is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
| forecaster.py (prophet-1.0) | : | forecaster.py (prophet-1.1) |
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| skipping to change at line 29 | | skipping to change at line 29 |
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| from prophet.plot import (plot, plot_components) | | from prophet.plot import (plot, plot_components) |
| | |
| logger = logging.getLogger('prophet') | | logger = logging.getLogger('prophet') |
| logger.setLevel(logging.INFO) | | logger.setLevel(logging.INFO) |
| | |
| class Prophet(object): | | class Prophet(object): |
| """Prophet forecaster. | | """Prophet forecaster. |
| | |
| Parameters | | Parameters |
| ---------- | | ---------- |
|
| growth: String 'linear' or 'logistic' to specify a linear or logistic | | growth: String 'linear', 'logistic' or 'flat' to specify a linear, logistic |
| trend. | | or |
| | flat trend. |
| changepoints: List of dates at which to include potential changepoints. If | | changepoints: List of dates at which to include potential changepoints. If |
| not specified, potential changepoints are selected automatically. | | not specified, potential changepoints are selected automatically. |
| n_changepoints: Number of potential changepoints to include. Not used | | n_changepoints: Number of potential changepoints to include. Not used |
| if input `changepoints` is supplied. If `changepoints` is not supplied, | | if input `changepoints` is supplied. If `changepoints` is not supplied, |
| then n_changepoints potential changepoints are selected uniformly from | | then n_changepoints potential changepoints are selected uniformly from |
| the first `changepoint_range` proportion of the history. | | the first `changepoint_range` proportion of the history. |
| changepoint_range: Proportion of history in which trend changepoints will | | changepoint_range: Proportion of history in which trend changepoints will |
| be estimated. Defaults to 0.8 for the first 80%. Not used if | | be estimated. Defaults to 0.8 for the first 80%. Not used if |
| `changepoints` is specified. | | `changepoints` is specified. |
| yearly_seasonality: Fit yearly seasonality. | | yearly_seasonality: Fit yearly seasonality. |
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| skipping to change at line 536 | | skipping to change at line 536 |
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| ------- | | ------- |
| holiday_features: pd.DataFrame with a column for each holiday. | | holiday_features: pd.DataFrame with a column for each holiday. |
| prior_scale_list: List of prior scales for each holiday column. | | prior_scale_list: List of prior scales for each holiday column. |
| holiday_names: List of names of holidays | | holiday_names: List of names of holidays |
| """ | | """ |
| # Holds columns of our future matrix. | | # Holds columns of our future matrix. |
| expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0])) | | expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0])) |
| prior_scales = {} | | prior_scales = {} |
| # Makes an index so we can perform `get_loc` below. | | # Makes an index so we can perform `get_loc` below. |
| # Strip to just dates. | | # Strip to just dates. |
|
| row_index = pd.DatetimeIndex(dates.apply(lambda x: x.date())) | | row_index = pd.DatetimeIndex(dates.dt.date) |
| | |
|
| for _ix, row in holidays.iterrows(): | | for row in holidays.itertuples(): |
| dt = row.ds.date() | | dt = row.ds.date() |
| try: | | try: |
|
| lw = int(row.get('lower_window', 0)) | | lw = int(getattr(row, 'lower_window', 0)) |
| uw = int(row.get('upper_window', 0)) | | uw = int(getattr(row, 'upper_window', 0)) |
| except ValueError: | | except ValueError: |
| lw = 0 | | lw = 0 |
| uw = 0 | | uw = 0 |
|
| ps = float(row.get('prior_scale', self.holidays_prior_scale)) | | ps = float(getattr(row, 'prior_scale', self.holidays_prior_scale)) |
| if np.isnan(ps): | | if np.isnan(ps): |
| ps = float(self.holidays_prior_scale) | | ps = float(self.holidays_prior_scale) |
| if row.holiday in prior_scales and prior_scales[row.holiday] != ps: | | if row.holiday in prior_scales and prior_scales[row.holiday] != ps: |
| raise ValueError( | | raise ValueError( |
| 'Holiday {holiday!r} does not have consistent prior ' | | 'Holiday {holiday!r} does not have consistent prior ' |
| 'scale specification.'.format(holiday=row.holiday) | | 'scale specification.'.format(holiday=row.holiday) |
| ) | | ) |
| if ps <= 0: | | if ps <= 0: |
| raise ValueError('Prior scale must be > 0') | | raise ValueError('Prior scale must be > 0') |
| prior_scales[row.holiday] = ps | | prior_scales[row.holiday] = ps |
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| skipping to change at line 895 | | skipping to change at line 895 |
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| group: List of components that form the group. | | group: List of components that form the group. |
| | |
| Returns | | Returns |
| ------- | | ------- |
| Dataframe with components. | | Dataframe with components. |
| """ | | """ |
| new_comp = components[components['component'].isin(set(group))].copy() | | new_comp = components[components['component'].isin(set(group))].copy() |
| group_cols = new_comp['col'].unique() | | group_cols = new_comp['col'].unique() |
| if len(group_cols) > 0: | | if len(group_cols) > 0: |
| new_comp = pd.DataFrame({'col': group_cols, 'component': name}) | | new_comp = pd.DataFrame({'col': group_cols, 'component': name}) |
|
| components = components.append(new_comp) | | components = pd.concat([components, new_comp]) |
| return components | | return components |
| | |
| def parse_seasonality_args(self, name, arg, auto_disable, default_order): | | def parse_seasonality_args(self, name, arg, auto_disable, default_order): |
| """Get number of fourier components for built-in seasonalities. | | """Get number of fourier components for built-in seasonalities. |
| | |
| Parameters | | Parameters |
| ---------- | | ---------- |
| name: string name of the seasonality component. | | name: string name of the seasonality component. |
| arg: 'auto', True, False, or number of fourier components as provided. | | arg: 'auto', True, False, or number of fourier components as provided. |
| auto_disable: bool if seasonality should be disabled when 'auto'. | | auto_disable: bool if seasonality should be disabled when 'auto'. |
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| skipping to change at line 1579 | | skipping to change at line 1579 |
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| freq=freq) | | freq=freq) |
| dates = dates[dates > last_date] # Drop start if equals last_date | | dates = dates[dates > last_date] # Drop start if equals last_date |
| dates = dates[:periods] # Return correct number of periods | | dates = dates[:periods] # Return correct number of periods |
| | |
| if include_history: | | if include_history: |
| dates = np.concatenate((np.array(self.history_dates), dates)) | | dates = np.concatenate((np.array(self.history_dates), dates)) |
| | |
| return pd.DataFrame({'ds': dates}) | | return pd.DataFrame({'ds': dates}) |
| | |
| def plot(self, fcst, ax=None, uncertainty=True, plot_cap=True, | | def plot(self, fcst, ax=None, uncertainty=True, plot_cap=True, |
|
| xlabel='ds', ylabel='y', figsize=(10, 6)): | | xlabel='ds', ylabel='y', figsize=(10, 6), include_legend=False): |
| """Plot the Prophet forecast. | | """Plot the Prophet forecast. |
| | |
| Parameters | | Parameters |
| ---------- | | ---------- |
| fcst: pd.DataFrame output of self.predict. | | fcst: pd.DataFrame output of self.predict. |
| ax: Optional matplotlib axes on which to plot. | | ax: Optional matplotlib axes on which to plot. |
| uncertainty: Optional boolean to plot uncertainty intervals. | | uncertainty: Optional boolean to plot uncertainty intervals. |
| plot_cap: Optional boolean indicating if the capacity should be shown | | plot_cap: Optional boolean indicating if the capacity should be shown |
| in the figure, if available. | | in the figure, if available. |
| xlabel: Optional label name on X-axis | | xlabel: Optional label name on X-axis |
| ylabel: Optional label name on Y-axis | | ylabel: Optional label name on Y-axis |
| figsize: Optional tuple width, height in inches. | | figsize: Optional tuple width, height in inches. |
|
| | include_legend: Optional boolean to add legend to the plot. |
| | |
| Returns | | Returns |
| ------- | | ------- |
| A matplotlib figure. | | A matplotlib figure. |
| """ | | """ |
| return plot( | | return plot( |
| m=self, fcst=fcst, ax=ax, uncertainty=uncertainty, | | m=self, fcst=fcst, ax=ax, uncertainty=uncertainty, |
| plot_cap=plot_cap, xlabel=xlabel, ylabel=ylabel, | | plot_cap=plot_cap, xlabel=xlabel, ylabel=ylabel, |
|
| figsize=figsize | | figsize=figsize, include_legend=include_legend |
| ) | | ) |
| | |
| def plot_components(self, fcst, uncertainty=True, plot_cap=True, | | def plot_components(self, fcst, uncertainty=True, plot_cap=True, |
| weekly_start=0, yearly_start=0, figsize=None): | | weekly_start=0, yearly_start=0, figsize=None): |
| """Plot the Prophet forecast components. | | """Plot the Prophet forecast components. |
| | |
| Will plot whichever are available of: trend, holidays, weekly | | Will plot whichever are available of: trend, holidays, weekly |
| seasonality, and yearly seasonality. | | seasonality, and yearly seasonality. |
| | |
| Parameters | | Parameters |
| | | |
| End of changes. 9 change blocks. |
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| 10 lines changed or deleted | | 12 lines changed or added |
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"Fossies" - the Fresh Open Source Software Archive 