In [21]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
bike_df = pd.read_csv('./train.csv')
print(bike_df.shape)
bike_df.head(3)
(10886, 12)
Out[21]:
| 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 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
In [22]:
bike_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 datetime 10886 non-null object
1 season 10886 non-null int64
2 holiday 10886 non-null int64
3 workingday 10886 non-null int64
4 weather 10886 non-null int64
5 temp 10886 non-null float64
6 atemp 10886 non-null float64
7 humidity 10886 non-null int64
8 windspeed 10886 non-null float64
9 casual 10886 non-null int64
10 registered 10886 non-null int64
11 count 10886 non-null int64
dtypes: float64(3), int64(8), object(1)
memory usage: 1020.7+ KB
In [23]:
bike_df.datetime.apply(pd.to_datetime)
Out[23]:
0 2011-01-01 00:00:00
1 2011-01-01 01:00:00
2 2011-01-01 02:00:00
3 2011-01-01 03:00:00
4 2011-01-01 04:00:00
...
10881 2012-12-19 19:00:00
10882 2012-12-19 20:00:00
10883 2012-12-19 21:00:00
10884 2012-12-19 22:00:00
10885 2012-12-19 23:00:00
Name: datetime, Length: 10886, dtype: datetime64[ns]In [24]:
# 문자열을 datetime 타입으로 변경.
bike_df['datetime'] = bike_df.datetime.apply(pd.to_datetime)
# datetime 타입에서 년, 월, 일, 시간 추출
bike_df['year'] = bike_df.datetime.apply(lambda x : x.year)
bike_df['month'] = bike_df.datetime.apply(lambda x : x.month)
bike_df['day'] = bike_df.datetime.apply(lambda x : x.day)
bike_df['hour'] = bike_df.datetime.apply(lambda x: x.hour)
bike_df.head(3)
Out[24]:
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | year | month | day | hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 | 2011 | 1 | 1 | 0 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 | 2011 | 1 | 1 | 1 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 | 2011 | 1 | 1 | 2 |
불필요한 데이터 컬럼 삭제¶
axis =1 열 삭제
In [25]:
drop_columns = ['datetime','casual','registered']
bike_df.drop(drop_columns, axis=1,inplace=True)
In [26]:
bike_df.isnull().sum()
Out[26]:
season 0
holiday 0
workingday 0
weather 0
temp 0
atemp 0
humidity 0
windspeed 0
count 0
year 0
month 0
day 0
hour 0
dtype: int64In [27]:
from sklearn.metrics import mean_squared_error, mean_absolute_error
# log 값 변환 시 NaN등의 이슈로 log() 가 아닌 log1p() 를 이용하여 RMSLE 계산
# 하지만 bike_df에는 널값이 없기 떄문에 log()사용가능
def rmsle(y, pred):
log_y = np.log1p(y)
log_pred = np.log1p(pred)
squared_error = (log_y - log_pred) ** 2
rmsle = np.sqrt(np.mean(squared_error))
return rmsle
# 사이킷런의 mean_square_error() 를 이용하여 RMSE 계산
def rmse(y,pred):
return np.sqrt(mean_squared_error(y,pred))
# MSE, RMSE, RMSLE 를 모두 계산
def evaluate_regr(y,pred):
rmsle_val = rmsle(y,pred)
rmse_val = rmse(y,pred)
# MAE 는 scikit learn의 mean_absolute_error() 로 계산
mae_val = mean_absolute_error(y,pred)
print('RMSLE: {0:.3f}, RMSE: {1:.3F}, MAE: {2:.3F}'.format(rmsle_val, rmse_val, mae_val))
로그 변환, 피처 인코딩, 모델 학습/예측/평가¶
In [28]:
from sklearn.model_selection import train_test_split , GridSearchCV
from sklearn.linear_model import LinearRegression , Ridge , Lasso
y_target = bike_df['count']
X_features = bike_df.drop(['count'],axis=1,inplace=False)
X_train, X_test, y_train, y_test = train_test_split(X_features, y_target, test_size=0.3, random_state=0)
lr_reg = LinearRegression()
lr_reg.fit(X_train, y_train)
pred = lr_reg.predict(X_test)
evaluate_regr(y_test ,pred)
RMSLE: 1.165, RMSE: 140.900, MAE: 105.924
In [29]:
def get_top_error_data(y_test, pred, n_tops = 5):
# DataFrame에 컬럼들로 실제 대여횟수(count)와 예측 값을 서로 비교 할 수 있도록 생성.
result_df = pd.DataFrame(y_test.values, columns=['real_count'])
result_df['predicted_count']= np.round(pred)
result_df['diff'] = np.abs(result_df['real_count'] - result_df['predicted_count'])
# 예측값과 실제값이 가장 큰 데이터 순으로 출력.
print(result_df.sort_values('diff', ascending=False)[:n_tops])
get_top_error_data(y_test,pred,n_tops=5)
real_count predicted_count diff
1618 890 322.0 568.0
3151 798 241.0 557.0
966 884 327.0 557.0
412 745 194.0 551.0
2817 856 310.0 546.0
In [30]:
y_target.hist()
Out[30]:
<AxesSubplot:>
In [31]:
y_log_transform = np.log1p(y_target)
y_log_transform.hist()
Out[31]:
<AxesSubplot:>
In [32]:
# 타겟 컬럼인 count 값을 log1p 로 Log 변환
y_target_log = np.log1p(y_target)
# 로그 변환된 y_target_log를 반영하여 학습/테스트 데이터 셋 분할
X_train, X_test, y_train, y_test = train_test_split(X_features, y_target_log, test_size=0.3, random_state=0)
lr_reg = LinearRegression()
lr_reg.fit(X_train, y_train)
pred = lr_reg.predict(X_test)
# 테스트 데이터 셋의 Target 값은 Log 변환되었으므로 다시 expm1를 이용하여 원래 scale로 변환
y_test_exp = np.expm1(y_test)
# 예측 값 역시 Log 변환된 타겟 기반으로 학습되어 예측되었으므로 다시 exmpl으로 scale변환
pred_exp = np.expm1(pred)
evaluate_regr(y_test_exp ,pred_exp)
RMSLE: 1.017, RMSE: 162.594, MAE: 109.286
In [33]:
coef = pd.Series(lr_reg.coef_, index=X_features.columns)
coef_sort = coef.sort_values(ascending=False)
sns.barplot(x=coef_sort.values, y=coef_sort.index)
Out[33]:
<AxesSubplot:>
In [34]:
# 'year', month', 'day', hour'등의 피처들을 One Hot Encoding
X_features_ohe = pd.get_dummies(X_features, columns=['year', 'month','day', 'hour', 'holiday',
'workingday','season','weather'])
In [38]:
X_features_ohe
Out[38]:
| temp | atemp | humidity | windspeed | year_2011 | year_2012 | month_1 | month_2 | month_3 | month_4 | ... | workingday_0 | workingday_1 | season_1 | season_2 | season_3 | season_4 | weather_1 | weather_2 | weather_3 | weather_4 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9.84 | 14.395 | 81 | 0.0000 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 9.02 | 13.635 | 80 | 0.0000 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 9.02 | 13.635 | 80 | 0.0000 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 9.84 | 14.395 | 75 | 0.0000 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | 9.84 | 14.395 | 75 | 0.0000 | 1 | 0 | 1 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10881 | 15.58 | 19.695 | 50 | 26.0027 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 10882 | 14.76 | 17.425 | 57 | 15.0013 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 10883 | 13.94 | 15.910 | 61 | 15.0013 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 10884 | 13.94 | 17.425 | 61 | 6.0032 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 10885 | 13.12 | 16.665 | 66 | 8.9981 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
10886 rows × 73 columns
In [35]:
# 원-핫 인코딩이 적용된 feature 데이터 세트 기반으로 학습/예측 데이터 분할.
X_train, X_test, y_train, y_test = train_test_split(X_features_ohe, y_target_log,
test_size=0.3, random_state=0)
# 모델과 학습/테스트 데이터 셋을 입력하면 성능 평가 수치를 반환
def get_model_predict(model, X_train, X_test, y_train, y_test, is_expm1=False):
model.fit(X_train, y_train)
pred = model.predict(X_test)
if is_expm1 :
y_test = np.expm1(y_test)
pred = np.expm1(pred)
print('###',model.__class__.__name__,'###')
evaluate_regr(y_test, pred)
# end of function get_model_predict
# model 별로 평가 수행
lr_reg = LinearRegression()
ridge_reg = Ridge(alpha=10)
lasso_reg = Lasso(alpha=0.01)
for model in [lr_reg, ridge_reg, lasso_reg]:
get_model_predict(model,X_train, X_test, y_train, y_test,is_expm1=True)
### LinearRegression ###
RMSLE: 0.590, RMSE: 97.690, MAE: 63.383
### Ridge ###
RMSLE: 0.590, RMSE: 98.529, MAE: 63.893
### Lasso ###
RMSLE: 0.635, RMSE: 113.219, MAE: 72.803
In [36]:
coef = pd.Series(lr_reg.coef_ , index=X_features_ohe.columns)
coef_sort = coef.sort_values(ascending=False)[:10]
sns.barplot(x=coef_sort.values , y=coef_sort.index)
Out[36]:
<AxesSubplot:>
In [37]:
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
# 랜덤 포레스트, GBM, XGBoost, LightGBM model 별로 평가 수행
rf_reg = RandomForestRegressor(n_estimators=500)
gbm_reg = GradientBoostingRegressor(n_estimators=500)
for model in [rf_reg, gbm_reg]:
# XGBoost의 경우 DataFrame이 입력 될 경우 버전에 따라 오류 발생 가능. ndarray로 변환.
get_model_predict(model,X_train.values, X_test.values, y_train.values, y_test.values,is_expm1=True)
### RandomForestRegressor ###
RMSLE: 0.354, RMSE: 50.196, MAE: 31.034
### GradientBoostingRegressor ###
RMSLE: 0.330, RMSE: 53.344, MAE: 32.747
In [ ]:
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