如何使用 python 打印精度、召回率、fscore？

Question

我想在 python 中使用 sklearn.metrics 計算和打印精度、召回率、fscore 和支持。 我是 doig NLP，所以我的 y_test 和 y_pred 基本上是向量化步驟之前的單詞。

下面是一些可以幫助您的信息：

y_test:  [0 0 0 1 1 0 1 1 1 0]
y_pred [0.86 0.14 1.   0.   1.   0.   0.04 0.96 0.01 0.99 1.   0.   0.01 0.99
 0.41 0.59 0.02 0.98 1.   0.  ]

x_train 50
y_train 50
x_test 10
y_test 10
x_valid 6
y_valid 6

y_pred dimension:  (20,)
y_test dimension:  (10,)

完整的引用錯誤：

  Traceback (most recent call last):
  File "C:\Users\iduboc\Documents\asd-dev\train.py", line 324, in <module>
    precision, recall, fscore, support = score(y_test, y_pred)
  File "C:\Users\iduboc\Python1\envs\asd-v3-1\lib\site-packages\sklearn\metrics\classification.py", line 1415, in precision_recall_fscore_support
    pos_label)
  File "C:\Users\iduboc\Python1\envs\asd-v3-1\lib\site-packages\sklearn\metrics\classification.py", line 1239, in _check_set_wise_labels
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "C:\Users\iduboc\Python1\envs\asd-v3-1\lib\site-packages\sklearn\metrics\classification.py", line 71, in _check_targets
    check_consistent_length(y_true, y_pred)
  File "C:\Users\iduboc\Python1\envs\asd-v3-1\lib\site-packages\sklearn\utils\validation.py", line 205, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [10, 20]

我的代碼：

 from sklearn.metrics import precision_recall_fscore_support as score
    precision, recall, fscore, support = score(y_test, y_pred)
    print('precision: {}'.format(precision))
    print('recall: {}'.format(recall))
    print('fscore: {}'.format(fscore))
    print('support: {}'.format(support))

我的代碼來預測值：

elif clf == 'rndforest':

    # No validation data in rnd forest
    x_train = np.concatenate((x_train, x_valid))
    y_train = np.concatenate((y_train, y_valid))

    model = RandomForestClassifier(n_estimators=int(clf_params['n_estimators']),
                                   max_features=clf_params['max_features'])
    model.fit(pipe_vect.transform(x_train), y_train)

    datetoday = datetime.today().strftime('%d-%b-%Y-%H_%M')
    model_name_save = abspath(os.path.join("models", dataset,  name_file + '-' + 
    vect + reduction + '-rndforest'\
                                   + datetoday + '.pickle'))
    print("Model d'enregistrement : ", model_name_save)




    x_test_vect = pipe_vect.transform(x_test)

    y_pred = model.predict_proba(x_test_vect)  

Answer 1

錯誤是由於預測向量和地面實況向量的大小不同造成的。 函數precision_recall_fscore_support僅在這些大小相同時才有效。

查看文檔：

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html

此外，上述函數期望接收非連續值，否則。 如果您將浮點數介於 0 和 1 之間的列表（ y_pred列表）作為參數傳遞，則會出現下一個錯誤：

ValueError: Classification metrics can't handle a mix of binary and continuous targets

產生錯誤的示例代碼是這樣的：

y_test =  [0., 0., 0., 1., 1.]
y_pred = [0.86, 0.14, 1., 0., 1.]

from sklearn.metrics import precision_recall_fscore_support as score

precision, recall, fscore, support = score(y_test, y_pred)
print('precision: {}'.format(precision))
print('recall: {}'.format(recall))
print('fscore: {}'.format(fscore))
print('support: {}'.format(support))

因此，如果您想計算這些指標，您必須以某種方式決定預測向量的哪些值為 1（正預測），哪些值為 0（負預測）。 例如，您可以使用一個閾值（例如 0.5）或多個閾值，然后選擇最佳閾值或繪制具有不同閾值級別（例如 0.1、0.2、0.3 等）的不同指標的曲線。