def get_imputer(cls): """Get the model object for this instance, loading it if it's not already loaded.""" if cls.imputer is None: imputer = SimpleImputer.load(model_path) print(imputer.input_columns) imputer.load_hpo_model() print(imputer.input_columns) imputer.imputer.batch_size = 1 cls.imputer = imputer return cls.imputer
Run SimpleImputer with hyperparameter optimization """ # Initialize a SimpleImputer model imputer = SimpleImputer(input_columns=['title', 'text'], output_column='finish', output_path='imputer_model') # Fit an imputer model with default list of hyperparameters imputer.fit_hpo(train_df=df_train) # Fit an imputer model with customized HPO imputer.fit_hpo(train_df=df_train, num_epochs=5, patience=3, learning_rate_candidates=[1e-3, 1e-4], num_hash_bucket_candidates=[2**15], tokens_candidates=['words', 'chars']) # ------------------------------------------------------------------------------------ """ Load saved model and get metrics from SimpleImputer """ # Load saved model imputer = SimpleImputer.load('./imputer_model') # Load a dictionary of metrics from the validation set metrics = imputer.load_metrics() weighted_f1 = metrics['weighted_f1'] avg_precision = metrics['avg_precision'] # ... explore other metrics stored in this dictionary!
def get_imputer(cls): """Get the model object for this instance, loading it if it's not already loaded.""" if cls.imputer == None: cls.imputer = SimpleImputer.load(model_path) return cls.imputer