Kaggle：Predicting a Biological Response-优快云博客

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    # 基本CSV读写操作
    # 我们需要读取给定的训练数据，再进行后续的数据(特征等)处理

    def read_data(file_name):
        f = open(file_name)
        #ignore header
        f.readline()
        samples = []
        target = []
        for line in f:
            line = line.strip().split(",")
            sample = [float(x) for x in line]
            samples.append(sample)
        return samples

    def write_delimited_file(file_path, data,header=None, delimiter=","):
        f_out = open(file_path,"w")
        if header is not None:
            f_out.write(delimiter.join(header) + "\n")
        for line in data:
            if isinstance(line, str):
                f_out.write(line + "\n")
            else:
                f_out.write(delimiter.join(line) + "\n")
        f_out.close()

from sklearn.linear_model import LogisticRegression
import csv_io
import math
import scipy

def train_and_predict():
    #read in the training file
    train = read_data("train.csv")
    print '读取训练数据完毕\n...\n'
    #set the training responses
    target = [x[0] for x in train]
    #set the training features
    train = [x[1:] for x in train]
    #read in the test file
    realtest = read_data("test.csv")
    print '读取待预测数据\n...\n'

    # code for logistic regression
    lr = LogisticRegression()
    lr.fit(train, target)
    print 'Logistic Regression训练完毕!\n...\n'
    predicted_probs = lr.predict_proba(realtest)

    # write solutions to file
    predicted_probs = ["%f" % x[1] for x in predicted_probs]
    write_delimited_file("lr_solution.csv", predicted_probs)

    print 'Logistic Regression预测完毕! 请提交lr_solution.csv文件到Kaggle'

if __name__=="__main__":
    train_and_predict()