kraken/backend/bci_core/utils.py

import numpy as np
import itertools
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
import logging
import os


def parse_model_type(model_path):
    model_path = os.path.normpath(model_path)
    file_name = model_path.split(os.sep)[-1]
    model_type, events, _ = file_name.split('_')
    events = events.split('+')
    return model_type.lower(), events


def event_metric(event_true, event_pred, fs, hit_time_range=(0, 3), ignore_event=(0,), f_beta=1.):
    """评价单试次f_alpha score
    Args: 
        event_true:
        event_pred:
        fs:
        hit_time_range (tuple): 
        ignore_event (tuple): ignore certain events
        f_beta (float): f_(alpha) score
    Return:
        f_beta score (float): f_alpha score
    """
    event_true = event_true.copy()[np.logical_not(np.isin(event_true[:, 2], ignore_event))]
    event_pred = event_pred.copy()[np.logical_not(np.isin(event_pred[:, 2], ignore_event))]
    true_idx = 0
    pred_idx = 0
    correct_count = 0
    hit_time_range = (int(fs * hit_time_range[0]), int(fs * hit_time_range[1]))
    while true_idx < len(event_true) and pred_idx < len(event_pred):
        if event_true[true_idx, 0] + hit_time_range[0] <= event_pred[pred_idx, 0] < event_true[true_idx, 0] + hit_time_range[1]:
            if event_true[true_idx, 2] == event_pred[pred_idx, 2]:
                correct_count += 1
                true_idx += 1
                pred_idx += 1
            else:
                pred_idx += 1
        elif event_pred[pred_idx, 0] < event_true[true_idx, 0] + hit_time_range[0]:
            pred_idx += 1
        else:
            true_idx += 1
    precision = correct_count / len(event_pred)
    recall = correct_count / len(event_true)
    fbeta_score = (1 + f_beta ** 2) * (precision * recall) / (f_beta ** 2 * precision + recall)
    return precision, recall, fbeta_score


def cut_epochs(t, data, timestamps):
    """
    cutting raw data into epochs
    :param t: tuple (start, end, samplerate)
    :param data: ndarray (..., n_times), the last dimension should be the times
    :param timestamps: list of timestamps
    :return: ndarray (n_epochs, ... , n_times), the first dimension be the epochs
    """
    timestamps = np.array(timestamps)
    start = timestamps + int(t[0] * t[2])
    end = timestamps + int(t[1] * t[2])
    # do boundary check
    if start[0] < 0:
        start = start[1:]
        end = end[1:]
    if end[-1] > data.shape[-1]:
        start = start[:-1]
        end = end[:-1]
    epochs = np.stack([data[..., s:e] for s, e in zip(start, end)], axis=0)
    return epochs


def product_dict(**kwargs):
    keys = kwargs.keys()
    vals = kwargs.values()
    for instance in itertools.product(*vals):
        yield dict(zip(keys, instance))


def param_search(model_func, X, y, params: dict, random_state=123):
    """

    :param model_func: model builder
    :param X: ndarray (n_trials, n_channels, n_times)
    :param y: ndarray (n_trials, )
    :param params: dict of params, key is param name and value is search range
    :param random_state:
    :return:
    """
    kfold = KFold(n_splits=10, shuffle=True, random_state=random_state)

    best_auc = -1
    best_param = None
    for p_dict in product_dict(**params):
        model = model_func(**p_dict)

        n_classes = len(np.unique(y))

        y_pred = np.zeros((len(y), n_classes))

        for train_idx, test_idx in kfold.split(X):
            X_train, y_train = X[train_idx], y[train_idx]
            X_test = X[test_idx]
            model.fit(X_train, y_train)
            y_pred[test_idx] = model.predict_proba(X_test)

        if n_classes == 3:
            auc = roc_auc_score(y, y_pred, multi_class='ovr')
        elif n_classes == 2:
            auc = roc_auc_score(y, y_pred[:, 1])
        else:
            raise ValueError

        # update
        if auc > best_auc:
            best_param = p_dict
            best_auc = auc
        
        # print each steps
        logging.debug(f'Current: {p_dict}, {auc}; Best: {best_param}, {best_auc}')

    return best_auc, best_param