regression_modeling.py 文件源码

def log_regression(wine_set):
    # # examining the data before recoding
    # print(wine_set["sulphates"].describe())
    # wine_set["sulphates_c"] = pd.qcut(wine_set["sulphates"], 4)
    # print(wine_set.groupby("sulphates_c").size())
    # print()
    # #
    # print(wine_set["alcohol"].describe())
    # wine_set["alcohol_c"] = pd.qcut(wine_set["alcohol"], 4)
    # print(wine_set.groupby("alcohol_c").size())
    # print()
    #
    # print(wine_set["quality"].describe())
    # wine_set["quality_c"] = pd.qcut(wine_set["quality"], 3)
    # print(wine_set.groupby("quality_c").size())
    # print()


    # recode quality into 2 groups: 0:{3,4,5,6}, 1:{7,8,9}
    recode = {3: 0, 4: 0, 5:0, 6:0, 7:1, 8:1, 9:1}
    wine_set['quality_c'] = wine_set['quality'].map(recode)

    # recode sulphates into 2 groups: 0: <= mean, 1: > mean
    def sulphates_to_cat(x):
       if x['sulphates'] <= wine_set['sulphates'].mean():
          return 0
       else:
          return 1
    wine_set['sulphates_c'] = wine_set.apply(lambda x: sulphates_to_cat(x), axis=1)

    # recode alcohol into 2 groups: 0: <= mean , 1: > mean
    def alcohol_to_cat(x):
       if x['alcohol'] <= wine_set['alcohol'].mean():
          return 0
       else:
          return 1
    wine_set['alcohol_c'] = wine_set.apply(lambda x: alcohol_to_cat(x), axis=1)
    # print(wine_set.head(10))

    # logistic regression for sulphates+alcohol -> quality
    print ("Logistic regression model for the association between wine's quality and sulphates&alcohol")
    model1 = smf.logit(formula="quality_c ~ sulphates_c + alcohol_c", data=wine_set)
    results1 = model1.fit()
    print(results1.summary())

    # odds ratios with 95% confidence intervals
    print("\nConfidence intervals")
    conf = results1.conf_int()
    conf['Odds ratio'] = results1.params
    conf.columns = ['Lower conf.int.', 'Upper conf.int.', 'Odds ratio']
    print(numpy.exp(conf))