def _store_evaluate_result(preds, feature_file_name, label_desc, result_table, result_column_names, validation_metrics, conn):
    '\n    Save the evaluation result in the table.\n\n    Args:\n        preds: the prediction result.\n        feature_file_name (str): the file path where the feature dumps.\n        label_desc (FieldDesc): the label FieldDesc object.\n        result_table (str): the result table name.\n        result_column_names (list[str]): the result column names.\n        validation_metrics (list[str]): the evaluation metric names.\n        conn: the database connection object.\n\n    Returns:\n        None.\n    '
    y_test = []
    with open(feature_file_name, 'r') as f:
        for line in f.readlines():
            row = [i for i in line.strip().split('\t')]
            if (label_desc.dtype == DataType.INT64):
                y_test.append(int(row[0]))
            elif (label_desc.dtype == DataType.FLOAT32):
                y_test.append(float(row[0]))
            else:
                raise TypeError('unsupported data type {}'.format(label_desc.dtype))
    y_test = np.array(y_test)
    evaluate_results = dict()
    for metric_name in validation_metrics:
        metric_name = metric_name.strip()
        if (metric_name not in SKLEARN_METRICS):
            raise ValueError(('unsupported metrics %s' % metric_name))
        metric_func = getattr(sklearn.metrics, metric_name)
        metric_value = metric_func(y_test, preds)
        evaluate_results[metric_name] = metric_value
    with db.buffered_db_writer(conn, result_table, result_column_names) as w:
        row = ['0.0']
        for mn in validation_metrics:
            row.append(str(evaluate_results[mn]))
        w.write(row)
Save the evaluation result in the table.

Args:
    preds: the prediction result.
    feature_file_name (str): the file path where the feature dumps.
    label_desc (FieldDesc): the label FieldDesc object.
    result_table (str): the result table name.
    result_column_names (list[str]): the result column names.
    validation_metrics (list[str]): the evaluation metric names.
    conn: the database connection object.

Returns:
    None.
def _store_evaluate_result(preds, feature_file_name, label_desc, result_table, result_column_names, validation_metrics, conn):
    '\n    Save the evaluation result in the table.\n\n    Args:\n        preds: the prediction result.\n        feature_file_name (str): the file path where the feature dumps.\n        label_desc (FieldDesc): the label FieldDesc object.\n        result_table (str): the result table name.\n        result_column_names (list[str]): the result column names.\n        validation_metrics (list[str]): the evaluation metric names.\n        conn: the database connection object.\n\n    Returns:\n        None.\n    '
    y_test = []
    with open(feature_file_name, 'r') as f:
        for line in f.readlines():
            row = [i for i in line.strip().split('\t')]
            if (label_desc.dtype == DataType.INT64):
                y_test.append(int(row[0]))
            elif (label_desc.dtype == DataType.FLOAT32):
                y_test.append(float(row[0]))
            else:
                raise TypeError('unsupported data type {}'.format(label_desc.dtype))
    y_test = np.array(y_test)
    evaluate_results = dict()
    for metric_name in validation_metrics:
        metric_name = metric_name.strip()
        if (metric_name not in SKLEARN_METRICS):
            raise ValueError(('unsupported metrics %s' % metric_name))
        metric_func = getattr(sklearn.metrics, metric_name)
        metric_value = metric_func(y_test, preds)
        evaluate_results[metric_name] = metric_value
    with db.buffered_db_writer(conn, result_table, result_column_names) as w:
        row = ['0.0']
        for mn in validation_metrics:
            row.append(str(evaluate_results[mn]))
        w.write(row)