def evaluate(data_set_name, layer_size, n_hidden_layers): with open(os.path.join('../specs', data_set_name + '.json')) as specs_file: specs = json.load(specs_file) data_set = pandas.read_csv(os.path.join('../resources', specs['file']), sep=specs['separator'], engine=specs['engine']) print(data_set.head()) with open(os.path.join(os.path.dirname(__file__), 'neural-net.json')) as config_file: config = json.load(config_file) x = data_set.ix[:, :2].values estimator = Estimator(x, config, layer_size, n_hidden_layers) y = data_set.ix[:, 2].values.reshape(-1, 1) if specs['scaling']: y = sklearn.preprocessing.MaxAbsScaler().fit_transform(numpy.log(y)) return estimator.estimate(y, config['batch_size'], specs['test_size'], specs['metric'])