"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()
max_depth = None max_leaf_nodes = 3 random_state = 0 class_weight = "balanced" n_jobs = -1 tag = 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() modelDriver.run_model()