Upload dynamic_shap_plot.py

dynamic_shap_plot.py

@@ -0,0 +1,115 @@
+from shap_plots import shap_summary_plot, shap_dependence_plot
+import plotly.tools as tls
+import dash_core_components as dcc
+import pandas as pd
+import numpy as np
+import xgboost
+import shap
+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
+from sklearn import preprocessing 
+
+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)
+
+def summary_plot_plotly_fig(shap_values, df_shap, feature_names, max_display = 8):
+    #data = pd.read_csv(dataset, encoding="ISO-8859-1")
+    #X = data.drop(['target column'], axis=1)
+
+    #y = data[target]
+    #y = y/max(y)
+
+    #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
+
+    #X_train.fillna((-999), inplace=True) 
+    #X_test.fillna((-999), inplace=True)
+
+    #_, shap_values, feature_names = train_model_and_return_shap_values(X, y, target)
+
+    mpl_fig = shap_summary_plot(shap_values, df_shap, feature_names=feature_names, max_display=20)
+
+    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 = [feature_names[i] for i in feature_order]
+    text = iter(text)
+
+    for i in range(1, len(plotly_fig['data']), 2):
+        t = text.__next__()
+        plotly_fig['data'][i]['name'] = ''
+        plotly_fig['data'][i]['text'] = t
+        plotly_fig['data'][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)
+                            )
+    return plotly_fig