From 783cdd1ff6c0463c1725247028ddbcd5fd103658 Mon Sep 17 00:00:00 2001 From: hypox64 Date: Sun, 8 Dec 2019 19:27:24 +0800 Subject: [PATCH] 0.11953 --- analysis.py | 14 + dataloader.py | 75 +- description_map.py | 36 +- evaluation.py | 2 +- images/0.026778_85_krr.png | Bin 0 -> 26464 bytes images/0.028606_114_model_svr.png | Bin 0 -> 33309 bytes images/0.029568_138_bay.png | Bin 0 -> 25865 bytes images/0.030053_137_las.png | Bin 0 -> 25244 bytes images/0.030281_138_ENet.png | Bin 0 -> 23886 bytes images/0.033188_65_model_lgb.png | Bin 0 -> 53272 bytes images/0.036021_82_GBoost.png | Bin 0 -> 37975 bytes images/0.040413_121_model_xgb.png | Bin 0 -> 39049 bytes ml.py | 109 +- model.py | 2 +- result/ml_best/best_0.026778_krr.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.028606_model_svr.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.029568_bay.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.030053_las.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.030281_ENet.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.033188_model_lgb.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.036021_GBoost.csv | 1460 +++++++++++++++++++ result/ml_best/best_0.040413_model_xgb.csv | 1460 +++++++++++++++++++ result/submission/0.02677_0.11953.csv | 1460 +++++++++++++++++++ result/{ => submission}/0.02839_0.12229.csv | 0 result/{ => submission}/0.03246_0.14142.csv | 0 result/{ => submission}/0.03688_0.14435.csv | 0 result/{ => submission}/0.04145_0.15960.csv | 0 train.py | 4 +- transformer.py | 15 +- 29 files changed, 13314 insertions(+), 83 deletions(-) create mode 100644 analysis.py create mode 100644 images/0.026778_85_krr.png create mode 100644 images/0.028606_114_model_svr.png create mode 100644 images/0.029568_138_bay.png create mode 100644 images/0.030053_137_las.png create mode 100644 images/0.030281_138_ENet.png create mode 100644 images/0.033188_65_model_lgb.png create mode 100644 images/0.036021_82_GBoost.png create mode 100644 images/0.040413_121_model_xgb.png create mode 100644 result/ml_best/best_0.026778_krr.csv create mode 100644 result/ml_best/best_0.028606_model_svr.csv create mode 100644 result/ml_best/best_0.029568_bay.csv create mode 100644 result/ml_best/best_0.030053_las.csv create mode 100644 result/ml_best/best_0.030281_ENet.csv create mode 100644 result/ml_best/best_0.033188_model_lgb.csv create mode 100644 result/ml_best/best_0.036021_GBoost.csv create mode 100644 result/ml_best/best_0.040413_model_xgb.csv create mode 100644 result/submission/0.02677_0.11953.csv rename result/{ => submission}/0.02839_0.12229.csv (100%) rename result/{ => submission}/0.03246_0.14142.csv (100%) rename result/{ => submission}/0.03688_0.14435.csv (100%) rename result/{ => submission}/0.04145_0.15960.csv (100%) diff --git a/analysis.py b/analysis.py new file mode 100644 index 0000000..949e1de --- /dev/null +++ b/analysis.py @@ -0,0 +1,14 @@ +import dataloader +import transformer +import numpy as np + +correlates = [] +desc_map,price_map = dataloader.load_train() +price_map['price'] = transformer.normlize(price_map['price']) +key = '' +desc_map[key] = transformer.normlize(desc_map[key]) +print(np.correlate(desc_map[key],price_map['price'])) +# for key in desc_map.keys(): +# desc_map[key] = transformer.normlize(desc_map[key]) +# correlates.append(np.correlate(desc_map[key],price_map['price'])[0][1]) +# print(correlates) diff --git a/dataloader.py b/dataloader.py index c477a3d..6ba723d 100644 --- a/dataloader.py +++ b/dataloader.py @@ -3,11 +3,8 @@ import csv import numpy as np import random from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV -from sklearn.kernel_ridge import KernelRidge -from sklearn.svm import SVR import evaluation -from description_map import value_map,fix_key,fix_miss,add_future +from description_map import value_map,fix_key,fix_miss,add_future,fix_LotFrontage # load description_txt description_txt = [] @@ -52,13 +49,6 @@ for i in range(len(colon_indexs)-1): ori_map[key] = interspace-j-1 #change word to vector Full_map[desc_key]=ori_map -# def normlize(npdata,justprice = False): -# _mean = np.mean(npdata) -# _std = np.std(npdata) -# if justprice: -# _mean = 180921.195 -# _std = 79415.2918 -# return (npdata-_mean)/_std def normlize(npdata,justprice = False): _min = np.min(npdata) @@ -68,8 +58,6 @@ def normlize(npdata,justprice = False): _max = 755000.0 return (npdata-_min)/(_max-_min) -# def convert2price(tensor): -# return tensor*79415.2918+180921.195 def convert2price(tensor): return tensor*(755000.0-34900.0)+34900 @@ -163,28 +151,32 @@ def dict2numpy(dict_data): return np_data def load_all(dimension): - desc_map,price_map = load_train() - desc_map = add_future(desc_map) - # print(len(desc_map)) - # print(desc_map) - # print(desc_map) - train_price = np.array(price_map['price']) - train_desc = dict2numpy(desc_map) - desc_map = load_test() + train_desc_map,train_price_map = load_train() + test_desc_map = load_test() + desc_map = {} + train_length = len(list(train_desc_map.values())[0]) + + for key in train_desc_map.keys(): + desc_map[key] = np.concatenate((train_desc_map[key],test_desc_map[key]),axis=0) + # desc_map[key] = normlize(desc_map[key]) + + desc_map['LotFrontage'] = fix_LotFrontage(desc_map) + desc_map['YearBuilt'] = (desc_map['YearBuilt']-1800)/10 + desc_map['YearRemodAdd'] = (desc_map['YearRemodAdd']-1800)/10 desc_map = add_future(desc_map) - test_desc = dict2numpy(desc_map) - desc_all = np.concatenate((train_desc,test_desc),axis=0) - for i in range(len(desc_all[0])): - desc_all[:,i] = normlize(desc_all[:,i]) - # print(desc_all) + for key in desc_map.keys(): + desc_map[key] = normlize(desc_map[key]) + + desc_all = dict2numpy(desc_map) + pca=PCA(n_components=dimension) #加载PCA算法，设置降维后主成分数目为 desc_all=pca.fit_transform(desc_all)#对样本进行降维 - train_price = normlize(train_price,True) - train_desc = desc_all[:len(train_desc)] - test_desc = desc_all[len(train_desc):] + train_price = normlize(np.array(train_price_map['price']),True) + train_desc = desc_all[:train_length] + test_desc = desc_all[train_length:] return train_desc.astype(np.float32),train_price.astype(np.float32),test_desc.astype(np.float32) @@ -197,30 +189,11 @@ def write_csv(prices,path): csvFile.close() def main(): + load_all(80) - dimension = 80 - - - train_desc,train_price,test_desc = load_all(dimension) - - # # KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5) - - # kr = GridSearchCV(KernelRidge(kernel='polynomial', gamma=0.1), - # param_grid={"alpha": [1e0, 0.1, 1e-2, 1e-3], - # "gamma": np.logspace(-2, 2, 5)}) - - - # kr.fit(train_desc, train_price) - # y_kr = kr.predict(test_desc) - # for i in range(len(y_kr)): - # y_kr[i] = convert2price(y_kr[i]) - # # print(y_kr.shape) - # print(dimension,evaluation.eval_test(y_kr)) + # dimension = 80 + # train_desc,train_price,test_desc = load_all(dimension) - # write_csv(train_price, './result.csv') - # # print(data) - # plt.plot(data[1]) - # plt.show() if __name__ == '__main__': main() diff --git a/description_map.py b/description_map.py index 440db1d..0fea2dd 100644 --- a/description_map.py +++ b/description_map.py @@ -1,5 +1,7 @@ import numpy as np +import pandas as pd + value_map = {} value_map["MSSubClass"] = {'180':1, '30':2, '45':2, @@ -110,10 +112,18 @@ def fix_miss(name): else: return 0 -# def fix_LotFrontage(Full_map): -# a = np.zeros(25) -# for i in range(25): -# a[Full_map['Neighborhood'][i]-1] += +def fix_LotFrontage(Full_map): + data_df = pd.DataFrame(Full_map) + data_df["LotFrontage"] = data_df.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) + return data_df["LotFrontage"].to_numpy() + +def binary(npdata): + for i in range(len(npdata)): + if npdata[i]>0: + npdata[i] = 1 + else: + npdata[i] = 0 + return npdata def add_future(features): features["TotalHouse"] = features["TotalBsmtSF"] + features["1stFlrSF"] + features["2ndFlrSF"] @@ -141,7 +151,9 @@ def add_future(features): features["Rooms"] = features["FullBath"]+features["TotRmsAbvGrd"] features["PorchArea"] = features["OpenPorchSF"]+features["EnclosedPorch"]+features["3SsnPorch"]+features["ScreenPorch"] features["TotalPlace"] = features["TotalBsmtSF"] + features["1stFlrSF"] + features["2ndFlrSF"] + features["GarageArea"] + features["OpenPorchSF"]+features["EnclosedPorch"]+features["3SsnPorch"]+features["ScreenPorch"] - + features['all_quality'] = (features['ExterQual'] +features['BsmtFinType1']+features['BsmtFinType2']+ + features['KitchenQual']+features['FireplaceQu']+features['GarageQual']+ + features['PoolQC']+features['Fence']) features['YrBltAndRemod']=features['YearBuilt']+features['YearRemodAdd'] features['TotalSF']=features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF'] @@ -153,11 +165,15 @@ def add_future(features): features['Total_porch_sf'] = (features['OpenPorchSF'] + features['3SsnPorch'] + features['EnclosedPorch'] + features['ScreenPorch'] + features['WoodDeckSF']) - # features['haspool'] = features['PoolArea'].apply(lambda x: 1 if x > 0 else 0) - # features['has2ndfloor'] = features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0) - # features['hasgarage'] = features['GarageArea'].apply(lambda x: 1 if x > 0 else 0) - # features['hasbsmt'] = features['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0) - # features['hasfireplace'] = features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0) + #random features + random_list = ['GrLivArea','OverallQual','2ndFlrSF','YearBuilt','1stFlrSF','TotalBsmtSF','OverallCond', + 'my_Neighborhood','my_SaleCondition','BsmtFinSF1','my_MSZoning','LotArea','GarageCars','YearRemodAdd','GarageArea'] + length = len(random_list) + for i in range(length): + for j in range(i,length): + if i != j: + features[random_list[i]+'*'+random_list[j]]=features[random_list[i]]*features[random_list[j]] + return features \ No newline at end of file diff --git a/evaluation.py b/evaluation.py index 24b2527..a6bca41 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sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone from sklearn.model_selection import KFold, cross_val_score, train_test_split from sklearn.metrics import mean_squared_error from sklearn.model_selection import GridSearchCV -# import xgboost as xgb -# import lightgbm as lgb +from sklearn.svm import SVR +import xgboost as xgb +import lightgbm as lgb import numpy as np -import torch import dataloader import evaluation import time import transformer +import time +import warnings +warnings.filterwarnings('ignore') +import matplotlib.pyplot as plt + + +def eval(model,train_x,train_y,test_x): + model.fit(train_x, train_y) + y_pre = model.predict(test_x) + for i in range(len(y_pre)): + y_pre[i] = dataloader.convert2price(y_pre[i]) + return evaluation.eval_test(y_pre),y_pre + # print(dimension,evaluation.eval_test(y_pre)) + +# KernelRidge() +krr = GridSearchCV(KernelRidge(kernel='polynomial'),cv = 3, + param_grid={"alpha": np.logspace(-1, 2, 10), + "gamma": np.logspace(-1, 2, 10)}) + -dimension = 85 -train_desc,train_price,test_desc = dataloader.load_all(dimension) +las = LassoCV(alphas=np.logspace(-5, 2, 50),eps=np.logspace(-5, 2, 20),max_iter=10000) -kr = GridSearchCV(KernelRidge(kernel='polynomial'), +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) + + +# ElasticNet +ENet = GridSearchCV(ElasticNet(max_iter = 10000), param_grid={"alpha": np.logspace(-3, 2, 6), - "gamma": np.logspace(-2, 2, 5)}) - -# print(np.logspace(-2, 2, 5)) -kr.fit(train_desc, train_price) -y_kr = kr.predict(test_desc) -for i in range(len(y_kr)): - y_kr[i] = dataloader.convert2price(y_kr[i]) -# print(y_kr.shape) -print(dimension,evaluation.eval_test(y_kr)) -dataloader.write_csv(y_kr, './result/result.csv') \ No newline at end of file + "l1_ratio": np.logspace(-2, 2, 5)}) + +#BayesianRidge +bay = BayesianRidge() + +#GradientBoostingRegressor +GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.01, + max_depth=4, max_features='sqrt', + min_samples_leaf=15, min_samples_split=10, + loss='huber', random_state =5) + +#LGBMRegressor +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) + +#SVR +model_svr = GridSearchCV(SVR(kernel="rbf"), + param_grid={"C": np.logspace(0, 2, 5), + "gamma": np.logspace(-4, -3, 8), + "epsilon":np.logspace(-4, -3, 5)}) + + +models = [krr,las,model_xgb,ENet,bay,GBoost,model_lgb,model_svr] +model_names = ['krr','las','model_xgb','ENet','bay','GBoost','model_lgb','model_svr'] +for model,model_name in zip(models,model_names): + print(model_name) + losss = [] + start_dimension = 60 + end_dimension = 150 + for dimension in range(start_dimension,end_dimension): + t1 = time.time() + train_desc,train_price,test_desc = dataloader.load_all(dimension) + loss,_ = eval(model,train_desc,train_price,test_desc) + losss.append(loss) + + t2 = time.time() + print(dimension,loss,' cost time:','%.3f'%(t2-t1),'s') + t1 = time.time() + + best_dimension = losss.index(min(losss))+start_dimension + print('Best:',min(losss),' dimension:',best_dimension) + + train_desc,train_price,test_desc = dataloader.load_all(best_dimension) + loss,pre = eval(model,train_desc,train_price,test_desc) + dataloader.write_csv(pre, './result/best_'+'%.6f'%loss+'_'+model_name+'.csv') + + plt.plot(np.linspace(start_dimension,dimension,dimension-start_dimension+1),losss) + plt.xlabel('PCA dimension') + plt.ylabel('loss') + plt.title(model_name+' :loss_PCA') + plt.savefig('./images/'+'%.6f'%loss+'_'+str(best_dimension)+'_'+model_name+".png") + plt.cla() + # plt.show() + diff --git a/model.py b/model.py index 131b12e..14224be 100644 --- a/model.py +++ b/model.py @@ -10,7 +10,7 @@ class Linear(nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Linear, self).__init__() self.fc1 = torch.nn.Linear(n_feature, n_hidden) # hidden layer - self.relu = nn.ReLU(inplace=True) + self.relu = nn.Sigmoid() self.dropout = nn.Dropout(0.2) self.fc2 = torch.nn.Linear(n_hidden, n_output) # output layer diff --git a/result/ml_best/best_0.026778_krr.csv b/result/ml_best/best_0.026778_krr.csv new file mode 100644 index 0000000..a2c81e4 --- /dev/null +++ b/result/ml_best/best_0.026778_krr.csv @@ -0,0 +1,1460 @@ +Id,SalePrice +1461,118501.266 +1462,156096.55 +1463,183318.73 +1464,194792.97 +1465,188233.08 +1466,174171.56 +1467,186916.95 +1468,163941.58 +1469,188074.73 +1470,130860.75 +1471,193235.14 +1472,98897.47 +1473,97302.39 +1474,146399.3 +1475,112574.125 +1476,383031.28 +1477,261349.06 +1478,264311.56 +1479,275483.25 +1480,497056.6 +1481,329975.72 +1482,207847.53 +1483,176846.44 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result/submission/0.03688_0.14435.csv diff --git a/result/0.04145_0.15960.csv b/result/submission/0.04145_0.15960.csv similarity index 100% rename from result/0.04145_0.15960.csv rename to result/submission/0.04145_0.15960.csv diff --git a/train.py b/train.py index 2d90cd5..b8fc176 100644 --- a/train.py +++ b/train.py @@ -8,13 +8,13 @@ import time import transformer #parameter -LR = 0.0001 +LR = 0.001 EPOCHS = 1000 BATCHSIZE = 64 CONTINUE = False use_gpu = True SAVE_FRE = 5 -Dimension = 120 +Dimension = 128 #load data train_desc,train_price,test_desc = dataloader.load_all(Dimension) train_desc.tolist() diff --git a/transformer.py b/transformer.py index 5418378..bff2f5e 100644 --- a/transformer.py +++ b/transformer.py @@ -8,4 +8,17 @@ def match_random(a,b): np.random.shuffle(b) def random_transform(a,alpha): - return a*random.uniform(1-alpha,1+alpha) \ No newline at end of file + return a*random.uniform(1-alpha,1+alpha) + + +def normlize(npdata,justprice = False): + _min = np.min(npdata) + _max = np.max(npdata) + if justprice: + _min = 34900.0 + _max = 755000.0 + return (npdata-_min)/(_max-_min) + + +def convert2price(tensor): + return tensor*(755000.0-34900.0)+34900 \ No newline at end of file -- GitLab