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管线中LogisticReturnal的_coef值太多

coefficients sklearn-pandas logistic-regression scikit-learn

4

Christopher · 技术社区 · 7 年前

我在利用这些小熊猫 DataFrameMapper 在一个学习管道中。为了评估特征联合管道中的特征贡献,我喜欢测量估计器的系数(逻辑回归)。对于下面的代码示例,有三个文本内容列 a, b 和 c X_train :

import pandas as pd
import numpy as np
import pickle
from sklearn_pandas import DataFrameMapper
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
np.random.seed(1)

data = pd.read_csv('https://pastebin.com/raw/WZHwqLWr')
#data.columns

X = data.copy()
y = data.result
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

mapper = DataFrameMapper([
        ('a', CountVectorizer()),
        ('b', CountVectorizer()),
        ('c', CountVectorizer())
])

pipeline = Pipeline([
        ('featurize', mapper),
        ('clf', LogisticRegression(random_state=1))
        ])

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

print(abs(pipeline.named_steps['clf'].coef_))
#array([[0.3567311 , 0.3567311 , 0.46215153, 0.10542043, 0.3567311 ,
#        0.46215153, 0.46215153, 0.3567311 , 0.3567311 , 0.3567311 ,
#        0.3567311 , 0.46215153, 0.46215153, 0.3567311 , 0.46215153,
#        0.3567311 , 0.3567311 , 0.3567311 , 0.3567311 , 0.46215153,
#        0.46215153, 0.46215153, 0.3567311 , 0.3567311 ]])

print(len(pipeline.named_steps['clf'].coef_[0]))
#24

b) 访问数据库的最佳方法是什么 coef_ 每个特征的值(“a”、“b”、“c”)?

期望输出:

a: coef_score (float)
b: coef_score (float)
c: coef_score (float)

2 回复 | 直到 7 年前

1

6

James Dellinger 7 年前

虽然您最初的数据帧确实只包含三个特性的列 a , b 和 c ,熊猫 DataFrameMapper() 课堂应用学习 CountVectorizer() 到每个列a、b和c的相应单词体。这导致总共创建了24个特性,然后将它们传递给您的用户 LogisticRegression() 分类器。这就是为什么在尝试访问分类器的 .coef_ 属性

coeff_ A. ,

原始数据帧如下所示:

             a                   b                c   result
2   here we go   hello here we are   this is a test        0
73  here we go   hello here we are   this is a test        0
...

如果我们运行下面一行,我们可以看到由 DataFrameMapper / 计数向量器() mapper 对象:

pipeline.named_steps['featurize'].transformed_names_

['a_another',
 'a_example',
 'a_go',
 'a_here',
 'a_is',
 'a_we',
 'b_are',
 'b_column',
 'b_content',
 'b_every',
 'b_has',
 'b_hello',
 'b_here',
 'b_text',
 'b_we',
 'c_can',
 'c_deal',
 'c_feature',
 'c_how',
 'c_is',
 'c_test',
 'c_this',
 'c_union',
 'c_with']

len(pipeline.named_steps['featurize'].transformed_names_)

24

B /

col_names = list(data.drop(['result'], axis=1).columns.values)
vect_feats = pipeline.named_steps['featurize'].transformed_names_
clf_coef_scores = abs(pipeline.named_steps['clf'].coef_)

def get_avg_coef_scores(col_names, vect_feats, clf_coef_scores):
    scores = {}
    start_pos = 0
    for n in col_names:
        num_vect_feats = len([i for i in vect_feats if i[0] == n])
        end_pos = start_pos + num_vect_feats
        scores[n + '_avg_coef_score'] = np.mean(clf_coef_scores[0][start_pos:end_pos])
        start_pos = end_pos
    return scores

如果我们调用刚刚编写的函数,我们将得到以下输出:

get_avg_coef_scores(col_names, vect_feats, clf_coef_scores)

{'a_avg_coef_score': 0.3499861323284858,
 'b_avg_coef_score': 0.40358462487685853,
 'c_avg_coef_score': 0.3918712435073411}

{key:clf_coef_scores[0][i] for i, key in enumerate(vect_feats)}

{'a_another': 0.3567310993987888,
 'a_example': 0.3567310993987888,
 'a_go': 0.4621515317244458,
 'a_here': 0.10542043232565701,
 'a_is': 0.3567310993987888,
 'a_we': 0.4621515317244458,
 'b_are': 0.4621515317244458,
 'b_column': 0.3567310993987888,
 'b_content': 0.3567310993987888,
 'b_every': 0.3567310993987888,
 'b_has': 0.3567310993987888,
 'b_hello': 0.4621515317244458,
 'b_here': 0.4621515317244458,
 'b_text': 0.3567310993987888,
 'b_we': 0.4621515317244458,
 'c_can': 0.3567310993987888,
 'c_deal': 0.3567310993987888,
 'c_feature': 0.3567310993987888,
 'c_how': 0.3567310993987888,
 'c_is': 0.4621515317244458,
 'c_test': 0.4621515317244458,
 'c_this': 0.4621515317244458,
 'c_union': 0.3567310993987888,
 'c_with': 0.3567310993987888}

2

5

Luca Massaron 7 年前

在恢复安装的 DataFrameMapper Pipeline ,您可以使用 .features CountVectorizer 用于将字符串转换为一个热编码变量的函数。每个CountVectorIzer都有一个 .vocabulary_ 方法,它确切地告诉您字符串所代表的列。

因此,您只需按顺序拉出每个 计数矢量器 数据帧映射器 对于它们中的每一个,按顺序提取表示输入矩阵中每一列的字符串。这将允许您拥有一个精确表示系数标签的序列。

根据您的示例,此代码片段应该满足您的需要,我在上面详细描述了这一点(如果您遇到任何错误,请警告我,我将根据您的反馈进行更正):

# recover the fitted mapper
fitted_mapper = pipeline.named_steps['featurize'] 

mapped_labels = list()
# iterate through the CountVectorizers
for label, fun in fitted_mapper.features:
    # Iterate through the sorted vocabulary
    for level, _ in sorted(fun.vocabulary_.items()):
        mapped_labels.append(label+'_'+level)

# the ordered sequence of vectorized strings
print(mapped_labels)

# pick up the coefficients
coefs = pipeline.named_steps['clf'].coef_[0]

# pair mapped labels and coefs and print them
for label, coef in zip(mapped_labels, coefs):
    print("%s:%0.5f" % (label, coef))