本文的大部分代码都是参考了这篇文章 数据集来自Kaggle官网的Titanic项目，很容易找到的，但是需要注册一个Kaggle账号才可以下载哦。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestRegressor
import sklearn.preprocessing as preprocessing
import warnings
from sklearn import model_selection
from sklearn.ensemble import BaggingRegressor
warnings.filterwarnings('ignore')
data_train = pd.read_csv('train.csv')
# 打印数据所有的列标签
print(data_train.columns)
# 了解数据信息
print(data_train.info())
# 统计数据中每个特征的缺失值个数
print(data_train.isnull().sum())
# 可视化，初步探索数据
plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置图形里面中文为黑体
fig = plt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
plt.subplot2grid((3, 2), (0, 0)) # 在一张大图里分列几个小图
data_train.Survived.value_counts().plot(kind='bar') # 柱状图
plt.title(u"获救情况 (1为获救)") # 标题
plt.ylabel(u"人数")
plt.subplot2grid((3, 2), (0, 1))
data_train.Pclass.value_counts().plot(kind="bar")
plt.ylabel(u"人数")
plt.title(u"乘客等级分布")
plt.subplot2grid((3,2), (1,0))
plt.scatter(data_train.Survived, data_train.Age)
plt.ylabel(u"年龄") # 设定纵坐标名称
plt.grid(b=True, which='major', axis='y')
plt.title(u"按年龄看获救分布 (1为获救)")
plt.subplot2grid((3, 2), (1, 1), colspan=2)
data_train.Age[data_train.Pclass == 1].plot(kind='kde')
data_train.Age[data_train.Pclass == 2].plot(kind='kde')
data_train.Age[data_train.Pclass == 3].plot(kind='kde')
plt.xlabel(u"年龄") # plots an axis lable
plt.ylabel(u"密度")
plt.title(u"各等级的乘客年龄分布")
plt.legend((u'头等舱', u'2等舱', u'3等舱'), loc='best') # sets our legend for our graph.
plt.subplot2grid((3, 2), (2, 0))
data_train.Embarked.value_counts().plot(kind='bar')
plt.title(u"各登船口岸上船人数")
plt.ylabel(u"人数")
# 自动调整子图间距
plt.tight_layout()
# plt.show()
# 看看各乘客等级的获救情况
fig.set(alpha=0.2) # 设定图表颜色alpha参数
Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()
df = pd.DataFrame({u'获救': Survived_1, u'未获救': Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各乘客等级的获救情况")
plt.xlabel(u"乘客等级")
plt.ylabel(u"人数")
# plt.show()
# 然后我们再来看看各种舱级别情况下各性别的获救情况
fig = plt.figure()
fig.set(alpha=0.65) # 设置图像透明度，无所谓
plt.suptitle("根据舱等级和性别的获救情况")
ax1 = plt.subplot(141)
data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(
kind='bar', label="female highclass", color='#FA2479')
ax1.set_xticklabels([u"获救", u"未获救"], rotation=0)
ax1.legend([u"女性/高级舱"], loc='best')
ax2 = plt.subplot(142, sharey=ax1)
data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(
kind='bar', label='female, low class', color='pink')
ax2.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend([u"女性/低级舱"], loc='best')
ax3 = plt.subplot(143, sharey=ax1)
data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts().plot(
kind='bar', label='male, high class',color='lightblue')
ax3.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend([u"男性/高级舱"], loc='best')
ax4 = plt.subplot(144, sharey=ax1)
data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts().plot(
kind='bar', label='male low class', color='steelblue')
ax4.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend([u"男性/低级舱"], loc='best')
# 自动调整子图间距
plt.tight_layout()
# plt.show()
# 各登船港口的获救情况
fig.set(alpha=0.2) # 设定图表颜色alpha参数
Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()
df = pd.DataFrame({u'获救': Survived_1, u'未获救': Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各登录港口乘客的获救情况")
plt.xlabel(u"登录港口")
plt.ylabel(u"人数")
# plt.show()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
Survived_cabin = data_train.Survived[pd.notnull(data_train.Cabin)].value_counts()
Survived_nocabin = data_train.Survived[pd.isnull(data_train.Cabin)].value_counts()
df = pd.DataFrame({u'有': Survived_cabin, u'无': Survived_nocabin}).transpose()
df.plot(kind='bar', stacked=True)
plt.title(u"按Cabin有无看获救情况")
plt.xlabel(u"Cabin有无")
plt.ylabel(u"人数")
plt.show()
# 使用随机森林来拟合缺失的年龄数据
def set_missing_ages(df):
# 把已有的数值型特征取出来丢进Random Forest Regressor中
age_df = df[['Age', 'Fare', 'Parch', 'SibSp', 'Pclass']]
# 乘客分成已知年龄和未知年龄两部分
known_age = age_df[age_df.Age.notnull()].values
unknown_age = age_df[age_df.Age.isnull()].values
# y即目标年龄
y = known_age[:, 0]
# X即特征属性值
X = known_age[:, 1:]
# fit到RandomForestRegressor之中
rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1) # 设置2000棵树进行训练
rfr.fit(X, y)
# 用得到的模型进行未知年龄结果预测
predictedAges = rfr.predict(unknown_age[:, 1::])
# 用得到的预测结果填补原缺失数据
df.loc[(df.Age.isnull()), 'Age'] = predictedAges
return df, rfr
def set_Cabin_type(df):
df.loc[(df.Cabin.notnull()), 'Cabin'] = "Yes"
df.loc[(df.Cabin.isnull()), 'Cabin'] = "No"
return df
data_train, rfr = set_missing_ages(data_train)
data_train = set_Cabin_type(data_train)
# print(data_train.isnull().sum())
dummies_Cabin = pd.get_dummies(data_train['Cabin'], prefix= 'Cabin')
dummies_Embarked = pd.get_dummies(data_train['Embarked'], prefix= 'Embarked')
dummies_Sex = pd.get_dummies(data_train['Sex'], prefix= 'Sex')
dummies_Pclass = pd.get_dummies(data_train['Pclass'], prefix= 'Pclass')
df = pd.concat([data_train, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)
df.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)
scaler = preprocessing.StandardScaler()
df['Age_scaled'] = scaler.fit_transform(np.array(df['Age']).reshape(-1, 1))
df['Fare_scaled'] = scaler.fit_transform(np.array(df['Fare']).reshape(-1, 1))
print('df->columns:', df.columns)
# 建立逻辑回归模型
# 用正则取出我们要的属性值
train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
train_np = train_df.values
# y即Survival结果
y = train_np[:, 0]
# X即特征属性值
X = train_np[:, 1:]
# fit到RandomForestRegressor之中
clf = LogisticRegression(C=1.0, tol=1e-6)
clf.fit(X, y)
# 读取测试数据集，并进行处理
data_test = pd.read_csv("test.csv")
data_test.loc[ (data_test.Fare.isnull()), 'Fare'] = 0
# 接着我们对test_data做和train_data中一致的特征变换
# 首先用同样的RandomForestRegressor模型填上丢失的年龄
tmp_df = data_test[['Age', 'Fare', 'Parch', 'SibSp', 'Pclass']]
null_age = tmp_df[data_test.Age.isnull()].values
# 根据特征属性X预测年龄并补上
X = null_age[:, 1:]
predictedAges = rfr.predict(X)
data_test.loc[(data_test.Age.isnull()), 'Age' ] = predictedAges
data_test = set_Cabin_type(data_test)
dummies_Cabin = pd.get_dummies(data_test['Cabin'], prefix= 'Cabin')
dummies_Embarked = pd.get_dummies(data_test['Embarked'], prefix= 'Embarked')
dummies_Sex = pd.get_dummies(data_test['Sex'], prefix= 'Sex')
dummies_Pclass = pd.get_dummies(data_test['Pclass'], prefix= 'Pclass')
df_test = pd.concat([data_test, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)
df_test.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)
df_test['Age_scaled'] = scaler.fit_transform(np.array(df_test['Age']).reshape(-1, 1))
df_test['Fare_scaled'] = scaler.fit_transform(np.array(df_test['Fare']).reshape(-1, 1))
print('df_test->columns:', df_test.columns)
test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
predictions = clf.predict(test)
# predictions = clf.predict(data_test)
print('Prediction:\n', predictions)
# 简单看看打分情况
clf = LogisticRegression(C=1.0, tol=1e-6)
all_data = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
X = all_data.values[:, 1:]
y = all_data.values[:, 0]
print(model_selection.cross_val_score(clf, X, y, cv=5))
# 分割数据，按照 训练数据:cv数据 = 7:3的比例
split_train, split_cv = model_selection.train_test_split(df, test_size=0.3, random_state=0)
train_df = split_train.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
# 生成模型
clf = LogisticRegression(C=1.0, tol=1e-6)
clf.fit(train_df.values[:, 1:], train_df.values[:, 0])
# 对cross validation数据进行预测
cv_df = split_cv.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')
predictions = clf.predict(cv_df.values[:, 1:])
origin_data_train = pd.read_csv("train.csv")
bad_cases = origin_data_train.loc[origin_data_train['PassengerId'].isin(split_cv[predictions != cv_df.values[:, 0]]['PassengerId'].values)]
print(bad_cases)
# 用sklearn的learning_curve得到training_score和cv_score，使用matplotlib画出learning curve
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1,
train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):
"""
画出data在某模型上的learning curve.
参数解释
----------
estimator : 你用的分类器。
title : 表格的标题。
X : 输入的feature，numpy类型
y : 输入的target vector
ylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点
cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)
n_jobs : 并行的的任务数(默认1)
"""
train_sizes, train_scores, test_scores = model_selection.learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
if plot:
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel(u"训练样本数")
plt.ylabel(u"得分")
plt.gca().invert_yaxis()
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,
alpha=0.1, color="b")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,
alpha=0.1, color="r")
plt.plot(train_sizes, train_scores_mean, 'o-', color="b", label=u"训练集上得分")
plt.plot(train_sizes, test_scores_mean, 'o-', color="r", label=u"交叉验证集上得分")
plt.legend(loc="best")
plt.draw()
plt.show()
plt.gca().invert_yaxis()
midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2
diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1])
return midpoint, diff
plot_learning_curve(clf, u"学习曲线", X, y)
# 模型融合
train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*|Mother|Child|Family|Title')
train_np = train_df.values
# y即Survival结果
y = train_np[:, 0]
# X即特征属性值
X = train_np[:, 1:]
# fit到BaggingRegressor之中
clf = LogisticRegression(C=1.0, tol=1e-6)
bagging_clf = BaggingRegressor(clf, n_estimators=20, max_samples=0.8, max_features=1.0, bootstrap=True, bootstrap_features=False, n_jobs=-1)
bagging_clf.fit(X, y)
test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*|Mother|Child|Family|Title')
predictions = bagging_clf.predict(test)
result = pd.DataFrame({'PassengerId': data_test['PassengerId'].values, 'Survived':predictions.astype(np.int32)})
# 将预测生存结果加入测试数据集中并输出成csv文件
result.to_csv('resut.csv', index=False) # 行索引不写入文件中数据可视化
Learning Curve
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
