some new features
This commit is contained in:
ilgazca
14
.venv/lib/python3.12/site-packages/prophet/__init__.py
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.venv/lib/python3.12/site-packages/prophet/__init__.py
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# 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 prophet.forecaster import Prophet
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from pathlib import Path
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about = {}
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here = Path(__file__).parent.resolve()
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with open(here / "__version__.py", "r") as f:
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exec(f.read(), about)
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__version__ = about["__version__"]
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__version__ = "1.1.7"
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.venv/lib/python3.12/site-packages/prophet/diagnostics.py
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.venv/lib/python3.12/site-packages/prophet/diagnostics.py
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# -*- coding: utf-8 -*-
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# Copyright (c) Facebook, Inc. and its affiliates.
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from __future__ import absolute_import, division, print_function
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import logging
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from tqdm.auto import tqdm
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from copy import deepcopy
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import concurrent.futures
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import numpy as np
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import pandas as pd
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logger = logging.getLogger('prophet')
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def generate_cutoffs(df, horizon, initial, 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 forecast horizon.
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initial: pd.Timedelta window of the initial forecast period.
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period: pd.Timedelta simulated forecasts are done with 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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if cutoff < df['ds'].min():
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raise ValueError('Less data than horizon.')
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result = [cutoff]
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while result[-1] >= min(df['ds']) + initial:
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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 'last date before cutoff in data - horizon'
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if cutoff > df['ds'].min():
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closest_date = df[df['ds'] <= cutoff].max()['ds']
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cutoff = closest_date - horizon
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# else no data left, leave cutoff as is, it will be dropped.
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result.append(cutoff)
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result = result[:-1]
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if len(result) == 0:
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raise ValueError(
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'Less data than horizon after initial window. '
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'Make horizon or initial shorter.'
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)
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logger.info('Making {} forecasts with cutoffs between {} and {}'.format(
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len(result), result[-1], result[0]
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))
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return list(reversed(result))
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def cross_validation(model, horizon, period=None, initial=None, parallel=None, cutoffs=None, disable_tqdm=False, extra_output_columns=None):
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"""Cross-Validation for time series.
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Computes forecasts from historical cutoff points, which user can input.
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If not provided, begins from (end - horizon) and works backwards, making
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cutoffs with a spacing of period until initial is reached.
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When period is equal to the time interval of the data, this is the
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technique described in https://robjhyndman.com/hyndsight/tscv/ .
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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 with pd.Timedelta compatible style, e.g., '5 days',
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'3 hours', '10 seconds'.
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period: string with pd.Timedelta compatible style. Simulated forecast will
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be done at every this period. If not provided, 0.5 * horizon is used.
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initial: string with pd.Timedelta compatible style. The first training
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period will include at least this much data. If not provided,
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3 * horizon is used.
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cutoffs: list of pd.Timestamp specifying cutoffs to be used during
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cross validation. If not provided, they are generated as described
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above.
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parallel: {None, 'processes', 'threads', 'dask', object}
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How to parallelize the forecast computation. By default no parallelism
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is used.
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* None: No parallelism.
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* 'processes': Parallelize with concurrent.futures.ProcessPoolExecutor.
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* 'threads': Parallelize with concurrent.futures.ThreadPoolExecutor.
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Note that some operations currently hold Python's Global Interpreter
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Lock, so parallelizing with threads may be slower than training
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sequentially.
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* 'dask': Parallelize with Dask.
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This requires that a dask.distributed Client be created.
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* object: Any instance with a `.map` method. This method will
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be called with :func:`single_cutoff_forecast` and a sequence of
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iterables where each element is the tuple of arguments to pass to
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:func:`single_cutoff_forecast`
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.. code-block::
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class MyBackend:
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def map(self, func, *iterables):
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results = [
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func(*args)
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for args in zip(*iterables)
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]
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return results
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disable_tqdm: if True it disables the progress bar that would otherwise show up when parallel=None
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extra_output_columns: A String or List of Strings e.g. 'trend' or ['trend'].
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Additional columns to 'yhat' and 'ds' to be returned in output.
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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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if model.history is None:
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raise Exception('Model has not been fit. Fitting the model provides contextual parameters for cross validation.')
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df = model.history.copy().reset_index(drop=True)
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horizon = pd.Timedelta(horizon)
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predict_columns = ['ds', 'yhat']
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if model.uncertainty_samples:
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predict_columns.extend(['yhat_lower', 'yhat_upper'])
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if extra_output_columns is not None:
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if isinstance(extra_output_columns, str):
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extra_output_columns = [extra_output_columns]
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predict_columns.extend([c for c in extra_output_columns if c not in predict_columns])
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# Identify the largest seasonality period
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period_max = 0.
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for s in model.seasonalities.values():
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period_max = max(period_max, s['period'])
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seasonality_dt = pd.Timedelta(str(period_max) + ' days')
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if cutoffs is None:
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# Set period
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period = 0.5 * horizon if period is None else pd.Timedelta(period)
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# Set initial
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initial = (
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max(3 * horizon, seasonality_dt) if initial is None
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else pd.Timedelta(initial)
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)
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# Compute Cutoffs
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cutoffs = generate_cutoffs(df, horizon, initial, period)
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else:
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# add validation of the cutoff to make sure that the min cutoff is strictly greater than the min date in the history
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if min(cutoffs) <= df['ds'].min():
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raise ValueError("Minimum cutoff value is not strictly greater than min date in history")
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# max value of cutoffs is <= (end date minus horizon)
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end_date_minus_horizon = df['ds'].max() - horizon
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if max(cutoffs) > end_date_minus_horizon:
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raise ValueError("Maximum cutoff value is greater than end date minus horizon, no value for cross-validation remaining")
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initial = cutoffs[0] - df['ds'].min()
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# Check if the initial window
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# (that is, the amount of time between the start of the history and the first cutoff)
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# is less than the maximum seasonality period
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if initial < seasonality_dt:
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msg = 'Seasonality has period of {} days '.format(period_max)
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msg += 'which is larger than initial window. '
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msg += 'Consider increasing initial.'
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logger.warning(msg)
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if parallel:
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valid = {"threads", "processes", "dask"}
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if parallel == "threads":
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pool = concurrent.futures.ThreadPoolExecutor()
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elif parallel == "processes":
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pool = concurrent.futures.ProcessPoolExecutor()
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elif parallel == "dask":
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try:
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from dask.distributed import get_client
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except ImportError as e:
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raise ImportError("parallel='dask' requires the optional "
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"dependency dask.") from e
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pool = get_client()
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# delay df and model to avoid large objects in task graph.
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df, model = pool.scatter([df, model])
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elif hasattr(parallel, "map"):
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pool = parallel
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else:
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msg = ("'parallel' should be one of {} for an instance with a "
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"'map' method".format(', '.join(valid)))
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raise ValueError(msg)
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iterables = ((df, model, cutoff, horizon, predict_columns)
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for cutoff in cutoffs)
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iterables = zip(*iterables)
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logger.info("Applying in parallel with %s", pool)
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predicts = pool.map(single_cutoff_forecast, *iterables)
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if parallel == "dask":
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# convert Futures to DataFrames
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predicts = pool.gather(predicts)
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else:
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predicts = [
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single_cutoff_forecast(df, model, cutoff, horizon, predict_columns)
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for cutoff in (tqdm(cutoffs) if not disable_tqdm else cutoffs)
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]
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# Combine all predicted pd.DataFrame into one pd.DataFrame
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return pd.concat(predicts, axis=0).reset_index(drop=True)
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def single_cutoff_forecast(df, model, cutoff, horizon, predict_columns):
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"""Forecast for single cutoff. Used in cross validation function
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when evaluating for multiple cutoffs either sequentially or in parallel.
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Parameters
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----------
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df: pd.DataFrame.
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DataFrame with history to be used for single
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cutoff forecast.
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model: Prophet model object.
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cutoff: pd.Timestamp cutoff date.
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Simulated Forecast will start from this date.
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horizon: pd.Timedelta forecast horizon.
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predict_columns: List of strings e.g. ['ds', 'yhat'].
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Columns with date and forecast to be returned in output.
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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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# Generate new object with copying fitting options
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m = prophet_copy(model, cutoff)
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# Train model
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history_c = df[df['ds'] <= cutoff]
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if history_c.shape[0] < 2:
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raise Exception(
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'Less than two datapoints before cutoff. '
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'Increase initial window.'
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)
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m.fit(history_c, **model.fit_kwargs)
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# Calculate yhat
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index_predicted = (df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)
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# Get the columns for the future dataframe
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columns = ['ds']
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if m.growth == 'logistic':
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columns.append('cap')
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if m.logistic_floor:
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columns.append('floor')
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columns.extend(m.extra_regressors.keys())
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columns.extend([
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props['condition_name']
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for props in m.seasonalities.values()
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if props['condition_name'] is not None])
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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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m.stan_backend.cleanup()
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return pd.concat([
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yhat[predict_columns],
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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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def prophet_copy(m, cutoff=None):
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"""Copy Prophet object
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Parameters
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----------
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m: Prophet model.
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cutoff: pd.Timestamp or None, default None.
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cutoff Timestamp for changepoints member variable.
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changepoints are only retained if 'changepoints <= cutoff'
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Returns
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-------
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Prophet class object with the same parameter with model variable
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"""
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if m.history is None:
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raise Exception('This is for copying a fitted Prophet object.')
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if m.specified_changepoints:
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changepoints = m.changepoints
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if cutoff is not None:
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# Filter change points '< cutoff'
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last_history_date = max(m.history['ds'][m.history['ds'] <= cutoff])
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changepoints = changepoints[changepoints < last_history_date]
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else:
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changepoints = None
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# Auto seasonalities are set to False because they are already set in
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# m.seasonalities.
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m2 = m.__class__(
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growth=m.growth,
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n_changepoints=m.n_changepoints,
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changepoint_range=m.changepoint_range,
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changepoints=changepoints,
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yearly_seasonality=False,
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weekly_seasonality=False,
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daily_seasonality=False,
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holidays=m.holidays,
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holidays_mode=m.holidays_mode,
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seasonality_mode=m.seasonality_mode,
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seasonality_prior_scale=m.seasonality_prior_scale,
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changepoint_prior_scale=m.changepoint_prior_scale,
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holidays_prior_scale=m.holidays_prior_scale,
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mcmc_samples=m.mcmc_samples,
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interval_width=m.interval_width,
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uncertainty_samples=m.uncertainty_samples,
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stan_backend=(
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m.stan_backend.get_type() if m.stan_backend is not None
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else None
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),
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)
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m2.extra_regressors = deepcopy(m.extra_regressors)
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m2.seasonalities = deepcopy(m.seasonalities)
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m2.country_holidays = deepcopy(m.country_holidays)
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return m2
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PERFORMANCE_METRICS=dict()
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def register_performance_metric(func):
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"""Register custom performance metric
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Parameters that your metric should contain
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----------
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df: Cross-validation results dataframe.
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w: Aggregation window size.
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Registered metric should return following
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-------
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Dataframe with columns horizon and metric.
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"""
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PERFORMANCE_METRICS[func.__name__] = func
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return func
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def performance_metrics(df, metrics=None, rolling_window=0.1, monthly=False):
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"""Compute performance metrics from cross-validation results.
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Computes a suite of performance metrics on the output of cross-validation.
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By default, the following metrics are included:
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'mse': mean squared error
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'rmse': root mean squared error
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'mae': mean absolute error
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'mape': mean absolute percent error
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'mdape': median absolute percent error
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'smape': symmetric mean absolute percentage error
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'coverage': coverage of the upper and lower intervals
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A subset of these can be specified by passing a list of names as the
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`metrics` argument.
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Metrics are calculated over a rolling window of cross validation
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predictions, after sorting by horizon. Averaging is first done within each
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value of horizon, and then across horizons as needed to reach the window
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size. The size of that window (number of simulated forecast points) is
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determined by the rolling_window argument, which specifies a proportion of
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simulated forecast points to include in each window. rolling_window=0 will
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compute it separately for each horizon. The default of rolling_window=0.1
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will use 10% of the rows in df in each window. rolling_window=1 will
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compute the metric across all simulated forecast points. The results are
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set to the right edge of the window.
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If rolling_window < 0, then metrics are computed at each datapoint with no
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averaging (i.e., 'mse' will actually be squared error with no mean).
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The output is a dataframe containing column 'horizon' along with columns
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for each of the metrics computed.
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Parameters
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----------
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df: The dataframe returned by cross_validation.
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metrics: A list of performance metrics to compute. If not provided, will
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use ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'].
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rolling_window: Proportion of data to use in each rolling window for
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computing the metrics. Should be in [0, 1] to average.
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monthly: monthly=True will compute horizons as numbers of calendar months
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from the cutoff date, starting from 0 for the cutoff month.
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Returns
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-------
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Dataframe with a column for each metric, and column 'horizon'
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"""
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valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage']
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if metrics is None:
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metrics = valid_metrics
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if ('yhat_lower' not in df or 'yhat_upper' not in df) and ('coverage' in metrics):
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metrics.remove('coverage')
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if len(set(metrics)) != len(metrics):
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raise ValueError('Input metrics must be a list of unique values')
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if not set(metrics).issubset(set(PERFORMANCE_METRICS)):
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raise ValueError(
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'Valid values for metrics are: {}'.format(valid_metrics)
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)
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df_m = df.copy()
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if monthly:
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df_m['horizon'] = df_m['ds'].dt.to_period('M').astype(int) - df_m['cutoff'].dt.to_period('M').astype(int)
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else:
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df_m['horizon'] = df_m['ds'] - df_m['cutoff']
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df_m.sort_values('horizon', inplace=True)
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if 'mape' in metrics and df_m['y'].abs().min() < 1e-8:
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logger.info('Skipping MAPE because y close to 0')
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metrics.remove('mape')
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if len(metrics) == 0:
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return None
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w = int(rolling_window * df_m.shape[0])
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if w >= 0:
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w = max(w, 1)
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w = min(w, df_m.shape[0])
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# Compute all metrics
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dfs = {}
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for metric in metrics:
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dfs[metric] = PERFORMANCE_METRICS[metric](df_m, w)
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res = dfs[metrics[0]]
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for i in range(1, len(metrics)):
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res_m = dfs[metrics[i]]
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assert np.array_equal(res['horizon'].values, res_m['horizon'].values)
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res[metrics[i]] = res_m[metrics[i]]
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||||
return res
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||||
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||||
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||||
def rolling_mean_by_h(x, h, w, name):
|
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"""Compute a rolling mean of x, after first aggregating by h.
|
||||
|
||||
Right-aligned. Computes a single mean for each unique value of h. Each
|
||||
mean is over at least w samples.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x: Array.
|
||||
h: Array of horizon for each value in x.
|
||||
w: Integer window size (number of elements).
|
||||
name: Name for metric in result dataframe
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and name, the rolling mean of x.
|
||||
"""
|
||||
# Aggregate over h
|
||||
df = pd.DataFrame({'x': x, 'h': h})
|
||||
df2 = (
|
||||
df.groupby('h').agg(['sum', 'count']).reset_index().sort_values('h')
|
||||
)
|
||||
xs = df2['x']['sum'].values
|
||||
ns = df2['x']['count'].values
|
||||
hs = df2.h.values
|
||||
|
||||
trailing_i = len(df2) - 1
|
||||
x_sum = 0
|
||||
n_sum = 0
|
||||
# We don't know output size but it is bounded by len(df2)
|
||||
res_x = np.empty(len(df2))
|
||||
|
||||
# Start from the right and work backwards
|
||||
for i in range(len(df2) - 1, -1, -1):
|
||||
x_sum += xs[i]
|
||||
n_sum += ns[i]
|
||||
while n_sum >= w:
|
||||
# Include points from the previous horizon. All of them if still
|
||||
# less than w, otherwise weight the mean by the difference
|
||||
excess_n = n_sum - w
|
||||
excess_x = excess_n * xs[i] / ns[i]
|
||||
res_x[trailing_i] = (x_sum - excess_x)/ w
|
||||
x_sum -= xs[trailing_i]
|
||||
n_sum -= ns[trailing_i]
|
||||
trailing_i -= 1
|
||||
|
||||
res_h = hs[(trailing_i + 1):]
|
||||
res_x = res_x[(trailing_i + 1):]
|
||||
|
||||
return pd.DataFrame({'horizon': res_h, name: res_x})
|
||||
|
||||
|
||||
|
||||
def rolling_median_by_h(x, h, w, name):
|
||||
"""Compute a rolling median of x, after first aggregating by h.
|
||||
|
||||
Right-aligned. Computes a single median for each unique value of h. Each
|
||||
median is over at least w samples.
|
||||
|
||||
For each h where there are fewer than w samples, we take samples from the previous h,
|
||||
moving backwards. (In other words, we ~ assume that the x's are shuffled within each h.)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x: Array.
|
||||
h: Array of horizon for each value in x.
|
||||
w: Integer window size (number of elements).
|
||||
name: Name for metric in result dataframe
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and name, the rolling median of x.
|
||||
"""
|
||||
# Aggregate over h
|
||||
df = pd.DataFrame({'x': x, 'h': h})
|
||||
grouped = df.groupby('h')
|
||||
df2 = grouped.size().reset_index().sort_values('h')
|
||||
hs = df2['h']
|
||||
|
||||
res_h = []
|
||||
res_x = []
|
||||
# Start from the right and work backwards
|
||||
i = len(hs) - 1
|
||||
while i >= 0:
|
||||
h_i = hs[i]
|
||||
xs = grouped.get_group(h_i).x.tolist()
|
||||
|
||||
# wrap in array so this works if h is pandas Series with custom index or numpy array
|
||||
next_idx_to_add = np.array(h == h_i).argmax() - 1
|
||||
while (len(xs) < w) and (next_idx_to_add >= 0):
|
||||
# Include points from the previous horizon. All of them if still
|
||||
# less than w, otherwise just enough to get to w.
|
||||
xs.append(x[next_idx_to_add])
|
||||
next_idx_to_add -= 1
|
||||
if len(xs) < w:
|
||||
# Ran out of points before getting enough.
|
||||
break
|
||||
res_h.append(hs[i])
|
||||
res_x.append(np.median(xs))
|
||||
i -= 1
|
||||
res_h.reverse()
|
||||
res_x.reverse()
|
||||
return pd.DataFrame({'horizon': res_h, name: res_x})
|
||||
|
||||
|
||||
# The functions below specify performance metrics for cross-validation results.
|
||||
# Each takes as input the output of cross_validation, and returns the statistic
|
||||
# as a dataframe, given a window size for rolling aggregation.
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def mse(df, w):
|
||||
"""Mean squared error
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and mse.
|
||||
"""
|
||||
se = (df['y'] - df['yhat']) ** 2
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mse': se})
|
||||
return rolling_mean_by_h(
|
||||
x=se.values, h=df['horizon'].values, w=w, name='mse'
|
||||
)
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def rmse(df, w):
|
||||
"""Root mean squared error
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and rmse.
|
||||
"""
|
||||
res = mse(df, w)
|
||||
res['mse'] = np.sqrt(res['mse'])
|
||||
res.rename({'mse': 'rmse'}, axis='columns', inplace=True)
|
||||
return res
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def mae(df, w):
|
||||
"""Mean absolute error
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and mae.
|
||||
"""
|
||||
ae = np.abs(df['y'] - df['yhat'])
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mae': ae})
|
||||
return rolling_mean_by_h(
|
||||
x=ae.values, h=df['horizon'].values, w=w, name='mae'
|
||||
)
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def mape(df, w):
|
||||
"""Mean absolute percent error
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and mape.
|
||||
"""
|
||||
ape = np.abs((df['y'] - df['yhat']) / df['y'])
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mape': ape})
|
||||
return rolling_mean_by_h(
|
||||
x=ape.values, h=df['horizon'].values, w=w, name='mape'
|
||||
)
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def mdape(df, w):
|
||||
"""Median absolute percent error
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and mdape.
|
||||
"""
|
||||
ape = np.abs((df['y'] - df['yhat']) / df['y'])
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mdape': ape})
|
||||
return rolling_median_by_h(
|
||||
x=ape.values, h=df['horizon'], w=w, name='mdape'
|
||||
)
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def smape(df, w):
|
||||
"""Symmetric mean absolute percentage error
|
||||
based on Chen and Yang (2004) formula
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and smape.
|
||||
"""
|
||||
sape = np.abs(df['y'] - df['yhat']) / ((np.abs(df['y']) + np.abs(df['yhat'])) / 2)
|
||||
sape = sape.fillna(0)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'smape': sape})
|
||||
return rolling_mean_by_h(
|
||||
x=sape.values, h=df['horizon'].values, w=w, name='smape'
|
||||
)
|
||||
|
||||
|
||||
@register_performance_metric
|
||||
def coverage(df, w):
|
||||
"""Coverage
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df: Cross-validation results dataframe.
|
||||
w: Aggregation window size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with columns horizon and coverage.
|
||||
"""
|
||||
is_covered = (df['y'] >= df['yhat_lower']) & (df['y'] <= df['yhat_upper'])
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'coverage': is_covered})
|
||||
return rolling_mean_by_h(
|
||||
x=is_covered.values, h=df['horizon'].values, w=w, name='coverage'
|
||||
)
|
||||
1939
.venv/lib/python3.12/site-packages/prophet/forecaster.py
Normal file
1939
.venv/lib/python3.12/site-packages/prophet/forecaster.py
Normal file
File diff suppressed because it is too large
Load Diff
77
.venv/lib/python3.12/site-packages/prophet/make_holidays.py
Normal file
77
.venv/lib/python3.12/site-packages/prophet/make_holidays.py
Normal file
@ -0,0 +1,77 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
import holidays
|
||||
|
||||
|
||||
def get_country_holidays_class(country):
|
||||
"""Get class for a supported country.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
country: country code
|
||||
|
||||
Returns
|
||||
-------
|
||||
A valid country holidays class
|
||||
"""
|
||||
substitutions = {
|
||||
"TU": "TR", # For compatibility with Turkey as 'TU' cases.
|
||||
}
|
||||
|
||||
country = substitutions.get(country, country)
|
||||
if not hasattr(holidays, country):
|
||||
raise AttributeError(f"Holidays in {country} are not currently supported!")
|
||||
|
||||
return getattr(holidays, country)
|
||||
|
||||
|
||||
def get_holiday_names(country):
|
||||
"""Return all possible holiday names of given country
|
||||
|
||||
Parameters
|
||||
----------
|
||||
country: country name
|
||||
|
||||
Returns
|
||||
-------
|
||||
A set of all possible holiday names of given country
|
||||
"""
|
||||
country_holidays = get_country_holidays_class(country)
|
||||
return set(country_holidays(language="en_US", years=np.arange(1995, 2045)).values())
|
||||
|
||||
|
||||
def make_holidays_df(year_list, country, province=None, state=None):
|
||||
"""Make dataframe of holidays for given years and countries
|
||||
|
||||
Parameters
|
||||
----------
|
||||
year_list: a list of years
|
||||
country: country name
|
||||
province: province name
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dataframe with 'ds' and 'holiday', which can directly feed
|
||||
to 'holidays' params in Prophet
|
||||
"""
|
||||
country_holidays = get_country_holidays_class(country)
|
||||
holidays = country_holidays(expand=False, language="en_US", subdiv=province, years=year_list)
|
||||
|
||||
holidays_df = pd.DataFrame(
|
||||
[(date, holidays.get_list(date)) for date in holidays],
|
||||
columns=["ds", "holiday"],
|
||||
)
|
||||
holidays_df = holidays_df.explode("holiday")
|
||||
holidays_df.reset_index(inplace=True, drop=True)
|
||||
holidays_df["ds"] = pd.to_datetime(holidays_df["ds"])
|
||||
|
||||
return holidays_df
|
||||
283
.venv/lib/python3.12/site-packages/prophet/models.py
Normal file
283
.venv/lib/python3.12/site-packages/prophet/models.py
Normal file
@ -0,0 +1,283 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
from abc import abstractmethod, ABC
|
||||
from dataclasses import dataclass
|
||||
from typing import Sequence, Tuple
|
||||
from collections import OrderedDict
|
||||
from enum import Enum
|
||||
import importlib_resources
|
||||
import pathlib
|
||||
import platform
|
||||
|
||||
import logging
|
||||
logger = logging.getLogger('prophet.models')
|
||||
|
||||
PLATFORM = "win" if platform.platform().startswith("Win") else "unix"
|
||||
|
||||
class TrendIndicator(Enum):
|
||||
LINEAR = 0
|
||||
LOGISTIC = 1
|
||||
FLAT = 2
|
||||
|
||||
@dataclass
|
||||
class ModelInputData:
|
||||
T: int
|
||||
S: int
|
||||
K: int
|
||||
tau: float
|
||||
trend_indicator: int
|
||||
y: Sequence[float] # length T
|
||||
t: Sequence[float] # length T
|
||||
cap: Sequence[float] # length T
|
||||
t_change: Sequence[float] # length S
|
||||
s_a: Sequence[int] # length K
|
||||
s_m: Sequence[int] # length K
|
||||
X: Sequence[Sequence[float]] # shape (T, K)
|
||||
sigmas: Sequence[float] # length K
|
||||
|
||||
@dataclass
|
||||
class ModelParams:
|
||||
k: float
|
||||
m: float
|
||||
delta: Sequence[float] # length S
|
||||
beta: Sequence[float] # length K
|
||||
sigma_obs: float
|
||||
|
||||
|
||||
class IStanBackend(ABC):
|
||||
def __init__(self):
|
||||
self.model = self.load_model()
|
||||
self.stan_fit = None
|
||||
self.newton_fallback = True
|
||||
|
||||
def set_options(self, **kwargs):
|
||||
"""
|
||||
Specify model options as kwargs.
|
||||
* newton_fallback [bool]: whether to fallback to Newton if L-BFGS fails
|
||||
"""
|
||||
for k, v in kwargs.items():
|
||||
if k == 'newton_fallback':
|
||||
self.newton_fallback = v
|
||||
else:
|
||||
raise ValueError(f'Unknown option {k}')
|
||||
|
||||
def cleanup(self):
|
||||
"""Clean up temporary files created during model fitting."""
|
||||
pass
|
||||
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def get_type():
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def load_model(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def fit(self, stan_init, stan_data, **kwargs) -> dict:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:
|
||||
pass
|
||||
|
||||
|
||||
class CmdStanPyBackend(IStanBackend):
|
||||
CMDSTAN_VERSION = "2.33.1"
|
||||
def __init__(self):
|
||||
import cmdstanpy
|
||||
# this must be set before super.__init__() for load_model to work on Windows
|
||||
local_cmdstan = importlib_resources.files("prophet") / "stan_model" / f"cmdstan-{self.CMDSTAN_VERSION}"
|
||||
if local_cmdstan.exists():
|
||||
cmdstanpy.set_cmdstan_path(str(local_cmdstan))
|
||||
super().__init__()
|
||||
|
||||
@staticmethod
|
||||
def get_type():
|
||||
return StanBackendEnum.CMDSTANPY.name
|
||||
|
||||
def load_model(self):
|
||||
import cmdstanpy
|
||||
model_file = importlib_resources.files("prophet") / "stan_model" / "prophet_model.bin"
|
||||
return cmdstanpy.CmdStanModel(exe_file=str(model_file))
|
||||
|
||||
def fit(self, stan_init, stan_data, **kwargs):
|
||||
if 'inits' not in kwargs and 'init' in kwargs:
|
||||
stan_init = self.sanitize_custom_inits(stan_init, kwargs['init'])
|
||||
del kwargs['init']
|
||||
|
||||
inits_list, data_list = self.prepare_data(stan_init, stan_data)
|
||||
args = dict(
|
||||
data=data_list,
|
||||
inits=inits_list,
|
||||
algorithm='Newton' if data_list['T'] < 100 else 'LBFGS',
|
||||
iter=int(1e4),
|
||||
)
|
||||
args.update(kwargs)
|
||||
|
||||
try:
|
||||
self.stan_fit = self.model.optimize(**args)
|
||||
except RuntimeError as e:
|
||||
# Fall back on Newton
|
||||
if not self.newton_fallback or args['algorithm'] == 'Newton':
|
||||
raise e
|
||||
logger.warning('Optimization terminated abnormally. Falling back to Newton.')
|
||||
args['algorithm'] = 'Newton'
|
||||
self.stan_fit = self.model.optimize(**args)
|
||||
params = self.stan_to_dict_numpy(
|
||||
self.stan_fit.column_names, self.stan_fit.optimized_params_np)
|
||||
for par in params:
|
||||
params[par] = params[par].reshape((1, -1))
|
||||
return params
|
||||
|
||||
def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:
|
||||
if 'inits' not in kwargs and 'init' in kwargs:
|
||||
stan_init = self.sanitize_custom_inits(stan_init, kwargs['init'])
|
||||
del kwargs['init']
|
||||
|
||||
inits_list, data_list = self.prepare_data(stan_init, stan_data)
|
||||
args = dict(
|
||||
data=data_list,
|
||||
inits=inits_list,
|
||||
)
|
||||
if 'chains' not in kwargs:
|
||||
kwargs['chains'] = 4
|
||||
iter_half = samples // 2
|
||||
kwargs['iter_sampling'] = iter_half
|
||||
if 'iter_warmup' not in kwargs:
|
||||
kwargs['iter_warmup'] = iter_half
|
||||
args.update(kwargs)
|
||||
|
||||
self.stan_fit = self.model.sample(**args)
|
||||
res = self.stan_fit.draws()
|
||||
(samples, c, columns) = res.shape
|
||||
res = res.reshape((samples * c, columns))
|
||||
params = self.stan_to_dict_numpy(self.stan_fit.column_names, res)
|
||||
|
||||
for par in params:
|
||||
s = params[par].shape
|
||||
if s[1] == 1:
|
||||
params[par] = params[par].reshape((s[0],))
|
||||
|
||||
if par in ['delta', 'beta'] and len(s) < 2:
|
||||
params[par] = params[par].reshape((-1, 1))
|
||||
|
||||
return params
|
||||
|
||||
def cleanup(self):
|
||||
import cmdstanpy
|
||||
|
||||
if hasattr(self, "stan_fit"):
|
||||
fit_result: cmdstanpy.CmdStanMLE | cmdstanpy.CmdStanMCMC = self.stan_fit
|
||||
to_remove = (
|
||||
fit_result.runset.csv_files +
|
||||
fit_result.runset.diagnostic_files +
|
||||
fit_result.runset.stdout_files +
|
||||
fit_result.runset.profile_files
|
||||
)
|
||||
for fpath in to_remove:
|
||||
if pathlib.Path(fpath).is_file():
|
||||
pathlib.Path(fpath).unlink()
|
||||
|
||||
@staticmethod
|
||||
def sanitize_custom_inits(default_inits, custom_inits):
|
||||
"""Validate that custom inits have the correct type and shape, otherwise use defaults."""
|
||||
sanitized = {}
|
||||
for param in ['k', 'm', 'sigma_obs']:
|
||||
try:
|
||||
sanitized[param] = float(custom_inits.get(param))
|
||||
except Exception:
|
||||
sanitized[param] = default_inits[param]
|
||||
for param in ['delta', 'beta']:
|
||||
if default_inits[param].shape == custom_inits[param].shape:
|
||||
sanitized[param] = custom_inits[param]
|
||||
else:
|
||||
sanitized[param] = default_inits[param]
|
||||
return sanitized
|
||||
|
||||
@staticmethod
|
||||
def prepare_data(init, data) -> Tuple[dict, dict]:
|
||||
"""Converts np.ndarrays to lists that can be read by cmdstanpy."""
|
||||
cmdstanpy_data = {
|
||||
'T': data['T'],
|
||||
'S': data['S'],
|
||||
'K': data['K'],
|
||||
'tau': data['tau'],
|
||||
'trend_indicator': data['trend_indicator'],
|
||||
'y': data['y'].tolist(),
|
||||
't': data['t'].tolist(),
|
||||
'cap': data['cap'].tolist(),
|
||||
't_change': data['t_change'].tolist(),
|
||||
's_a': data['s_a'].tolist(),
|
||||
's_m': data['s_m'].tolist(),
|
||||
'X': data['X'].to_numpy().tolist(),
|
||||
'sigmas': data['sigmas']
|
||||
}
|
||||
|
||||
cmdstanpy_init = {
|
||||
'k': init['k'],
|
||||
'm': init['m'],
|
||||
'delta': init['delta'].tolist(),
|
||||
'beta': init['beta'].tolist(),
|
||||
'sigma_obs': init['sigma_obs']
|
||||
}
|
||||
return (cmdstanpy_init, cmdstanpy_data)
|
||||
|
||||
@staticmethod
|
||||
def stan_to_dict_numpy(column_names: Tuple[str, ...], data: 'np.array'):
|
||||
import numpy as np
|
||||
|
||||
output = OrderedDict()
|
||||
|
||||
prev = None
|
||||
|
||||
start = 0
|
||||
end = 0
|
||||
two_dims = len(data.shape) > 1
|
||||
for cname in column_names:
|
||||
parsed = cname.split(".") if "." in cname else cname.split("[")
|
||||
curr = parsed[0]
|
||||
if prev is None:
|
||||
prev = curr
|
||||
|
||||
if curr != prev:
|
||||
if prev in output:
|
||||
raise RuntimeError(
|
||||
"Found repeated column name"
|
||||
)
|
||||
if two_dims:
|
||||
output[prev] = np.array(data[:, start:end])
|
||||
else:
|
||||
output[prev] = np.array(data[start:end])
|
||||
prev = curr
|
||||
start = end
|
||||
end += 1
|
||||
if prev in output:
|
||||
raise RuntimeError(
|
||||
"Found repeated column name"
|
||||
)
|
||||
if two_dims:
|
||||
output[prev] = np.array(data[:, start:end])
|
||||
else:
|
||||
output[prev] = np.array(data[start:end])
|
||||
return output
|
||||
|
||||
|
||||
|
||||
|
||||
class StanBackendEnum(Enum):
|
||||
CMDSTANPY = CmdStanPyBackend
|
||||
|
||||
@staticmethod
|
||||
def get_backend_class(name: str) -> IStanBackend:
|
||||
try:
|
||||
return StanBackendEnum[name].value
|
||||
except KeyError as e:
|
||||
raise ValueError(f"Unknown stan backend: {name}") from e
|
||||
1025
.venv/lib/python3.12/site-packages/prophet/plot.py
Normal file
1025
.venv/lib/python3.12/site-packages/prophet/plot.py
Normal file
File diff suppressed because it is too large
Load Diff
218
.venv/lib/python3.12/site-packages/prophet/serialize.py
Normal file
218
.venv/lib/python3.12/site-packages/prophet/serialize.py
Normal file
@ -0,0 +1,218 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
from io import StringIO
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from prophet.forecaster import Prophet
|
||||
|
||||
about = {}
|
||||
here = Path(__file__).parent.resolve()
|
||||
with open(here / "__version__.py", "r") as f:
|
||||
exec(f.read(), about)
|
||||
|
||||
SIMPLE_ATTRIBUTES = [
|
||||
'growth', 'n_changepoints', 'specified_changepoints', 'changepoint_range',
|
||||
'yearly_seasonality', 'weekly_seasonality', 'daily_seasonality',
|
||||
'seasonality_mode', 'seasonality_prior_scale', 'changepoint_prior_scale',
|
||||
'holidays_prior_scale', 'mcmc_samples', 'interval_width', 'uncertainty_samples',
|
||||
'y_scale', 'y_min', 'scaling', 'logistic_floor', 'country_holidays', 'component_modes',
|
||||
'holidays_mode'
|
||||
]
|
||||
|
||||
PD_SERIES = ['changepoints', 'history_dates', 'train_holiday_names']
|
||||
|
||||
PD_TIMESTAMP = ['start']
|
||||
|
||||
PD_TIMEDELTA = ['t_scale']
|
||||
|
||||
PD_DATAFRAME = ['holidays', 'history', 'train_component_cols']
|
||||
|
||||
NP_ARRAY = ['changepoints_t']
|
||||
|
||||
ORDEREDDICT = ['seasonalities', 'extra_regressors']
|
||||
|
||||
|
||||
def model_to_dict(model):
|
||||
"""Convert a Prophet model to a dictionary suitable for JSON serialization.
|
||||
|
||||
Model must be fitted. Skips Stan objects that are not needed for predict.
|
||||
|
||||
Can be reversed with model_from_dict.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model: Prophet model object.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict that can be used to serialize a Prophet model as JSON or loaded back
|
||||
into a Prophet model.
|
||||
"""
|
||||
if model.history is None:
|
||||
raise ValueError(
|
||||
"This can only be used to serialize models that have already been fit."
|
||||
)
|
||||
|
||||
model_dict = {
|
||||
attribute: getattr(model, attribute) for attribute in SIMPLE_ATTRIBUTES
|
||||
}
|
||||
# Handle attributes of non-core types
|
||||
for attribute in PD_SERIES:
|
||||
if getattr(model, attribute) is None:
|
||||
model_dict[attribute] = None
|
||||
else:
|
||||
model_dict[attribute] = getattr(model, attribute).to_json(
|
||||
orient='split', date_format='iso'
|
||||
)
|
||||
for attribute in PD_TIMESTAMP:
|
||||
model_dict[attribute] = getattr(model, attribute).timestamp()
|
||||
for attribute in PD_TIMEDELTA:
|
||||
model_dict[attribute] = getattr(model, attribute).total_seconds()
|
||||
for attribute in PD_DATAFRAME:
|
||||
if getattr(model, attribute) is None:
|
||||
model_dict[attribute] = None
|
||||
else:
|
||||
model_dict[attribute] = getattr(model, attribute).to_json(orient='table', index=False)
|
||||
for attribute in NP_ARRAY:
|
||||
model_dict[attribute] = getattr(model, attribute).tolist()
|
||||
for attribute in ORDEREDDICT:
|
||||
model_dict[attribute] = [
|
||||
list(getattr(model, attribute).keys()),
|
||||
getattr(model, attribute),
|
||||
]
|
||||
# Other attributes with special handling
|
||||
# fit_kwargs -> Transform any numpy types before serializing.
|
||||
# They do not need to be transformed back on deserializing.
|
||||
fit_kwargs = deepcopy(model.fit_kwargs)
|
||||
if 'init' in fit_kwargs:
|
||||
for k, v in fit_kwargs['init'].items():
|
||||
if isinstance(v, np.ndarray):
|
||||
fit_kwargs['init'][k] = v.tolist()
|
||||
elif isinstance(v, np.floating):
|
||||
fit_kwargs['init'][k] = float(v)
|
||||
model_dict['fit_kwargs'] = fit_kwargs
|
||||
|
||||
# Params (Dict[str, np.ndarray])
|
||||
model_dict['params'] = {k: v.tolist() for k, v in model.params.items()}
|
||||
# Attributes that are skipped: stan_fit, stan_backend
|
||||
model_dict['__prophet_version'] = about["__version__"]
|
||||
return model_dict
|
||||
|
||||
|
||||
def model_to_json(model):
|
||||
"""Serialize a Prophet model to json string.
|
||||
|
||||
Model must be fitted. Skips Stan objects that are not needed for predict.
|
||||
|
||||
Can be deserialized with model_from_json.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model: Prophet model object.
|
||||
|
||||
Returns
|
||||
-------
|
||||
json string that can be deserialized into a Prophet model.
|
||||
"""
|
||||
model_json = model_to_dict(model)
|
||||
return json.dumps(model_json)
|
||||
|
||||
|
||||
def _handle_simple_attributes_backwards_compat(model_dict):
|
||||
"""Handle backwards compatibility for SIMPLE_ATTRIBUTES."""
|
||||
# prophet<1.1.5: handle scaling parameters introduced in #2470
|
||||
if 'scaling' not in model_dict:
|
||||
model_dict['scaling'] = 'absmax'
|
||||
model_dict['y_min'] = 0.
|
||||
# prophet<1.1.5: handle holidays_mode parameter introduced in #2477
|
||||
if 'holidays_mode' not in model_dict:
|
||||
model_dict['holidays_mode'] = model_dict['seasonality_mode']
|
||||
|
||||
def model_from_dict(model_dict):
|
||||
"""Recreate a Prophet model from a dictionary.
|
||||
|
||||
Recreates models that were converted with model_to_dict.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model_dict: Dictionary containing model, created with model_to_dict.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Prophet model.
|
||||
"""
|
||||
model = Prophet() # We will overwrite all attributes set in init anyway
|
||||
# Simple types
|
||||
_handle_simple_attributes_backwards_compat(model_dict)
|
||||
for attribute in SIMPLE_ATTRIBUTES:
|
||||
setattr(model, attribute, model_dict[attribute])
|
||||
for attribute in PD_SERIES:
|
||||
if model_dict[attribute] is None:
|
||||
setattr(model, attribute, None)
|
||||
else:
|
||||
s = pd.read_json(StringIO(model_dict[attribute]), typ='series', orient='split')
|
||||
if s.name == 'ds':
|
||||
if len(s) == 0:
|
||||
s = pd.to_datetime(s)
|
||||
s = s.dt.tz_localize(None)
|
||||
setattr(model, attribute, s)
|
||||
for attribute in PD_TIMESTAMP:
|
||||
setattr(model, attribute, pd.Timestamp.utcfromtimestamp(model_dict[attribute]).tz_localize(None))
|
||||
for attribute in PD_TIMEDELTA:
|
||||
setattr(model, attribute, pd.Timedelta(seconds=model_dict[attribute]))
|
||||
for attribute in PD_DATAFRAME:
|
||||
if model_dict[attribute] is None:
|
||||
setattr(model, attribute, None)
|
||||
else:
|
||||
df = pd.read_json(StringIO(model_dict[attribute]), typ='frame', orient='table', convert_dates=['ds'])
|
||||
if attribute == 'train_component_cols':
|
||||
# Special handling because of named index column
|
||||
df.columns.name = 'component'
|
||||
df.index.name = 'col'
|
||||
setattr(model, attribute, df)
|
||||
for attribute in NP_ARRAY:
|
||||
setattr(model, attribute, np.array(model_dict[attribute]))
|
||||
for attribute in ORDEREDDICT:
|
||||
key_list, unordered_dict = model_dict[attribute]
|
||||
od = OrderedDict()
|
||||
for key in key_list:
|
||||
od[key] = unordered_dict[key]
|
||||
setattr(model, attribute, od)
|
||||
# Other attributes with special handling
|
||||
# fit_kwargs
|
||||
model.fit_kwargs = model_dict['fit_kwargs']
|
||||
# Params (Dict[str, np.ndarray])
|
||||
model.params = {k: np.array(v) for k, v in model_dict['params'].items()}
|
||||
# Skipped attributes
|
||||
model.stan_backend = None
|
||||
model.stan_fit = None
|
||||
return model
|
||||
|
||||
|
||||
def model_from_json(model_json):
|
||||
"""Deserialize a Prophet model from json string.
|
||||
|
||||
Deserializes models that were serialized with model_to_json.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model_json: Serialized model string
|
||||
|
||||
Returns
|
||||
-------
|
||||
Prophet model.
|
||||
"""
|
||||
model_dict = json.loads(model_json)
|
||||
return model_from_dict(model_dict)
|
||||
Binary file not shown.
BIN
.venv/lib/python3.12/site-packages/prophet/stan_model/cmdstan-2.33.1/bin/print
Executable file
BIN
.venv/lib/python3.12/site-packages/prophet/stan_model/cmdstan-2.33.1/bin/print
Executable file
Binary file not shown.
BIN
.venv/lib/python3.12/site-packages/prophet/stan_model/cmdstan-2.33.1/bin/stanc
Executable file
BIN
.venv/lib/python3.12/site-packages/prophet/stan_model/cmdstan-2.33.1/bin/stanc
Executable file
Binary file not shown.
Binary file not shown.
@ -0,0 +1,64 @@
|
||||
arena.o: ../tbb_2020.3/src/tbb/arena.cpp \
|
||||
../tbb_2020.3/include/tbb/global_control.h \
|
||||
../tbb_2020.3/include/tbb/tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h ../tbb_2020.3/src/tbb/scheduler.h \
|
||||
../tbb_2020.3/src/tbb/scheduler_common.h \
|
||||
../tbb_2020.3/include/tbb/tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_generic.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_ia32_common.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_itsx.h \
|
||||
../tbb_2020.3/include/tbb/machine/linux_common.h \
|
||||
../tbb_2020.3/include/tbb/cache_aligned_allocator.h \
|
||||
../tbb_2020.3/src/tbb/tbb_statistics.h \
|
||||
../tbb_2020.3/include/tbb/tbb_stddef.h ../tbb_2020.3/include/tbb/task.h \
|
||||
../tbb_2020.3/include/tbb/internal/_deprecated_header_message_guard.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/internal/_warning_suppress_enable_notice.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/tbb_profiling.h \
|
||||
../tbb_2020.3/include/tbb/internal/_tbb_strings.h \
|
||||
../tbb_2020.3/include/tbb/atomic.h \
|
||||
../tbb_2020.3/include/tbb/internal/_warning_suppress_disable_notice.h \
|
||||
../tbb_2020.3/include/tbb/tbb_exception.h \
|
||||
../tbb_2020.3/include/tbb/tbb_allocator.h \
|
||||
../tbb_2020.3/include/tbb/spin_mutex.h \
|
||||
../tbb_2020.3/include/tbb/aligned_space.h \
|
||||
../tbb_2020.3/include/tbb/internal/_mutex_padding.h \
|
||||
../tbb_2020.3/include/tbb/internal/_x86_eliding_mutex_impl.h \
|
||||
../tbb_2020.3/src/tbb/mailbox.h ../tbb_2020.3/include/tbb/atomic.h \
|
||||
../tbb_2020.3/src/tbb/tbb_misc.h ../tbb_2020.3/include/tbb/info.h \
|
||||
../tbb_2020.3/src/tbb/itt_notify.h \
|
||||
../tbb_2020.3/src/tbb/tools_api/ittnotify.h \
|
||||
../tbb_2020.3/src/tbb/tools_api/legacy/ittnotify.h \
|
||||
../tbb_2020.3/src/tbb/../rml/include/rml_tbb.h \
|
||||
../tbb_2020.3/src/tbb/../rml/include/rml_base.h \
|
||||
../tbb_2020.3/src/tbb/intrusive_list.h \
|
||||
../tbb_2020.3/include/tbb/internal/_template_helpers.h \
|
||||
../tbb_2020.3/src/tbb/cilk-tbb-interop.h ../tbb_2020.3/src/tbb/arena.h \
|
||||
../tbb_2020.3/src/tbb/task_stream.h \
|
||||
../tbb_2020.3/include/tbb/tbb_allocator.h \
|
||||
../tbb_2020.3/src/tbb/observer_proxy.h \
|
||||
../tbb_2020.3/include/tbb/task_scheduler_observer.h \
|
||||
../tbb_2020.3/include/tbb/task_arena.h ../tbb_2020.3/include/tbb/task.h \
|
||||
../tbb_2020.3/include/tbb/tbb_exception.h \
|
||||
../tbb_2020.3/include/tbb/internal/_template_helpers.h \
|
||||
../tbb_2020.3/include/tbb/info.h \
|
||||
../tbb_2020.3/include/tbb/spin_rw_mutex.h \
|
||||
../tbb_2020.3/include/tbb/internal/_x86_rtm_rw_mutex_impl.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_profiling.h \
|
||||
../tbb_2020.3/include/tbb/internal/../spin_rw_mutex.h \
|
||||
../tbb_2020.3/include/tbb/aligned_space.h ../tbb_2020.3/src/tbb/market.h \
|
||||
../tbb_2020.3/src/tbb/governor.h \
|
||||
../tbb_2020.3/include/tbb/task_scheduler_init.h \
|
||||
../tbb_2020.3/src/tbb/tls.h ../tbb_2020.3/src/tbb/concurrent_monitor.h \
|
||||
../tbb_2020.3/src/tbb/semaphore.h \
|
||||
../tbb_2020.3/include/tbb/internal/_flow_graph_impl.h \
|
||||
../tbb_2020.3/include/tbb/internal/../task.h \
|
||||
../tbb_2020.3/include/tbb/internal/../internal/_deprecated_header_message_guard.h \
|
||||
../tbb_2020.3/include/tbb/internal/../task_arena.h \
|
||||
../tbb_2020.3/include/tbb/internal/../flow_graph_abstractions.h \
|
||||
../tbb_2020.3/src/tbb/scheduler_utility.h \
|
||||
../tbb_2020.3/include/tbb/task_arena.h
|
||||
Binary file not shown.
@ -0,0 +1,32 @@
|
||||
backend.o: ../tbb_2020.3/src/tbbmalloc/backend.cpp \
|
||||
../tbb_2020.3/src/tbbmalloc/tbbmalloc_internal.h \
|
||||
../tbb_2020.3/src/tbbmalloc/TypeDefinitions.h \
|
||||
../tbb_2020.3/src/tbbmalloc/Customize.h \
|
||||
../tbb_2020.3/include/tbb/tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h \
|
||||
../tbb_2020.3/src/tbbmalloc/Synchronize.h \
|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
../tbb_2020.3/src/tbb/../rml/include/rml_base.h \
|
||||
../tbb_2020.3/src/tbb/tbb_misc.h ../tbb_2020.3/include/tbb/atomic.h \
|
||||
../tbb_2020.3/include/tbb/info.h ../tbb_2020.3/src/tbb/tls.h \
|
||||
../tbb_2020.3/src/tbb/cilk-tbb-interop.h \
|
||||
../tbb_2020.3/src/tbb/scheduler.h ../tbb_2020.3/src/tbb/mailbox.h \
|
||||
../tbb_2020.3/src/tbb/itt_notify.h \
|
||||
../tbb_2020.3/src/tbb/tools_api/ittnotify.h \
|
||||
../tbb_2020.3/src/tbb/tools_api/legacy/ittnotify.h \
|
||||
../tbb_2020.3/src/tbb/intrusive_list.h \
|
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../tbb_2020.3/include/tbb/internal/_template_helpers.h \
|
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../tbb_2020.3/src/tbb/arena.h ../tbb_2020.3/src/tbb/task_stream.h \
|
||||
../tbb_2020.3/include/tbb/tbb_allocator.h \
|
||||
../tbb_2020.3/src/tbb/observer_proxy.h \
|
||||
../tbb_2020.3/include/tbb/task_scheduler_observer.h \
|
||||
../tbb_2020.3/include/tbb/task_arena.h ../tbb_2020.3/include/tbb/task.h \
|
||||
../tbb_2020.3/include/tbb/tbb_exception.h \
|
||||
../tbb_2020.3/include/tbb/internal/_template_helpers.h \
|
||||
../tbb_2020.3/include/tbb/info.h \
|
||||
../tbb_2020.3/include/tbb/spin_rw_mutex.h \
|
||||
../tbb_2020.3/include/tbb/internal/_x86_rtm_rw_mutex_impl.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_profiling.h \
|
||||
../tbb_2020.3/include/tbb/internal/../spin_rw_mutex.h \
|
||||
../tbb_2020.3/include/tbb/aligned_space.h ../tbb_2020.3/src/tbb/market.h \
|
||||
../tbb_2020.3/src/tbb/concurrent_monitor.h \
|
||||
../tbb_2020.3/src/tbb/semaphore.h \
|
||||
../tbb_2020.3/include/tbb/partitioner.h
|
||||
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|
||||
task_group_context.o: ../tbb_2020.3/src/tbb/task_group_context.cpp \
|
||||
../tbb_2020.3/src/tbb/scheduler.h \
|
||||
../tbb_2020.3/src/tbb/scheduler_common.h \
|
||||
../tbb_2020.3/include/tbb/tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_generic.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_ia32_common.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_itsx.h \
|
||||
../tbb_2020.3/include/tbb/machine/linux_common.h \
|
||||
../tbb_2020.3/include/tbb/cache_aligned_allocator.h \
|
||||
../tbb_2020.3/src/tbb/tbb_statistics.h \
|
||||
../tbb_2020.3/include/tbb/tbb_stddef.h ../tbb_2020.3/include/tbb/task.h \
|
||||
../tbb_2020.3/include/tbb/internal/_deprecated_header_message_guard.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/internal/_warning_suppress_enable_notice.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/tbb_profiling.h \
|
||||
../tbb_2020.3/include/tbb/internal/_tbb_strings.h \
|
||||
../tbb_2020.3/include/tbb/atomic.h \
|
||||
../tbb_2020.3/include/tbb/internal/_warning_suppress_disable_notice.h \
|
||||
../tbb_2020.3/include/tbb/tbb_exception.h \
|
||||
../tbb_2020.3/include/tbb/tbb_allocator.h \
|
||||
../tbb_2020.3/include/tbb/spin_mutex.h \
|
||||
../tbb_2020.3/include/tbb/aligned_space.h \
|
||||
../tbb_2020.3/include/tbb/internal/_mutex_padding.h \
|
||||
../tbb_2020.3/include/tbb/internal/_x86_eliding_mutex_impl.h \
|
||||
../tbb_2020.3/src/tbb/mailbox.h ../tbb_2020.3/include/tbb/atomic.h \
|
||||
../tbb_2020.3/src/tbb/tbb_misc.h ../tbb_2020.3/include/tbb/info.h \
|
||||
../tbb_2020.3/src/tbb/itt_notify.h \
|
||||
../tbb_2020.3/src/tbb/tools_api/ittnotify.h \
|
||||
../tbb_2020.3/src/tbb/tools_api/legacy/ittnotify.h \
|
||||
../tbb_2020.3/src/tbb/../rml/include/rml_tbb.h \
|
||||
../tbb_2020.3/src/tbb/../rml/include/rml_base.h \
|
||||
../tbb_2020.3/src/tbb/intrusive_list.h \
|
||||
../tbb_2020.3/include/tbb/internal/_template_helpers.h \
|
||||
../tbb_2020.3/src/tbb/cilk-tbb-interop.h ../tbb_2020.3/src/tbb/arena.h \
|
||||
../tbb_2020.3/src/tbb/task_stream.h \
|
||||
../tbb_2020.3/include/tbb/tbb_allocator.h \
|
||||
../tbb_2020.3/src/tbb/observer_proxy.h \
|
||||
../tbb_2020.3/include/tbb/task_scheduler_observer.h \
|
||||
../tbb_2020.3/include/tbb/task_arena.h ../tbb_2020.3/include/tbb/task.h \
|
||||
../tbb_2020.3/include/tbb/tbb_exception.h \
|
||||
../tbb_2020.3/include/tbb/internal/_template_helpers.h \
|
||||
../tbb_2020.3/include/tbb/info.h \
|
||||
../tbb_2020.3/include/tbb/spin_rw_mutex.h \
|
||||
../tbb_2020.3/include/tbb/internal/_x86_rtm_rw_mutex_impl.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_profiling.h \
|
||||
../tbb_2020.3/include/tbb/internal/../spin_rw_mutex.h \
|
||||
../tbb_2020.3/include/tbb/aligned_space.h ../tbb_2020.3/src/tbb/market.h \
|
||||
../tbb_2020.3/src/tbb/governor.h \
|
||||
../tbb_2020.3/include/tbb/task_scheduler_init.h \
|
||||
../tbb_2020.3/src/tbb/tls.h ../tbb_2020.3/src/tbb/concurrent_monitor.h \
|
||||
../tbb_2020.3/src/tbb/semaphore.h
|
||||
Binary file not shown.
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|
||||
task_v2.o: ../tbb_2020.3/src/old/task_v2.cpp \
|
||||
../tbb_2020.3/include/tbb/task.h \
|
||||
../tbb_2020.3/include/tbb/internal/_deprecated_header_message_guard.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/internal/_warning_suppress_enable_notice.h \
|
||||
../tbb_2020.3/include/tbb/internal/../tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/tbb_stddef.h \
|
||||
../tbb_2020.3/include/tbb/tbb_config.h \
|
||||
../tbb_2020.3/include/tbb/tbb_machine.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_generic.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_ia32_common.h \
|
||||
../tbb_2020.3/include/tbb/machine/gcc_itsx.h \
|
||||
../tbb_2020.3/include/tbb/machine/linux_common.h \
|
||||
../tbb_2020.3/include/tbb/tbb_profiling.h \
|
||||
../tbb_2020.3/include/tbb/internal/_tbb_strings.h \
|
||||
../tbb_2020.3/include/tbb/atomic.h \
|
||||
../tbb_2020.3/include/tbb/internal/_warning_suppress_disable_notice.h
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user