def model_train_validation(ins_file, oos_file, classifier, var_list_filename,
result_dir, output_suffix):
"""
train model
evaluate on the train and validation data
evaluate the model performance on the train and validation data
"""
#################### Load train and validation data ####################
print 'Loading data for modeling starts ...'
t0 = time.time()
target_name = 'target'
X, y = load_data(ins_file, var_list_filename, target_name)
Xv, yv = load_data(oos_file, var_list_filename, target_name)
print "Loading data done, taking ", time.time() - t0, "secs"
# Train Model
print '\nModel training starts...'
t0 = time.time()
model = classifier
model.fit(X, y)
print "Model training done, taking ", time.time() - t0, "secs"
pickle.dump(model, open(result_dir + "model.p",
'wb')) # save model to disk
# Predict Train
y_pred = model.predict(X)
p_pred = model.predict_proba(X)
p_pred = p_pred[:, 1]
# Predict Validation
yv_pred = model.predict(Xv)
pv_pred = model.predict_proba(Xv)
pv_pred = pv_pred[:, 1]
# Performance Evaluation: Train and Validation
performance_eval_train_validation(y, p_pred, yv, pv_pred, result_dir,
output_suffix)
#################### Random Forest Feature Importance ######################
try:
varlist_file = open(var_list_filename, 'rU')
varlist_csv = csv.reader(varlist_file)
var_list = []
for row in varlist_csv:
var_list.append(row[0])
out_feat_import = open(
result_dir + 'feature_import_' + str(output_suffix) + '.csv', 'wb')
feat_import_csv = csv.writer(out_feat_import)
var_import = zip(range(len(var_list)), var_list,
model.feature_importances_)
feat_import_csv.writerow(['var seq num', 'var name', 'importance'])
print "RandomForest classifier, var importance was output"
for row in var_import:
feat_import_csv.writerow(row)
except:
print "Not RandomForest classifier, var importance not created"
def model_train_validation(ins_file, oos_file, classifier, var_list_filename, output_dir, outpu):
"""
train model
evaluate on the train and validation data
evaluate the model performance on the train and validation data
"""
#################### Load train and validation data ####################
print 'Loading data for modeling starts ...'
t0=time.time()
target_name='target'
X,y = load_data_fast(ins_file, var_list_filename, target_name)
Xv,yv = load_data_fast(oos_file, var_list_filename, target_name)
print "Loading data done, taking ",time.time()-t0,"secs"
# prepare trivial input values for generating reason code in production
trivial_input_values_file = output_dir+'trivial_input_values.p'
trivial_input_values = median(X,axis=0)
pickle.dump(trivial_input_values,open(trivial_input_values_file,'wb'))
# Train Model
print '\nModel training starts...'
t0=time.time()
model = classifier
model.fit(X, y)
print "Model training done, taking ",time.time()-t0,"secs"
pickle.dump(model,open(output_dir+"model.p",'wb')) # save model to disk
'''
#export to tree graph in DOT format, tree only
tree.export_graphviz(model,out_file=output_dir+'tree.dot')
os.system("dot -Tpng "+output_dir+"tree.dot -o "+output_dir+"tree.png")
'''
# Predict Train
y_pred = model.predict(X)
p_pred = model.predict_proba(X)
p_pred = p_pred[:,1]
# Predict Validation
yv_pred = model.predict(Xv)
pv_pred = model.predict_proba(Xv)
pv_pred = pv_pred[:,1]
# Performance Evaluation: Train and Validation
ks, auc, lorenz_curve_capt_rate = performance_eval_train_validation(y,p_pred,yv,pv_pred,output_dir,output_suffix)
#################### Random Forest Feature Importance ######################
try:
varlist_file=open(var_list_filename,'rU')
varlist_csv=csv.reader(varlist_file)
var_list=[]
for row in varlist_csv:
var_list.append(row[0])
out_feat_import = open(output_dir + 'feature_import_' + str(output_suffix)+'.csv', 'wb')
feat_import_csv = csv.writer(out_feat_import)
var_import = zip(range(len(var_list)),var_list,model.feature_importances_)
feat_import_csv.writerow(['var seq num','var name','importance'])
print "RandomForest classifier, var importance was output"
for row in var_import:
feat_import_csv.writerow(row)
except:
print "Not RandomForest classifier, var importance not created"
return ks, auc, lorenz_curve_capt_rate
