线性回归 随机森林 回归预测1：机器学习处理共享单车数据集

目录

1 介绍2 导入工具箱3 数据探索3.1 分析数据相关系数3.2 分析时间和数量的关系

4 数据预处理4.1 日期拆分，将datetime拆分为年月日等4.2 windspeed列有0值，使用随机森林对0值进行填充4.3 类别变量处理4.4 分割出类别变量4.5 删除不需要的变量

5 构建目标函数6 预测

使用线性回归、随机森林处理共享单车预测问题，数据集中包含了缺失值，我们使用随机森林先对缺失值进行预测，并且该数据集是和时间序列相关，但是我们将时间特征拆分为多个特征，将问题转换为了回归预测问题。

1 介绍

kaggle数据集介绍：https://www.kaggle.com/competitions/bike-sharing-demand/code?competitionId=3948&sortBy=voteCount 特征：

datetime：时间，我们后面会对其进行拆分，拆分为：年月日小时等；season：季节holiday：是否是假期workingday：是否是工作日weather :天气情况temp ：温度atemp:体感温度humidity：相对湿度windspeed ：风速count -总出租数量（预测值）

2 导入工具箱

import numpy as np

import pandas as pd

from datetime import datetime

import warnings

import calendar

pd.options.mode.chained_assignment = None

warnings.filterwarnings("ignore", category=DeprecationWarning)

import matplotlib.pyplot as plt

import seaborn as sn

3 数据探索

3.1 分析数据相关系数

data = pd.read_csv("train.csv")

# 相关性分析

corrMatt = data[["temp", "atemp", "casual", "registered", "humidity", "windspeed", "count"]].corr()

mask = np.array(corrMatt)

mask[np.tril_indices_from(mask)] = False

fig, ax = plt.subplots()

fig.set_size_inches(20, 10)

sn.heatmap(corrMatt, mask=mask, vmax=.8, square=True, annot=True)

plt.show()

3.2 分析时间和数量的关系

data["date"] = data.datetime.apply(lambda x : x.split()[0])

data["hour"] = data.datetime.apply(lambda x : x.split()[1].split(":")[0])

data["weekday"] = data.date.apply(lambda dateString : calendar.day_name[datetime.strptime(dateString,"%Y-%m-%d").weekday()])

data["month"] = data.date.apply(lambda dateString : calendar.month_name[datetime.strptime(dateString,"%Y-%m-%d").month])

data["season"] = data.season.map({1: "Spring", 2 : "Summer", 3 : "Fall", 4 :"Winter" })

fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=4)

fig.set_size_inches(12, 20)

sortOrder = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October",

"November", "December"]

hueOrder = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"]

monthAggregated = pd.DataFrame(data.groupby("month")["count"].mean()).reset_index()

monthSorted = monthAggregated.sort_values(by="count", ascending=False)

sn.barplot(data=monthSorted, x="month", y="count", ax=ax1, order=sortOrder)

ax1.set(xlabel='Month', ylabel='Avearage Count', title="Average Count By Month")

hourAggregated = pd.DataFrame(data.groupby(["hour", "season"], sort=True)["count"].mean()).reset_index()

sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["count"], hue=hourAggregated["season"], data=hourAggregated,

join=True, ax=ax2)

ax2.set(xlabel='Hour Of The Day', ylabel='Users Count', title="Average Users Count By Hour Of The Day Across Season",

label='big')

hourAggregated = pd.DataFrame(data.groupby(["hour", "weekday"], sort=True)["count"].mean()).reset_index()

sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["count"], hue=hourAggregated["weekday"], hue_order=hueOrder,

data=hourAggregated, join=True, ax=ax3)

ax3.set(xlabel='Hour Of The Day', ylabel='Users Count', title="Average Users Count By Hour Of The Day Across Weekdays",

label='big')

hourTransformed = pd.melt(data[["hour", "casual", "registered"]], id_vars=['hour'], value_vars=['casual', 'registered'])

hourAggregated = pd.DataFrame(hourTransformed.groupby(["hour", "variable"], sort=True)["value"].mean()).reset_index()

sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["value"], hue=hourAggregated["variable"],

hue_order=["casual", "registered"], data=hourAggregated, join=True, ax=ax4)

ax4.set(xlabel='Hour Of The Day', ylabel='Users Count', title="Average Users Count By Hour Of The Day Across User Type",

label='big')

plt.show()

4 数据预处理

4.1 日期拆分，将datetime拆分为年月日等

data = pd.read_csv("train.csv")

# 日期数据拆分

data["date"] = data.datetime.apply(lambda x: x.split()[0])

data["hour"] = data.datetime.apply(lambda x: x.split()[1].split(":")[0]).astype("int")

data["year"] = data.datetime.apply(lambda x: x.split()[0].split("-")[0])

data["weekday"] = data.date.apply(lambda dateString: datetime.strptime(dateString, "%Y-%m-%d").weekday())

data["month"] = data.date.apply(lambda dateString: datetime.strptime(dateString, "%Y-%m-%d").month)

4.2 windspeed列有0值，使用随机森林对0值进行填充

print(len(data[data["windspeed"] == 0]))

print(len(data[data["windspeed"] != 0]))

# 2 填充数据，Windspeed有好多0列，我们使用随机森林进行预测，将预测值进行填充

from sklearn.ensemble import RandomForestRegressor

dataWind0 = data[data["windspeed"] == 0]

dataWindNot0 = data[data["windspeed"] != 0]

rfModel_wind = RandomForestRegressor()

windColumns = ["season", "weather", "humidity", "month", "temp", "year", "atemp"]

rfModel_wind.fit(dataWindNot0[windColumns], dataWindNot0["windspeed"])

wind0Values = rfModel_wind.predict(X=dataWind0[windColumns])

dataWind0["windspeed"] = wind0Values

data = dataWindNot0.append(dataWind0)

data.reset_index(inplace=True)

data.drop('index', inplace=True, axis=1)

print(len(data[data["windspeed"] == 0]))

print(len(data[data["windspeed"] != 0]))

原来0值的数量：1313

现在0值的数量：0

4.3 类别变量处理

categoricalFeatureNames = ["season", "holiday", "workingday", "weather", "weekday", "month", "year", "hour"]

for var in categoricalFeatureNames:

data[var] = data[var].astype("category")

4.4 分割出类别变量

yLabels = data["count"]

4.5 删除不需要的变量

dropFeatures = ['casual', "count", "datetime", "date", "registered"]

data = data.drop(dropFeatures, axis=1)

5 构建目标函数

def rmsle(y, y_, convertExp=True):

if convertExp:

y = np.exp(y),

y_ = np.exp(y_)

log1 = np.nan_to_num(np.array([np.log(v + 1) for v in y]))

log2 = np.nan_to_num(np.array([np.log(v + 1) for v in y_]))

calc = (log1 - log2) ** 2

return np.sqrt(np.mean(calc))

6 预测

from sklearn.linear_model import LinearRegression

lModel = LinearRegression()

yLabelsLog = np.log1p(yLabels)

lModel.fit(X=data, y=yLabelsLog)

preds = lModel.predict(X=data)

print("RMSLE Value For Linear Regression: ", rmsle(np.exp(yLabelsLog), np.exp(preds), False))

from sklearn.ensemble import RandomForestRegressor

rfModel = RandomForestRegressor(n_estimators=100)

yLabelsLog = np.log1p(yLabels)

rfModel.fit(data, yLabelsLog)

preds = rfModel.predict(X=data)

print("RMSLE Value For Random Forest: ", rmsle(np.exp(yLabelsLog), np.exp(preds), False))

RMSLE Value For Linear Regression: 0.9779011300976505

RMSLE Value For Random Forest: 0.1025119847901103

通过结果我们看出随机森林效果很好。

待完善：超参数调优。

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