머신러닝
자전거 수요 예측- 실습
J.H_DA
2022. 4. 25. 10:27
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore', category= RuntimeWarning)
In [22]:
bike_df = pd.read_csv("./datasets/bike/train_bike.csv")
In [8]:
bike_df.head(3)
Out[8]:
datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcount012
| 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 |
| 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 |
| 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
In [10]:
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']= bike_df.datetime.apply(pd.to_datetime)
In [24]:
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)
drop_columns = ['datetime', 'casual', 'registered']
bike_df.drop(drop_columns, axis=1, inplace= True)
In [29]:
from sklearn.metrics import mean_squared_error, mean_absolute_error
# log 값 변환시 nan등의 이슈로 log() 가아닌 loglp()를 이요하여 RMSLE 계산
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_squre_error()를 이요하여 RMSE 계산
def rmse(y,pred):
return np.sqrt(mean_squared_error(y,pred))
# MAE, RMSE< rMSLE를 모두 계산
def evaluate_regr(y, pred):
rmsle_val = rmsle(y, pred)
rmse_val = rmse(y, pred)
# MAE 는 scikit leanr dml mean_absolute_error()로 계산
mae_val = mean_absolute_error(y, pred)
print(f"RMSLE: {rmsle_val:.3f}, RMSE:{rmse_val:.3f}, MAE: {mae_val:.3f}")
In [31]:
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 [34]:
def get_top_error_data(y_test, pred, n_tops = 5):
# DataFrame에 컬럼들로 실제 대여횟수와 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'])
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 [36]:
# 타겟 컬럼인 count 값을 log1p로 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 exmp1으로 scale변환
pred_exp = np.expm1(pred)
evaluate_regr(y_test_exp, pred_exp)
RMSLE: 1.017, RMSE:162.594, MAE: 109.286
In [40]:
import seaborn as sns
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[40]:
<AxesSubplot:>
In [41]:
# 'year', 'month' , 'hour', 'season', 'weather' feature들을 one Hot Encoding
X_features_ohe= pd.get_dummies(X_features, columns = ['year', 'month' , 'hour', 'holiday', 'workingday','season', 'weather'])
In [48]:
# 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)
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: 0.589, RMSE:97.483, MAE: 63.106
### Ridge ###
RMSLE: 0.589, RMSE:98.407, MAE: 63.648
### Lasso ###
RMSLE: 0.634, RMSE:113.031, MAE: 72.658
In [51]:
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[51]:
<AxesSubplot:>
In [ ]:
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 DataFrame 이 입력 될 경우 버전에 따라 오류 발생 가능 .ndarray로 변환
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