def plot_global_decisions(self,
colors=None,
enable_node_id=True,
random_state=0,
file_name="interpretable_tree.png",
show_img=False,
fig_size=(20, 8)):
""" Visualizes the decision policies of the surrogate tree.
"""
graph_inst = plot_tree(self.__model,
self.__model_type,
feature_names=self.feature_names,
color_list=colors,
class_names=self.class_names,
enable_node_id=enable_node_id,
seed=random_state)
f_name = "interpretable_tree.png" if file_name is None else file_name
graph_inst.write_png(f_name)
try:
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
except ImportError:
raise exceptions.MatplotlibUnavailableError(
"Matplotlib is required but unavailable on the system.")
except RuntimeError:
raise exceptions.MatplotlibDisplayError(
"Matplotlib unable to open display")
if show_img:
plt.rcParams["figure.figsize"] = fig_size
img = plt.imread(f_name)
if self.__model_type == 'regressor':
cax = plt.imshow(img,
cmap=plt.cm.get_cmap(
graph_inst.get_colorscheme()))
plt.colorbar(cax)
else:
plt.imshow(img)
return graph_inst
from sklearn.externals.six import StringIO
from sklearn.tree import export_graphviz
import pydotplus
import numpy as np
from skater.util import exceptions
try:
from matplotlib.colors import rgb2hex
import matplotlib.pyplot as plt
except ImportError:
raise (exceptions.MatplotlibUnavailableError(
"matplotlib is required but unavailable on the system."))
# reference: http://wingraphviz.sourceforge.net/wingraphviz/language/colorname.htm
# TODO: Make the color scheme for regression and classification homogeneous
color_schemes = [
'aliceblue', 'antiquewhite', 'aquamarine', 'azure', 'beige', 'bisque',
'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood',
'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue',
'cornsilk', 'crimson', 'cyan', 'darkgoldenrod', 'darkgreen', 'darkkhaki',
'darkolivegreen', 'darkorange', 'darkorchid', 'darksalmon', 'darkseagreen',
'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise',
'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey',
'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'gainsboro',
'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey',
'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender',
'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan',
'lightgoldenrod', 'lightgoldenrodyellow', 'lightgray', 'lightgrey',
'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue',
'lightslateblue', 'lightslategray', 'lightslategrey', 'lightsteelblue',
'lightyellow', 'limegreen', 'linen', 'magenta', 'maroon',
from skater.util import exceptions
try:
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
from plotly import tools
except ImportError:
raise exceptions.PlotlyUnavailableError("plotly is required but unavailable on your system.")
try:
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib import colors as mcolors
except ImportError:
raise exceptions.MatplotlibUnavailableError("Matplotlib is required but unavailable on your system.")
_enable_axis = lambda ax, flag: ax.axis("on") if flag is True else ax.axis("off")
def _create_meshgrid(xx, yy, plot_step=0.02):
xmin, xmax = xx.min() - 0.5, xx.max() + 0.5
ymin, ymax = yy.min() - 0.5, yy.max() + 0.5
xx, yy = np.meshgrid(np.arange(xmin, xmax, plot_step),
np.arange(ymin, ymax, plot_step))
x_ = pd.DataFrame({'F1': xx.ravel(), 'F2': yy.ravel()})
return x_, xx, yy
def _generate_contours(est, X_, xx, yy, color_map, ax, **params):
Z = est.predict(X_).reshape(xx.shape)