我创建了一个模型来对我的8类数据集进行分类,并使用MLP从中获得一些分数。为此,我决定使用sklearn.metrics.cross_验证,使用10倍
以下代码可以正常工作:
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import cross_validate
from sklearn.metrics import accuracy_score, make_scorer, f1_score
import pandas as pd
def MLPClasify(sample):
df = pd.read_csv('my_path\\my_file.csv', header=None)
y = df[NumberOfFeatures]
x = df.drop([NumberOfFeatures], axis=1)
clf = MLPClassifier(hidden_layer_sizes=(27), activation='logistic', max_iter=500, alpha=0.0001,
solver='adam', verbose=10, random_state=21, tol=0.000000001)
clf.out_activation_ = 'softmax'
scoring = {'Accuracy': make_scorer(accuracy_score), 'F1': make_scorer(f1_score,
average='weighted')}
scores = cross_validate(clf, x, y, cv=10, scoring=scoring)
return scores
一切正常,我得到了大约60%的准确度。所以我决定使用一种热编码,看看是否能得到更好的结果。因此,我编写了以下代码:
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import cross_validate
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.metrics import accuracy_score, make_scorer, f1_score
import pandas as pd
def MLPClasify(sample):
df = pd.read_csv('my_path\\my_file.csv', header=None)
y = df[NumberOfFeatures]
x = df.drop([NumberOfFeatures], axis=1)
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(y)
onehot_encoder = OneHotEncoder()
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
y = onehot_encoded
clf = MLPClassifier(hidden_layer_sizes=(27), activation='logistic', max_iter=500, alpha=0.0001,
solver='adam', verbose=10, random_state=21, tol=0.000000001)
clf.out_activation_ = 'softmax'
scoring = {'Accuracy': make_scorer(accuracy_score), 'F1': make_scorer(f1_score,
average='weighted')}
scores = cross_validate(clf, x, y, cv=10, scoring=scoring)
return scores
代码运行,但我得到以下警告:
UndefinedMetricWarning:F分数定义不清,在没有真实或预测样本的标签中设置为0.0。使用zero_division
参数
控制这种行为。
平均值,“真实或预测”,“F分数为”,len(真实和)
而且,我的准确率下降到2%以下
你知道我做错了什么吗
谢谢你的帮助
目前没有回答
相关问题 更多 >
编程相关推荐