Kaggle - Bike Sharing Prediction

import pylab
import calendar
import numpy as np
import pandas as pd
import seaborn as sn
from scipy import stats
import missingno as msno
from datetime import datetime
import matplotlib.pyplot as plt
import warnings
pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)
%matplotlib inline

In [2]:

dailyData = pd.read_csv("C:/Users/_lc/BikeSharingDemand/train.csv")
dailyData.head(2)

Out[2]:

	datetime	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count
0	2011-01-01 00:00:00	1	0	0	1	9.84	14.395	81	0.0	3	13	16
1	2011-01-01 01:00:00	1	0	0	1	9.02	13.635	80	0.0	8	32	40

 

Out[58]:

	time	day
0	2011-01-01 00:00:00	1
1	2011-01-01 01:00:00	1
2	2011-01-01 02:00:00	1
3	2011-01-01 03:00:00	1
4	2011-01-01 04:00:00	1

In [5]:

datetime - hourly date + timestamp
season - 1 = spring, 2 = summer, 3 = fall, 4 = winter
holiday - whether the day is considered a holiday
workingday - whether the day is neither a weekend nor holiday
weather -
1: Clear, Few clouds, Partly cloudy, Partly cloudy
2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
temp - temperature in Celsius
atemp - "feels like" temperature in Celsius
humidity - relative humidity
windspeed - wind speed
casual - number of non-registered user rentals initiated
registered - number of registered user rentals initiated
count - number of total rentals (Dependent Variable)

dailyData.info()

RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
datetime      10886 non-null object
season        10886 non-null int64
holiday       10886 non-null int64
workingday    10886 non-null int64
weather       10886 non-null int64
temp          10886 non-null float64
atemp         10886 non-null float64
humidity      10886 non-null int64
windspeed     10886 non-null float64
casual        10886 non-null int64
registered    10886 non-null int64
count         10886 non-null int64
dtypes: float64(3), int64(8), object(1)
memory usage: 1020.6+ KB

In [3]:

dailyData['date'] = dailyData['datetime'].map(lambda x: x.split()[0])
dailyData['datetime'] = pd.to_datetime(dailyData.datetime)
dailyData['month'] = dailyData['datetime'].map(lambda x: x.month)
dailyData['day'] = dailyData['datetime'].map(lambda x: x.day)
dailyData['hour'] = dailyData['datetime'].map(lambda x: x.hour)
dailyData['weekday'] = dailyData['datetime'].map(lambda x: x.weekday())
dailyData.head(2)

Out[3]:

	datetime	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count	date	month	day	hour	weekday
0	2011-01-01 00:00:00	1	0	0	1	9.84	14.395	81	0.0	3	13	16	2011-01-01	1	1	0	5
1	2011-01-01 01:00:00	1	0	0	1	9.02	13.635	80	0.0	8	32	40	2011-01-01	1	1	1	5

In [4]:

dailyData["season"] = dailyData.season.map({1: "Spring", 2 : "Summer", 3 : "Fall", 4 :"Winter" })
dailyData["weather"] = dailyData.weather.map({1: " Clear + Few clouds + Partly cloudy + Partly cloudy",\
                                        2 : " Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist ", \
                                        3 : " Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds", \
                                        4 :" Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog " })
dailyData.head(2)

Out[4]:

	datetime	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count	date	month	day	hour	weekday
0	2011-01-01 00:00:00	Spring	0	0	Clear + Few clouds + Partly cloudy + Partly c...	9.84	14.395	81	0.0	3	13	16	2011-01-01	1	1	0	5
1	2011-01-01 01:00:00	Spring	0	0	Clear + Few clouds + Partly cloudy + Partly c...	9.02	13.635	80	0.0	8	32	40	2011-01-01	1	1	1	5

In [5]:

categoryVariableList = ["hour","weekday","month","season","weather","holiday","workingday"]
for var in categoryVariableList:
    dailyData[var] = dailyData[var].astype("category")
dailyData.head(2)

Out[5]:

	datetime	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count	date	month	day	hour	weekday
0	2011-01-01 00:00:00	Spring	0	0	Clear + Few clouds + Partly cloudy + Partly c...	9.84	14.395	81	0.0	3	13	16	2011-01-01	1	1	0	5
1	2011-01-01 01:00:00	Spring	0	0	Clear + Few clouds + Partly cloudy + Partly c...	9.02	13.635	80	0.0	8	32	40	2011-01-01	1	1	1	5

In [6]:

dailyData.drop('datetime', axis=1, inplace=True)
dailyData.head(2)

Out[6]:

	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count	date	month	day	hour	weekday
0	Spring	0	0	Clear + Few clouds + Partly cloudy + Partly c...	9.84	14.395	81	0.0	3	13	16	2011-01-01	1	1	0	5
1	Spring	0	0	Clear + Few clouds + Partly cloudy + Partly c...	9.02	13.635	80	0.0	8	32	40	2011-01-01	1	1	1	5

In [7]:

dataTypeDf = dailyData.dtypes.value_counts().to_frame().reset_index().rename(columns={'index':'variableType', 0:'count'})
sn.set_style('darkgrid')
ax = plt.figure(figsize=(10,4)).add_subplot(111)
dataTypeDf.plot(kind='bar',x="variableType",y="count",ax=ax)
ax.set(xlabel='variableTypeariable Type', ylabel='Count',title="Variables DataType Count")

Out[7]:

[Text(0, 0.5, 'Count'),
 Text(0.5, 0, 'variableTypeariable Type'),
 Text(0.5, 1.0, 'Variables DataType Count')]

In [8]:

msno.matrix(dailyData,figsize=(12,5))

Out[8]:

In [8]:

fig, axes = plt.subplots(nrows=2,ncols=2)
fig.set_size_inches(12, 10)
sn.boxplot(data=dailyData,y="count",orient="v",ax=axes[0][0])
sn.boxplot(data=dailyData,y="count",x="season",orient="v",ax=axes[0][1])
sn.boxplot(data=dailyData,y="count",x="hour",orient="v",ax=axes[1][0])
sn.boxplot(data=dailyData,y="count",x="workingday",orient="v",ax=axes[1][1])

axes[0][0].set(ylabel='Count',title="Box Plot On Count")
axes[0][1].set(xlabel='Season', ylabel='Count',title="Box Plot On Count Across Season")
axes[1][0].set(xlabel='Hour Of The Day', ylabel='Count',title="Box Plot On Count Across Hour Of The Day")
axes[1][1].set(xlabel='Working Day', ylabel='Count',title="Box Plot On Count Across Working Day")

Out[8]:

[Text(0, 0.5, 'Count'),
 Text(0.5, 0, 'Working Day'),
 Text(0.5, 1.0, 'Box Plot On Count Across Working Day')]

In [9]:

dailyDataWithoutOutliers = dailyData[np.abs(dailyData["count"]-dailyData["count"].mean())<=(3*dailyData["count"].std())]
len(dailyDataWithoutOutliers)

Out[9]:

10739

In [10]:

corrMatt = dailyData[["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)

Out[10]:

In [11]:

fig,(ax1,ax2,ax3) = plt.subplots(ncols=3)
fig.set_size_inches(12, 5)
sn.regplot(x="temp", y="count", data=dailyData,ax=ax1)
sn.regplot(x="windspeed", y="count", data=dailyData,ax=ax2)
sn.regplot(x="humidity", y="count", data=dailyData,ax=ax3)

Out[11]:

In [12]:

fig,axes = plt.subplots(ncols=2,nrows=2)
fig.set_size_inches(12, 10)
sn.distplot(dailyData["count"],ax=axes[0][0])
stats.probplot(dailyData["count"], dist='norm', fit=True, plot=axes[0][1])
sn.distplot(np.log(dailyDataWithoutOutliers["count"]),ax=axes[1][0])
stats.probplot(np.log1p(dailyDataWithoutOutliers["count"]), dist='norm', fit=True, plot=axes[1][1])

Out[12]:

((array([-3.82819677, -3.60401975, -3.48099008, ...,  3.48099008,
          3.60401975,  3.82819677]),
  array([0.69314718, 0.69314718, 0.69314718, ..., 6.5971457 , 6.59850903,
         6.5998705 ])),
 (1.3486990121229765, 4.562423868087808, 0.9581176780909608))

In [13]:

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(dailyData.groupby("month")["count"].mean()).reset_index()
sn.barplot(data=monthAggregated,x="month",y="count",ax=ax1)
ax1.set(xlabel='Month', ylabel='Avearage Count',title="Average Count By Month")

hourAggregated = pd.DataFrame(dailyData.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(dailyData.groupby(["hour","weekday"],sort=True)["count"].mean()).reset_index()
sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["count"],hue=hourAggregated["weekday"], 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(dailyData[["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')

Out[13]:

[Text(0, 0.5, 'Users Count'),
 Text(0.5, 0, 'Hour Of The Day'),
 Text(0.5, 1.0, 'Average Users Count By Hour Of The Day Across User Type'),
 None]

In [14]:

dataTrain = pd.read_csv("C:/Users/_lc/BikeSharingDemand/train.csv")
dataTest = pd.read_csv("C:/Users/_lc/BikeSharingDemand/test.csv")
dataTest.head(2)

Out[14]:

	datetime	season	holiday	workingday	weather	temp	atemp	humidity	windspeed
0	2011-01-20 00:00:00	1	0	1	1	10.66	11.365	56	26.0027
1	2011-01-20 01:00:00	1	0	1	1	10.66	13.635	56	0.0000

In [35]:

data = dataTrain.append(dataTest)
data.reset_index(inplace=True)
data.drop('index',inplace=True,axis=1)
data.head()

Out[35]:

	atemp	casual	count	datetime	holiday	humidity	registered	season	temp	weather	windspeed	workingday
0	14.395	3.0	16.0	2011-01-01 00:00:00	0	81	13.0	1	9.84	1	0.0	0
1	13.635	8.0	40.0	2011-01-01 01:00:00	0	80	32.0	1	9.02	1	0.0	0
2	13.635	5.0	32.0	2011-01-01 02:00:00	0	80	27.0	1	9.02	1	0.0	0
3	14.395	3.0	13.0	2011-01-01 03:00:00	0	75	10.0	1	9.84	1	0.0	0
4	14.395	0.0	1.0	2011-01-01 04:00:00	0	75	1.0	1	9.84	1	0.0	0

In [36]:

data["datetime"] = pd.to_datetime(data.datetime)
data["year"] = data.datetime.map(lambda x : x.year)
data["month"] = data.datetime.apply(lambda x: x.month)
data["day"] = data.datetime.apply(lambda x : x.day)
data["hour"] = data.datetime.apply(lambda x : x.hour)
data['weekday'] = data.datetime.map(lambda x: x.weekday())
data.head()

Out[36]:

	atemp	casual	count	datetime	holiday	humidity	registered	season	temp	weather	windspeed	workingday	year	month	day	hour	weekday
0	14.395	3.0	16.0	2011-01-01 00:00:00	0	81	13.0	1	9.84	1	0.0	0	2011	1	1	0	5
1	13.635	8.0	40.0	2011-01-01 01:00:00	0	80	32.0	1	9.02	1	0.0	0	2011	1	1	1	5
2	13.635	5.0	32.0	2011-01-01 02:00:00	0	80	27.0	1	9.02	1	0.0	0	2011	1	1	2	5
3	14.395	3.0	13.0	2011-01-01 03:00:00	0	75	10.0	1	9.84	1	0.0	0	2011	1	1	3	5
4	14.395	0.0	1.0	2011-01-01 04:00:00	0	75	1.0	1	9.84	1	0.0	0	2011	1	1	4	5

In [37]:

# ---------预测风速为零的---------
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)
data.head()

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)

Out[37]:

	atemp	casual	count	datetime	holiday	humidity	registered	season	temp	weather	windspeed	workingday	year	month	day	hour	weekday
0	12.880	0.0	1.0	2011-01-01 05:00:00	0	75	1.0	1	9.84	2	6.0032	0	2011	1	1	5	5
1	19.695	12.0	36.0	2011-01-01 10:00:00	0	76	24.0	1	15.58	1	16.9979	0	2011	1	1	10	5
2	16.665	26.0	56.0	2011-01-01 11:00:00	0	81	30.0	1	14.76	1	19.0012	0	2011	1	1	11	5
3	21.210	29.0	84.0	2011-01-01 12:00:00	0	77	55.0	1	17.22	1	19.0012	0	2011	1	1	12	5
4	22.725	47.0	94.0	2011-01-01 13:00:00	0	72	47.0	1	18.86	2	19.9995	0	2011	1	1	13	5

In [39]:

categoricalFeatureNames = ["season","holiday","workingday","weather","weekday","month","year","hour"]
numericalFeatureNames = ["temp","humidity","windspeed","atemp"]
dropFeatures = ['casual',"count","datetime","day","registered"]
for var in categoricalFeatureNames:
    data[var] = data[var].astype("category")

In [40]:

dataTrain = data[pd.notnull(data['count'])].sort_values(by=["datetime"])
dataTest = data[~pd.notnull(data['count'])].sort_values(by=["datetime"])
datetimecol = dataTest["datetime"]
yLabels = dataTrain["count"]
yLablesRegistered = dataTrain["registered"]
yLablesCasual = dataTrain["casual"]

dataTrain  = dataTrain.drop(dropFeatures,axis=1)
dataTest  = dataTest.drop(dropFeatures,axis=1)

In [47]:

# 对数均方误差
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))

In [48]:

from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
import warnings
pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)

lModel = LinearRegression()

yLabelsLog = np.log1p(yLabels)
lModel.fit(X = dataTrain,y = yLabelsLog)

preds = lModel.predict(X= dataTrain)
print ("RMSLE Value For Linear Regression: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

RMSLE Value For Linear Regression:  0.9779688131592883

In [53]:

ridge_m_ = Ridge()
ridge_params_ = { 'max_iter':[3000],'alpha':[0.1, 1, 2, 3, 4, 10, 30,100,200,300,400,800,900,1000]}
rmsle_scorer = metrics.make_scorer(rmsle, greater_is_better=False)
grid_ridge_m = GridSearchCV( ridge_m_,
                          ridge_params_,
                          scoring = rmsle_scorer,
                          cv=5)
yLabelsLog = np.log1p(yLabels)
grid_ridge_m.fit( dataTrain, yLabelsLog )
preds = grid_ridge_m.predict(X= dataTrain)
print (grid_ridge_m.best_params_)
print ("RMSLE Value For Ridge Regression: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

fig,ax= plt.subplots()
fig.set_size_inches(12,5)
df = pd.DataFrame(grid_ridge_m.cv_results_)
df["alpha"] = df["params"].apply(lambda x:x["alpha"])
df["rmsle"] = df["mean_test_score"].apply(lambda x:-x)
sn.pointplot(data=df,x="alpha",y="rmsle",ax=ax)

{'alpha': 0.1, 'max_iter': 3000}
RMSLE Value For Ridge Regression:  0.9779687981095982

Out[53]:

In [55]:

lasso_m_ = Lasso()

alpha  = 1/np.array([0.1, 1, 2, 3, 4, 10, 30,100,200,300,400,800,900,1000])
lasso_params_ = { 'max_iter':[3000],'alpha':alpha}

grid_lasso_m = GridSearchCV( lasso_m_,lasso_params_,scoring = rmsle_scorer,cv=5)
yLabelsLog = np.log1p(yLabels)
grid_lasso_m.fit( dataTrain, yLabelsLog )
preds = grid_lasso_m.predict(X= dataTrain)
print (grid_lasso_m.best_params_)
print ("RMSLE Value For Lasso Regression: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

fig,ax= plt.subplots()
fig.set_size_inches(12,5)
df = pd.DataFrame(grid_lasso_m.cv_results_)
df["alpha"] = df["params"].apply(lambda x:x["alpha"])
df["rmsle"] = df["mean_test_score"].apply(lambda x:-x)
sn.pointplot(data=df,x="alpha",y="rmsle",ax=ax)

{'alpha': 0.005, 'max_iter': 3000}
RMSLE Value For Lasso Regression:  0.9781068303163186

Out[55]:

In [56]:

from sklearn.ensemble import GradientBoostingRegressor
gbm = GradientBoostingRegressor(n_estimators=4000,alpha=0.01); ### Test 0.41
yLabelsLog = np.log1p(yLabels)
gbm.fit(dataTrain,yLabelsLog)
preds = gbm.predict(X= dataTrain)
print ("RMSLE Value For Gradient Boost: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

predsTest = gbm.predict(X= dataTest)
fig,(ax1,ax2)= plt.subplots(ncols=2)
fig.set_size_inches(12,5)
sn.distplot(yLabels,ax=ax1,bins=50)
sn.distplot(np.exp(predsTest),ax=ax2,bins=50)

RMSLE Value For Gradient Boost:  0.1896091619650034

Out[56]: