予期しないキーワード引数のpython

Question

私は正しいパラメータを渡していますが、私はまだ、次のエラーを取得しています：

Starting classification 

Classification running ... 

Traceback (most recent call last): 
    File "C:/Classify/classifier.py", line 95, in <module> 

    train_avg, test_avg, cms = train_model(X, y, "ceps", plot=True) 
    File "C:/Classify/classifier.py", line 25, in train_model 

    cv = ShuffleSplit(n=len(X), n_iterations=1, test_fraction=0.3, indices=True, random_state=0) 

TypeError: __init__() got an unexpected keyword argument 'test_fraction' 

Process finished with exit code 1 

私のコードは次のとおりです。

def train_model(X, Y, name, plot=False): 

    labels = np.unique(Y) 

    cv = ShuffleSplit(n=len(X), n_iterations=1, test_fraction=0.3, indices=True, random_state=0) 

    train_errors = [] 
    test_errors = [] 

    scores = [] 
    pr_scores = defaultdict(list) 
    precisions, recalls, thresholds = defaultdict(list), defaultdict(list), defaultdict(list) 

    roc_scores = defaultdict(list) 
    tprs = defaultdict(list) 
    fprs = defaultdict(list) 

    clfs = [] # for the median 

    cms = [] 

    for train, test in cv: 
     X_train, y_train = X[train], Y[train] 
     X_test, y_test = X[test], Y[test] 

     clf = LogisticRegression() 
     clf.fit(X_train, y_train) 
     clfs.append(clf) 

     train_score = clf.score(X_train, y_train) 
     test_score = clf.score(X_test, y_test) 
     scores.append(test_score) 

     train_errors.append(1 - train_score) 
     test_errors.append(1 - test_score) 

     y_pred = clf.predict(X_test) 
     cm = confusion_matrix(y_test, y_pred) 
     cms.append(cm) 

     for label in labels: 
      y_label_test = np.asarray(y_test == label, dtype=int) 
      proba = clf.predict_proba(X_test) 
      proba_label = proba[:, label] 

      fpr, tpr, roc_thresholds = roc_curve(y_label_test, proba_label) 
      roc_scores[label].append(auc(fpr, tpr)) 
      tprs[label].append(tpr) 
      fprs[label].append(fpr) 

    if plot: 
     for label in labels: 
      scores_to_sort = roc_scores[label] 
      median = np.argsort(scores_to_sort)[len(scores_to_sort)/2] 
      desc = "%s %s" % (name, genre_list[label]) 
      plot_roc_curves(roc_scores[label][median], desc, tprs[label][median],fprs[label][median], label='%s vs rest' % genre_list[label]) 

    all_pr_scores = np.asarray(pr_scores.values()).flatten() 
    summary = (np.mean(scores), np.std(scores), np.mean(all_pr_scores), np.std(all_pr_scores)) 

    #print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary) 

    #save the trained model to disk 
    joblib.dump(clf, 'saved_model/model_ceps.pkl') 

    return np.mean(train_errors), np.mean(test_errors), np.asarray(cms) 

Answer 1

それはパラメータn_iterationsの名前だ、これShuffleSplit documentationで説明されているようにn_iterです。パラメータ名を変更すると、エラーがなくなります。

あなたは私たちがscikit-学ぶ0.15使用することを考えると、sklearn.cross_validation.ShuffleSplitで次のパラメータを送信することができます： - 学びscikitの

n 
n_iter 
test_size 
train_size 
indices 
random_state 
n_iterations