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Separate preprocessing step from fit() method for easier debugging (#2505)
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Cuong Duong
GitHub
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commit
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2 changed files with 109 additions and 56 deletions
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@ -6,6 +6,7 @@
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from __future__ import absolute_import, division, print_function
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import dataclasses
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import logging
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from collections import OrderedDict, defaultdict
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from copy import deepcopy
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@ -17,7 +18,7 @@ import pandas as pd
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from numpy.typing import NDArray
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from prophet.make_holidays import get_holiday_names, make_holidays_df
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from prophet.models import StanBackendEnum
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from prophet.models import StanBackendEnum, ModelInputData, ModelParams, TrendIndicator
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from prophet.plot import (plot, plot_components)
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logger = logging.getLogger('prophet')
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@ -1114,6 +1115,76 @@ class Prophet(object):
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m = df['y_scaled'].mean()
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return k, m
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def preprocess(self, df: pd.DataFrame, **kwargs) -> ModelInputData:
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"""
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Reformats historical data, standardizes y and extra regressors, sets seasonalities and changepoints.
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Saves the preprocessed data to the instantiated object, and also returns the relevant components
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as a ModelInputData object.
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"""
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if ('ds' not in df) or ('y' not in df):
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raise ValueError(
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'Dataframe must have columns "ds" and "y" with the dates and '
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'values respectively.'
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)
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history = df[df['y'].notnull()].copy()
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if history.shape[0] < 2:
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raise ValueError('Dataframe has less than 2 non-NaN rows.')
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self.history_dates = pd.to_datetime(pd.Series(history['ds'].unique(), name='ds')).sort_values()
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self.history = self.setup_dataframe(history, initialize_scales=True)
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self.set_auto_seasonalities()
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seasonal_features, prior_scales, component_cols, modes = (
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self.make_all_seasonality_features(self.history))
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self.train_component_cols = component_cols
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self.component_modes = modes
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self.fit_kwargs = deepcopy(kwargs)
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self.set_changepoints()
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if self.growth in ['linear', 'flat']:
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cap = np.zeros(self.history.shape[0])
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else:
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cap = self.history['cap_scaled']
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return ModelInputData(
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T=self.history.shape[0],
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S=len(self.changepoints_t),
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K=seasonal_features.shape[1],
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tau=self.changepoint_prior_scale,
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trend_indicator=TrendIndicator[self.growth.upper()].value,
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y=self.history['y_scaled'],
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t=self.history['t'],
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t_change=self.changepoints_t,
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X=seasonal_features,
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sigmas=prior_scales,
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s_a=component_cols['additive_terms'],
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s_m=component_cols['multiplicative_terms'],
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cap=cap,
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)
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def calculate_initial_params(self, num_total_regressors: int) -> ModelParams:
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"""
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Calculates initial parameters for the model based on the preprocessed history.
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Parameters
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----------
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num_total_regressors: the count of seasonality fourier components plus holidays plus extra regressors.
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"""
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if self.growth == 'linear':
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k, m = self.linear_growth_init(self.history)
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elif self.growth == 'flat':
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k, m = self.flat_growth_init(self.history)
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elif self.growth == 'logistic':
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k, m = self.logistic_growth_init(self.history)
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return ModelParams(
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k=k,
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m=m,
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delta=np.zeros_like(self.changepoints_t),
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beta=np.zeros(num_total_regressors),
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sigma_obs=1.0,
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)
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def fit(self, df, **kwargs):
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"""Fit the Prophet model.
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@ -1142,63 +1213,14 @@ class Prophet(object):
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if self.history is not None:
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raise Exception('Prophet object can only be fit once. '
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'Instantiate a new object.')
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if ('ds' not in df) or ('y' not in df):
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raise ValueError(
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'Dataframe must have columns "ds" and "y" with the dates and '
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'values respectively.'
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)
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history = df[df['y'].notnull()].copy()
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if history.shape[0] < 2:
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raise ValueError('Dataframe has less than 2 non-NaN rows.')
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self.history_dates = pd.to_datetime(pd.Series(df['ds'].unique(), name='ds')).sort_values()
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history = self.setup_dataframe(history, initialize_scales=True)
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self.history = history
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self.set_auto_seasonalities()
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seasonal_features, prior_scales, component_cols, modes = (
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self.make_all_seasonality_features(history))
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self.train_component_cols = component_cols
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self.component_modes = modes
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self.fit_kwargs = deepcopy(kwargs)
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model_inputs = self.preprocess(df, **kwargs)
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initial_params = self.calculate_initial_params(model_inputs.K)
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self.set_changepoints()
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dat = dataclasses.asdict(model_inputs)
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stan_init = dataclasses.asdict(initial_params)
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trend_indicator = {'linear': 0, 'logistic': 1, 'flat': 2}
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dat = {
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'T': history.shape[0],
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'K': seasonal_features.shape[1],
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'S': len(self.changepoints_t),
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'y': history['y_scaled'],
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't': history['t'],
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't_change': self.changepoints_t,
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'X': seasonal_features,
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'sigmas': prior_scales,
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'tau': self.changepoint_prior_scale,
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'trend_indicator': trend_indicator[self.growth],
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's_a': component_cols['additive_terms'],
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's_m': component_cols['multiplicative_terms'],
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}
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if self.growth == 'linear':
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dat['cap'] = np.zeros(self.history.shape[0])
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kinit = self.linear_growth_init(history)
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elif self.growth == 'flat':
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dat['cap'] = np.zeros(self.history.shape[0])
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kinit = self.flat_growth_init(history)
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else:
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dat['cap'] = history['cap_scaled']
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kinit = self.logistic_growth_init(history)
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stan_init = {
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'k': kinit[0],
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'm': kinit[1],
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'delta': np.zeros(len(self.changepoints_t)),
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'beta': np.zeros(seasonal_features.shape[1]),
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'sigma_obs': 1,
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}
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if history['y'].min() == history['y'].max() and \
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if self.history['y'].min() == self.history['y'].max() and \
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(self.growth == 'linear' or self.growth == 'flat'):
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self.params = stan_init
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self.params['sigma_obs'] = 1e-9
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@ -6,7 +6,8 @@
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from __future__ import absolute_import, division, print_function
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from abc import abstractmethod, ABC
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from typing import Tuple
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from dataclasses import dataclass
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from typing import Sequence, Tuple
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from collections import OrderedDict
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from enum import Enum
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import importlib_resources
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@ -17,6 +18,36 @@ logger = logging.getLogger('prophet.models')
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PLATFORM = "win" if platform.platform().startswith("Win") else "unix"
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class TrendIndicator(Enum):
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LINEAR = 0
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LOGISTIC = 1
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FLAT = 2
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@dataclass
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class ModelInputData:
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T: int
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S: int
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K: int
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tau: float
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trend_indicator: int
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y: Sequence[float] # length T
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t: Sequence[float] # length T
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cap: Sequence[float] # length T
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t_change: Sequence[float] # length S
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s_a: Sequence[int] # length K
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s_m: Sequence[int] # length K
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X: Sequence[Sequence[float]] # shape (T, K)
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sigmas: Sequence[float] # length K
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@dataclass
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class ModelParams:
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k: float
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m: float
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delta: Sequence[float] # length S
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beta: Sequence[float] # length K
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sigma_obs: float
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class IStanBackend(ABC):
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def __init__(self):
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self.model = self.load_model()
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