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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 |