kaggle入门-Bike Sharing Demand自行车需求预测

接触机器学习断断续续有一年了，一直没有真正做点什么事，今天终于开始想刷刷kaggle的问题了，慢慢熟悉和理解机器学习以及深度学习。

今天第一题是一个比较基础的Bike Sharing Demand题，根据日期时间、天气、温度等特征，预测自行车的租借量。训练与测试数据集大概长这样：

// train
datetime,season,holiday,workingday,weather,temp,atemp,humidity,windspeed,casual,registered,count
2011-01-01 00:00:00,1,0,0,1,9.84,14.395,81,0,3,13,16
2011-01-01 01:00:00,1,0,0,1,9.02,13.635,80,0,8,32,40// test
datetime,season,holiday,workingday,weather,temp,atemp,humidity,windspeed
2011-01-20 00:00:00,1,0,1,1,10.66,11.365,56,26.0027
2011-01-20 01:00:00,1,0,1,1,10.66,13.635,56,

观察上面的数据，我们可以发现：租借量等于注册用户租借量加上未注册用户租借量，即casual + registered。评价指标是loss函数RMSLE (Root Mean Squared Logarithmic Error)：

其中，

为预测的租借量，为实际的租借量，为样本数。实际上，RMSLE就是一个误差函数。

以下是对数据的描述：

Data Fields

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

整个过程：

# coding: utf-8# In[54]:import numpy as np
import pandas as pd
get_ipython().magic('matplotlib inline')from sklearn import cross_validation
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor# In[4]:df_origin = pd.read_csv("train.csv",sep=",")
df_origin.head()# ### 查看完整24小时的时间# In[5]:df_origin.head(24)# In[6]:df_origin.tail(24)# ### 查看描述信息# In[7]:df_origin.info()# In[9]:df_origin.describe()# In[10]:df_origin.columns# In[12]:df_origin.shape# In[11]:df_test = pd.read_csv("test.csv",sep=",")
df_test.head()# In[13]:df_test.shape# ### 检测异常值# In[14]:df_origin.isnull# In[18]:#df_test.isnull# ## 特征工程# ### 时间离散化# In[25]:df_origin['hour'] = df_origin['datetime'].str[11:13].astype(int)
df_origin.head()# In[26]:from datetime import datetime # In[42]:week = [datetime.date(datetime.strptime(time, '%Y-%m-%d')).weekday()  for time in df_origin['datetime'].str[:10]]
df_origin['week'] = week
df_origin.head()# In[43]:df_origin['month'] = df_origin['datetime'].str[5:7].astype(int)
df_origin['year'] = df_origin['datetime'].str[0:4].astype(int)
df_origin.head()# In[45]:df_origin.columns.values# In[46]:df_clean = df_origin.loc[:,['season', 'holiday', 'workingday', 'weather', 'temp','atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count','hour', 'week', 'year', 'month']]
df_clean.head()# #### 同理 处理test数据# In[47]:#temp = pd.DatetimeIndex(train['datetime'])
#train['year'] = temp.year
#train['month'] = temp.month
#train['hour'] = temp.hour
#train['weekday'] = temp.weekdaydf_test['hour'] = df_test['datetime'].str[11:13].astype(int)
week1 = [datetime.date(datetime.strptime(time, '%Y-%m-%d')).weekday()  for time in df_test['datetime'].str[:10]]
df_test['week'] = week1
df_test['month'] = df_test['datetime'].str[5:7].astype(int)
df_test['year'] = df_test['datetime'].str[0:4].astype(int)
df_clean_test = df_test.loc[:,['season', 'holiday', 'workingday', 'weather', 'temp','atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count','hour', 'week', 'year', 'month']]
df_test.head()# ## 检查数据均衡# ### log casual和register，然后相加# In[51]:df_origin['casual'].hist()# In[52]:df_origin['registered'].hist()# In[57]:df_clean['log_cas'] = np.log(df_origin['casual'] + 1)
df_clean['log_reg'] = np.log(df_origin['registered'] + 1)
df_clean.head()# ### 随机森林特征选择# In[58]:df_clean.head(10)# In[59]:fea_cols=['season', 'holiday', 'workingday', 'weather', 'temp','atemp', 'humidity', 'windspeed','hour', 'week', 'year']# ### 许多特征之间有太多相关性
#
# #### season和month，二选一
# #### temp和atemp，二选一
# #### humidity和weather，windspeed，看rf的特征重要度
# #### week和workingday
#
# # In[60]:df_clean[fea_cols].corr()# ### 剔除特征重要度< 0.01的特征# In[62]:clf_cal = RandomForestRegressor(n_estimators=1000, min_samples_split=11, oob_score=True)
clf_cal# In[63]:clf_cal.fit(df_clean[fea_cols].values, df_clean['log_cas'].values)
pd.DataFrame(clf_cal.feature_importances_).plot(kind='bar')
clf_cal.oob_score_# In[64]:clf_cal.feature_importances_# In[65]:fea_cas = ['season', 'workingday', 'weather', 'temp','humidity', 'windspeed','hour', 'week', 'year']# In[66]:clf_cal.fit(df_clean[fea_cas].values, df_clean['log_cas'].values)
pd.DataFrame(clf_cal.feature_importances_).plot(kind='bar')
clf_cal.oob_score_# In[67]:clf_reg = RandomForestRegressor(n_estimators=1000, min_samples_split=11, oob_score=True)# In[68]:clf_reg.fit(df_clean[fea_cols].values, df_clean['log_reg'].values)
pd.DataFrame(clf_reg.feature_importances_).plot(kind='bar')
clf_reg.oob_score_# In[69]:clf_reg.feature_importances_# In[70]:fea_regs=['season', 'workingday', 'weather', 'temp', 'humidity', 'hour', 'week', 'year']# In[71]:clf_reg.fit(df_clean[fea_regs].values, df_clean['log_reg'].values)
pd.DataFrame(clf_reg.feature_importances_).plot(kind='bar')
clf_reg.oob_score_# In[73]:y_pred7 = np.exp(clf_cal.predict(df_clean_test[fea_cas])) + np.exp(clf_reg.predict(df_clean_test[fea_regs])) - 2
y_pred7[:40]# ### 对结果四舍五入# In[74]:y_pred7 = [round(x) for x in y_pred7]
df_test['count'] = y_pred7
df_test['count'] = df_test['count'].astype(int)
df_test.head()# In[75]:df_test.shape# In[77]:df_test.to_csv('result.csv', sep=',', columns=['datetime', 'count'], header=['datetime', 'count'], index = False)# In[ ]:

参考：

1. http://www.cnblogs.com/en-heng/p/6907839.html

2. http://efavdb.com/bike-share-forecasting/

3. http://nbviewer.jupyter.org/gist/whbzju/ff06fce9fd738dcf8096#%E6%97%B6%E9%97%B4%E7%A6%BB%E6%95%A3%E5%8C%96

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