机器学习：基于逻辑回归和高斯贝叶斯对人口普查数据集的分类与预测

• 机器学习：基于逻辑回归和高斯贝叶斯对人口普查数据集的分类与预测

1、数据说明

2、数据探索

导入模块

from plotly import __version__


import plotly.offline as offline
from plotly.offline import init_notebook_mode, iplot

init_notebook_mode(connected=true)
from plotly.graph_objs import *
import colorlover as cl
from plotly import tools
import pandas as pd
import numpy as np
colors = ['#e43620', '#f16d30','#d99a6c','#fed976', '#b3cb95', '#41bfb3','#229bac', 
'#256894', '#fed936', '#f36d30', '#b3cb25', '#f14d30','#49bfb5','#252bac',]

导入数据

data_train = pd.read_csv(r'/home/mw/income_census_train.csv')
data_test = pd.read_csv(r'/home/mw/income_census_test.csv')

数据集实例数与特征数，缺失值查看

print('原训练集shape:',data_train.shape)
print('原测试集shape:',data_test.shape)
print('原数据集实例数：',data_train.shape[0]+data_test.shape[0])

print('缺失值检测\n原训练集：',data_train.isnull().values.sum(), ',原测试集：',data_test.isnull().values.sum())

print('所有字段名：',data_train.columns)

各个特征的描述与相关总结

data_train.describe()

展示所有类型特征

data_train.describe(include=['o'])

查看8个categorial数据类型的属性唯一值

print('workclass: ',len(data_train['workclass'].unique()),'个\n',data_train['workclass'].unique())
print('education: ',len(data_train['education'].unique()),'个\n',data_train['education'].unique())
print('marital_status: ',len(data_train['marital_status'].unique()),'个\n',data_train['marital_status'].unique())
print('occupation: ',len(data_train['occupation'].unique()),'个\n',data_train['occupation'].unique())
print('relationship: ',len(data_train['relationship'].unique()),'个\n',data_train['relationship'].unique())
print('race: ',len(data_train['race'].unique()),'个\n',data_train['race'].unique())
print('gender: ',len(data_train['gender'].unique()),'个\n',data_train['gender'].unique())
print('native_country: ',len(data_train['native_country'].unique()),'个\n',data_train['native_country'].unique())

data = data_train.drop(['id'],axis = 1)
data.head()

3、数据预处理

将oject数据转化为int类型

for feature in data.columns:
    if data[feature].dtype == 'object':
        data[feature] = pd.categorical(data[feature]).codes # codes	这个分类的分类代码
data.head()

data.info()

4、建立模型

选取特征数据与类别数据

from sklearn.preprocessing import standardscaler
from sklearn.linear_model import logisticregression
from sklearn.model_selection import train_test_split
x_df = data.iloc[:,data.columns != 'income_bracket']
y_df = data.iloc[:,data.columns == 'income_bracket']
x = np.array(x_df)
y = np.array(y_df)

标准化数据

scaler = standardscaler()
x = scaler.fit_transform(x)

5、特征选取（feature selection）

from sklearn.tree import decisiontreeclassifier
# from sklearn.decomposition import pca

# fit an extra tree model to the data
tree = decisiontreeclassifier(random_state=0)
tree.fit(x, y)

# 显示每个属性的相对重要性得分
relval = tree.feature_importances_

trace = bar(x = x_df.columns.tolist(), y = relval, text = [round(i,2) for i in relval], textposition= "outside", marker = dict(color = colors))
iplot(figure(data = [trace], layout = layout(title="特征重要性", width = 800, height = 400, yaxis = dict(range = [0,0.25]))))

from sklearn.feature_selection import rfe

# 使用决策树作为模型
lr = decisiontreeclassifier()
names = x_df.columns.tolist()

# 将所有特征排序
selector = rfe(lr, n_features_to_select = 10)
selector.fit(x,y.ravel())

print("排序后的特征：",sorted(zip(map(lambda x:round(x,4), selector.ranking_), names)))

# 得到新的dataframe
x_df_new = x_df.iloc[:, selector.get_support(indices = false)]
x_df_new.columns

x_new = scaler.fit_transform(np.array(x_df_new)) # 数组化 + 标准化
x_train, x_test, y_train, y_test = train_test_split(x_new,y) # 切分

from sklearn.linear_model import logisticregression
from sklearn.naive_bayes import gaussiannb
from sklearn import metrics
from sklearn.metrics import confusion_matrix,roc_curve, auc, recall_score, classification_report

import matplotlib.pyplot as plt

import itertools

# 绘制混淆矩阵
def plot_confusion_matrix(cm, classes,
                          title='confusion matrix',
                          cmap=plt.cm.blues):
    """
    this function prints and plots the confusion matrix.
    normalization can be applied by setting `normalize=true`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=0)
    plt.yticks(tick_marks, classes)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('true label')
    plt.xlabel('predicted label')

6、逻辑回归（logistic regression）

# instance
lr = logisticregression()
# fit
lr_clf = lr.fit(x_train,y_train.ravel())
# predict
y_pred = lr_clf.predict(x_test)

print('logisticregression %s' % metrics.accuracy_score(y_test, y_pred))

cm = confusion_matrix(y_test, y_pred)

class_names = [0,1]
plt.figure()
plot_confusion_matrix(cm , classes=class_names, title='confusion matrix')
plt.show()

7、高斯贝叶斯（gaussian naive bayes）

gnb = gaussiannb()
gnb_clf = gnb.fit(x_train, y_train.ravel())
y_pred2 = gnb_clf.predict(x_test)

print('gaussiannb %s' % metrics.accuracy_score(y_test, y_pred2))

cm = confusion_matrix(y_test, y_pred2)

class_names = [0,1]
plt.figure()
plot_confusion_matrix(cm , classes=class_names, title='confusion matrix')
plt.show()