House Priceの分析6
大まかな流れを把握 --> 提出まで
読み込み
#import some necessary librairies import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) %matplotlib inline import matplotlib.pyplot as plt # Matlab-style plotting import seaborn as sns color = sns.color_palette() sns.set_style('darkgrid') import warnings def ignore_warn(*args, **kwargs): pass warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn) from scipy import stats from scipy.stats import norm, skew #for some statistics pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points from subprocess import check_output print(check_output(["ls", "kaggle/kaggle1/"]).decode("utf8")) #check the files available in the directory train = pd.read_csv('kaggle/kaggle1/train.csv') test = pd.read_csv('kaggle/kaggle1/test.csv')
前処理
Idを消す
#check the numbers of samples and features print("The train data size before dropping Id feature is : {} ".format(train.shape)) print("The test data size before dropping Id feature is : {} ".format(test.shape)) #Save the 'Id' column train_ID = train['Id'] test_ID = test['Id'] #Now drop the 'Id' colum since it's unnecessary for the prediction process. train.drop("Id", axis = 1, inplace = True) test.drop("Id", axis = 1, inplace = True) #check again the data size after dropping the 'Id' variable print("\nThe train data size after dropping Id feature is : {} ".format(train.shape)) print("The test data size after dropping Id feature is : {} ".format(test.shape))
SalePriceの外れ値削除
fig, ax = plt.subplots() ax.scatter(x = train['GrLivArea'], y = train['SalePrice']) plt.ylabel('SalePrice', fontsize=13) plt.xlabel('GrLivArea', fontsize=13) plt.show() #Deleting outliers train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index) #Check the graphic again fig, ax = plt.subplots() ax.scatter(train['GrLivArea'], train['SalePrice']) plt.ylabel('SalePrice', fontsize=13) plt.xlabel('GrLivArea', fontsize=13) plt.show()
SalePriceのhistgramと対数変換の確認
sns.distplot(train['SalePrice'] , fit=norm); sns.distplot(train['SalePrice'] , fit=norm); (mu, sigma) = norm.fit(train['SalePrice']) print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma)) plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best') plt.ylabel('Frequency') plt.title('SalePrice distribution') #Get also the QQ-plot fig = plt.figure() res = stats.probplot(train['SalePrice'], plot=plt) plt.show() #We use the numpy fuction log1p which applies log(1+x) to all elements of the column train["SalePrice"] = np.log1p(train["SalePrice"]) #Check the new distribution sns.distplot(train['SalePrice'] , fit=norm); # Get the fitted parameters used by the function (mu, sigma) = norm.fit(train['SalePrice']) print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma)) #Now plot the distribution plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best') plt.ylabel('Frequency') plt.title('SalePrice distribution') #Get also the QQ-plot fig = plt.figure() res = stats.probplot(train['SalePrice'], plot=plt) plt.show()
訓練データとテストデータを結合 + 欠損値の確認
ntrain = train.shape[0] ntest = test.shape[0] y_train = train.SalePrice.values all_data = pd.concat((train, test)).reset_index(drop=True) all_data.drop(['SalePrice'], axis=1, inplace=True) print("all_data size is : {}".format(all_data.shape)) all_data_na = (all_data.isnull().sum() / len(all_data)) * 100 all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30] missing_data = pd.DataFrame({'Missing Ratio' :all_data_na}) missing_data.head(20) f, ax = plt.subplots(figsize=(15, 12)) plt.xticks(rotation='90') sns.barplot(x=all_data_na.index, y=all_data_na) plt.xlabel('Features', fontsize=15) plt.ylabel('Percent of missing values', fontsize=15) plt.title('Percent missing data by feature', fontsize=15)
SalePriceと他の特徴の関係を確認
#Correlation map to see how features are correlated with SalePrice corrmat = train.corr() plt.subplots(figsize=(12,9)) sns.heatmap(corrmat, vmax=0.9, square=True) all_data["PoolQC"] = all_data["PoolQC"].fillna("None") all_data["MiscFeature"] = all_data["MiscFeature"].fillna("None") all_data["Alley"] = all_data["Alley"].fillna("None") all_data["Fence"] = all_data["Fence"].fillna("None") all_data["FireplaceQu"] = all_data["FireplaceQu"].fillna("None")
欠損値の補填
#Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform( lambda x: x.fillna(x.median())) for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'): all_data[col] = all_data[col].fillna('None') for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): all_data[col] = all_data[col].fillna(0) for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'): all_data[col] = all_data[col].fillna(0) for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'): all_data[col] = all_data[col].fillna('None') all_data["MasVnrType"] = all_data["MasVnrType"].fillna("None") all_data["MasVnrArea"] = all_data["MasVnrArea"].fillna(0) all_data = all_data.drop(['Utilities'], axis=1) ## mode は最頻値を集計 all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0]) all_data["Functional"] = all_data["Functional"].fillna("Typ") all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0]) all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0]) all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) all_data['MSSubClass'] = all_data['MSSubClass'].fillna("None") #Check remaining missing values if any all_data_na = (all_data.isnull().sum() / len(all_data)) * 100 all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False) missing_data = pd.DataFrame({'Missing Ratio' :all_data_na}) missing_data.head()
カテゴリの数値を str 変換
#MSSubClass=The building class all_data['MSSubClass'] = all_data['MSSubClass'].apply(str) #Changing OverallCond into a categorical variable all_data['OverallCond'] = all_data['OverallCond'].astype(str) #Year and month sold are transformed into categorical features. all_data['YrSold'] = all_data['YrSold'].astype(str) all_data['MoSold'] = all_data['MoSold'].astype(str)
カテゴリ変数のラベルエンコーディング
from sklearn.preprocessing import LabelEncoder cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', 'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', 'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope', 'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', 'YrSold', 'MoSold') # process columns, apply LabelEncoder to categorical features for c in cols: lbl = LabelEncoder() lbl.fit(list(all_data[c].values)) all_data[c] = lbl.transform(list(all_data[c].values)) # shape print('Shape all_data: {}'.format(all_data.shape)) # Adding total sqfootage feature all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']
ナンバー変数の歪度を確認
- 歪度の絶対値が0.75以上であれば、boxcox1p変換
numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index # Check the skew of all numerical features skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False) print("\nSkew in numerical features: \n") skewness = pd.DataFrame({'Skew' :skewed_feats}) skewness.head(10) skewness = skewness[abs(skewness) > 0.75] print("There are {} skewed numerical features to Box Cox transform".format(skewness.shape[0])) from scipy.special import boxcox1p skewed_features = skewness.index lam = 0.15 for feat in skewed_features: #all_data[feat] += 1 all_data[feat] = boxcox1p(all_data[feat], lam) #all_data[skewed_features] = np.log1p(all_data[skewed_features])
カテゴリ変数をダミー変数に変換
all_data = pd.get_dummies(all_data)
print(all_data.shape)
前処理完了
train = all_data[:ntrain] test = all_data[ntrain:]
モデル作成
## RobustScaler: 中央値と四分位数を用いて変換（外れ値を無視）。 lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1)) ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3)) KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5) GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05, max_depth=4, max_features='sqrt', min_samples_leaf=15, min_samples_split=10, loss='huber', random_state =5) model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, learning_rate=0.05, max_depth=3, min_child_weight=1.7817, n_estimators=2200, reg_alpha=0.4640, reg_lambda=0.8571, subsample=0.5213, silent=1, random_state =7, nthread = -1) model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5, learning_rate=0.05, n_estimators=720, max_bin = 55, bagging_fraction = 0.8, bagging_freq = 5, feature_fraction = 0.2319, feature_fraction_seed=9, bagging_seed=9, min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)
モデル評価
#Validation function n_folds = 5 def rmsle_cv(model): kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values) print('kf:', kf) rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring="neg_mean_squared_error", cv = kf)) return(rmse) score = rmsle_cv(lasso) print("\nLasso score: {:.4f} ({:.4f})\n".format(score.mean(), score.std())) score = rmsle_cv(ENet) print("ElasticNet score: {:.4f} ({:.4f})\n".format(score.mean(), score.std())) score = rmsle_cv(KRR) print("Kernel Ridge score: {:.4f} ({:.4f})\n".format(score.mean(), score.std())) score = rmsle_cv(GBoost) print("Gradient Boosting score: {:.4f} ({:.4f})\n".format(score.mean(), score.std())) score = rmsle_cv(model_xgb) print("Xgboost score: {:.4f} ({:.4f})\n".format(score.mean(), score.std())) score = rmsle_cv(model_lgb) print("LGBM score: {:.4f} ({:.4f})\n" .format(score.mean(), score.std()))
アンサンブル
- Averaged Base Model
class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin): def __init__(self, models): self.models = models # we define clones of the original models to fit the data in def fit(self, X, y): self.models_ = [clone(x) for x in self.models] # Train cloned base models for model in self.models_: model.fit(X, y) return self #Now we do the predictions for cloned models and average them def predict(self, X): predictions = np.column_stack([ model.predict(X) for model in self.models_ ]) return np.mean(predictions, axis=1) averaged_models = AveragingModels(models = (ENet, GBoost, KRR, lasso)) score = rmsle_cv(averaged_models) print(" Averaged base models score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
- Stacking Averaged Model
class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin): def __init__(self, base_models, meta_model, n_folds=5): self.base_models = base_models self.meta_model = meta_model self.n_folds = n_folds # We again fit the data on clones of the original models def fit(self, X, y): self.base_models_ = [list() for x in self.base_models] self.meta_model_ = clone(self.meta_model) kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156) # Train cloned base models then create out-of-fold predictions # that are needed to train the cloned meta-model out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models))) for i, model in enumerate(self.base_models): for train_index, holdout_index in kfold.split(X, y): instance = clone(model) self.base_models_[i].append(instance) instance.fit(X[train_index], y[train_index]) y_pred = instance.predict(X[holdout_index]) out_of_fold_predictions[holdout_index, i] = y_pred # Now train the cloned meta-model using the out-of-fold predictions as new feature self.meta_model_.fit(out_of_fold_predictions, y) return self #Do the predictions of all base models on the test data and use the averaged predictions as #meta-features for the final prediction which is done by the meta-model def predict(self, X): meta_features = np.column_stack([ np.column_stack([model.predict(X) for model in base_models]).mean(axis=1) for base_models in self.base_models_ ]) return self.meta_model_.predict(meta_features) stacked_averaged_models = StackingAveragedModels(base_models = (ENet, GBoost, KRR), meta_model = lasso) score = rmsle_cv(stacked_averaged_models) print("Stacking Averaged models score: {:.4f} ({:.4f})".format(score.mean(), score.std())) def rmsle(y, y_pred): return np.sqrt(mean_squared_error(y, y_pred)) stacked_averaged_models.fit(train.values, y_train) stacked_train_pred = stacked_averaged_models.predict(train.values) stacked_pred = np.expm1(stacked_averaged_models.predict(test.values)) print(rmsle(y_train, stacked_train_pred)) model_xgb.fit(train, y_train) xgb_train_pred = model_xgb.predict(train) xgb_pred = np.expm1(model_xgb.predict(test)) print(rmsle(y_train, xgb_train_pred)) model_lgb.fit(train, y_train) lgb_train_pred = model_lgb.predict(train) lgb_pred = np.expm1(model_lgb.predict(test.values)) print(rmsle(y_train, lgb_train_pred)) '''RMSE on the entire Train data when averaging''' print('RMSLE score on train data:') print(rmsle(y_train,stacked_train_pred*0.70 + xgb_train_pred*0.15 + lgb_train_pred*0.15 ))
モデル予測
ensemble = stacked_pred*0.70 + xgb_pred*0.15 + lgb_pred*0.15 ## 提出 sub = pd.DataFrame() sub['Id'] = test_ID sub['SalePrice'] = ensemble sub.to_csv('submission.csv',index=False)
