#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from bigdl.chronos.forecaster.abstract import Forecaster
from bigdl.chronos.model.prophet import ProphetModel
[docs]class ProphetForecaster(Forecaster):
"""
Example:
>>> #The dataset is split into data, validation_data
>>> model = ProphetForecaster(changepoint_prior_scale=0.05, seasonality_mode='additive')
>>> model.fit(data, validation_data)
>>> predict_result = model.predict(horizon=24)
"""
def __init__(self,
changepoint_prior_scale=0.05,
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
seasonality_mode='additive',
changepoint_range=0.8,
metric="mse",
):
"""
Build a Prophet Forecast Model.
User can customize changepoint_prior_scale, seasonality_prior_scale,
holidays_prior_scale, seasonality_mode, changepoint_range and metric
of the Prophet model, for details of the Prophet model hyperparameters, refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
:param changepoint_prior_scale: hyperparameter changepoint_prior_scale for the
Prophet model.
:param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
Prophet model.
:param holidays_prior_scale: hyperparameter holidays_prior_scale for the
Prophet model.
:param seasonality_mode: hyperparameter seasonality_mode for the
Prophet model.
:param changepoint_range: hyperparameter changepoint_range for the
Prophet model.
:param metric: the metric for validation and evaluation. For regression, we support
Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
Cosine Proximity: ("cosine_proximity", "cosine")
"""
self.model_config = {
"changepoint_prior_scale": changepoint_prior_scale,
"seasonality_prior_scale": seasonality_prior_scale,
"holidays_prior_scale": holidays_prior_scale,
"seasonality_mode": seasonality_mode,
"changepoint_range": changepoint_range,
"metric": metric
}
self.internal = ProphetModel()
super().__init__()
[docs] def fit(self, data, validation_data=None):
"""
Fit(Train) the forecaster.
:param data: training data, a pandas dataframe with Td rows,
and 2 columns, with column 'ds' indicating date and column 'y' indicating value
and Td is the time dimension
:param validation_data: evaluation data, should be the same type as data
:return: the evaluation metric value
"""
self._check_data(data, validation_data)
return self.internal.fit_eval(data=data,
validation_data=validation_data,
**self.model_config)
def _check_data(self, data, validation_data):
from bigdl.nano.utils.common import invalidInputError
invalidInputError('ds' in data.columns and 'y' in data.columns,
"data should be a pandas dataframe that has at"
" least 2 columns 'ds' and 'y'.")
if validation_data is not None:
invalidInputError('ds' in validation_data.columns and 'y' in validation_data.columns,
"validation_data should be a dataframe that has at least"
" 2 columns 'ds' and 'y'.")
[docs] def predict(self, horizon=1, freq="D", ds_data=None):
"""
Predict using a trained forecaster.
:param horizon: the number of steps forward to predict, the value defaults to 1.
:param freq: the freqency of the predicted dataframe, defaulted to day("D"),
the frequency can be anything from the pandas list of frequency strings here:
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
:param ds_data: a dataframe that has 1 column 'ds' indicating date.
:return: A pandas DataFrame of length horizon,
including "trend" and "seasonality" and inference values, etc.
where the "yhat" column is the inference value.
"""
if self.internal.model is None:
from bigdl.nano.utils.common import invalidInputError
invalidInputError(False,
"You must call fit or restore first before calling predict!")
return self.internal.predict(horizon=horizon, freq=freq, ds_data=ds_data)
[docs] def evaluate(self, data, metrics=['mse']):
"""
Evaluate using a trained forecaster.
:param data: evaluation data, a pandas dataframe with Td rows,
and 2 columns, with column 'ds' indicating date and column 'y' indicating value
and Td is the time dimension
:param metrics: A list contains metrics for test/valid data.
:return: A list of evaluation results. Calculation results for each metrics.
"""
from bigdl.nano.utils.common import invalidInputError
if data is None:
invalidInputError(False, "Input invalid data of None")
if self.internal.model is None:
invalidInputError(False,
"You must call fit or restore first before calling evaluate!")
return self.internal.evaluate(target=data,
metrics=metrics)
[docs] def save(self, checkpoint_file):
"""
Save the forecaster.
:param checkpoint_file: The location you want to save the forecaster, should be a json file
"""
if self.internal.model is None:
from bigdl.nano.utils.common import invalidInputError
invalidInputError(False,
"You must call fit or restore first before calling save!")
self.internal.save(checkpoint_file)
[docs] def restore(self, checkpoint_file):
"""
Restore the forecaster.
:param checkpoint_file: The checkpoint file location you want to load the forecaster.
"""
self.internal.restore(checkpoint_file)