Spaces:
Sleeping
Sleeping
File size: 15,077 Bytes
8a6cf88 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 |
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
from sklearn.model_selection import train_test_split
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(dataset, shap_values, target='target column', max_display = 20):
feature_names=dataset.columns
mpl_fig = shap_summary_plot(shap_values, dataset, 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
def train_model_and_return_shap_values(X, y, target):
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)
# Some of values are float or integer and some object. This is why we need to cast them:
for f in X_train.columns:
if X_train[f].dtype=='object':
lbl = preprocessing.LabelEncoder()
lbl.fit(list(X_train[f].values))
X_train[f] = lbl.transform(list(X_train[f].values))
for f in X_test.columns:
if X_test[f].dtype=='object':
lbl = preprocessing.LabelEncoder()
lbl.fit(list(X_test[f].values))
X_test[f] = lbl.transform(list(X_test[f].values))
X_train=np.array(X_train)
X_test=np.array(X_test)
X_train = X_train.astype(float)
X_test = X_test.astype(float)
d_train = xgboost.DMatrix(X_train, label=y_train, feature_names=list(X))
d_test = xgboost.DMatrix(X_test, label=y_test, feature_names=list(X))
# train the model
params = {
"eta": 0.01,
"subsample": 0.5,
"base_score": np.mean(y_train),
"silent": 1
}
model = xgboost.train(params, d_train, 5000, evals = [(d_test, "test")], verbose_eval=None, early_stopping_rounds=50)
feature_names = model.feature_names
shap_values = shap.TreeExplainer(model).shap_values(pd.DataFrame(X_train, columns=X.columns))
return model, shap_values, feature_names
def dependence_plot_to_plotly_fig(dataset, target='target column', max_display=10):
data = pd.read_csv(dataset, encoding="ISO-8859-1")
X = data.drop(['target column'], axis=1)
y = data[target]
y = y/max(y)
xgb_full = xgboost.DMatrix(X, label=y)
# create a train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
xgb_train = xgboost.DMatrix(X_train, label=y_train)
xgb_test = xgboost.DMatrix(X_test, label=y_test)
# use validation set to choose # of trees
params = {
# "eta": 0.002,
# "max_depth": 3,
# "subsample": 0.5,
"silent": 1
}
model_train = xgboost.train(params, xgb_train, 3000, evals = [(xgb_test, "test")], verbose_eval=None)
# train final model on the full data set
params = {
# "eta": 0.002,
# "max_depth": 3,
# "subsample": 0.5,
"silent": 1
}
model = xgboost.train(params, xgb_full, 1500, evals = [(xgb_full, "test")], verbose_eval=None)
features = model.feature_names
shap_values = shap.TreeExplainer(model).shap_values(X)
feature_order = np.argsort(np.sum(np.abs(shap_values), axis=0)[:-1])
feature_order = feature_order[-min(max_display, len(feature_order)):]
features = [features[i] for i in feature_order[::-1]]
lis = []
for i in features:
mpl_fig, interaction_index = shap_dependence_plot(i, shap_values, X)
plotly_fig = tls.mpl_to_plotly(mpl_fig)
# The x-tick labels start by default from 0, which is not necessarily the min value of the feature.
# So, we need to increment the x-tick labels by 1. But while doing so, the y-axis gets shifted.
# To prevent that, we need to manually control the x-axis range from r_min to r_max
new_x = []
for j in plotly_fig['data'][0]['x']:
new_x.append(j)
r_min = min(plotly_fig['data'][0]['x'])
r_max = max(plotly_fig['data'][0]['x'])
plotly_fig['layout']['xaxis'].update(range=[r_min-1, r_max+1])
plotly_fig['data'][0]['x'] = tuple(new_x)
# Define the colorbar
colorbar_trace = go.Scatter(x=[None],
y=[None],
mode='markers',
marker=dict(
colorscale=red_blue,
showscale=True,
colorbar=dict(thickness=5, outlinewidth=0),
color=[min(X[X.columns[interaction_index]]), max(X[X.columns[interaction_index]])],
),
hoverinfo='none'
)
plotly_fig['layout']['showlegend'] = False
plotly_fig['layout']['hovermode'] = 'closest'
plotly_fig['layout']['height']=380
plotly_fig['layout']['width']=450
plotly_fig['layout']['xaxis'].update(zeroline=True,
showline=True,
ticklen=4,
showgrid=False,
tickmode='linear')
title = plotly_fig['layout']['yaxis']['title']
plotly_fig['layout']['yaxis'].update(title=title.split(' -')[0])
plotly_fig.add_trace(colorbar_trace)
plotly_fig.layout.update(
annotations=[dict(
x=1.23,
align="right",
valign="top",
text=X.columns[interaction_index],
showarrow=False,
xref="paper",
yref="paper",
xanchor="right",
yanchor="middle",
textangle=-90,
font=dict(family='Calibri', size=14)
)
],
margin=dict(t=50, b=50, l=50, r=80)
)
lis.append(plotly_fig)
return lis, features
def interaction_plot_to_plotly_fig(dataset, target_col='target column', max_display=10):
data = pd.read_csv(dataset, encoding="ISO-8859-1")
X = data.drop(['target column'], axis=1)
y = data[target_col]
y = y/max(y)
xgb_full = xgboost.DMatrix(X, label=y)
# create a train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
xgb_train = xgboost.DMatrix(X_train, label=y_train)
xgb_test = xgboost.DMatrix(X_test, label=y_test)
# use validation set to choose # of trees
params = {
# "eta": 0.002,
# "max_depth": 3,
# "subsample": 0.5,
"silent": 1
}
model_train = xgboost.train(params, xgb_train, 3000, evals = [(xgb_test, "test")], verbose_eval=None)
# train final model on the full data set
params = {
# "eta": 0.002,
# "max_depth": 3,
# "subsample": 0.5,
"silent": 1
}
model = xgboost.train(params, xgb_full, 1500, evals = [(xgb_full, "test")], verbose_eval=None)
features = model.feature_names
shap_values = shap.TreeExplainer(model).shap_values(X)
feature_order = np.argsort(np.sum(np.abs(shap_values), axis=0)[:-1])
feature_order = feature_order[-min(max_display, len(feature_order)):]
features = [features[i] for i in feature_order[::-1]]
shap_interaction_values = shap.TreeExplainer(model).shap_interaction_values(X)
lis = []
for i in features:
for j in features:
mpl_fig = pl.figure()
ax = mpl_fig.add_subplot(111)
_, interaction_index = shap_dependence_plot ( (i, j), shap_interaction_values, X.iloc[:2000,:] )
plotly_fig = tls.mpl_to_plotly(mpl_fig)
r_min = min(plotly_fig['data'][0]['x'])
r_max = max(plotly_fig['data'][0]['x'])
plotly_fig['layout']['xaxis'].update(range=[r_min-1, r_max+1])
plotly_fig['layout']['showlegend'] = False
plotly_fig['layout']['hovermode'] = 'closest'
plotly_fig['layout']['height']=380
plotly_fig['layout']['width']=450
plotly_fig['layout']['xaxis'].update(zeroline=True,
showline=True,
ticklen=4,
showgrid=False,
tickmode='linear')
plotly_fig['layout']['yaxis'].update(showline=True)
if i!=j:
# plotly_fig['layout']['height']=380
plotly_fig['layout']['width']=480
plotly_fig['layout']['yaxis']['title'] = "SHAP interaction value for {} and {}".format(i.split('-')[0], j.split('-')[0])
# Define the colorbar
colorbar_trace = go.Scatter(x=[None],
y=[None],
mode='markers',
marker=dict(
colorscale=red_blue,
showscale=True,
colorbar=dict(thickness=5, outlinewidth=0),
color=[min(X[X.columns[interaction_index]]), max(X[X.columns[interaction_index]])],
),
hoverinfo='none'
)
plotly_fig.add_trace(colorbar_trace)
plotly_fig.layout.update(
annotations=[dict(
x=1.23,
align="right",
valign="top",
text=X.columns[interaction_index],
showarrow=False,
xref="paper",
yref="paper",
xanchor="right",
yanchor="middle",
textangle=-90,
font=dict(family='Calibri', size=14)
)
],
margin=dict(t=30, b=30, l=60, r=80)
)
else:
plotly_fig['layout']['yaxis']['title'] = "SHAP main effect value for {}".format(i.split('-')[0])
lis.append(plotly_fig)
return lis, features
|