is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
| plot.py (prophet-1.0) | : | plot.py (prophet-1.1) |
|---|
| | |
| skipping to change at line 41 | | skipping to change at line 41 |
|---|
| logger.error('Importing matplotlib failed. Plotting will not work.') | | logger.error('Importing matplotlib failed. Plotting will not work.') |
| | |
| try: | | try: |
| import plotly.graph_objs as go | | import plotly.graph_objs as go |
| from plotly.subplots import make_subplots | | from plotly.subplots import make_subplots |
| except ImportError: | | except ImportError: |
| logger.error('Importing plotly failed. Interactive plots will not work.') | | logger.error('Importing plotly failed. Interactive plots will not work.') |
| | |
| def plot( | | def plot( |
| m, fcst, ax=None, uncertainty=True, plot_cap=True, xlabel='ds', ylabel='y', | | m, fcst, ax=None, uncertainty=True, plot_cap=True, xlabel='ds', ylabel='y', |
|
| figsize=(10, 6) | | figsize=(10, 6), include_legend=False |
| ): | | ): |
| """Plot the Prophet forecast. | | """Plot the Prophet forecast. |
| | |
| Parameters | | Parameters |
| ---------- | | ---------- |
| m: Prophet model. | | m: Prophet model. |
| fcst: pd.DataFrame output of m.predict. | | fcst: pd.DataFrame output of m.predict. |
| ax: Optional matplotlib axes on which to plot. | | ax: Optional matplotlib axes on which to plot. |
| uncertainty: Optional boolean to plot uncertainty intervals, which will | | uncertainty: Optional boolean to plot uncertainty intervals, which will |
| only be done if m.uncertainty_samples > 0. | | only be done if m.uncertainty_samples > 0. |
| 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. |
| """ | | """ |
| if ax is None: | | if ax is None: |
| fig = plt.figure(facecolor='w', figsize=figsize) | | fig = plt.figure(facecolor='w', figsize=figsize) |
| ax = fig.add_subplot(111) | | ax = fig.add_subplot(111) |
| else: | | else: |
| fig = ax.get_figure() | | fig = ax.get_figure() |
| fcst_t = fcst['ds'].dt.to_pydatetime() | | fcst_t = fcst['ds'].dt.to_pydatetime() |
|
| ax.plot(m.history['ds'].dt.to_pydatetime(), m.history['y'], 'k.') | | ax.plot(m.history['ds'].dt.to_pydatetime(), m.history['y'], 'k.', |
| ax.plot(fcst_t, fcst['yhat'], ls='-', c='#0072B2') | | label='Observed data points') |
| | ax.plot(fcst_t, fcst['yhat'], ls='-', c='#0072B2', label='Forecast') |
| if 'cap' in fcst and plot_cap: | | if 'cap' in fcst and plot_cap: |
|
| ax.plot(fcst_t, fcst['cap'], ls='--', c='k') | | ax.plot(fcst_t, fcst['cap'], ls='--', c='k', label='Maximum capacity') |
| if m.logistic_floor and 'floor' in fcst and plot_cap: | | if m.logistic_floor and 'floor' in fcst and plot_cap: |
|
| ax.plot(fcst_t, fcst['floor'], ls='--', c='k') | | ax.plot(fcst_t, fcst['floor'], ls='--', c='k', label='Minimum capacity') |
| if uncertainty and m.uncertainty_samples: | | if uncertainty and m.uncertainty_samples: |
| ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'], | | ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'], |
|
| color='#0072B2', alpha=0.2) | | color='#0072B2', alpha=0.2, label='Uncertainty interval'
) |
| # Specify formatting to workaround matplotlib issue #12925 | | # Specify formatting to workaround matplotlib issue #12925 |
| locator = AutoDateLocator(interval_multiples=False) | | locator = AutoDateLocator(interval_multiples=False) |
| formatter = AutoDateFormatter(locator) | | formatter = AutoDateFormatter(locator) |
| ax.xaxis.set_major_locator(locator) | | ax.xaxis.set_major_locator(locator) |
| ax.xaxis.set_major_formatter(formatter) | | ax.xaxis.set_major_formatter(formatter) |
| ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2) | | ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2) |
| ax.set_xlabel(xlabel) | | ax.set_xlabel(xlabel) |
| ax.set_ylabel(ylabel) | | ax.set_ylabel(ylabel) |
|
| | if include_legend: |
| | ax.legend() |
| fig.tight_layout() | | fig.tight_layout() |
| return fig | | return fig |
| | |
| def plot_components( | | def plot_components( |
| m, fcst, uncertainty=True, plot_cap=True, weekly_start=0, yearly_start=0, | | m, fcst, uncertainty=True, plot_cap=True, weekly_start=0, yearly_start=0, |
| figsize=None | | 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 |
| | |
| skipping to change at line 460 | | skipping to change at line 464 |
|---|
| if trend: | | if trend: |
| artists.append(ax.plot(fcst['ds'], fcst['trend'], c=cp_color)) | | artists.append(ax.plot(fcst['ds'], fcst['trend'], c=cp_color)) |
| signif_changepoints = m.changepoints[ | | signif_changepoints = m.changepoints[ |
| np.abs(np.nanmean(m.params['delta'], axis=0)) >= threshold | | np.abs(np.nanmean(m.params['delta'], axis=0)) >= threshold |
| ] if len(m.changepoints) > 0 else [] | | ] if len(m.changepoints) > 0 else [] |
| for cp in signif_changepoints: | | for cp in signif_changepoints: |
| artists.append(ax.axvline(x=cp, c=cp_color, ls=cp_linestyle)) | | artists.append(ax.axvline(x=cp, c=cp_color, ls=cp_linestyle)) |
| return artists | | return artists |
| | |
| def plot_cross_validation_metric( | | def plot_cross_validation_metric( |
|
| df_cv, metric, rolling_window=0.1, ax=None, figsize=(10, 6), color='b' | | df_cv, metric, rolling_window=0.1, ax=None, figsize=(10, 6), color='b', |
| | point_color='gray' |
| ): | | ): |
| """Plot a performance metric vs. forecast horizon from cross validation. | | """Plot a performance metric vs. forecast horizon from cross validation. |
| | |
| Cross validation produces a collection of out-of-sample model predictions | | Cross validation produces a collection of out-of-sample model predictions |
| that can be compared to actual values, at a range of different horizons | | that can be compared to actual values, at a range of different horizons |
| (distance from the cutoff). This computes a specified performance metric | | (distance from the cutoff). This computes a specified performance metric |
| for each prediction, and aggregated over a rolling window with horizon. | | for each prediction, and aggregated over a rolling window with horizon. |
| | |
| This uses prophet.diagnostics.performance_metrics to compute the metrics. | | This uses prophet.diagnostics.performance_metrics to compute the metrics. |
| Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'. | | Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'. |
| | |
| skipping to change at line 530 | | skipping to change at line 535 |
|---|
| 10 ** 3, | | 10 ** 3, |
| 1., | | 1., |
| ] | | ] |
| for i, dt in enumerate(dts): | | for i, dt in enumerate(dts): |
| if np.timedelta64(1, dt) < np.timedelta64(tick_w, 'ns'): | | if np.timedelta64(1, dt) < np.timedelta64(tick_w, 'ns'): |
| break | | break |
| | |
| x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / floa
t(dt_conversions[i]) | | x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / floa
t(dt_conversions[i]) |
| x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float
(dt_conversions[i]) | | x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float
(dt_conversions[i]) |
| | |
|
| ax.plot(x_plt, df_none[metric], '.', alpha=0.1, c=color) | | ax.plot(x_plt, df_none[metric], '.', alpha=0.1, c=point_color) |
| ax.plot(x_plt_h, df_h[metric], '-', c=color) | | ax.plot(x_plt_h, df_h[metric], '-', c=color) |
| ax.grid(True) | | ax.grid(True) |
| | |
| ax.set_xlabel('Horizon ({})'.format(dt_names[i])) | | ax.set_xlabel('Horizon ({})'.format(dt_names[i])) |
| ax.set_ylabel(metric) | | ax.set_ylabel(metric) |
| return fig | | return fig |
| | |
| def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoi
nts=False, | | def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoi
nts=False, |
| changepoints_threshold=0.01, xlabel='ds', ylabel='y', figsize=(9
00, 600)): | | changepoints_threshold=0.01, xlabel='ds', ylabel='y', figsize=(9
00, 600)): |
| """Plot the Prophet forecast with Plotly offline. | | """Plot the Prophet forecast with Plotly offline. |
| | | |
| End of changes. 9 change blocks. |
|---|
| 8 lines changed or deleted | | 13 lines changed or added |
|---|
"Fossies" - the Fresh Open Source Software Archive 