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| 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 = [df_shap.index[i] for i in df_shap.index] | |
| 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' | |
| #plotly_fig['data'][i]['text'] = df_shap.index | |
| plotly_fig['data'][i]['y'] = feature_names[feature_order] | |
| 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=True)) | |
| plotly_fig.add_trace(colorbar_trace) | |
| plotly_fig.layout.update( | |
| annotations=[dict( | |
| x=1.18, | |
| align="right", | |
| valign="top", | |
| text='Gene', | |
| 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 |