import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression


df=pd.read_csv("train.csv",index_col=False)
df1=pd.read_csv("test.csv",index_col=False)

def handle_non_numerical_data(df):
    columns = df.columns.values
    for column in columns:
        text_digit_vals = {}
        def convert_to_int(val):
            return text_digit_vals[val]

        if df[column].dtype != np.int64 and df[column].dtype != np.float64:
            column_contents = df[column].values.tolist()
            unique_elements = set(column_contents)
            x = 0
            for unique in unique_elements:
                if unique not in text_digit_vals:
                    text_digit_vals[unique] = x
                    x+=1

            df[column] = list(map(convert_to_int, df[column]))

    return df




HouseStyle={"1Story":1,
       "1.5Fin":2,
       "1.5Unf":3,
       "2Story":4,
       "2.5Fin":5,
       "2.5Unf":6,
       "SFoyer":7,
       "SLvl":8}

#print(HouseStyle)
#df['HouseStyle'].update( df['HouseStyle'].map(HouseStyle) )


#print(df['HouseStyle'])
LotShape={"Reg":1,
       "IR1":2,
       "IR2":3,
       "IR3":4}

Utilities={"AllPub":1,
       "NoSewr":2,
       "NoSeWa":3,
       "ELO":4}
LandSlope={"Gtl":1,
       "Mod":2,
       "Sev":3}
ExterQual={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5}
ExterCond={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5}
BsmtQual={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5,
       "NA":6}
BsmtCond={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5,
       "NA":6}
BsmtExposure={
       "Gd":1,
       "Av":2,
       "Mn":3,
       "No":4,
       "NA":5}
BsmtFinType1={"GLQ":1,
       "ALQ":2,
       "BLQ":3,
       "Rec":4,
       "LwQ":5,
       "Unf":6,
       "NA":7}
BsmtFinType2={"GLQ":1,
       "ALQ":2,
       "BLQ":3,
       "Rec":4,
       "LwQ":5,
       "Unf":6,
       "NA":7}
HeatinQC={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5}
Electrical={"SBrkr":1,
       "FuseA":2,
       "FuseF":3,
       "FuseP":4,
       "Mix":5}
KitchenQual={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5}
Functional={"Typ":1,
       "Min1":2,
       "Min2":3,
       "Mod":4,
       "Maj1":5,
       "Maj2":6,
       "Sev":7,
       "Sal":8}
FireplaceQu={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5,
       "NA":6}

GarageFinish={"Fin":1,
       "Rfn":2,
       "Unf":3,
       "NA":4}

GarageQual={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5,
       "NA":6}
GarageCond={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "Po":5,
       "NA":6}
PavedDrive={"Y":1,
       "P":2,
       "N":3}
PoolQC={"Ex":1,
       "Gd":2,
       "TA":3,
       "Fa":4,
       "NA":5}
Fence={"GdPrv":1,
       "MnPrv":2,
       "GdWo":3,
       "MnWw":4,
       "NA":5}


depfeatures={'HouseStyle':HouseStyle,'Fence':Fence,'PoolQC':PoolQC,'PavedDrive':PavedDrive,'GarageCond':GarageCond,
                  'GarageQual':GarageQual,'GarageFinish':GarageFinish,'FireplaceQu':FireplaceQu,'Functional':Functional
             ,'KitchenQual':KitchenQual,'Electrical':Electrical,'BsmtFinType2':BsmtFinType2,'BsmtFinType1':BsmtFinType1,
              'BsmtExposure':BsmtExposure,'BsmtCond':BsmtCond,'BsmtQual':BsmtQual,'ExterCond':ExterCond,'ExterQual':ExterQual,
             'LandSlope':LandSlope,'Utilities':Utilities,'LotShape':LotShape,'HeatinQC':HeatinQC}



df=handle_non_numerical_data(df)
df1=handle_non_numerical_data(df1)

df = df.replace(np.nan, 0, regex=True)
df1 = df1.replace(np.nan, 0, regex=True)


test_ID=df1['Id'].values
del df1['Id']
del df['Id']
y=df['SalePrice'].values
x=df1.values
X=df.loc[:, df.columns != 'SalePrice'].values
w=np.random.rand(len(y))
model=LinearRegression(fit_intercept=True, normalize=True, copy_X=True, n_jobs=1)

model.fit(X,y,w)
y_pred=model.predict(x)

submission = pd.DataFrame({
    "Id": test_ID,
    "SalePrice": y_pred
    })
submission.to_csv('houseprice.csv', index=False)