ``hyperparameter_grid`` search space
Runs a full grid search if ``hyperparameter_budget`` is sufficient to exhaust full
``hyperparameter_grid``, otherwise samples uniformly at random from the space.
If None, uses defaults:
* full grid search if all values are constant
* 10 if any value is a distribution to sample from
n_jobs : `int` or None, default `~greykite.framework.constants.COMPUTATION_N_JOBS`
Number of jobs to run in parallel
(the maximum number of concurrently running workers).
``-1`` uses all CPUs. ``-2`` uses all CPUs but one.
``None`` is treated as 1 unless in a `joblib.Parallel` backend context
that specifies otherwise.
verbose : `int`, default 1
Verbosity level during CV.
if > 0, prints number of fits
if > 1, prints fit parameters, total score + fit time
if > 2, prints train/test scores
cv_horizon : `int` or None, default None
Number of periods in each CV test set
If None, default is ``forecast_horizon``.
Set either ``cv_horizon`` or ``cv_max_splits`` to 0 to skip CV.
cv_min_train_periods : `int` or None, default None
Minimum number of periods for training each CV fold.
If cv_expanding_window is False, every training period is this size
If None, default is 2 * ``cv_horizon``
cv_expanding_window : `bool`, default False
If True, training window for each CV split is fixed to the first available date.
Otherwise, train start date is sliding, determined by ``cv_min_train_periods``.
cv_use_most_recent_splits: `bool`, default False
If True, splits from the end of the dataset are used.
Else a sampling strategy is applied. Check
`~greykite.sklearn.cross_validation.RollingTimeSeriesSplit._sample_splits`
for details.
cv_periods_between_splits : `int` or None, default None
Number of periods to slide the test window between CV splits
If None, default is ``cv_horizon``
cv_periods_between_train_test : `int` or None, default None
Number of periods for the gap between train and test in a CV split.
If None, default is ``periods_between_train_test``.
cv_max_splits : `int` or None, default 3
Maximum number of CV splits.
Given the above configuration, samples up to max_splits train/test splits,
preferring splits toward the end of available data. If None, uses all splits.
Set either ``cv_horizon`` or ``cv_max_splits`` to 0 to skip CV.
Returns
-------
forecast_result : :class:`~greykite.framework.pipeline.pipeline.ForecastResult`
Forecast result. See :class:`~greykite.framework.pipeline.pipeline.ForecastResult`
for details.
* If ``cv_horizon=0``, ``forecast_result.grid_search.best_estimator_``
and ``forecast_result.grid_search.best_params_`` attributes are defined
according to the provided single set of parameters. There must be a single
set of parameters to skip cross-validation.
* If ``test_horizon=0``, ``forecast_result.backtest`` is None.
"""
if hyperparameter_grid is None or hyperparameter_grid == []:
hyperparameter_grid = {}
# When hyperparameter_grid is a singleton list, unlist it
if isinstance(hyperparameter_grid, list) and len(hyperparameter_grid) == 1:
hyperparameter_grid = hyperparameter_grid[0]
# Loads full dataset
ts = UnivariateTimeSeries()
ts.load_data(
df=df,
time_col=time_col,
value_col=value_col,
freq=freq,
date_format=date_format,
tz=tz,
train_end_date=train_end_date,
regressor_cols=regressor_cols,
lagged_regressor_cols=lagged_regressor_cols,
anomaly_info=anomaly_info)
# Splits data into training and test sets. ts.df uses standardized column names
if test_horizon == 0:
train_df = ts.fit_df
train_y = ts.fit_y
test_df = pd.DataFrame(columns=list(df.columns))
else:
# Make sure to refit best_pipeline appropriately
train_df, test_df, train_y, test_y = train_test_split(
ts.fit_df,
ts.fit_y,
train_size=ts.fit_df.shape[0] - test_horizon - periods_between_train_test,
test_size=test_horizon + periods_between_train_test,
shuffle=False) # this is important since this is timeseries forecasting!
log_message(f"Train size: {train_df.shape[0]}. Test size: {test_df.shape[0]}", LoggingLevelEnum.INFO)
# Defines default training pipeline
if pipeline is None:
pipeline = get_basic_pipeline(
estimator=estimator,
score_func=score_func,
score_func_greater_is_better=score_func_greater_is_better,
agg_periods=agg_periods,
agg_func=agg_func,
relative_error_tolerance=relative_error_tolerance,
coverage=coverage,
null_model_params=null_model_params,
regressor_cols=ts.regressor_cols,
lagged_regressor_cols=ts.lagged_regressor_cols)
# Searches for the best parameters, and refits model with selected parameters on the entire training set
if cv_horizon == 0 or cv_max_splits == 0:
# No cross-validation. Only one set of hyperparameters is allowed.
try:
if len(ParameterGrid(hyperparameter_grid)) > 1:
raise ValueError(
"CV is required to identify the best model because there are multiple options "
"in `hyperparameter_grid`. Either provide a single option or set `cv_horizon` and `cv_max_splits` "
"to nonzero values.")
except TypeError: # Parameter value is not iterable
raise ValueError(
"CV is required to identify the best model because `hyperparameter_grid` contains "
"a distribution. Either remove the distribution or set `cv_horizon` and `cv_max_splits` "
"to nonzero values.")
# Fits model to entire train set. Params must be set manually since it's not done by grid search
params = {k: v[0] for k, v in hyperparameter_grid.items()} # unpack lists, `v` is a singleton list with the parameter value
best_estimator = pipeline.set_params(**params).fit(train_df, train_y)
# Wraps this model in a dummy RandomizedSearchCV object to return the backtest model
grid_search = get_hyperparameter_searcher(
hyperparameter_grid=hyperparameter_grid,
model=pipeline,
cv=None, # no cross-validation
hyperparameter_budget=hyperparameter_budget,
n_jobs=n_jobs,
verbose=verbose,
score_func=score_func,
score_func_greater_is_better=score_func_greater_is_better,
cv_report_metrics=cv_report_metrics,
agg_periods=agg_periods,
agg_func=agg_func,
relative_error_tolerance=relative_error_tolerance)
# Sets relevant attributes. Others are undefined (cv_results_, best_score_, best_index_, scorer_, refit_time_)
grid_search.best_estimator_ = best_estimator
grid_search.best_params_ = params
grid_search.n_splits_ = 0
else:
# Defines cross-validation splitter
cv = RollingTimeSeriesSplit(
forecast_horizon=cv_horizon,
min_train_periods=cv_min_train_periods,
expanding_window=cv_expanding_window,
use_most_recent_splits=cv_use_most_recent_splits,
periods_between_splits=cv_periods_between_splits,
periods_between_train_test=cv_periods_between_train_test,
max_splits=cv_max_splits)
# Defines grid search approach for CV
grid_search = get_hyperparameter_searcher(
hyperparameter_grid=hyperparameter_grid,
model=pipeline,
cv=cv,
hyperparameter_budget=hyperparameter_budget,
n_jobs=n_jobs,
verbose=verbose,
score_func=score_func,
score_func_greater_is_better=score_func_greater_is_better,
cv_report_metrics=cv_report_metrics,
agg_periods=agg_periods,
agg_func=agg_func,
relative_error_tolerance=relative_error_tolerance)
grid_search.fit(train_df, train_y)
best_estimator = grid_search.best_estimator_
# Evaluates historical performance, fits model to all data (train+test)
if test_horizon > 0:
backtest_train_end_date = train_df[TIME_COL].max()
# Uses pd.date_range because pd.Timedelta does not work for complicated frequencies e.g. "W-MON"
backtest_test_start_date = pd.date_range(
start=backtest_train_end_date,
periods=periods_between_train_test + 2, # Adds 2 as start parameter is inclusive
freq=ts.freq)[-1]
backtest = get_forecast(
df=ts.fit_df, # Backtest needs to happen on fit_df, not on the entire df
trained_model=best_estimator,
train_end_date=backtest_train_end_date,
test_start_date=backtest_test_start_date,
forecast_horizon=test_horizon,
xlabel=time_col,
ylabel=value_col,
relative_error_tolerance=relative_error_tolerance)
best_pipeline = clone(best_estimator) # Copies optimal parameters
best_pipeline.fit(ts.fit_df, ts.y) # Refits this model on entire training dataset
else:
backtest = None # Backtest training metrics are the same as forecast training metrics
best_pipeline = best_estimator # best_model is already fit to all data
# Makes future predictions
periods = forecast_horizon + periods_between_train_test
future_df = ts.make_future_dataframe(
periods=periods,
include_history=True)
forecast_train_end_date = ts.train_end_date
# Uses pd.date_range because pd.Timedelta does not work for complicated frequencies e.g. "W-MON"
forecast_test_start_date = pd.date_range(
start=forecast_train_end_date,
periods=periods_between_train_test + 2, # Adds 2 as start parameter is inclusive
freq=ts.freq)[-1]
forecast = get_forecast(
df=future_df,
trained_model=best_pipeline,
train_end_date=forecast_train_end_date,
test_start_date=forecast_test_start_date,
forecast_horizon=forecast_horizon,
xlabel=time_col,
ylabel=value_col,
relative_error_tolerance=relative_error_tolerance)
result = ForecastResult(
timeseries=ts,
grid_search=grid_search,
model=best_pipeline,
backtest=backtest,
forecast=forecast
)
return result
def mock_pipeline(
# The arguments and defaults should be identical to forecast_pipeline() function
# input
df: pd.DataFrame,
time_col=TIME_COL,
value_col=VALUE_COL,
date_format=None,
tz=None,
freq=None,
train_end_date=None,
anomaly_info=None,
# model
pipeline=None,
regressor_cols=None,
lagged_regressor_cols=None,
estimator=SimpleSilverkiteEstimator(),
hyperparameter_grid=None,
hyperparameter_budget=None,
n_jobs=1,
verbose=1,
# forecast
forecast_horizon=None,
coverage: Optional[float] = 0.95,
test_horizon=None,
periods_between_train_test=None,
agg_periods=None,
agg_func=None,
# evaluation
score_func=EvaluationMetricEnum.MeanSquaredError.name,
score_func_greater_is_better=False,
cv_report_metrics=None,
null_model_params=None,
relative_error_tolerance: Optional[float] = 0.05,
# CV
cv_horizon=None,
cv_min_train_periods=None,
cv_expanding_window=False,
cv_use_most_recent_splits=False,
cv_periods_between_splits=None,
cv_periods_between_train_test=0,
cv_max_splits=3):
"""Create and returns custom pipeline parameters"""
return {
"df": df,
"time_col": time_col,
"value_col": value_col,
"date_format": date_format,
"tz": tz,
"freq": freq,
"train_end_date": train_end_date,
"anomaly_info": anomaly_info,
"pipeline": pipeline,
"regressor_cols": regressor_cols,
"lagged_regressor_cols": lagged_regressor_cols,
"estimator": estimator,
"hyperparameter_grid": hyperparameter_grid,
"hyperparameter_budget": hyperparameter_budget,
"n_jobs": n_jobs,
"verbose": verbose,
"forecast_horizon": forecast_horizon,
"coverage": coverage,
"test_horizon": test_horizon,
"periods_between_train_test": periods_between_train_test,
"agg_periods": agg_periods,
"agg_func": agg_func,
"score_func": score_func,
"score_func_greater_is_better": score_func_greater_is_better,
"cv_report_metrics": cv_report_metrics,
"null_model_params": null_model_params,
"relative_error_tolerance": relative_error_tolerance,
"cv_horizon": cv_horizon,
"cv_min_train_periods": cv_min_train_periods,
"cv_expanding_window": cv_expanding_window,
"cv_use_most_recent_splits": cv_use_most_recent_splits,
"cv_periods_between_splits": cv_periods_between_splits,
"cv_periods_between_train_test": cv_periods_between_train_test,
"cv_max_splits": cv_max_splits,
}
def get_basic_pipeline(
estimator=SimpleSilverkiteEstimator(),
score_func=EvaluationMetricEnum.MeanAbsolutePercentError.name,
score_func_greater_is_better=False,
agg_periods=None,
agg_func=None,
relative_error_tolerance=None,
coverage=0.95,
null_model_params=None,
regressor_cols=None):
"""Returns a basic pipeline for univariate forecasting.
Allows for outlier detection, normalization, null imputation,
degenerate column removal, and forecast model fitting. By default,
only null imputation is enabled. See source code for the pipeline steps.
Notes
-----
While ``score_func`` is used to define the estimator's score function, the
the ``scoring`` parameter of `~sklearn.model_selection.RandomizedSearchCV`
should be provided when using this pipeline in grid search.
Otherwise, grid search assumes higher values are better for ``score_func``.
Parameters
----------
estimator : instance of an estimator that implements `~greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator`, default SimpleSilverkiteEstimator() # noqa: E501
Estimator to use as the final step in the pipeline.
score_func : `str` or callable, default ``EvaluationMetricEnum.MeanAbsolutePercentError.name``
Score function used to select optimal model in CV.
If a callable, takes arrays ``y_true``, ``y_pred`` and returns a float.
If a string, must be either a
`~greykite.common.evaluation.EvaluationMetricEnum` member name
or `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
score_func_greater_is_better : `bool`, default False
True if ``score_func`` is a score function, meaning higher is better,
and False if it is a loss function, meaning lower is better.
Must be provided if ``score_func`` is a callable (custom function).
Ignored if ``score_func`` is a string, because the direction is known.
agg_periods : `int` or None, default None
Number of periods to aggregate before evaluation.
Model is fit at original frequency, and forecast is
aggregated according to ``agg_periods``
E.g. fit model on hourly data, and evaluate performance at daily level
If None, does not apply aggregation
agg_func : callable or None, default None
Takes an array and returns a number, e.g. np.max, np.sum
Used to aggregate data prior to evaluation (applied to actual and predicted)
Ignored if ``agg_periods`` is None
relative_error_tolerance : `float` or None, default None
Threshold to compute the
`~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`
metric, defined as the fraction of forecasted values whose relative
error is strictly greater than ``relative_error_tolerance``.
For example, 0.05 allows for 5% relative error.
Required if ``score_func`` is
`~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
coverage : `float` or None, optional, default=0.95
Intended coverage of the prediction bands (0.0 to 1.0)
If None, the upper/lower predictions are not returned
Ignored if `pipeline` is provided. Uses coverage of the ``pipeline`` estimator instead.
null_model_params : `dict` or None, default None
Defines baseline model to compute ``R2_null_model_score`` evaluation metric.
``R2_null_model_score`` is the improvement in the loss function relative
to a null model. It can be used to evaluate model quality with respect to
a simple baseline. For details, see
`~greykite.common.evaluation.r2_null_model_score`.
The null model is a `~sklearn.dummy.DummyRegressor`,
which returns constant predictions.
Valid keys are "strategy", "constant", "quantile".
See `~sklearn.dummy.DummyRegressor`. For example::
null_model_params = {
"strategy": "mean",
}
null_model_params = {
"strategy": "median",
}
null_model_params = {
"strategy": "quantile",
"quantile": 0.8,
}
null_model_params = {
"strategy": "constant",
"constant": 2.0,
}
If None, ``R2_null_model_score`` is not calculated.
Note: CV model selection always optimizes ``score_func`, not
the ``R2_null_model_score``.
regressor_cols : `list` [`str`] or None, default None
A list of regressor columns used in the training and prediction DataFrames.
It should contain only the regressors that are being used in the grid search.
If None, no regressor columns are used.
Regressor columns that are unavailable in ``df`` are dropped.
Returns
-------
pipeline : `sklearn.pipeline.Pipeline`
sklearn Pipeline for univariate forecasting.
"""
score_func, _, _ = get_score_func_with_aggregation(
score_func=score_func,
greater_is_better=score_func_greater_is_better,
agg_periods=agg_periods,
agg_func=agg_func,
relative_error_tolerance=relative_error_tolerance)
if regressor_cols is None:
regressor_cols = []
# A new unfitted estimator with the same parameters
estimator_clone = clone(estimator)
# Sets parameters common to all `BaseForecastEstimator`
estimator_clone.set_params(
score_func=score_func,
coverage=coverage,
null_model_params=null_model_params)
# Note:
# Unlike typical ML, "y" (target values) is part of "X" (training data).
# Some forecasting models require that all historical values are available
# (no gaps in the timeseries). By including "y" values as part of "X", they
# can be transformed prior to fitting the estimator. This allows outlier removal and
# null imputation that respects train/test boundaries, to avoid leaking future
# information into the past. Evaluation is always done against original "y".
# Parameters for this pipeline are set via `hyperparameter_grid`.
pipeline = Pipeline([
("input", PandasFeatureUnion([
("date", Pipeline([
("select_date", ColumnSelector([TIME_COL])) # leaves time column unmodified
])),
("response", Pipeline([ # applies outlier and null transformation to value column
("select_val", ColumnSelector([VALUE_COL])),
("outlier", ZscoreOutlierTransformer(z_cutoff=None)),
("null", NullTransformer(impute_algorithm="interpolate"))
])),
("regressors_numeric", Pipeline([
("select_reg", ColumnSelector(regressor_cols)),
("select_reg_numeric", DtypeColumnSelector(include="number")),
("outlier", ZscoreOutlierTransformer(z_cutoff=None)),
("normalize", NormalizeTransformer(normalize_algorithm=None)), # no normalization by default
("null", NullTransformer(impute_algorithm="interpolate"))
])),
("regressors_other", Pipeline([
("select_reg", ColumnSelector(regressor_cols)),
("select_reg_non_numeric", DtypeColumnSelector(exclude="number"))
]))
])),
("degenerate", DropDegenerateTransformer()), # default `drop_degenerate=False`
# Sets BaseForecastEstimator parameters (`score_func`, etc.).
# Other parameters of the estimator are set by `hyperparameter_grid` later.
("estimator", estimator_clone)
])
return pipeline
def test_plot_components():
"""Tests plot_components.
Because component plots are implemented in `base_silverkite_estimator.py,` the bulk of
the testing is done there. This file only tests inheritance and compatibility of the
trained_model generated by this estimator's fit.
"""
daily_data = generate_df_with_reg_for_tests(
freq="D",
periods=20,
train_start_date=datetime.datetime(2018, 1, 1),
conti_year_origin=2018)
train_df = daily_data.get("train_df").copy()
model = SimpleSilverkiteEstimator(
fit_algorithm_dict={"fit_algorithm": "linear"},
yearly_seasonality=True,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=True,
daily_seasonality=False,
)
model.fit(train_df)
# Test plot_components
with pytest.warns(Warning) as record:
title = "Custom component plot"
fig = model.plot_components(
names=["trend", "YEARLY_SEASONALITY", "DUMMY"], title=title)
expected_rows = 3
assert len(fig.data) == expected_rows
assert [fig.data[i].name for i in range(expected_rows)] == \
[cst.VALUE_COL, "trend", "YEARLY_SEASONALITY"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == cst.TIME_COL
assert fig.layout.xaxis3.title["text"] == "Time of year"
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "trend"
assert fig.layout.yaxis3.title["text"] == "yearly"
assert fig.layout.title["text"] == title
assert f"The following components have not been specified in the model: " \
f"{{'DUMMY'}}, plotting the rest." in record[0].message.args[0]
# Test plot_trend
title = "Custom trend plot"
fig = model.plot_trend(title=title)
expected_rows = 2
assert len(fig.data) == expected_rows
assert [fig.data[i].name
for i in range(expected_rows)] == [cst.VALUE_COL, "trend"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == cst.TIME_COL
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "trend"
assert fig.layout.title["text"] == title
# Test plot_seasonalities
with pytest.warns(Warning):
# suppresses the warning on seasonalities removed
title = "Custom seasonality plot"
fig = model.plot_seasonalities(title=title)
expected_rows = 3
assert len(fig.data) == expected_rows
assert [fig.data[i].name for i in range(expected_rows)] == \
[cst.VALUE_COL, "WEEKLY_SEASONALITY", "YEARLY_SEASONALITY"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == "Day of week"
assert fig.layout.xaxis3.title["text"] == "Time of year"
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "weekly"
assert fig.layout.yaxis3.title["text"] == "yearly"
assert fig.layout.title["text"] == title
