替代文字

Kaggle Competition 的练习

1 2	# 安装 vecstack !pip install vecstack

# 数据分析库
import pandas as pd
import numpy as np
import random

# 数据可视化
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('ggplot')

# 机器学习库
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, BaggingRegressor
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.model_selection import RandomizedSearchCV, cross_val_score, StratifiedKFold, learning_curve, KFold, train_test_split

# Ensemble Models
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor

# Package for stacking models
from vecstack import stacking

导入数据

train = pd.read_csv('/train.csv', index_col='Id')
test = pd.read_csv('/test.csv', index_col='Id')

train.head()

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
Id
1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	0	150	856	GasA	Ex	Y	SBrkr	856	854	0	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	0	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	0	284	1262	GasA	Ex	Y	SBrkr	1262	0	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	0	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	0	434	920	GasA	Ex	Y	SBrkr	920	866	0	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	0	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	0	540	756	GasA	Gd	Y	SBrkr	961	756	0	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	0	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	0	490	1145	GasA	Ex	Y	SBrkr	1145	1053	0	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	0	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

浏览数据

1	train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1460 entries, 1 to 1460
Data columns (total 80 columns):
MSSubClass       1460 non-null int64
MSZoning         1460 non-null object
LotFrontage      1201 non-null float64
LotArea          1460 non-null int64
Street           1460 non-null object
Alley            91 non-null object
LotShape         1460 non-null object
LandContour      1460 non-null object
Utilities        1460 non-null object
LotConfig        1460 non-null object
LandSlope        1460 non-null object
Neighborhood     1460 non-null object
Condition1       1460 non-null object
Condition2       1460 non-null object
BldgType         1460 non-null object
HouseStyle       1460 non-null object
OverallQual      1460 non-null int64
OverallCond      1460 non-null int64
YearBuilt        1460 non-null int64
YearRemodAdd     1460 non-null int64
RoofStyle        1460 non-null object
RoofMatl         1460 non-null object
Exterior1st      1460 non-null object
Exterior2nd      1460 non-null object
MasVnrType       1452 non-null object
MasVnrArea       1452 non-null float64
ExterQual        1460 non-null object
ExterCond        1460 non-null object
Foundation       1460 non-null object
BsmtQual         1423 non-null object
BsmtCond         1423 non-null object
BsmtExposure     1422 non-null object
BsmtFinType1     1423 non-null object
BsmtFinSF1       1460 non-null int64
BsmtFinType2     1422 non-null object
BsmtFinSF2       1460 non-null int64
BsmtUnfSF        1460 non-null int64
TotalBsmtSF      1460 non-null int64
Heating          1460 non-null object
HeatingQC        1460 non-null object
CentralAir       1460 non-null object
Electrical       1459 non-null object
1stFlrSF         1460 non-null int64
2ndFlrSF         1460 non-null int64
LowQualFinSF     1460 non-null int64
GrLivArea        1460 non-null int64
BsmtFullBath     1460 non-null int64
BsmtHalfBath     1460 non-null int64
FullBath         1460 non-null int64
HalfBath         1460 non-null int64
BedroomAbvGr     1460 non-null int64
KitchenAbvGr     1460 non-null int64
KitchenQual      1460 non-null object
TotRmsAbvGrd     1460 non-null int64
Functional       1460 non-null object
Fireplaces       1460 non-null int64
FireplaceQu      770 non-null object
GarageType       1379 non-null object
GarageYrBlt      1379 non-null float64
GarageFinish     1379 non-null object
GarageCars       1460 non-null int64
GarageArea       1460 non-null int64
GarageQual       1379 non-null object
GarageCond       1379 non-null object
PavedDrive       1460 non-null object
WoodDeckSF       1460 non-null int64
OpenPorchSF      1460 non-null int64
EnclosedPorch    1460 non-null int64
3SsnPorch        1460 non-null int64
ScreenPorch      1460 non-null int64
PoolArea         1460 non-null int64
PoolQC           7 non-null object
Fence            281 non-null object
MiscFeature      54 non-null object
MiscVal          1460 non-null int64
MoSold           1460 non-null int64
YrSold           1460 non-null int64
SaleType         1460 non-null object
SaleCondition    1460 non-null object
SalePrice        1460 non-null int64
dtypes: float64(3), int64(34), object(43)
memory usage: 923.9+ KB

1	train.describe(include="O")

	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinType2	Heating	HeatingQC	CentralAir	Electrical	KitchenQual	Functional	FireplaceQu	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
count	1460	1460	91	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	1452	1460	1460	1460	1423	1423	1422	1423	1422	1460	1460	1460	1459	1460	1460	770	1379	1379	1379	1379	1460	7	281	54	1460	1460
unique	5	2	2	4	4	2	5	3	25	9	8	5	8	6	8	15	16	4	4	5	6	4	4	4	6	6	6	5	2	5	4	7	5	6	3	5	5	3	3	4	4	9	6
top	RL	Pave	Grvl	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Norm	Norm	1Fam	1Story	Gable	CompShg	VinylSd	VinylSd	None	TA	TA	PConc	TA	TA	No	Unf	Unf	GasA	Ex	Y	SBrkr	TA	Typ	Gd	Attchd	Unf	TA	TA	Y	Gd	MnPrv	Shed	WD	Normal
freq	1151	1454	50	925	1311	1459	1052	1382	225	1260	1445	1220	726	1141	1434	515	504	864	906	1282	647	649	1311	953	430	1256	1428	741	1365	1334	735	1360	380	870	605	1311	1326	1340	3	157	49	1267	1198

检查缺失数据

1
2
3

train_missing = train.isnull().sum()
train_missing = train_missing[train_missing > 0]
train_missing

LotFrontage      259
Alley           1369
MasVnrType         8
MasVnrArea         8
BsmtQual          37
BsmtCond          37
BsmtExposure      38
BsmtFinType1      37
BsmtFinType2      38
Electrical         1
FireplaceQu      690
GarageType        81
GarageYrBlt       81
GarageFinish      81
GarageQual        81
GarageCond        81
PoolQC          1453
Fence           1179
MiscFeature     1406
dtype: int64

1
2
3

test_missing = test.isnull().sum()
test_missing = test_missing[test_missing > 0]
test_missing

MSZoning           4
LotFrontage      227
Alley           1352
Utilities          2
Exterior1st        1
Exterior2nd        1
MasVnrType        16
MasVnrArea        15
BsmtQual          44
BsmtCond          45
BsmtExposure      44
BsmtFinType1      42
BsmtFinSF1         1
BsmtFinType2      42
BsmtFinSF2         1
BsmtUnfSF          1
TotalBsmtSF        1
BsmtFullBath       2
BsmtHalfBath       2
KitchenQual        1
Functional         2
FireplaceQu      730
GarageType        76
GarageYrBlt       78
GarageFinish      78
GarageCars         1
GarageArea         1
GarageQual        78
GarageCond        78
PoolQC          1456
Fence           1169
MiscFeature     1408
SaleType           1
dtype: int64

# 可视化缺失数据
def plot_missing(df):
    # 寻找缺失的列
    missing = df.isnull().sum()
    missing = missing[missing > 0]
    missing.sort_values(inplace=True)
    
    # 画出缺失值的柱状图。
    missing.plot.bar(figsize=(10,8))
    plt.xlabel('Columns with missing values')
    plt.ylabel('Count')
    
    # 搜索缺失值
    import missingno as msno
    msno.matrix(df=df, figsize=(10,8))
    # 查看相关性
    #msno.heatmap(df=df,figsize=(10,8))

1	plot_missing(train)

png

1	plot_missing(test)

png

分析概要

以下是缺失比较多的feature

Feature	Train miss	Test miss	Dispos
LotFrontage	259	227	填个中位数吧
Alley	1369	1352	删除
FireplaceQu	690	730	fireplaceQU 和 fireplaces 有关缺失项貌似都是没有fireplace的
PoolQC	1453	1456	删除
Fence	1179	1169	删除
MiscFeature	1406	1408	删除

处理缺失值

1
2
3

# 删除缺失过多的feature
train = train.drop(['Alley', 'PoolQC', 'MiscFeature', 'Fence'], axis=1)
test = test.drop(['Alley', 'PoolQC', 'MiscFeature', 'Fence'], axis=1)

1
2
3

# FireplaceQu 缺失的都认为是没有的
train['FireplaceQu'] = train['FireplaceQu'].fillna('NA')
test['FireplaceQu'] = test['FireplaceQu'].fillna('NA')

# 填充其他缺失值
def fill_missing_values(df):
    # 查找缺失值
    missing = df.isnull().sum()
    missing = missing[missing > 0]
    
    for column in missing.index:
        # 类型为 object 的填众数
        if df[column].dtype == 'object':
            df[column].fillna(df[column].value_counts().index[0], inplace=True)
        # 其他类型填中位数
        else:
            df[column].fillna(df[column].median(), inplace=True)

1 2	fill_missing_values(train) train.isnull().sum().max()

1 2	fill_missing_values(test) test.isnull().sum().max()

好的，已经没有缺失数据了。

整理 description 文件

数据描述文件记录了所有特征所代表的含义，其中许多特征是字符串，现在我们要整理为个字典，便于我们查询。

description_dict = {}
with open('/data_description.txt','r') as description:
    description_data = description.read()
    description.close()
    
description_data = description_data.split('\n')

for i in description_data:
    if ':' in i:
        key = i.split(':')[0]
        description_dict[key] = []
    elif i.split() and '       ' in i:
        value = i.split()[0]
        description_dict[key].append(value)

print(description_dict)

{'MSSubClass': ['20', '30', '40', '45', '50', '60', '70', '75', '80', '85', '90', '120', '150', '160', '180', '190'], 'MSZoning': ['A', 'C', 'FV', 'I', 'RH', 'RL', 'RP', 'RM'], 'LotFrontage': [], 'LotArea': [], 'Street': ['Grvl', 'Pave'], 'Alley': ['Grvl', 'Pave', 'NA'], 'LotShape': ['Reg', 'IR1', 'IR2', 'IR3'], 'LandContour': ['Lvl', 'Bnk', 'HLS', 'Low'], 'Utilities': ['AllPub', 'NoSewr', 'NoSeWa', 'ELO'], 'LotConfig': ['Inside', 'Corner', 'CulDSac', 'FR2', 'FR3'], 'LandSlope': ['Gtl', 'Mod', 'Sev'], 'Neighborhood': ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'Names', 'NoRidge', 'NPkVill', 'NridgHt', 'NWAmes', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker'], 'Condition1': ['Artery', 'Feedr', 'Norm', 'RRNn', 'RRAn', 'PosN', 'PosA', 'RRNe', 'RRAe'], 'Condition2': ['Artery', 'Feedr', 'Norm', 'RRNn', 'RRAn', 'PosN', 'PosA', 'RRNe', 'RRAe'], 'BldgType': ['1Fam', '2FmCon', 'Duplx', 'TwnhsE', 'TwnhsI'], 'HouseStyle': ['1Story'], '       1.5Fin\tOne and one-half story': [], '       1.5Unf\tOne and one-half story': ['2Story'], '       2.5Fin\tTwo and one-half story': [], '       2.5Unf\tTwo and one-half story': ['SFoyer', 'SLvl'], 'OverallQual': ['10', '9', '8', '7', '6', '5', '4', '3', '2', '1'], 'OverallCond': ['10', '9', '8', '7', '6', '5', '4', '3', '2', '1'], 'YearBuilt': [], 'YearRemodAdd': [], 'RoofStyle': ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed'], 'RoofMatl': ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl'], 'Exterior1st': ['AsbShng', 'AsphShn', 'BrkComm', 'BrkFace', 'CBlock', 'CemntBd', 'HdBoard', 'ImStucc', 'MetalSd', 'Other', 'Plywood', 'PreCast', 'Stone', 'Stucco', 'VinylSd', 'Wd', 'WdShing'], 'Exterior2nd': ['AsbShng', 'AsphShn', 'BrkComm', 'BrkFace', 'CBlock', 'CemntBd', 'HdBoard', 'ImStucc', 'MetalSd', 'Other', 'Plywood', 'PreCast', 'Stone', 'Stucco', 'VinylSd', 'Wd', 'WdShing'], 'MasVnrType': ['BrkCmn', 'BrkFace', 'CBlock', 'None', 'Stone'], 'MasVnrArea': [], 'ExterQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'ExterCond': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'Foundation': ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood'], 'BsmtQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'BsmtCond': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'BsmtExposure': ['Gd', 'Av', 'Mn', 'No', 'NA'], 'BsmtFinType1': ['GLQ', 'ALQ', 'BLQ', 'Rec', 'LwQ', 'Unf', 'NA'], 'BsmtFinSF1': [], 'BsmtFinType2': ['GLQ', 'ALQ', 'BLQ', 'Rec', 'LwQ', 'Unf', 'NA'], 'BsmtFinSF2': [], 'BsmtUnfSF': [], 'TotalBsmtSF': [], 'Heating': ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall'], 'HeatingQC': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'CentralAir': ['N', 'Y'], 'Electrical': ['SBrkr', 'FuseA', 'FuseF', 'FuseP', 'Mix'], '1stFlrSF': [], '2ndFlrSF': [], 'LowQualFinSF': [], 'GrLivArea': [], 'BsmtFullBath': [], 'BsmtHalfBath': [], 'FullBath': [], 'HalfBath': [], 'Bedroom': [], 'Kitchen': [], 'KitchenQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'TotRmsAbvGrd': [], 'Functional': ['Typ', 'Min1', 'Min2', 'Mod', 'Maj1', 'Maj2', 'Sev', 'Sal'], 'Fireplaces': [], 'FireplaceQu': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'GarageType': ['2Types', 'Attchd', 'Basment', 'BuiltIn', 'CarPort', 'Detchd', 'NA'], 'GarageYrBlt': [], 'GarageFinish': ['Fin', 'RFn', 'Unf', 'NA'], 'GarageCars': [], 'GarageArea': [], 'GarageQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'GarageCond': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'PavedDrive': ['Y', 'P', 'N'], 'WoodDeckSF': [], 'OpenPorchSF': [], 'EnclosedPorch': [], '3SsnPorch': [], 'ScreenPorch': [], 'PoolArea': [], 'PoolQC': ['Ex', 'Gd', 'TA', 'Fa', 'NA'], 'Fence': ['GdPrv', 'MnPrv', 'GdWo', 'MnWw', 'NA'], 'MiscFeature': ['Elev', 'Gar2', 'Othr', 'Shed', 'TenC', 'NA'], 'MiscVal': [], 'MoSold': [], 'YrSold': [], 'SaleType': ['WD', 'CWD', 'VWD', 'New', 'COD', 'Con', 'ConLw', 'ConLI', 'ConLD', 'Oth'], 'SaleCondition': ['Normal', 'Abnorml', 'AdjLand', 'Alloca', 'Family', 'Partial']}

字符串类型的 feature 重编码

# 这个函数的作用是得到数据集中非数字的feature column
def get_object_column(df):
    object_column = []
    for column in df.columns:
        if df[column].dtype == 'object':
            object_column.append(column)
    return object_column

1	#object_column = get_object_column(train)

# 这个函数的作用是把 description_dict 中的 value 转换为对应数字的字典
def generate_map(map_list, end_index=1):
    d = {}
    j = len(map_list) - end_index
    for i in map_list:
        d[i] = j
        j -= 1
    return d

def preprocess_order_feature(df):
    for i in get_object_column(df):
        order_map = generate_map(description_dict[i], 0)
        df[i] = df[i].map(order_map)
        df[i] = df[i].fillna(0)
    return df

1 2	train = preprocess_order_feature(train) test = preprocess_order_feature(test)

观察数据

1	train.describe()

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
count	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.00000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000
mean	56.897260	2.825342	69.863699	10516.828082	1.004110	3.591781	3.814384	3.998630	4.583562	2.937671	10.747945	6.969178	6.993151	4.334247	0.497260	6.099315	5.575342	1971.267808	1984.865753	4.589726	6.924658	5.958904	5.271918	2.552740	103.117123	3.39589	3.083562	4.603425	4.565068	4.010959	2.656164	4.571233	443.639726	2.273288	46.549315	567.240411	1057.429452	4.963699	4.145205	1.065068	4.889726	1162.626712	346.992466	5.844521	1515.463699	0.425342	0.057534	1.565068	0.382877	2.866438	1.046575	3.511644	6.517808	7.841781	0.613014	2.825342	4.791781	1978.589041	2.771233	1.767123	472.980137	3.976712	3.975342	2.856164	94.244521	46.660274	21.954110	3.409589	15.060959	2.758904	43.489041	6.321918	2007.815753	9.509589	5.417808	180921.195890
std	42.300571	1.020174	22.027677	9981.264932	0.063996	0.582296	0.606509	0.052342	0.773448	0.276232	7.565716	0.878349	0.248272	1.555218	0.500164	1.382997	1.112799	30.202904	20.645407	0.834998	0.599127	4.426038	4.263353	1.046204	180.731373	0.57428	0.351054	0.722394	0.678071	0.284178	1.039123	2.070649	456.098091	0.869859	161.319273	441.866955	438.705324	0.295124	0.959501	0.246731	0.394658	386.587738	436.528436	48.623081	525.480383	0.518911	0.238753	0.550916	0.502885	0.815778	0.220338	0.663760	1.625393	0.667698	0.644666	1.810877	1.759864	23.997022	0.811835	0.747315	213.804841	0.241665	0.232860	0.496592	125.338794	66.256028	61.119149	29.317331	55.757415	40.177307	496.123024	2.703626	1.328095	1.368616	1.475209	79442.502883
min	20.000000	0.000000	21.000000	1300.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	1.000000	1.000000	1872.000000	1950.000000	1.000000	1.000000	0.000000	0.000000	1.000000	0.000000	2.00000	1.000000	1.000000	3.000000	2.000000	2.000000	2.000000	0.000000	2.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	334.000000	0.000000	0.000000	334.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	2.000000	2.000000	0.000000	1.000000	2.000000	1900.000000	2.000000	0.000000	0.000000	2.000000	2.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2006.000000	1.000000	1.000000	34900.000000
25%	20.000000	3.000000	60.000000	7553.500000	1.000000	3.000000	4.000000	4.000000	4.000000	3.000000	4.000000	7.000000	7.000000	5.000000	0.000000	5.000000	5.000000	1954.000000	1967.000000	5.000000	7.000000	3.000000	3.000000	2.000000	0.000000	3.00000	3.000000	4.000000	4.000000	4.000000	2.000000	2.000000	0.000000	2.000000	0.000000	223.000000	795.750000	5.000000	3.000000	1.000000	5.000000	882.000000	0.000000	0.000000	1129.500000	0.000000	0.000000	1.000000	0.000000	2.000000	1.000000	3.000000	5.000000	8.000000	0.000000	1.000000	2.000000	1962.000000	2.000000	1.000000	334.500000	4.000000	4.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2007.000000	10.000000	6.000000	129975.000000
50%	50.000000	3.000000	69.000000	9478.500000	1.000000	4.000000	4.000000	4.000000	5.000000	3.000000	10.000000	7.000000	7.000000	5.000000	0.000000	6.000000	5.000000	1973.000000	1994.000000	5.000000	7.000000	3.000000	3.000000	2.000000	0.000000	3.00000	3.000000	5.000000	5.000000	4.000000	2.000000	5.000000	383.500000	2.000000	0.000000	477.500000	991.500000	5.000000	5.000000	1.000000	5.000000	1087.000000	0.000000	0.000000	1464.000000	0.000000	0.000000	2.000000	0.000000	3.000000	1.000000	3.000000	6.000000	8.000000	1.000000	3.000000	6.000000	1980.000000	3.000000	2.000000	480.000000	4.000000	4.000000	3.000000	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2008.000000	10.000000	6.000000	163000.000000
75%	70.000000	3.000000	79.000000	11601.500000	1.000000	4.000000	4.000000	4.000000	5.000000	3.000000	18.000000	7.000000	7.000000	5.000000	1.000000	7.000000	6.000000	2000.000000	2004.000000	5.000000	7.000000	9.000000	9.000000	4.000000	164.250000	4.00000	3.000000	5.000000	5.000000	4.000000	3.000000	7.000000	712.250000	2.000000	0.000000	808.000000	1298.250000	5.000000	5.000000	1.000000	5.000000	1391.250000	728.000000	0.000000	1776.750000	1.000000	0.000000	2.000000	1.000000	3.000000	1.000000	4.000000	7.000000	8.000000	1.000000	5.000000	6.000000	2001.000000	3.000000	2.000000	576.000000	4.000000	4.000000	3.000000	168.000000	68.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	2009.000000	10.000000	6.000000	214000.000000
max	190.000000	6.000000	313.000000	215245.000000	2.000000	4.000000	4.000000	4.000000	5.000000	3.000000	25.000000	9.000000	9.000000	5.000000	1.000000	10.000000	9.000000	2010.000000	2010.000000	6.000000	8.000000	17.000000	17.000000	5.000000	1600.000000	5.00000	5.000000	6.000000	6.000000	5.000000	5.000000	7.000000	5644.000000	7.000000	1474.000000	2336.000000	6110.000000	6.000000	5.000000	2.000000	5.000000	4692.000000	2065.000000	572.000000	5642.000000	3.000000	2.000000	3.000000	2.000000	8.000000	3.000000	5.000000	14.000000	8.000000	3.000000	6.000000	7.000000	2010.000000	4.000000	4.000000	1418.000000	6.000000	6.000000	3.000000	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	15500.000000	12.000000	2010.000000	10.000000	6.000000	755000.000000

观察 feature 之间的相关性

corr_mat = train[["SalePrice","MSSubClass","MSZoning","LotFrontage","LotArea", "BldgType",
                       "OverallQual", "OverallCond","YearBuilt", "BedroomAbvGr", "PoolArea", "GarageArea",
                       "SaleType", "MoSold"]].corr()
# corr_mat = train.corr()
f, ax = plt.subplots(figsize=(16, 8))
sns.heatmap(corr_mat, vmax=1 , square=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fb6c086fa20>

png

观察热力图，颜色越浅相关性越大。关于热力图-> this video.

观察年限和售价的规律

1 2	f, ax = plt.subplots(figsize=(16, 8)) sns.lineplot(x='YearBuilt', y='SalePrice', data=train)

<matplotlib.axes._subplots.AxesSubplot at 0x7fb6a5211320>

png

在二十世纪的时候价格增长迅速。

综合质量和售价有明显的相关性

1 2	f, ax = plt.subplots(figsize=(12, 8)) sns.lineplot(x='OverallQual', y='SalePrice', color='green',data=train)

<matplotlib.axes._subplots.AxesSubplot at 0x7fb6a3e6d8d0>

png

我们可以看到，随着房屋整体质量的提高，销售价格快速上涨，这是非常合理的。

观察售价

1 2	f, ax = plt.subplots(figsize=(10, 6)) sns.distplot(train['SalePrice'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fb6a25cdba8>

png

大部分的房屋售价在 100000 到 200000 之间。

构建预测模型

X = train.drop('SalePrice', axis=1)
y = np.ravel(np.array(train[['SalePrice']]))
print(y.shape)
y

(1460,)





array([208500, 181500, 223500, ..., 266500, 142125, 147500])

1 2	# Use train_test_split from sci-kit learn to segment our data into train and a local testset X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

定义评估函数

评估函数基于预测值的对数与观察到的销售价格的对数之间的均方根误差(RMSE)。

1 2	def rmse(y, y_pred): return np.sqrt(mean_squared_error(np.log(y), np.log(y_pred)))

随机森林

random_forest = RandomForestRegressor(n_estimators=1200,
                                      max_depth=15,
                                      min_samples_split=5,
                                      min_samples_leaf=5,
                                      max_features=None,
                                      random_state=42,
                                      oob_score=True)
kf = KFold(n_splits=5)
y_pred = cross_val_score(random_forest, X, y, cv=kf)
y_pred.mean()

0.8500001566166802

1	random_forest.fit(X, y)

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=15,
                      max_features=None, max_leaf_nodes=None,
                      min_impurity_decrease=0.0, min_impurity_split=None,
                      min_samples_leaf=5, min_samples_split=5,
                      min_weight_fraction_leaf=0.0, n_estimators=1200,
                      n_jobs=None, oob_score=True, random_state=42, verbose=0,
                      warm_start=False)

1	rf_pred = random_forest.predict(test)

rf_pred

array([126945.71699684, 153924.56961003, 182182.80294353, ...,
       156066.28489667, 117296.65091637, 224995.13115853])

XG Boost

xg_boost = XGBRegressor(learning_rate=0.01,
                        n_estimators=6000,
                        max_depth=4, 
                        min_child_weight=1,
                        gamma=0.6,
                        subsample=0.7,
                        colsample_bytree=0.2,
                        objective='reg:linear',
                        nthread=-1,
                        scale_pos_weight=1,
                        seed=27,
                        reg_alpha=0.00006)

kf = KFold(n_splits=5)
y_pred = cross_val_score(xg_boost, X, y, cv=kf)
y_pred.mean()

[03:11:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[03:11:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[03:11:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[03:12:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[03:12:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.





0.8959027545454475

1	xg_boost.fit(X, y)

[03:12:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.





XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=0.2, gamma=0.6,
             importance_type='gain', learning_rate=0.01, max_delta_step=0,
             max_depth=4, min_child_weight=1, missing=None, n_estimators=6000,
             n_jobs=1, nthread=-1, objective='reg:linear', random_state=0,
             reg_alpha=6e-05, reg_lambda=1, scale_pos_weight=1, seed=27,
             silent=None, subsample=0.7, verbosity=1)

1 2	xgb_pred = xg_boost.predict(test) xgb_pred

array([127263.77 , 163056.02 , 193208.25 , ..., 173218.75 , 115712.914,
       211616.88 ], dtype=float32)

Gradient Boost Regressor(GBM)

g_boost = GradientBoostingRegressor(n_estimators=6000,
                                    learning_rate=0.01,
                                    max_depth=5,
                                    max_features='sqrt',
                                    min_samples_leaf=15,
                                    min_samples_split=10,
                                    loss='ls',
                                    random_state=42
                                    )

kf = KFold(n_splits=5)
y_pred = cross_val_score(g_boost, X, y, cv=kf)
y_pred.mean()

0.8905525972502479

1	g_boost.fit(X, y)

GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                          learning_rate=0.01, loss='ls', max_depth=5,
                          max_features='sqrt', max_leaf_nodes=None,
                          min_impurity_decrease=0.0, min_impurity_split=None,
                          min_samples_leaf=15, min_samples_split=10,
                          min_weight_fraction_leaf=0.0, n_estimators=6000,
                          n_iter_no_change=None, presort='auto',
                          random_state=42, subsample=1.0, tol=0.0001,
                          validation_fraction=0.1, verbose=0, warm_start=False)

1 2	gbm_pred = g_boost.predict(test) gbm_pred

array([125909.87154943, 163184.8652622 , 187872.72372976, ...,
       176429.22616544, 119620.23912638, 211953.00041747])

Light GBM

lightgbm = LGBMRegressor(objective='regression',
                         num_leaves=6,
                         learning_rate=0.01,
                         n_estimators=6400,
                         verbose=-1,
                         bagging_fraction=0.8,
                         bagging_freq=4,
                         bagging_seed=6,
                         feature_fraction=0.2,
                         feature_fraction_seed=7
                         )
kf = KFold(n_splits=5)
y_pred = cross_val_score(lightgbm, X, y, cv=kf)
y_pred.mean()

0.8881867437856128

1	lightgbm.fit(X,y)

LGBMRegressor(bagging_fraction=0.8, bagging_freq=4, bagging_seed=6,
              boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
              feature_fraction=0.2, feature_fraction_seed=7,
              importance_type='split', learning_rate=0.01, max_depth=-1,
              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
              n_estimators=6400, n_jobs=-1, num_leaves=6,
              objective='regression', random_state=None, reg_alpha=0.0,
              reg_lambda=0.0, silent=True, subsample=1.0,
              subsample_for_bin=200000, subsample_freq=0, verbose=-1)

1 2	lgb_pred = lightgbm.predict(test) lgb_pred

array([124759.4703253 , 161206.70919126, 187680.444818  , ...,
       168310.83365532, 123698.90457326, 206480.92047866])

Logistic Regression

logreg = LogisticRegression()
kf = KFold(n_splits=5)
y_pred = cross_val_score(logreg, X, y, cv=kf)
y_pred.mean()

1	logreg.fit(X, y)

/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)
/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)





LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)

1	round(logreg.score(X, y) * 100, 2)

88.9

1 2	log_pred = logreg.predict(test) log_pred

array([135500, 128950, 175000, ..., 133900, 190000, 187500])

模型的叠加

叠加(也称为元集成)是一种模型集成技术，用于组合来自多个预测模型的信息，生成性能更好的新模型。在这个项目中，我们使用名为vecstack的python包，它可以帮助我们对前面导入的模型进行堆栈。它实际上非常容易使用，可以查看文档了解更多信息。vecstack

1	models = [g_boost, xg_boost, lightgbm, random_forest]

Strain, S_test = stacking(models,
                          X_train,
                          y_train,
                          X_test,
                          regression=True,
                          mode='oof_pred_bag',
                          metric=rmse,
                          n_folds=5,
                          random_state=25,
                          verbose=2)

task:         [regression]
metric:       [rmse]
mode:         [oof_pred_bag]
n_models:     [4]

model  0:     [GradientBoostingRegressor]
    fold  0:  [0.12653004]
    fold  1:  [0.13818165]
    fold  2:  [0.10747644]
    fold  3:  [0.14980732]
    fold  4:  [0.11127270]
    ----
    MEAN:     [0.12665363] + [0.01595833]
    FULL:     [0.12764756]

model  1:     [XGBRegressor]
[03:23:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
    fold  0:  [0.11631560]
[03:24:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
    fold  1:  [0.14701253]
[03:24:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
    fold  2:  [0.10450330]
[03:24:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
    fold  3:  [0.14328067]
[03:24:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
    fold  4:  [0.10632026]
    ----
    MEAN:     [0.12348647] + [0.01817552]
    FULL:     [0.12481458]

model  2:     [LGBMRegressor]
    fold  0:  [0.12668239]
    fold  1:  [0.14251415]
    fold  2:  [0.11409004]
    fold  3:  [0.15394461]
    fold  4:  [0.11576550]
    ----
    MEAN:     [0.13059934] + [0.01545902]
    FULL:     [0.13150292]

model  3:     [RandomForestRegressor]
    fold  0:  [0.13803357]
    fold  1:  [0.16746496]
    fold  2:  [0.13370269]
    fold  3:  [0.17907099]
    fold  4:  [0.13625091]
    ----
    MEAN:     [0.15090463] + [0.01867560]
    FULL:     [0.15204350]

1	Strain, S_test

(array([[145154.57609501, 140247.640625  , 144708.92814448,
         136304.80002144],
        [441586.84575012, 453786.875     , 476049.8262998 ,
         433615.5690085 ],
        [205559.38156983, 199459.953125  , 204548.62741617,
         189012.87710637],
        ...,
        [229773.83814053, 245324.03125   , 222988.34529258,
         233726.54189435],
        [ 78529.68615301,  81706.46875   ,  74919.86211206,
          94651.91862458],
        [126564.42955093, 118016.921875  , 131591.97464745,
         134449.34870648]]),
 array([[156946.11019358, 162235.903125  , 156274.58204718,
         174271.19166786],
        [168719.74644755, 171368.26875   , 172660.15892698,
         168988.4858204 ],
        [165875.73697659, 167827.703125  , 166511.09786993,
         144720.7621456 ],
        ...,
        [235105.18179731, 240780.803125  , 236012.18528746,
         224310.44197611],
        [311340.99357469, 306275.29375   , 305036.50098821,
         319953.95931372],
        [100400.26285948,  97430.8671875 ,  97576.05463032,
         109458.70614352]]))

# Initialize 2nd level model
xgb_lev2 = XGBRegressor(learning_rate=0.1, 
                        n_estimators=500,
                        max_depth=3,
                        n_jobs=-1,
                        random_state=17
                       )

# Fit the 2nd level model on the output of level 1
xgb_lev2.fit(Strain, y_train)

[03:25:26] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.





XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0,
             importance_type='gain', learning_rate=0.1, max_delta_step=0,
             max_depth=3, min_child_weight=1, missing=None, n_estimators=500,
             n_jobs=-1, nthread=None, objective='reg:linear', random_state=17,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
             silent=None, subsample=1, verbosity=1)

1
2
3

# Make predictions on the localized test set
stacked_pred = xgb_lev2.predict(S_test)
print("RMSE of Stacked Model: {}".format(rmse(y_test,stacked_pred)))

RMSE of Stacked Model: 0.12290530277450722

y1_pred_L1 = models[0].predict(test)
y2_pred_L1 = models[1].predict(test)
y3_pred_L1 = models[2].predict(test)
y4_pred_L1 = models[3].predict(test)
S_test_L1 = np.c_[y1_pred_L1, y2_pred_L1, y3_pred_L1, y4_pred_L1]

1	test_stacked_pred = xgb_lev2.predict(S_test_L1)

# Save the predictions in form of a dataframe
submission = pd.DataFrame()

submission['Id'] = np.array(test.index)
submission['SalePrice'] = test_stacked_pred

1	submission.to_csv('/submissionV2.csv', index=False)

混合较好得分的 submission

因为不知道最终的测试集合的正真数据是什么，只能一遍一遍提交去蒙，看到别人的方法是混合他人较好的提交去验证，尝试下看看。

1
2
3

submission_v1 = pd.read_csv('/House_price_submission_v44.csv')
submission_v2 = pd.read_csv('/submissionV19.csv')
submission_v3 = pd.read_csv('/blended_submission.csv')

final_blend = 0.5*submission_v1.SalePrice.values + 0.2*submission_v2.SalePrice.values + 0.3*submission_v3.SalePrice.values

blended_submission = pd.DataFrame()

blended_submission['Id'] = submission_v1.Id.values
blended_submission['SalePrice'] = final_blend

1 2	blended_submission.to_csv('/submissionV20.csv', index=False) blended_submission

Top4

呃，就这样吧，最终和前十名差不到0.004。

用 Tensorfolw 试试

import math
import tensorflow as tf
from tensorflow.python.data import Dataset
from sklearn.model_selection import train_test_split
from sklearn import metrics
tf.logging.set_verbosity(tf.logging.ERROR)

correlation_dataframe = train.copy()
saleprice_corr = correlation_dataframe.corr()['SalePrice']
saleprice_corr = saleprice_corr[saleprice_corr > 0]
corr_feature = saleprice_corr.index

1	corr_feature

Index(['MSZoning', 'LotFrontage', 'LotArea', 'Utilities', 'BldgType',
       'OverallQual', 'YearBuilt', 'YearRemodAdd', 'MasVnrType', 'MasVnrArea',
       'ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'BsmtExposure',
       'BsmtFinType1', 'BsmtFinSF1', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
       'HeatingQC', 'Electrical', '1stFlrSF', '2ndFlrSF', 'GrLivArea',
       'BsmtFullBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenQual',
       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', '3SsnPorch',
       'ScreenPorch', 'PoolArea', 'MoSold', 'SalePrice'],
      dtype='object')

def preprocess_feature(df, corr_feature):
    newdf = pd.DataFrame()
    for feature in corr_feature:
        newdf[feature] = df[feature]
    return newdf

1 2	X = preprocess_feature(train, corr_feature).drop('SalePrice', axis=1) y = np.ravel(np.array(train[['SalePrice']]))

1	X.describe()

	MSZoning	LotFrontage	LotArea	Utilities	BldgType	OverallQual	YearBuilt	YearRemodAdd	MasVnrType	MasVnrArea	ExterQual	ExterCond	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	FullBath	HalfBath	BedroomAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	3SsnPorch	ScreenPorch	PoolArea	MoSold
count	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.00000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000
mean	2.825342	69.863699	10516.828082	3.998630	4.334247	6.099315	1971.267808	1984.865753	2.552740	103.117123	3.39589	3.083562	4.565068	4.010959	2.656164	4.571233	443.639726	567.240411	1057.429452	4.963699	4.145205	4.889726	1162.626712	346.992466	1515.463699	0.425342	1.565068	0.382877	2.866438	3.511644	6.517808	7.841781	0.613014	2.825342	4.791781	1978.589041	2.771233	1.767123	472.980137	3.976712	3.975342	2.856164	94.244521	46.660274	3.409589	15.060959	2.758904	6.321918
std	1.020174	22.027677	9981.264932	0.052342	1.555218	1.382997	30.202904	20.645407	1.046204	180.731373	0.57428	0.351054	0.678071	0.284178	1.039123	2.070649	456.098091	441.866955	438.705324	0.295124	0.959501	0.394658	386.587738	436.528436	525.480383	0.518911	0.550916	0.502885	0.815778	0.663760	1.625393	0.667698	0.644666	1.810877	1.759864	23.997022	0.811835	0.747315	213.804841	0.241665	0.232860	0.496592	125.338794	66.256028	29.317331	55.757415	40.177307	2.703626
min	0.000000	21.000000	1300.000000	2.000000	0.000000	1.000000	1872.000000	1950.000000	1.000000	0.000000	2.00000	1.000000	3.000000	2.000000	2.000000	2.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	334.000000	0.000000	334.000000	0.000000	0.000000	0.000000	0.000000	2.000000	2.000000	2.000000	0.000000	1.000000	2.000000	1900.000000	2.000000	0.000000	0.000000	2.000000	2.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	3.000000	60.000000	7553.500000	4.000000	5.000000	5.000000	1954.000000	1967.000000	2.000000	0.000000	3.00000	3.000000	4.000000	4.000000	2.000000	2.000000	0.000000	223.000000	795.750000	5.000000	3.000000	5.000000	882.000000	0.000000	1129.500000	0.000000	1.000000	0.000000	2.000000	3.000000	5.000000	8.000000	0.000000	1.000000	2.000000	1962.000000	2.000000	1.000000	334.500000	4.000000	4.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000
50%	3.000000	69.000000	9478.500000	4.000000	5.000000	6.000000	1973.000000	1994.000000	2.000000	0.000000	3.00000	3.000000	5.000000	4.000000	2.000000	5.000000	383.500000	477.500000	991.500000	5.000000	5.000000	5.000000	1087.000000	0.000000	1464.000000	0.000000	2.000000	0.000000	3.000000	3.000000	6.000000	8.000000	1.000000	3.000000	6.000000	1980.000000	3.000000	2.000000	480.000000	4.000000	4.000000	3.000000	0.000000	25.000000	0.000000	0.000000	0.000000	6.000000
75%	3.000000	79.000000	11601.500000	4.000000	5.000000	7.000000	2000.000000	2004.000000	4.000000	164.250000	4.00000	3.000000	5.000000	4.000000	3.000000	7.000000	712.250000	808.000000	1298.250000	5.000000	5.000000	5.000000	1391.250000	728.000000	1776.750000	1.000000	2.000000	1.000000	3.000000	4.000000	7.000000	8.000000	1.000000	5.000000	6.000000	2001.000000	3.000000	2.000000	576.000000	4.000000	4.000000	3.000000	168.000000	68.000000	0.000000	0.000000	0.000000	8.000000
max	6.000000	313.000000	215245.000000	4.000000	5.000000	10.000000	2010.000000	2010.000000	5.000000	1600.000000	5.00000	5.000000	6.000000	5.000000	5.000000	7.000000	5644.000000	2336.000000	6110.000000	6.000000	5.000000	5.000000	4692.000000	2065.000000	5642.000000	3.000000	3.000000	2.000000	8.000000	5.000000	14.000000	8.000000	3.000000	6.000000	7.000000	2010.000000	4.000000	4.000000	1418.000000	6.000000	6.000000	3.000000	857.000000	547.000000	508.000000	480.000000	738.000000	12.000000

def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
    features = {key: np.array(value) for key, value in dict(features).items()}
    
    ds = Dataset.from_tensor_slices((features, targets))
    ds = ds.batch(batch_size).repeat(num_epochs)
    
    if shuffle:
        ds = ds.shuffle(10000)
    
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels

def construct_feature_columns(input_features):
    """构造TensorFlow特征列
        参数:
            input_features:要使用的数字输入特性的名称。
        返回:
            一个 feature columns 集合
    """
    return set([tf.feature_column.numeric_column(my_feature) 
                for my_feature in input_features])

def train_model(
    learning_rate,
    strps,
    batch_size,
    training_examples,
    training_targets,
    validation_examples,
    validation_targets):
    """训练多元特征的线性回归模型
        除训练外，此功能还打印训练进度信息，
        以及随着时间的推移而失去的训练和验证。
    参数:
        learning_rate:一个float，表示学习率
        steps:一个非零的int，训练步骤的总数。训练步骤
            由使用单个批处理的向前和向后传递组成。
        batch_size:一个非零的int
        training_example: DataFrame 包含一个或多个列
        '  california_housing_dataframe '作为训练的输入feature
        training_targets:一个' DataFrame '，它只包含一列
        ' california_housing_dataframe '作为训练的目标。
        validation_example: ' DataFrame '包含一个或多个列
        ' california_housing_dataframe '作为验证的输入feature
        validation_targets: ' DataFrame '，仅包含来自其中的一列
        ' california_housing_dataframe '作为验证的目标。
    返回:
        在训练数据上训练的“线性回归器”对象
    """
    periods = 10
    steps_per_period = strps / periods
    
    # 创建一个线性回归对象
    my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
    my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
    linear_regressor = tf.estimator.LinearRegressor(
        feature_columns=construct_feature_columns(training_examples),
        optimizer=my_optimizer
    )
    
    # 创建输入函数
    training_input_fn = lambda: my_input_fn(
        training_examples,
        training_targets,
        batch_size=batch_size)
    
    predict_training_input_fn = lambda: my_input_fn(
      training_examples, 
      training_targets, 
      num_epochs=1, 
      shuffle=False)
    
    predict_validation_input_fn = lambda: my_input_fn(
        validation_examples, 
        validation_targets,
        num_epochs=1,
        shuffle=False)
    
    #训练模型，但要在循环中进行，这样我们才能定期评估
    #损失指标
    print("Training model...")
    print("RMSE (on training data):")
    training_rmse = []
    validation_rmse = []
    for period in range (0, periods):
      # Train the model, starting from the prior state.
      linear_regressor.train(
          input_fn=training_input_fn,
          steps=steps_per_period,
      )
      # Take a break and compute predictions.
      training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)
      training_predictions = np.array([item['predictions'][0] for item in training_predictions])
      
      validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)
      validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])
      

      # Compute training and validation loss.
      training_root_mean_squared_error = math.sqrt(
          metrics.mean_squared_error(training_predictions, training_targets))
      validation_root_mean_squared_error = math.sqrt(
          metrics.mean_squared_error(validation_predictions, validation_targets))
      # Occasionally print the current loss.
      print("  period %02d : %0.2f" % (period, training_root_mean_squared_error))
      # Add the loss metrics from this period to our list.
      training_rmse.append(training_root_mean_squared_error)
      validation_rmse.append(validation_root_mean_squared_error)
    print("Model training finished.")

    # Output a graph of loss metrics over periods.
    plt.ylabel("RMSE")
    plt.xlabel("Periods")
    plt.title("Root Mean Squared Error vs. Periods")
    plt.tight_layout()
    plt.plot(training_rmse, label="training")
    plt.plot(validation_rmse, label="validation")
    plt.legend()

    return linear_regressor

linear_regressor = train_model(
    learning_rate=0.5,
    strps=200,
    batch_size=5,
    training_examples=X_train,
    training_targets=y_train,
    validation_examples=X_test,
    validation_targets=y_test)

Training model...
RMSE (on training data):
  period 00 : 115236.06
  period 01 : 135729.29
  period 02 : 94007.13
  period 03 : 94940.56
  period 04 : 78776.40
  period 05 : 73407.86
  period 06 : 79100.02
  period 07 : 76995.32
  period 08 : 94657.55
  period 09 : 55721.83
Model training finished.

png

linear_regressor2 = train_model(
    learning_rate=0.25,
    strps=500,
    batch_size=5,
    training_examples=X_train,
    training_targets=y_train,
    validation_examples=X_test,
    validation_targets=y_test)

Training model...
RMSE (on training data):
  period 00 : 126663.18
  period 01 : 89900.76
  period 02 : 67300.53
  period 03 : 73597.13
  period 04 : 59429.95
  period 05 : 60645.34
  period 06 : 57056.42
  period 07 : 55974.51
  period 08 : 59490.64
  period 09 : 59963.44
Model training finished.

png

predict_test_input_fn = lambda: my_input_fn(
      test, 
      test, 
      num_epochs=1, 
      shuffle=False)

test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)
test_predictions = np.array([item['predictions'][0] for item in test_predictions])
test_predictions

array([152403.8 , 178793.02, 186845.23, ..., 203018.39, 142496.02,
       197809.12], dtype=float32)

就先这样吧。