active_learning.py 文件源码

def CAL_v(name, label_p, label_n, oracle, n_features, ftype, test_x, test_y):
    online = OnlineBase(name, label_p, label_n, oracle, n_features, ftype, error=.5)
    x, y = online.collect_pts(100, -1)
    i = 0
    q = online.get_n_query()
    C_range = np.logspace(-2, 5, 10, base=10)
    gamma_range = np.logspace(-5, 1, 10, base=10)
    param_grid = dict(gamma=gamma_range, C=C_range)
    while q < 3500:
        i += 1
        # h_ = ex.fit(x, y)

        cv = StratifiedShuffleSplit(y, n_iter=5, test_size=0.2, random_state=42)
        grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv, verbose=0, n_jobs=-1)
        grid.fit(x, y)
        h_ = grid.best_estimator_

        online_ = OnlineBase('', label_p, label_n, h_.predict, n_features, ftype, error=.1)
        x_, _ = online_.collect_pts(10, 200)
        if x_ is not None and len(x_) > 0:
            x.extend(x_)
            y.extend(oracle(x_))
        q += online_.get_n_query()
        pred_y = h_.predict(test_x)
        print len(x), q, sm.accuracy_score(test_y, pred_y)