"min-sample-split_" + str(min_sample_split) + "_" +\
"max-depth_" + str(max_depth) + "_" +\
"max-leaf-nodes_" + str(max_leaf_nodes) + "_" +\
"random-state_" + str(random_state) + "_" +\
"class-weight_" + class_weight
tag = writestr + "_" + specific_info
## END OF PARAMS TO MODIFY ##
PARAMETERS = {
"X_train" : X_train,
"Y_train" : Y_train,
"X_dev" : X_dev,
"Y_dev" : Y_dev,
"architecture" : architecture,
"num_estimators" : num_estimators,
"min_sample_split" : min_sample_split,
"max_depth" : max_depth,
"max_leaf_nodes" : max_leaf_nodes,
"random_state" : random_state,
"class_weight" : class_weight,
"n_jobs" : n_jobs,
"tag" : tag,
"print_cost" : True
}
modelDriver = ModelDriver(PARAMETERS)
modelDriver.load()
modelDriver.init_model()
out = modelDriver.run_model()
"random-state_" + str(random_state) + "_" +\
"class-weight_" + class_weight
tag = writestr + "_" + specific_info
## END OF PARAMS TO MODIFY ##
PARAMETERS = {
"X_train": X_train,
"Y_train": Y_train,
"X_dev": X_dev,
"Y_dev": Y_dev,
"X_test": X_test,
"Y_test": Y_test,
"architecture": architecture,
"num_estimators": num_estimators,
"min_sample_split": min_sample_split,
"max_depth": max_depth,
"max_leaf_nodes": max_leaf_nodes,
"random_state": random_state,
"class_weight": class_weight,
"n_jobs": n_jobs,
"tag": tag,
"print_cost": True
}
modelDriver = ModelDriver(PARAMETERS)
#modelDriver.load()
#modelDriver.init_model()
#out = modelDriver.run_model()
modelDriver.cross_validation()
