machine_learn/logistic/train.py

# -*- encoding:utf-8 -*-
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict
from numpy import shape
from sklearn import metrics
from sklearn.metrics import log_loss
import numpy as np
import matplotlib.pyplot as plt


def read_data(path):
    with open(path) as f:
        lines = f.readlines()
    lines = [eval(line.strip()) for line in lines]
    X, y = zip(*lines)
    X = np.array(X)
    y = np.array(y)
    return X, y


def curve(x_train, w, w0):
    results = x_train.tolist()
    for i in range(0, 100):
        x1 = 1.0 * i / 10
        x2 = -1 * (w[0] * x1 + w0) / w[1]
        results.append([x1, x2])
    results = ["{},{}".format(x1, x2) for [x1, x2] in results]
    return results


def drawScatterAndLine(p, q):
    x1 = []
    x2 = []
    y1 = []
    y2 = []

    for idx,i in enumerate(q):
        if i == 0:
            x1.append(p[idx][0])
            y1.append(p[idx][1])
        else:
            x2.append(p[idx][0])
            y2.append(p[idx][1])

    plt.scatter(x1, y1)
    plt.scatter(x2, y2)
    plt.xlabel('p')
    plt.ylabel('q')
    plt.title('line regesion')
    plt.show()


def main():
    X_train, y_train = read_data("train_data")
    drawScatterAndLine(X_train, y_train)
    X_test, y_test = read_data("test_data")

    model = LogisticRegression()
    model.fit(X_train, y_train)

    print "w", model.coef_
    print "w0", model.intercept_

    y_pred = model.predict(X_test)
    print y_pred
    # y_pred = model.predict_proba(X_test)
    # print y_pred
    # loss=log_loss(y_test,y_pred)
    # print "KL_loss:",loss
    # loss=log_loss(y_pred,y_test)
    # print "KL_loss:",loss
    '''
    curve_results=curve(X_train,model.coef_.tolist()[0],model.intercept_.tolist()[0])
    with open("train_with_splitline","w") as f :
        f.writelines("\n".join(curve_results))
    '''

if __name__ == '__main__':
    main()