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https://github.com/saymrwulf/prophet.git
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Added a interactive Plotly plot of the forecast (#915)
This commit is contained in:
Olof Höjvall
Ben Letham
parent
a31a93480a
commit
c756d20100
1 changed files with 155 additions and 0 deletions
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@ -25,6 +25,12 @@ try:
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except ImportError:
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logger.error('Importing matplotlib failed. Plotting will not work.')
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try:
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import plotly.graph_objs as go
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import plotly.offline
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except ImportError:
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logger.error('Importing plotly failed. Interactive plots will not work.')
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def plot(
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m, fcst, ax=None, uncertainty=True, plot_cap=True, xlabel='ds', ylabel='y',
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@ -495,3 +501,152 @@ def plot_cross_validation_metric(
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ax.set_xlabel('Horizon ({})'.format(dt_names[i]))
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ax.set_ylabel(metric)
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return fig
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def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoints=False,
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changepoints_threshold=0.01, xlabel='ds', ylabel='y'):
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"""Plot the Prophet forecast with Plotly offline.
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Requires plotly.offline.init_notebook_mode() to have been run,
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see https://plot.ly/python/offline/ for details
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Parameters
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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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plot_cap: Optional boolean indicating if the capacity should be shown
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in the figure, if available.
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trend: Optional boolean to plot trend
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changepoints: Optional boolean to plot changepoints
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changepoints_threshold: Threshold on trend change magnitude for significance.
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xlabel: Optional label name on X-axis
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ylabel: Optional label name on Y-axis
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Returns
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-------
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A Plotly plot.
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"""
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prediction_color = '#0072B2'
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error_color = 'rgba(0, 114, 178, 0.2)' # '#0072B2' with 0.2 opacity
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actual_color = 'black'
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cap_color = 'black'
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trend_color = '#B23B00'
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line_width = 2
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marker_size = 4
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data = []
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# Add actual
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data.append(go.Scatter(
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name='Actual',
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x=m.history['ds'],
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y=m.history['y'],
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marker=dict(color=actual_color, size=marker_size),
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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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data.append(go.Scatter(
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x=fcst['ds'],
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y=fcst['yhat_lower'],
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mode='lines',
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line=dict(width=0),
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hoverinfo='skip'
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))
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# Add prediction
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data.append(go.Scatter(
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name='Predicted',
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x=fcst['ds'],
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y=fcst['yhat'],
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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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))
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# Add upper bound
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if uncertainty:
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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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mode='lines',
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line=dict(width=0),
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fillcolor=error_color,
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fill='tonexty',
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hoverinfo='skip'
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))
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# Add caps
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if 'cap' in fcst and plot_cap:
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data.append(go.Scatter(
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name='Cap',
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x=fcst['ds'],
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y=fcst['cap'],
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mode='lines',
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line=dict(color=cap_color, dash='dash', width=line_width),
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))
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if m.logistic_floor and 'floor' in fcst and plot_cap:
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data.append(go.Scatter(
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name='Floor',
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x=fcst['ds'],
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y=fcst['floor'],
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mode='lines',
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line=dict(color=cap_color, dash='dash', width=line_width),
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))
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# Add trend
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if trend:
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data.append(go.Scatter(
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name='Trend',
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x=fcst['ds'],
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y=fcst['trend'],
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mode='lines',
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line=dict(color=trend_color, width=line_width),
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))
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# Add changepoints
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if changepoints:
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signif_changepoints = m.changepoints[
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np.abs(np.nanmean(m.params['delta'], axis=0)) >= changepoints_threshold
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]
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data.append(go.Scatter(
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x=signif_changepoints,
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y=fcst.loc[fcst['ds'].isin(signif_changepoints), 'trend'],
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marker=dict(size=50, symbol='line-ns-open', color=trend_color,
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line=dict(width=line_width)),
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mode='markers',
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hoverinfo='skip'
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))
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layout = dict(
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showlegend=False,
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yaxis=dict(
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title=ylabel
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),
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xaxis=dict(
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title=xlabel,
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type='date',
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rangeselector=dict(
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buttons=list([
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dict(count=7,
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label='1w',
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step='day',
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stepmode='backward'),
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dict(count=1,
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label='1m',
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step='month',
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stepmode='backward'),
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dict(count=6,
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label='6m',
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step='month',
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stepmode='backward'),
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dict(count=1,
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label='1y',
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step='year',
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stepmode='backward'),
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dict(step='all')
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])
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),
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rangeslider=dict(
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visible=True
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),
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),
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)
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fig = go.Figure(data=data, layout=layout)
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return plotly.offline.iplot(fig)
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