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https://github.com/saymrwulf/prophet.git
synced 2026-07-23 19:32:25 +00:00
disable plotting uncertainties if m.uncertainty_samples is 0 or False
This commit is contained in:
Alexander Gawrilow
Ben Letham
parent
0679e69dba
commit
a6a1381a0a
1 changed files with 32 additions and 21 deletions
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@ -49,7 +49,8 @@ def plot(
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m: Prophet model.
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fcst: pd.DataFrame output of m.predict.
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ax: Optional matplotlib axes on which to plot.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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xlabel: Optional label name on X-axis
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@ -72,7 +73,7 @@ def plot(
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ax.plot(fcst_t, fcst['cap'], ls='--', c='k')
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if m.logistic_floor and 'floor' in fcst and plot_cap:
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ax.plot(fcst_t, fcst['floor'], ls='--', c='k')
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'],
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color='#0072B2', alpha=0.2)
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# Specify formatting to workaround matplotlib issue #12925
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@ -101,7 +102,8 @@ def plot_components(
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----------
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m: Prophet model.
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fcst: pd.DataFrame output of m.predict.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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weekly_start: Optional int specifying the start day of the weekly
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@ -196,7 +198,8 @@ def plot_forecast_component(
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fcst: pd.DataFrame output of m.predict.
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name: Name of the component to plot.
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ax: Optional matplotlib Axes to plot on.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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figsize: Optional tuple width, height in inches.
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@ -215,7 +218,7 @@ def plot_forecast_component(
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artists += ax.plot(fcst_t, fcst['cap'], ls='--', c='k')
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if m.logistic_floor and 'floor' in fcst and plot_cap:
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ax.plot(fcst_t, fcst['floor'], ls='--', c='k')
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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artists += [ax.fill_between(
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fcst_t, fcst[name + '_lower'], fcst[name + '_upper'],
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color='#0072B2', alpha=0.2)]
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@ -264,7 +267,8 @@ def plot_weekly(m, ax=None, uncertainty=True, weekly_start=0, figsize=(10, 6), n
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m: Prophet model.
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ax: Optional matplotlib Axes to plot on. One will be created if this
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is not provided.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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weekly_start: Optional int specifying the start day of the weekly
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seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
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by 1 day to Monday, and so on.
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@ -287,7 +291,7 @@ def plot_weekly(m, ax=None, uncertainty=True, weekly_start=0, figsize=(10, 6), n
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days = days.weekday_name
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artists += ax.plot(range(len(days)), seas[name], ls='-',
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c='#0072B2')
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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artists += [ax.fill_between(range(len(days)),
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seas[name + '_lower'], seas[name + '_upper'],
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color='#0072B2', alpha=0.2)]
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@ -309,7 +313,8 @@ def plot_yearly(m, ax=None, uncertainty=True, yearly_start=0, figsize=(10, 6), n
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m: Prophet model.
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ax: Optional matplotlib Axes to plot on. One will be created if
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this is not provided.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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yearly_start: Optional int specifying the start day of the yearly
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seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
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by 1 day to Jan 2, and so on.
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@ -331,7 +336,7 @@ def plot_yearly(m, ax=None, uncertainty=True, yearly_start=0, figsize=(10, 6), n
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seas = m.predict_seasonal_components(df_y)
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artists += ax.plot(
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df_y['ds'].dt.to_pydatetime(), seas[name], ls='-', c='#0072B2')
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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artists += [ax.fill_between(
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df_y['ds'].dt.to_pydatetime(), seas[name + '_lower'],
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seas[name + '_upper'], color='#0072B2', alpha=0.2)]
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@ -356,7 +361,8 @@ def plot_seasonality(m, name, ax=None, uncertainty=True, figsize=(10, 6)):
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name: Seasonality name, like 'daily', 'weekly'.
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ax: Optional matplotlib Axes to plot on. One will be created if
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this is not provided.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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figsize: Optional tuple width, height in inches.
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Returns
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@ -377,7 +383,7 @@ def plot_seasonality(m, name, ax=None, uncertainty=True, figsize=(10, 6)):
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seas = m.predict_seasonal_components(df_y)
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artists += ax.plot(df_y['ds'].dt.to_pydatetime(), seas[name], ls='-',
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c='#0072B2')
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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artists += [ax.fill_between(
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df_y['ds'].dt.to_pydatetime(), seas[name + '_lower'],
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seas[name + '_upper'], color='#0072B2', alpha=0.2)]
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@ -566,7 +572,7 @@ def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoi
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mode='markers'
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))
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# Add lower bound
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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data.append(go.Scatter(
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x=fcst['ds'],
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y=fcst['yhat_lower'],
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@ -582,10 +588,10 @@ def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoi
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mode='lines',
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line=dict(color=prediction_color, width=line_width),
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fillcolor=error_color,
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fill='tonexty' if uncertainty else 'none'
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fill='tonexty' if uncertainty and m.uncertainty_samples else 'none'
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))
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# Add upper bound
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if uncertainty:
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if uncertainty and m.uncertainty_samples:
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data.append(go.Scatter(
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x=fcst['ds'],
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y=fcst['yhat_upper'],
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@ -688,7 +694,8 @@ def plot_components_plotly(
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----------
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m: Prophet model.
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fcst: pd.DataFrame output of m.predict.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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figsize: Set the size for the subplots (in px).
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@ -746,7 +753,8 @@ def plot_forecast_component_plotly(m, fcst, name, uncertainty=True, plot_cap=Fal
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m: Prophet model.
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fcst: pd.DataFrame output of m.predict.
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name: Name of the component to plot.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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figsize: The plot's size (in px).
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@ -775,7 +783,8 @@ def plot_seasonality_plotly(m, name, uncertainty=True, figsize=(900, 300)):
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----------
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m: Prophet model.
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name: Seasonality name, like 'daily', 'weekly'.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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figsize: Set the plot's size (in px).
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Returns
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@ -802,7 +811,8 @@ def get_forecast_component_plotly_props(m, fcst, name, uncertainty=True, plot_ca
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m: Prophet model.
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fcst: pd.DataFrame output of m.predict.
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name: Name of the component to plot.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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@ -842,7 +852,7 @@ def get_forecast_component_plotly_props(m, fcst, name, uncertainty=True, plot_ca
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line=go.scatter.Line(color=prediction_color, width=line_width),
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text=text,
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))
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if uncertainty and (fcst[name + '_upper'] != fcst[name + '_lower']).any():
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if uncertainty and m.uncertainty_samples and (fcst[name + '_upper'] != fcst[name + '_lower']).any():
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if mode == 'markers':
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traces[0].update(
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error_y=dict(
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@ -906,7 +916,8 @@ def get_seasonality_plotly_props(m, name, uncertainty=True):
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----------
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m: Prophet model.
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name: Name of the component to plot.
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uncertainty: Optional boolean to plot uncertainty intervals.
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uncertainty: Optional boolean to plot uncertainty intervals, which will
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only be done if m.uncertainty_samples > 0.
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Returns
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-------
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@ -939,7 +950,7 @@ def get_seasonality_plotly_props(m, name, uncertainty=True):
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mode='lines',
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line=go.scatter.Line(color=prediction_color, width=line_width)
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))
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if uncertainty and (seas[name + '_upper'] != seas[name + '_lower']).any():
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if uncertainty and m.uncertainty_samples and (seas[name + '_upper'] != seas[name + '_lower']).any():
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traces.append(go.Scatter(
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name=name + '_upper',
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x=df_y['ds'],
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