def part_plot_1D(model, total_features, X_val, y_val, feature):
pdp_dist = pdp.pdp_isolate(model=model,
dataset=X_val,
model_features=total_features,
feature=feature)
pdp.pdp_plot(pdp_dist, feature)
plt.show()
def plot_pdp(m,
X,
features,
feature,
center=True,
classes=None,
percentile_range=None,
plot_params=None):
p = pdp.pdp_isolate(m,
X,
features,
feature,
n_jobs=-1,
percentile_range=percentile_range)
fig, axes = pdp.pdp_plot(p,
feature,
plot_lines=True,
center=center,
plot_pts_dist=True,
plot_params=plot_params)
if classes is not None:
_ = axes['pdp_ax']['_pdp_ax'].set_xticklabels(classes)
_ = axes['pdp_ax']['_count_ax'].set_xticklabels(classes)
_ = axes['pdp_ax']['_count_ax'].set_xlabel('')
_ = axes['pdp_ax']['_count_ax'].set_title('')
fig.autofmt_xdate()
plt.show()
def plot_pdp(feat, clusters=None, feat_name=None):
feat_name = feat_name or feat
p = pdp.pdp_isolate(m, x, feat)
return pdp.pdp_plot(p,
feat_name,
plot_lines=True,
cluster=clusters is not None,
n_cluster_centers=clusters)
def partial_dependence_plot(model, data: pd.DataFrame, model_features: list,
column: str):
pdp_df = pdp.pdp_isolate(model=model,
dataset=data,
model_features=model_features,
feature=column)
pdp.pdp_plot(pdp_df, column, figsize=(10, 8))
return plt.show()
def generateInsight(model,features,data):
pdp_airbnb = pdp.pdp_isolate(model=model,
dataset=data,
model_features=data.columns,
feature=features)
fig, axes = pdp.pdp_plot(pdp_isolate_out=pdp_airbnb,
feature_name=features,
plot_pts_dist=True,
)
def plot_pdp(df, model, feat, clusters=None, feat_name=None):
'''Use a sample from the dataframe using get_sample()'''
feat_name = feat_name or feat
p = pdp.pdp_isolate(model, df, df.columns, feat)
return pdp.pdp_plot(p,
feat_name,
plot_lines=True,
cluster=clusters is not None,
n_cluster_centers=clusters)
def partial_dependence_plot(feat_name, model, X_test, base_features, path):
pdp_dist = pdp.pdp_isolate(model=model,
dataset=X_test,
model_features=base_features,
feature=feat_name)
pdp.pdp_plot(pdp_dist, feat_name)
plt.savefig(path)
print('generate ' + path)
plt.close()
def ploting_pdp(f):
''' Function for ploting PDP '''
pdp_surv = pdp.pdp_isolate(model=rf,
dataset=X_train,
model_features=X_train.columns,
feature=f,
cust_grid_points=None)
pdp.pdp_plot(pdp_surv, f)
plt.show()
def plot_1D_partial_dependency(fitted_model, X_test : pd.DataFrame, model_features : list, feature : str ): # Create the data that we will plot pdp_obj = pdp.pdp_isolate(model = fitted_model, dataset = X_test, model_features = model_features, feature = feature) # plot it pdp.pdp_plot(pdp_obj, feature) plt.show()
def show_partial_dep_plots(lin_model, X_test):
"""Prints partial dependence plots for each feature in the dataset."""
for feat_name in X_test.columns:
pdp_dist = pdp.pdp_isolate(
model=lin_model,
dataset=X_test,
model_features=X_test.columns,
feature=feat_name,
)
pdp.pdp_plot(pdp_dist, feat_name)
plt.show()
def plot_pdp(model, x, feat, clusters=None, feat_name=None, filename=None):
feat_name = feat_name or feat
p = pdp.pdp_isolate(model, x, x.columns, feat)
fig, ax = pdp.pdp_plot(p,
feature_name=feat_name,
plot_lines=True,
cluster=clusters is not None,
n_cluster_centers=clusters)
if filename:
pdp.plt.savefig(filename + '.png', dpi=200)
pdp.plt.show()
return fig
def isolated(model, X, feature):
"""
isolated pair dependancy plot
"""
#instantiate and isolate variable
isolated = pdp_isolate(model = model,
dataset = X,
model_features = X.columns,
feature = feature)
#plot the variable
pdp_plot(isolated, feature_name = feature)
def pdpplot(feature_to_plot,
pdp_model=random_forest,
pdp_dataset=X_test,
pdp_model_features=list(X)):
pdp_cancer = pdp.pdp_isolate(model=pdp_model,
dataset=pdp_dataset,
model_features=pdp_model_features,
feature=feature_to_plot)
fig, axes = pdp.pdp_plot(pdp_cancer,
feature_to_plot,
figsize=(10, 5),
plot_params={})
def plot_pdp(self, feature_to_plot, i):
# creating data to plot
pdp_feature = pdp.pdp_isolate(model=self.model,
dataset=self.x,
model_features=list(self.x.columns),
feature=feature_to_plot)
# plot it
pdp.pdp_plot(pdp_feature, feature_to_plot)
# saving the plot
plt.tight_layout()
plt.savefig(self.out + '/dep_plot' + str(i) + '.jpg', dpi=400)
plt.close()