Update app.py

app.py

@@ -5,6 +5,17 @@ import pandas as pd
 import sklearn
 import xgboost
 import shap
+import plotly.tools as tls
+import dash_core_components as dcc
+import matplotlib
+import plotly.graph_objs as go
+try:
+    import matplotlib.pyplot as pl
+    from matplotlib.colors import LinearSegmentedColormap
+    from matplotlib.ticker import MaxNLocator
+except ImportError:
+    pass
+
 st.set_option('deprecation.showPyplotGlobalUse', False)
 
 seed=42
@@ -65,6 +76,36 @@ explainer = shap.TreeExplainer(xgb)
 def convert_df(df):
    return df.to_csv(index=False).encode('utf-8')
 
+
+cdict1 = {
+    'red': ((0.0, 0.11764705882352941, 0.11764705882352941),
+            (1.0, 0.9607843137254902, 0.9607843137254902)),
+
+    'green': ((0.0, 0.5333333333333333, 0.5333333333333333),
+              (1.0, 0.15294117647058825, 0.15294117647058825)),
+
+    'blue': ((0.0, 0.8980392156862745, 0.8980392156862745),
+             (1.0, 0.3411764705882353, 0.3411764705882353)),
+
+    'alpha': ((0.0, 1, 1),
+              (0.5, 1, 1),
+              (1.0, 1, 1))
+}  # #1E88E5 -> #ff0052
+red_blue = LinearSegmentedColormap('RedBlue', cdict1)
+
+def matplotlib_to_plotly(cmap, pl_entries):
+    h = 1.0/(pl_entries-1)
+    pl_colorscale = []
+
+    for k in range(pl_entries):
+        C = list(map(np.uint8, np.array(cmap(k*h)[:3])*255))
+        pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])
+
+    return pl_colorscale
+
+red_blue = matplotlib_to_plotly(red_blue, 255)
+
+
 if len(gene_list) > 1:
     df = df_total[df_total.index.isin(gene_list)]
     df['Gene'] = df.index
@@ -91,6 +132,57 @@ if len(gene_list) > 1:
     summary_plot = shap.summary_plot(shap_values, df_shap, show=False)
     st.caption("SHAP Summary Plot of All Input Genes")
     st.pyplot(fig=summary_plot)
+    st.caption("Interactive SHAP Summary Plot of All Input Genes")
+    mpl_fig = shap_summary_plot(shap_values, df_shap, show=False)
+    plotly_fig = tls.mpl_to_plotly(mpl_fig)
+    plotly_fig['layout'] = {'xaxis': {'title': 'SHAP value (impact on model output)'}}
+
+    feature_order = np.argsort(np.sum(np.abs(shap_values), axis=0)[:-1])
+    feature_order = feature_order[-min(max_display, len(feature_order)):]
+    text = gene_list
+    text = iter(text)
+    for i in range(1, len(plotly_fig['df_shap']), 2):
+        t = text.__next__()
+        plotly_fig['df_shap'][i]['name'] = ''
+        plotly_fig['df_shap'][i]['text'] = t
+        plotly_fig['df_shap'][i]['hoverinfo'] = 'text'
+    colorbar_trace  = go.Scatter(x=[None],
+                                 y=[None],
+                                 mode='markers',
+                                 marker=dict(
+                                     colorscale=red_blue, 
+                                     showscale=True,
+                                     cmin=-5,
+                                     cmax=5,
+                                     colorbar=dict(thickness=5, tickvals=[-5, 5], ticktext=['Low', 'High'], outlinewidth=0)
+                                 ),
+                                 hoverinfo='none'
+                                )
+    plotly_fig['layout']['showlegend'] = False
+    plotly_fig['layout']['hovermode'] = 'closest'
+    plotly_fig['layout']['height']=600
+    plotly_fig['layout']['width']=500
+    plotly_fig['layout']['xaxis'].update(zeroline=True, showline=True, ticklen=4, showgrid=False)
+    plotly_fig['layout']['yaxis'].update(dict(visible=False))
+    plotly_fig.add_trace(colorbar_trace)
+    plotly_fig.layout.update(
+                             annotations=[dict(
+                                  x=1.18,
+                                  align="right",
+                                  valign="top",
+                                  text='Feature value',
+                                  showarrow=False,
+                                  xref="paper",
+                                  yref="paper",
+                                  xanchor="right",
+                                  yanchor="middle",
+                                  textangle=-90,
+                                  font=dict(family='Calibri', size=14)
+                                )
+                             ],
+                             margin=dict(t=20)
+                            )
+    st.plotly_chart(plotly_fig)
 
 else:
     pass