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https://github.com/saymrwulf/prophet.git
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79d0793ce4
* Resolve conflict * Change comments and add error column to output DataFrame * Change file structure * Update * Modified diagnostics * Update diagnostics.py following the advice on Github * Add tests and documentation * Change copy method into Prophet class and reflect comments
131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
# Copyright (c) 2017-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree. An additional grant
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# of patent rights can be found in the PATENTS file in the same directory.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import logging
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logger = logging.getLogger(__name__)
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import numpy as np
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import pandas as pd
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from functools import reduce
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def _cutoffs(df, horizon, k, period):
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"""Generate cutoff dates
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Parameters
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----------
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df: pd.DataFrame with historical data
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horizon: pd.Timedelta.
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Forecast horizon
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k: Int number.
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The number of forecasts point.
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period: pd.Timedelta.
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Simulated Forecast will be done at every this period.
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Returns
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-------
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list of pd.Timestamp
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"""
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# Last cutoff is 'latest date in data - horizon' date
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cutoff = df['ds'].max() - horizon
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result = [cutoff]
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for i in range(1, k):
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cutoff -= period
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# If data does not exist in data range (cutoff, cutoff + horizon]
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if not (((df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)).any()):
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# Next cutoff point is 'closest date before cutoff in data - horizon'
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closest_date = df[df['ds'] <= cutoff].max()['ds']
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cutoff = closest_date - horizon
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if cutoff < df['ds'].min():
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logger.warning('Not enough data for requested number of cutoffs! Using {}.'.format(k))
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break
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result.append(cutoff)
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# Sort lines in ascending order
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return reversed(result)
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def simulated_historical_forecasts(model, horizon, k, period=None):
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"""Simulated Historical Forecasts.
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If you would like to know it in detail, read the original paper
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https://facebookincubator.github.io/prophet/static/prophet_paper_20170113.pdf
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Parameters
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----------
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model: Prophet class object.
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Fitted Prophet model
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horizon: string which has pd.Timedelta compatible style.
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Forecast horizon ('5 days', '3 hours', '10 seconds' etc)
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k: Int number.
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The number of forecasts point.
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period: string which has pd.Timedelta compatible style or None, default None.
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Simulated Forecast will be done at every this period.
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0.5 * horizon is used when it is None.
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Returns
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-------
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A pd.DataFrame with the forecast, actual value and cutoff.
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"""
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df = model.history.copy().reset_index(drop=True)
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horizon = pd.Timedelta(horizon)
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period = 0.5 * horizon if period is None else pd.Timedelta(period)
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cutoffs = _cutoffs(df, horizon, k, period)
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predicts = []
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for cutoff in cutoffs:
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# Generate new object with copying fitting options
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m = model.copy(cutoff)
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# Train model
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m.fit(df[df['ds'] <= cutoff])
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# Calculate yhat
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index_predicted = (df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)
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columns = ['ds'] + (['cap'] if m.growth == 'logistic' else [])
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yhat = m.predict(df[index_predicted][columns])
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# Merge yhat(predicts), y(df, original data) and cutoff
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predicts.append(pd.concat([
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yhat[['ds', 'yhat', 'yhat_lower', 'yhat_upper']],
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df[index_predicted][['y']].reset_index(drop=True),
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pd.DataFrame({'cutoff': [cutoff] * len(yhat)})
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], axis=1))
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# Combine all predicted pd.DataFrame into one pd.DataFrame
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return reduce(lambda x, y: x.append(y), predicts).reset_index(drop=True)
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def cross_validation(model, horizon, period, initial=None):
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"""Cross-Validation for time-series.
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This function is the same with Time series cross-validation described in https://robjhyndman.com/hyndsight/tscv/
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when the value of period is equal to the time interval of data.
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Parameters
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----------
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model: Prophet class object. Fitted Prophet model
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horizon: string which has pd.Timedelta compatible style.
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Forecast horizon ('5 days', '3 hours', '10 seconds' etc)
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period: string which has pd.Timedelta compatible style.
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Simulated Forecast will be done at every this period.
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initial: string which has pd.Timedelta compatible style or None, default None.
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First training period.
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3 * horizon is used when it is None.
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Returns
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-------
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A pd.DataFrame with the forecast, actual value and cutoff.
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"""
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te = model.history['ds'].max()
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ts = model.history['ds'].min()
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horizon = pd.Timedelta(horizon)
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period = pd.Timedelta(period)
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initial = 3 * horizon if initial is None else pd.Timedelta(initial)
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k = int(np.floor(((te - horizon) - (ts + initial)) / period))
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return simulated_historical_forecasts(model, horizon, k, period)
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