[Kaggle] Housing Prices 房价预测

import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import FeatureUnion
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score

train = pd.read_csv("./train.csv")
test = pd.read_csv("./test.csv")
# RangeIndex: 1460 entries, 0 to 1459
# Data columns (total 81 columns):

abs(train.corr()['SalePrice']).sort_values(ascending=False).plot.bar()

most_10_important = abs(corrmat["SalePrice"]).sort_values(ascending=False)[1:11].index

sns.pairplot(x_vars=most_10_important[0:5], y_vars=['SalePrice'], data=train, dropna=True)
sns.pairplot(x_vars=most_10_important[5:], y_vars=['SalePrice'], data=train, dropna=True)
# help(sns.pairplot)

#删除异常值
train = train.drop(train[(train['OverallQual']<5)&(train['SalePrice']>200000)].index)
train = train.drop(train[(train['GrLivArea']>4000)&(train['SalePrice']<300000)].index)
train = train.drop(train[(train['YearBuilt']<1900)&(train['SalePrice']>400000)].index)
train = train.drop(train[(train['TotalBsmtSF']>6000)&(train['SalePrice']<200000)].index)
sns.pairplot(x_vars=most_10_important[0:5], y_vars=['SalePrice'], data=train, dropna=True)
sns.pairplot(x_vars=most_10_important[5:], y_vars=['SalePrice'], data=train, dropna=True)
# help(sns.pairplot)

X_train = train[most_10_important]
X_test = test[most_10_important]
y_train = train['SalePrice']

X_train['YearBuilt'] = X_train['YearBuilt'].astype(str)
X_train['YearRemodAdd'] = X_train['YearRemodAdd'].astype(str)
X_test['YearBuilt'] = X_test['YearBuilt'].astype(str)
X_test['YearRemodAdd'] = X_test['YearRemodAdd'].astype(str)

def num_cat_splitor(X_train):
    s = (X_train.dtypes == 'object')
    object_cols = list(s[s].index)
    num_cols = list(set(X_train.columns) - set(object_cols))
    return num_cols, object_cols
num_cols, object_cols = num_cat_splitor(X_train)

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values
        
num_pipeline = Pipeline([
        ('selector', DataFrameSelector(num_cols)),
        ('imputer', SimpleImputer(strategy="median")),
        ('std_scaler', StandardScaler()),
    ])
cat_pipeline = Pipeline([
        ('selector', DataFrameSelector(object_cols)),
        ('cat_encoder', OneHotEncoder(sparse=False,handle_unknown='ignore')),
    ])
full_pipeline = FeatureUnion(transformer_list=[
        ("num_pipeline", num_pipeline),
        ("cat_pipeline", cat_pipeline),
    ])
X_prepared = full_pipeline.fit_transform(X_train)

prepare_select_and_predict_pipeline = Pipeline([
    ('preparation', full_pipeline),
    ('forst_reg', RandomForestRegressor(random_state=0))
])
param_grid = [{
    'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],
    'forst_reg__n_estimators' : [50,100, 150, 200,250,300,330,350],
    'forst_reg__max_features':[45,50, 55, 65]
}]

grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=7,
                                scoring='neg_mean_squared_error', verbose=2, n_jobs=-1)

grid_search_prep.fit(X_train,y_train)
grid_search_prep.best_params_
final_model = grid_search_prep.best_estimator_

y_pred_test = final_model.predict(X_test)
result = pd.DataFrame()
result['Id'] = test['Id']
result['SalePrice'] = y_pred_test
result.to_csv('housing_price_10_features.csv',index=False)