willkoehrsen / hyperparameter-optimization Goto Github PK

space = {
    'boosting_type': hp.choice('boosting_type', 
                                            [{'boosting_type': 'gbdt', 'subsample': hp.uniform('gbdt_subsample', 0.5, 1)}, 
                                             {'boosting_type': 'dart', 'subsample': hp.uniform('dart_subsample', 0.5, 1)},
                                             {'boosting_type': 'goss', 'subsample': 1.0}]),
    'num_leaves': hp.quniform('num_leaves', 30, 150, 1),
    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
    'subsample_for_bin': hp.quniform('subsample_for_bin', 20000, 300000, 20000),
    'min_child_samples': hp.quniform('min_child_samples', 20, 500, 5),
    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),
    'colsample_bytree': hp.uniform('colsample_by_tree', 0.6, 1.0),
    'objective': ['regression']
}

best = fmin(fn=objective, space= space, algo=tpe_algorithm, max_evals=MAX_EVAL, trials=bayes_trials, rstate=np.random.RandomState(50))

----> 5 best = fmin(fn=objective, space= space, algo=tpe_algorithm, max_evals=MAX_EVAL, trials=bayes_trials, rstate=np.random.RandomState(50))
...
...
TypeError: Unknown type of parameter:boosting_type, got:dict

def objective(params = sample(space)):
    """Objective function for AE Hyperparameter Optimization"""
    
    # Keep track of evals
    global ITERATION
    
    ITERATION += 1
    
    #generate parameters
    
    # Make sure parameters that need to be integers are integers
    for parameter_name in ['batch_size']:
        params[parameter_name] = int(params[parameter_name])
    
    start = timer()
    
    # run AE with pre-defined k-fold reps
    out, autoencoder, encoder, decoder =  functionalModel(data=train_ctr.X, batch_size=params['batch_size'], epochs=100, dr=params['dropout'], reg=params['regularization'], lr=params['learning_rate'], optimizer='rmsprop')
    run_time = timer() - start
    
    # Extract the best score (MSE)
    best_score = best_loss = out.history['loss'][-1]
    
    # Loss must be minimized
    loss = best_score

    # Write to the csv file ('a' means append)
    of_connection = open(out_file, 'a')
    writer = csv.writer(of_connection)
    writer.writerow([loss, params, ITERATION, run_time])
    
    # Dictionary with information for evaluation
    return {'loss': loss, 'params': params, 'iteration': ITERATION,
            'train_time': run_time, 'status': STATUS_OK}

#run fmin
from hyperopt import fmin
# Global variable
global  ITERATION

#initialize iteration to 0
ITERATION = 0
#max number of runs of algorithm
MAX_EVALS = 5

# Run optimization
best = fmin(fn = objective, space = space, algo = tpe.suggest, 
            max_evals = MAX_EVALS, trials = bayes_trials, rstate = np.random.RandomState(50))