House price prediction (top4% kernel) 참고.
아래는 캐글 집값예측 샘플데회에 대한 나의 코드이다. 보시다시피 EDA도 안하고 무작정 전처리 / 모델링이 끝이며, 스코어도 발산하는 코드이다.
House price prediction 분석 -2 부터는 나의 기본코드를 개선할 것이다.
House price prediction (top4% kernel) 에서는 정규화, 대체, 인코딩 등의 전처리 과정을 사전 학습하고 상위 4%의 모델을 만드는 과정이 담긴 커널이다. 이 커널에서 참조된 레퍼런스들을 집중적으로 참고할 것이다.
집값예측문제 개선과정을 git commit을 통해 남길 예정이니, 어떤 과정을 통해 개선했는지 궁금한 캐글러들은 확인해봐도 좋을듯 하다.
아래는 나의 베이스라인 커널이다.
House Pr
%matplotlib inlineimport pandas as pd
import numpy as np
import xgboost
import sklearn
import seaborn as sb
import math
import matplotlib.pyplot as plot
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, LabelEncoder
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, KFold
from sklearn.metrics import mean_absolute_error, mean_squared_error
from xgboost import XGBRegressor
train = pd.read_csv('./data/train.csv')
test = pd.read_csv('./data/test.csv')
train_y = train['SalePrice']
train.drop('SalePrice',axis=1, inplace=True)
# (1)
loss_cols = [col for col in train if train[col].isnull().sum() > 1000]
train_f = train.drop(loss_cols, axis=1)
# (2)
train_num_cols = [col for col in train_f if train_f[col].dtypes !='object' ]
sim = SimpleImputer()
train_num = pd.DataFrame(sim.fit_transform(train_f[train_num_cols]), columns=train_num_cols)
train_cat = train_f.select_dtypes(include='object')
cat_sim = SimpleImputer(strategy='most_frequent')
train_cat = pd.DataFrame(cat_sim.fit_transform(train_cat), columns=train_cat.columns)
# (3)
high_cardinal_cols = [col for col in train_cat.columns if train_cat[col].nunique() >= 10]
low_cardinal_cols = [col for col in train_cat.columns if train_cat[col].nunique() < 10]
ore = OrdinalEncoder()
train_ohe = pd.get_dummies(train_cat[low_cardinal_cols], prefix=low_cardinal_cols, prefix_sep='_') #pd.DataFrame(ohe.fit_transform(train_cat[low_cardinal_cols]))
train_ore = pd.DataFrame(ore.fit_transform(train_cat[high_cardinal_cols]), columns = high_cardinal_cols)
# # of joined dataframe's col is 223
print('====Validation====')
print(len(train_cat.columns), len(train_num.columns))
print(len(train_ohe.columns), len(train_ore.columns), len(train_num.columns))
print("Valid : " ,((len(train_ohe.columns)+len(train_ore.columns)+len(train_num.columns)) == 223))
# concatenation
train_f.drop(train_cat.columns, axis=1, inplace=True)
train_f.drop(train_num.columns, axis=1, inplace=True)
train_f = pd.concat([train_num, train_ohe, train_ore], axis=1)
====Validation====
39 37
183 3 37
Valid : Trueflag = 1
if flag == 1: # error :
train_x, valid_x, train_y, valid_y = train_test_split(train_f,train_y, train_size=0.8, test_size=0.2)
model = XGBRegressor(eta=0.1, colsample_bytree=0.75, max_depth= 3, min_child_weight=3)
model.fit(train_x,train_y)
pred = model.predict(valid_x)
score = mean_squared_error(pred, valid_y, squared=False)
print("mean_squared_error: ",score)
elif flag == 2:
model = XGBRegressor()
scores = -1*cross_val_score(model, train_f, train_y, cv=5, scoring='neg_mean_squared_error')
print("mean_squared_error mean: ",scores.mean())
elif flag == 3:
pass
elif flag == 4:
train_x, valid_x, train_y, valid_y = train_test_split(train_f, train_y, train_size=0.8, test_size = 0.2)
model = XGBRegressor()
kf = KFold(random_state=30, shuffle=True, n_splits=10)
params = {'eta':[0.05, 0.1, 0.15],'max_depth':[5,7], 'min_child_weight':[1,3], 'colsample_bytree':[0.5,0.75]}
gcv = GridSearchCV(estimator=model, cv=kf, n_jobs=10, scoring='neg_mean_squared_error', verbose=True, param_grid=params)
gcv.fit(train_x, train_y)
print(gcv.best_params_)
best_model = gcv.best_estimator_
pred = best_model.predict(valid_x)
score = mean_squared_error(pred, valid_y, squared=Fasle)
print("mean_squared_error: ",score)
# Fitting 10 folds for each of 8 candidates, totalling 80 fits
# {'colsample_bytree': 0.75, 'max_depth': 5, 'min_child_weight': 3}
# Mean_absolute_error: 17648.85913420377mean_squared_error: 25757.824396836273