scikit multilearn：accuracy_score ValueError：不支持多类多输出

Question

我想预测一次可以在 1 个以上标签中的样本（多标签分类）。 所以我使用了scikit-multilearn库并成功地安装了一个分类器，甚至可以预测测试数据。 它只是无法输出分类器的准确性。

我的数据（最多 1100 行）：

依赖变量（我预测的变量）是最后 4 个： N/xN、Sex、Maturity和CType 。 其余的是独立变量。

我所说的准确性是分类器与预测所有标签的接近程度。

这是代码：

import numpy as np
import pandas as pd
from scipy import sparse
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from skmultilearn.problem_transform import BinaryRelevance

# Prepare data
df = pd.read_csv("Data_Numeric.csv")
# remove crab_id for now
del df['Crab_id']

# independent vars: the rest
# dependent vars: N/xN, Gender, Maturity, CType
# n_samples = 1100
# n_features = 6
# n_labels = 4
X = df.iloc[:, :6].values
y = df.iloc[:, 6:df.shape[1]].astype(np.int64).values

X = sparse.csr_matrix(X)
y = sparse.csr_matrix(y, dtype=np.int64)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# generate model
classifier = BinaryRelevance(SVC())

# train
classifier.fit(X_train, y_train)

# predict
y_pred = classifier.predict(X_test)
y_pred_array = y_pred.toarray()

# my_data = X_test[0:4, :]
# my_data[0] = [64.7, 46, 12, 13, 0, 0]
# my_data_prediction = classifier.predict(my_data).toarray()
# my_data_true = y_test[0:4, :].toarray()

# error here
score = accuracy_score(y_test.toarray(), y_pred.toarray())

错误是

Traceback (most recent call last):
  File "<input>", line 42, in <module>
  File "/home/f4ww4z/anaconda3/envs/ayah/lib/python3.7/site-packages/sklearn/metrics/_classification.py", line 185, in accuracy_score
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "/home/f4ww4z/anaconda3/envs/ayah/lib/python3.7/site-packages/sklearn/metrics/_classification.py", line 97, in _check_targets
    raise ValueError("{0} is not supported".format(y_type))
ValueError: multiclass-multioutput is not supported

y_test

>>> y_test
<330x4 sparse matrix of type '<class 'numpy.longlong'>'
    with 578 stored elements in Compressed Sparse Row format>

y_test.toarray() ，形状为330x4 ：

y_pred

>>> y_pred
<330x4 sparse matrix of type '<class 'numpy.longlong'>'
    with 408 stored elements in Compressed Sparse Column format>

y_pred.toarray() ：

我如何正确地看到分类器的准确性？

Answer 1

from sklearn.model_selection import cross_validate, KFold

clf = BinaryRelevance(SVC())
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

scores = cross_validate(clf, X_train, y_train, cv=k_fold, scoring=['accuracy'])

或者

scores = cross_val_score(clf, X_train, y_train, cv=5)

通过使用交叉验证方法，您可以获得 5 个准确度分数，然后取它们的平均值。

您可以通过使用 MultioutputClassifier 和 RandomForestClassifier 来进行基本操作

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_validate, KFold
from sklearn.multioutput import MultiOutputClassifier
clf=MultiOutputClassifier(RandomForestClassifier(random_state=42,class_weight="balanced"))
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

scores = cross_validate(clf, X_train_tf, y_train, cv=k_fold, scoring=['f1_weighted'])

也许这会帮助你:)