abf37e5b9a3e46d2c9e07e32ee947b4b74e64057,examples/temp_do_not_use_lscp.py,,,#,66

Before Change

timestamp = today.strftime("%Y%m%d_%H%M%S")

// set numpy parameters
np.set_printoptions(suppress=True, precision=4)


// TODO: Clean up unnecessary residual comments
// TODO: Add proper documentation

After Change

        return iter(self.estimator_list)


if __name__ == "__main__":

    from pyod.models.lof import LOF
    import scipy.io as scio
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import roc_auc_score

    def loaddata(filename):
        
        load data
        :param filename:
        :return:
        
        mat = scio.loadmat(filename + ".mat")
        X_orig = mat["X"]
        y_orig = mat["y"].ravel()
        return X_orig, y_orig

    X, y = loaddata(r"C:\Users\znasrullah001\Documents\project-files\PyOD\LSCP\datasets\cardio")

    random_state = np.random.RandomState(0)

    el = []
    k_list = random_state.randint(5, 200, size=50).tolist()
    for k in k_list:
        el.append(LOF(k))

    // create the model
    lscp = LSCP(el, random_state=random_state, local_region_size=100)

    // split the data into training and testing
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=random_state)
    X_train, X_test = standardizer(X_train, X_test)

    // fit and predict
    lscp.fit(X_train)
    scores = lscp.decision_function(X_test)
    print(roc_auc_score(y_test, scores))

Instances