From c756d20100a2855cb49f2405883c6975ec174b12 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Olof=20H=C3=B6jvall?= <olof.hojvall@gmail.com>
Date: Thu, 18 Apr 2019 01:47:16 +0200
Subject: [PATCH] Added a interactive Plotly plot of the forecast (#915)

---
 python/fbprophet/plot.py | 155 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 155 insertions(+)

diff --git a/python/fbprophet/plot.py b/python/fbprophet/plot.py
index 96c1086..2f385a0 100644
--- a/python/fbprophet/plot.py
+++ b/python/fbprophet/plot.py
@@ -25,6 +25,12 @@ try:
 except ImportError:
     logger.error('Importing matplotlib failed. Plotting will not work.')
 
+try:
+    import plotly.graph_objs as go
+    import plotly.offline
+except ImportError:
+    logger.error('Importing plotly failed. Interactive plots will not work.')
+
 
 def plot(
     m, fcst, ax=None, uncertainty=True, plot_cap=True, xlabel='ds', ylabel='y',
@@ -495,3 +501,152 @@ def plot_cross_validation_metric(
     ax.set_xlabel('Horizon ({})'.format(dt_names[i]))
     ax.set_ylabel(metric)
     return fig
+
+
+def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoints=False,
+                changepoints_threshold=0.01, xlabel='ds', ylabel='y'):
+    """Plot the Prophet forecast with Plotly offline.
+
+    Requires plotly.offline.init_notebook_mode() to have been run,
+    see https://plot.ly/python/offline/ for details
+
+    Parameters
+    ----------
+    m: Prophet model.
+    fcst: pd.DataFrame output of m.predict.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+    plot_cap: Optional boolean indicating if the capacity should be shown
+        in the figure, if available.
+    trend: Optional boolean to plot trend
+    changepoints: Optional boolean to plot changepoints
+    changepoints_threshold: Threshold on trend change magnitude for significance.
+    xlabel: Optional label name on X-axis
+    ylabel: Optional label name on Y-axis
+
+    Returns
+    -------
+    A Plotly plot.
+    """
+    prediction_color = '#0072B2'
+    error_color = 'rgba(0, 114, 178, 0.2)'  # '#0072B2' with 0.2 opacity
+    actual_color = 'black'
+    cap_color = 'black'
+    trend_color = '#B23B00'
+    line_width = 2
+    marker_size = 4
+
+    data = []
+    # Add actual
+    data.append(go.Scatter(
+        name='Actual',
+        x=m.history['ds'],
+        y=m.history['y'],
+        marker=dict(color=actual_color, size=marker_size),
+        mode='markers'
+    ))
+    # Add lower bound
+    if uncertainty:
+        data.append(go.Scatter(
+            x=fcst['ds'],
+            y=fcst['yhat_lower'],
+            mode='lines',
+            line=dict(width=0),
+            hoverinfo='skip'
+        ))
+    # Add prediction
+    data.append(go.Scatter(
+        name='Predicted',
+        x=fcst['ds'],
+        y=fcst['yhat'],
+        mode='lines',
+        line=dict(color=prediction_color, width=line_width),
+        fillcolor=error_color,
+        fill='tonexty' if uncertainty else 'none'
+    ))
+    # Add upper bound
+    if uncertainty:
+        data.append(go.Scatter(
+            x=fcst['ds'],
+            y=fcst['yhat_upper'],
+            mode='lines',
+            line=dict(width=0),
+            fillcolor=error_color,
+            fill='tonexty',
+            hoverinfo='skip'
+        ))
+    # Add caps
+    if 'cap' in fcst and plot_cap:
+        data.append(go.Scatter(
+            name='Cap',
+            x=fcst['ds'],
+            y=fcst['cap'],
+            mode='lines',
+            line=dict(color=cap_color, dash='dash', width=line_width),
+        ))
+    if m.logistic_floor and 'floor' in fcst and plot_cap:
+        data.append(go.Scatter(
+            name='Floor',
+            x=fcst['ds'],
+            y=fcst['floor'],
+            mode='lines',
+            line=dict(color=cap_color, dash='dash', width=line_width),
+        ))
+    # Add trend
+    if trend:
+        data.append(go.Scatter(
+            name='Trend',
+            x=fcst['ds'],
+            y=fcst['trend'],
+            mode='lines',
+            line=dict(color=trend_color, width=line_width),
+        ))
+    # Add changepoints
+    if changepoints:
+        signif_changepoints = m.changepoints[
+            np.abs(np.nanmean(m.params['delta'], axis=0)) >= changepoints_threshold
+        ]
+        data.append(go.Scatter(
+            x=signif_changepoints,
+            y=fcst.loc[fcst['ds'].isin(signif_changepoints), 'trend'],
+            marker=dict(size=50, symbol='line-ns-open', color=trend_color,
+                        line=dict(width=line_width)),
+            mode='markers',
+            hoverinfo='skip'
+        ))
+
+    layout = dict(
+        showlegend=False,
+        yaxis=dict(
+            title=ylabel
+        ),
+        xaxis=dict(
+            title=xlabel,
+            type='date',
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=7,
+                         label='1w',
+                         step='day',
+                         stepmode='backward'),
+                    dict(count=1,
+                         label='1m',
+                         step='month',
+                         stepmode='backward'),
+                    dict(count=6,
+                         label='6m',
+                         step='month',
+                         stepmode='backward'),
+                    dict(count=1,
+                         label='1y',
+                         step='year',
+                         stepmode='backward'),
+                    dict(step='all')
+                ])
+            ),
+            rangeslider=dict(
+                visible=True
+            ),
+        ),
+    )
+    fig = go.Figure(data=data, layout=layout)
+    return plotly.offline.iplot(fig)