#!/usr/bin/python

import pandas as pd

import numpy as np

import csv as csv

import matplotlib

import matplotlib.pyplot as plt

from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV

from sklearn.model_selection import cross_val_score

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train = pd.read_csv('train.csv', header=0)        # Load the train file into a dataframe

df = pd.get_dummies(train.iloc[:,1:-1])

df = df.fillna(df.mean())

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X_train = df

y = train.price

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def rmse_cv(model):

    rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring="neg_mean_squared_error", cv = 3))

    return(rmse)

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#调用LassoCV函数，并进行交叉验证，默认cv=3

model_lasso = LassoCV(alphas = [0.1,1,0.001, 0.0005]).fit(X_train, y)

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#模型所选择的最优正则化参数alpha

print(model_lasso.alpha_)

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#各特征列的参数值或者说权重参数，为0代表该特征被模型剔除了

print(model_lasso.coef_)

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#输出看模型最终选择了几个特征向量，剔除了几个特征向量

coef = pd.Series(model_lasso.coef_, index = X_train.columns)

print("Lasso picked " + str(sum(coef != 0)) + " variables and eliminated the other " +  str(sum(coef == 0)) + " variables")

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#输出所选择的最优正则化参数情况下的残差平均值，因为是3折，所以看平均值

print(rmse_cv(model_lasso).mean())

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#画出特征变量的重要程度，这里面选出前3个重要，后3个不重要的举例

imp_coef = pd.concat([coef.sort_values().head(3),

                     coef.sort_values().tail(3)])

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matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)

imp_coef.plot(kind = "barh")

plt.title("Coefficients in the Lasso Model")

plt.show()