欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

scikit-learn 逻辑回归实现乳腺癌检测

程序员文章站 2022-07-14 13:03:24
...

随书代码,阅读笔记

  • 载入数据
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

# 载入数据
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
print('data shape: {0}; no. positive: {1}; no. negative: {2}'.format(
    X.shape, y[y==1].shape[0], y[y==0].shape[0]))
print(cancer.data[0])

#准备测试集和训练集
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

一共有569个样本,每个样本有30个特征,其中357个阳性,212个阴性(y=0)

  • 模型训练
# 模型训练
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)

train_score = model.score(X_train, y_train)
test_score = model.score(X_test, y_test)
print('train score: {train_score:.6f}; test score: {test_score:.6f}'.format(
    train_score=train_score, test_score=test_score))

#output: train score: 0.953846; test score: 0.956140
  • 预测
# 样本预测
y_pred = model.predict(X_test)
print('matchs: {0}/{1}'.format(np.equal(y_pred, y_test).shape[0], y_test.shape[0]))

# 预测概率:找出低于 90% 概率的样本个数
y_pred_proba = model.predict_proba(X_test)
print('sample of predict probability: {0}'.format(y_pred_proba[0]))
y_pred_proba_0 = y_pred_proba[:, 0] > 0.1 
result = y_pred_proba[y_pred_proba_0]
y_pred_proba_1 = result[:, 1] > 0.1
print(result[y_pred_proba_1])

模型优化

import time
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline

# 增加多项式预处理
def polynomial_model(degree=1, **kwarg):
    polynomial_features = PolynomialFeatures(degree=degree,
                                             include_bias=False)
    logistic_regression = LogisticRegression(**kwarg)
    pipeline = Pipeline([("polynomial_features", polynomial_features),
                         ("logistic_regression", logistic_regression)])
    return pipeline

model = polynomial_model(degree=2, penalty='l1')

start = time.clock()
model.fit(X_train, y_train)

train_score = model.score(X_train, y_train)
cv_score = model.score(X_test, y_test)
print('elaspe: {0:.6f}; train_score: {1:0.6f}; cv_score: {2:.6f}'.format(
    time.clock()-start, train_score, cv_score))

#output : train_score: 1.000000; cv_score: 0.973684

新特征

根据原始的30个特征,使用多项式组合出来495个特征,其中97个是有用的。

logistic_regression = model.named_steps['logistic_regression']
print('model parameters shape: {0}; count of non-zero element: {1}'.format(
    logistic_regression.coef_.shape, 
    np.count_nonzero(logistic_regression.coef_)))

#output:model parameters shape: (1, 495); count of non-zero element: 97

学习率曲线

from common.utils import plot_learning_curve
from sklearn.model_selection import ShuffleSplit

cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
title = 'Learning Curves (degree={0}, penalty={1})'
degrees = [1, 2]
penalty = 'l1'

start = time.clock()
plt.figure(figsize=(12, 4), dpi=144)
for i in range(len(degrees)):
    plt.subplot(1, len(degrees), i + 1)
    plot_learning_curve(plt, polynomial_model(degree=degrees[i], penalty=penalty), 
                        title.format(degrees[i], penalty), X, y, ylim=(0.8, 1.01), cv=cv)

print('elaspe: {0:.6f}'.format(time.clock()-start))

scikit-learn 逻辑回归实现乳腺癌检测


penalty = 'l2'

start = time.clock()
plt.figure(figsize=(12, 4), dpi=144)
for i in range(len(degrees)):
    plt.subplot(1, len(degrees), i + 1)
    plot_learning_curve(plt, polynomial_model(degree=degrees[i], penalty=penalty, solver='lbfgs'), 
                        title.format(degrees[i], penalty), X, y, ylim=(0.8, 1.01), cv=cv)

print('elaspe: {0:.6f}'.format(time.clock()-start))

scikit-learn 逻辑回归实现乳腺癌检测

扩展阅读