mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-07-29 20:14:08 +00:00
Style modifications to fbprophet
This commit is contained in:
baogorek
Ben Letham
parent
83f66dadfd
commit
3a6a338ac2
3 changed files with 86 additions and 88 deletions
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@ -144,17 +144,17 @@ class Prophet(object):
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"""Validates the inputs to Prophet."""
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if self.growth not in ('linear', 'logistic'):
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raise ValueError(
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"Parameter 'growth' should be 'linear' or 'logistic'.")
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'Parameter "growth" should be "linear" or "logistic".')
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if ((self.changepoint_range < 0) or (self.changepoint_range > 1)):
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raise ValueError("Parameter 'changepoint_range' must be in [0, 1]")
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raise ValueError('Parameter "changepoint_range" must be in [0, 1]')
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if self.holidays is not None:
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if not (
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isinstance(self.holidays, pd.DataFrame)
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and 'ds' in self.holidays # noqa W503
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and 'holiday' in self.holidays # noqa W503
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):
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raise ValueError("holidays must be a DataFrame with 'ds' and "
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"'holiday' columns.")
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raise ValueError('holidays must be a DataFrame with "ds" and '
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'"holiday" columns.')
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self.holidays['ds'] = pd.to_datetime(self.holidays['ds'])
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has_lower = 'lower_window' in self.holidays
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has_upper = 'upper_window' in self.holidays
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@ -170,7 +170,7 @@ class Prophet(object):
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self.validate_column_name(h, check_holidays=False)
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if self.seasonality_mode not in ['additive', 'multiplicative']:
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raise ValueError(
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"seasonality_mode must be 'additive' or 'multiplicative'"
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'seasonality_mode must be "additive" or "multiplicative"'
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)
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def validate_column_name(self, name, check_holidays=True,
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@ -198,21 +198,21 @@ class Prophet(object):
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reserved_names.extend([
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'ds', 'y', 'cap', 'floor', 'y_scaled', 'cap_scaled'])
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if name in reserved_names:
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raise ValueError('Name "{}" is reserved.'.format(name))
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raise ValueError(f'Name "{name}" is reserved.')
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if (check_holidays and self.holidays is not None and
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name in self.holidays['holiday'].unique()):
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raise ValueError(
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'Name "{}" already used for a holiday.'.format(name))
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f'Name "{name}" already used for a holiday.')
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if (check_holidays and self.country_holidays is not None and
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name in get_holiday_names(self.country_holidays)):
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raise ValueError(
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'Name "{}" is a holiday name in {}.'.format(name, self.country_holidays))
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f'Name "{name}" is a holiday name in {self.country_holidays}.')
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if check_seasonalities and name in self.seasonalities:
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raise ValueError(
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'Name "{}" already used for a seasonality.'.format(name))
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f'Name "{name}" already used for a seasonality.')
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if check_regressors and name in self.extra_regressors:
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raise ValueError(
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'Name "{}" already used for an added regressor.'.format(name))
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'Name "{name}" already used for an added regressor.')
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def setup_dataframe(self, df, initialize_scales=False):
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"""Prepare dataframe for fitting or predicting.
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@ -231,10 +231,9 @@ class Prophet(object):
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-------
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pd.DataFrame prepared for fitting or predicting.
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"""
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if 'y' in df:
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df['y'] = pd.to_numeric(df['y'])
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if np.isinf(df['y'].values).any():
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raise ValueError('Found infinity in column y.')
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df['y'] = pd.to_numeric(df['y'])
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if np.isinf(df['y'].values).any():
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raise ValueError('Found infinity in column y.')
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if df['ds'].dtype == np.int64:
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df['ds'] = df['ds'].astype(str)
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df['ds'] = pd.to_datetime(df['ds'])
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@ -248,18 +247,19 @@ class Prophet(object):
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for name in self.extra_regressors:
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if name not in df:
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raise ValueError(
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'Regressor "{}" missing from dataframe'.format(name))
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f'Regressor "{name}" missing from dataframe')
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df[name] = pd.to_numeric(df[name])
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if df[name].isnull().any():
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raise ValueError('Found NaN in column ' + name)
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raise ValueError(f'Found NaN in column {name}')
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for props in self.seasonalities.values():
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condition_name = props['condition_name']
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if condition_name is not None:
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if condition_name not in df:
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raise ValueError(
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'Condition "{}" missing from dataframe'.format(condition_name))
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f'Condition "{condition}" missing from dataframe')
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if not df[condition_name].isin([True, False]).all():
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raise ValueError('Found non-boolean in column ' + condition_name)
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raise ValueError(
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f'Found non-boolean in column {condition_name}')
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df[condition_name] = df[condition_name].astype('bool')
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if df.index.name == 'ds':
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@ -271,14 +271,14 @@ class Prophet(object):
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if self.logistic_floor:
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if 'floor' not in df:
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raise ValueError("Expected column 'floor'.")
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raise ValueError('Expected column "floor".')
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else:
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df['floor'] = 0
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if self.growth == 'logistic':
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if 'cap' not in df:
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raise ValueError(
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"Capacities must be supplied for logistic growth in "
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"column 'cap'"
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'Capacities must be supplied for logistic growth in '
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'column "cap"'
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)
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if (df['cap'] <= df['floor']).any():
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raise ValueError(
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@ -287,8 +287,7 @@ class Prophet(object):
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df['cap_scaled'] = (df['cap'] - df['floor']) / self.y_scale
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df['t'] = (df['ds'] - self.start) / self.t_scale
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if 'y' in df:
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df['y_scaled'] = (df['y'] - df['floor']) / self.y_scale
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df['y_scaled'] = (df['y'] - df['floor']) / self.y_scale
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for name, props in self.extra_regressors.items():
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df[name] = ((df[name] - props['mu']) / props['std'])
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@ -323,8 +322,7 @@ class Prophet(object):
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standardize = False
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if standardize == 'auto':
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if set(df[name].unique()) == set([1, 0]):
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# Don't standardize binary variables.
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standardize = False
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standardize = False # Don't standardize binary variables.
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else:
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standardize = True
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if standardize:
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@ -354,14 +352,14 @@ class Prophet(object):
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'Changepoints must fall within training data.')
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else:
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# Place potential changepoints evenly through first
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# changepoint_range proportion of the history
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# {changepoint_range} proportion of the history
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hist_size = int(np.floor(
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self.history.shape[0] * self.changepoint_range))
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if self.n_changepoints + 1 > hist_size:
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self.n_changepoints = hist_size - 1
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logger.info(
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'n_changepoints greater than number of observations.'
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'Using {}.'.format(self.n_changepoints)
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f'n_changepoints greater than number of observations. '
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'Using {self.n_changepoints}.'
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)
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if self.n_changepoints > 0:
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cp_indexes = (
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@ -454,10 +452,10 @@ class Prophet(object):
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country_holidays_df = make_holidays_df(
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year_list=year_list, country=self.country_holidays
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)
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all_holidays = pd.concat((all_holidays, country_holidays_df), sort=False)
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all_holidays = pd.concat((all_holidays, country_holidays_df),
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sort=False)
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all_holidays.reset_index(drop=True, inplace=True)
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# If the model has already been fit with a certain set of holidays,
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# make sure we are using those same ones.
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# Drop holidays that were not seen in data used to fit model
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if self.train_holiday_names is not None:
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# Remove holiday names didn't show up in fit
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index_to_drop = all_holidays.index[
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@ -469,10 +467,12 @@ class Prophet(object):
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# Add holiday names in fit but not in predict with ds as NA
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holidays_to_add = pd.DataFrame({
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'holiday': self.train_holiday_names[
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np.logical_not(self.train_holiday_names.isin(all_holidays.holiday))
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np.logical_not(self.train_holiday_names
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.isin(all_holidays.holiday))
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]
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})
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all_holidays = pd.concat((all_holidays, holidays_to_add), sort=False)
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all_holidays = pd.concat((all_holidays, holidays_to_add),
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sort=False)
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all_holidays.reset_index(drop=True, inplace=True)
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return all_holidays
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@ -509,12 +509,10 @@ class Prophet(object):
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ps = float(row.get('prior_scale', self.holidays_prior_scale))
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if np.isnan(ps):
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ps = float(self.holidays_prior_scale)
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if (
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row.holiday in prior_scales and prior_scales[row.holiday] != ps
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):
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if row.holiday in prior_scales and prior_scales[row.holiday] != ps:
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raise ValueError(
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'Holiday {} does not have consistent prior scale '
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'specification.'.format(row.holiday))
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f'Holiday {row.holiday} does not have consistent prior '
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'scale specification.')
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if ps <= 0:
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raise ValueError('Prior scale must be > 0')
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prior_scales[row.holiday] = ps
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@ -525,7 +523,6 @@ class Prophet(object):
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loc = row_index.get_loc(occurrence)
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except KeyError:
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loc = None
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key = '{}_delim_{}{}'.format(
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row.holiday,
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'+' if offset >= 0 else '-',
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@ -534,11 +531,11 @@ class Prophet(object):
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if loc is not None:
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expanded_holidays[key][loc] = 1.
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else:
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# Access key to generate value
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expanded_holidays[key]
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expanded_holidays[key] # Access key to generate value
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holiday_features = pd.DataFrame(expanded_holidays)
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# Make sure column order is consistent
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holiday_features = holiday_features[sorted(holiday_features.columns.tolist())]
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holiday_features = holiday_features[sorted(holiday_features.columns
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.tolist())]
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prior_scale_list = [
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prior_scales[h.split('_delim_')[0]]
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for h in holiday_features.columns
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@ -549,9 +546,8 @@ class Prophet(object):
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self.train_holiday_names = pd.Series(holiday_names)
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return holiday_features, prior_scale_list, holiday_names
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def add_regressor(
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self, name, prior_scale=None, standardize='auto', mode=None
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):
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def add_regressor(self, name, prior_scale=None, standardize='auto',
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mode=None):
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"""Add an additional regressor to be used for fitting and predicting.
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The dataframe passed to `fit` and `predict` will have a column with the
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@ -601,9 +597,8 @@ class Prophet(object):
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}
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return self
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def add_seasonality(
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self, name, period, fourier_order, prior_scale=None, mode=None, condition_name=None
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):
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def add_seasonality(self, name, period, fourier_order, prior_scale=None,
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mode=None, condition_name=None):
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"""Add a seasonal component with specified period, number of Fourier
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components, and prior scale.
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@ -621,9 +616,9 @@ class Prophet(object):
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Additive means the seasonality will be added to the trend,
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multiplicative means it will multiply the trend.
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If condition_name is provided, the dataframe passed to `fit` and `predict`
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should have a column with the specified condition_name containing booleans
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which decides when to apply seasonality.
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If condition_name is provided, the dataframe passed to `fit` and
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`predict` should have a column with the specified condition_name
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containing booleans which decides when to apply seasonality.
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Parameters
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----------
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@ -640,7 +635,7 @@ class Prophet(object):
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"""
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if self.history is not None:
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raise Exception(
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"Seasonality must be added prior to model fitting.")
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'Seasonality must be added prior to model fitting.')
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if name not in ['daily', 'weekly', 'yearly']:
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# Allow overwriting built-in seasonalities
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self.validate_column_name(name, check_seasonalities=False)
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@ -655,7 +650,7 @@ class Prophet(object):
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if mode is None:
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mode = self.seasonality_mode
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if mode not in ['additive', 'multiplicative']:
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raise ValueError("mode must be 'additive' or 'multiplicative'")
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raise ValueError('mode must be "additive" or "multiplicative"')
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if condition_name is not None:
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self.validate_column_name(condition_name)
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self.seasonalities[name] = {
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@ -698,9 +693,8 @@ class Prophet(object):
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# Set the holidays.
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if self.country_holidays is not None:
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logger.warning(
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'Changing country holidays from {} to {}'.format(
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self.country_holidays, country_name
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)
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f'Changing country holidays from {self.country_holidays} to '
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'{country_name}.'
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)
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self.country_holidays = country_name
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return self
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@ -828,10 +822,8 @@ class Prophet(object):
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# Remove the placeholder
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component_cols.drop('zeros', axis=1, inplace=True, errors='ignore')
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# Validation
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if (
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max(component_cols['additive_terms']
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+ component_cols['multiplicative_terms']) > 1
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):
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if (max(component_cols['additive_terms']
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+ component_cols['multiplicative_terms']) > 1):
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raise Exception('A bug occurred in seasonal components.')
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# Compare to the training, if set.
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if self.train_component_cols is not None:
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@ -879,14 +871,13 @@ class Prophet(object):
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fourier_order = 0
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if name in self.seasonalities:
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logger.info(
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'Found custom seasonality named "{name}", '
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'disabling built-in {name} seasonality.'.format(name=name)
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f'Found custom seasonality named "{name}", '
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'disabling built-in {name} seasonality.'
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)
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elif auto_disable:
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logger.info(
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'Disabling {name} seasonality. Run prophet with '
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'{name}_seasonality=True to override this.'.format(
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name=name)
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f'Disabling {name} seasonality. Run prophet with '
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'{name}_seasonality=True to override this.'
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)
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else:
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fourier_order = default_order
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@ -1049,8 +1040,8 @@ class Prophet(object):
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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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'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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@ -1100,10 +1091,8 @@ class Prophet(object):
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'sigma_obs': 1,
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}
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if (
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(history['y'].min() == history['y'].max())
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and self.growth == 'linear'
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):
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if ((history['y'].min() == history['y'].max())
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and (self.growth == 'linear')):
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# Nothing to fit.
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self.params = stan_init()
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self.params['sigma_obs'] = 1e-9
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@ -1120,7 +1109,8 @@ class Prophet(object):
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for par in self.stan_fit.model_pars:
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self.params[par] = self.stan_fit[par]
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# Shape vector parameters
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if par in ['delta', 'beta'] and len(self.params[par].shape) < 2:
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if (par in ['delta', 'beta']
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and len(self.params[par].shape) < 2):
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self.params[par] = self.params[par].reshape((-1, 1))
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else:
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args = dict(
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@ -1133,9 +1123,9 @@ class Prophet(object):
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try:
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self.stan_fit = model.optimizing(**args)
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except RuntimeError:
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# Fall back on Newton
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logger.warning(
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'Optimization terminated abnormally. Falling back to Newton.'
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'Optimization terminated abnormally. '
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'Falling back to Newton.'
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)
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args['algorithm'] = 'Newton'
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self.stan_fit = model.optimizing(**args)
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@ -1146,8 +1136,10 @@ class Prophet(object):
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# If no changepoints were requested, replace delta with 0s
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if len(self.changepoints) == 0:
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# Fold delta into the base rate k
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self.params['k'] = self.params['k'] + self.params['delta'].reshape(-1)
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self.params['delta'] = np.zeros(self.params['delta'].shape).reshape((-1, 1))
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self.params['k'] = (self.params['k']
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+ self.params['delta'].reshape(-1))
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self.params['delta'] = (np.zeros(self.params['delta'].shape)
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.reshape((-1, 1)))
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return self
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@ -1165,7 +1157,7 @@ class Prophet(object):
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A pd.DataFrame with the forecast components.
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"""
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if self.history is None:
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raise Exception('Model must be fit before predictions can be made.')
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raise Exception('Model has not been fit.')
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if df is None:
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df = self.history.copy()
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@ -1415,7 +1407,8 @@ class Prophet(object):
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trend = self.sample_predictive_trend(df, iteration)
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beta = self.params['beta'][iteration]
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Xb_a = np.matmul(seasonal_features.values, beta * s_a.values) * self.y_scale
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Xb_a = np.matmul(seasonal_features.values,
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beta * s_a.values) * self.y_scale
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Xb_m = np.matmul(seasonal_features.values, beta * s_m.values)
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sigma = self.params['sigma_obs'][iteration]
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@ -1493,7 +1486,7 @@ class Prophet(object):
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requested number of periods.
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"""
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if self.history_dates is None:
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raise Exception('Model must be fit before this can be used.')
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raise Exception('Model as not been fit.')
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||||
last_date = self.history_dates.max()
|
||||
dates = pd.date_range(
|
||||
start=last_date,
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||||
|
|
@ -1507,8 +1500,8 @@ class Prophet(object):
|
|||
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||||
return pd.DataFrame({'ds': dates})
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||||
|
||||
def plot(self, fcst, ax=None, uncertainty=True, plot_cap=True, xlabel='ds',
|
||||
ylabel='y', figsize=(10, 6)):
|
||||
def plot(self, fcst, ax=None, uncertainty=True, plot_cap=True,
|
||||
xlabel='ds', ylabel='y', figsize=(10, 6)):
|
||||
"""Plot the Prophet forecast.
|
||||
|
||||
Parameters
|
||||
|
|
|
|||
|
|
@ -259,8 +259,10 @@ class TestDiagnostics(TestCase):
|
|||
self.assertTrue((m1.holidays == m2.holidays).values.all())
|
||||
self.assertEqual(m1.country_holidays, m2.country_holidays)
|
||||
self.assertEqual(m1.seasonality_mode, m2.seasonality_mode)
|
||||
self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
|
||||
self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
|
||||
self.assertEqual(m1.seasonality_prior_scale,
|
||||
m2.seasonality_prior_scale)
|
||||
self.assertEqual(m1.changepoint_prior_scale,
|
||||
m2.changepoint_prior_scale)
|
||||
self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
|
||||
self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
|
||||
self.assertEqual(m1.interval_width, m2.interval_width)
|
||||
|
|
|
|||
|
|
@ -42,7 +42,8 @@ class TestProphet(TestCase):
|
|||
train = DATA.head(N // 2)
|
||||
future = DATA.tail(N // 2)
|
||||
|
||||
forecaster = Prophet(weekly_seasonality=False, yearly_seasonality=False)
|
||||
forecaster = Prophet(weekly_seasonality=False,
|
||||
yearly_seasonality=False)
|
||||
forecaster.fit(train)
|
||||
forecaster.predict(future)
|
||||
|
||||
|
|
@ -102,8 +103,10 @@ class TestProphet(TestCase):
|
|||
m = Prophet(uncertainty_samples=uncertainty)
|
||||
m.fit(train)
|
||||
fcst = m.predict(future)
|
||||
expected_cols = ['ds', 'trend', 'additive_terms', 'multiplicative_terms', 'weekly', 'yhat']
|
||||
self.assertTrue(all(col in expected_cols for col in fcst.columns.tolist()))
|
||||
expected_cols = ['ds', 'trend', 'additive_terms',
|
||||
'multiplicative_terms', 'weekly', 'yhat']
|
||||
self.assertTrue(all(col in expected_cols
|
||||
for col in fcst.columns.tolist()))
|
||||
|
||||
def test_setup_dataframe(self):
|
||||
m = Prophet()
|
||||
|
|
@ -223,7 +226,7 @@ class TestProphet(TestCase):
|
|||
mat = Prophet.fourier_series(DATA['ds'], 7, 3)
|
||||
# These are from the R forecast package directly.
|
||||
true_values = np.array([
|
||||
0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837, -0.9009689,
|
||||
0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837, -0.9009689
|
||||
])
|
||||
self.assertAlmostEqual(np.sum((mat[0] - true_values)**2), 0.0)
|
||||
|
||||
|
|
@ -231,7 +234,7 @@ class TestProphet(TestCase):
|
|||
mat = Prophet.fourier_series(DATA['ds'], 365.25, 3)
|
||||
# These are from the R forecast package directly.
|
||||
true_values = np.array([
|
||||
0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249, 0.6874572,
|
||||
0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249, 0.6874572
|
||||
])
|
||||
self.assertAlmostEqual(np.sum((mat[0] - true_values)**2), 0.0)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue