Python_机器学习_李宏毅老师作业二

概述#Author:Richardimportnumpyasnpimportmatplotlib.pyplotaspltnp.random.seed(0)#使得每次生成的随机数相同X_train_path=r"G:\课程学习\机器学习\Mr_Li_ML\HomeWorks\数据\hw2\data\X_train"Y_train_path=r"G:\课程学习\机器学习\Mr_Li_ML\HomeWorks\数

# Author:Richardimport numpy as npimport matplotlib.pyplot as pltnp.random.seed(0)  # 使得每次生成的随机数相同X_train_path = r"G:\课程学习\机器学习\Mr_li_ML\HomeWorks\数据\hw2\data\X_train"Y_train_path = r"G:\课程学习\机器学习\Mr_li_ML\HomeWorks\数据\hw2\data\Y_train"X_test_path = r"G:\课程学习\机器学习\Mr_li_ML\HomeWorks\数据\hw2\data\X_test"# 将数据转成numpy格式with open(X_train_path) as file:    head = next(file)  # 提取投文件，Str格式    # print(type(head),head[0])    X_train = np.array([line.strip('\n').split(',')[1:] for line in file], dtype=float)    # print(X_train.shape)  #(54256,510)with open(Y_train_path) as file:    head = next(file)    Y_train = np.array([line.strip('\n').split(',')[1] for line in file], dtype=float)    # print(Y_train.shape)  #(5425611)with open(X_test_path) as file:    head = next(file)    X_test = np.array([line.strip('\n').split(',')[1:] for line in file], dtype=float)    # print(X_test.shape)   #(27622,510)# set a normalize functiondef _normalize(X, train=True, specifIEd_column=None, X_mean=None, X_std=None):    # This function normalizes specific columns of X.    # The mean and standard variance of training data will be reused when processing testing data.    #    # Arguments:    #     X: data to be processed    #     train: 'True' when processing training data, 'False' for testing data    #     specific_column: indexes of the columns that will be normalized. If 'None', all columns    #         will be normalized.    #     X_mean: mean value of training data, used when train = 'True'    #     X_std: standard deviation of training data, used when train = 'True'    # Outputs:    #     X: normalized data    #     X_mean: computed mean value of training data    #     X_std: computed standard deviation of training data    if specifIEd_column == None:        specifIEd_column = np.arange(X.shape[1])    if train:        X_mean = np.mean(X[:, specifIEd_column], axis=0)        X_std = np.std(X[:, specifIEd_column], axis=0)    for i in range(X.shape[0]):        for j in range(X.shape[1]):            if X_std[j] != 0:                X[i, j] = (X[i, j] - X_mean[j]) / X_std[j]    return X, X_mean, X_std# 标准化训练数据和测试数据X_train, X_mean, X_std = _normalize(X_train, train=True)X_test, _, _ = _normalize(X_test, train=False, X_mean=X_mean, X_std=X_std)# _变量用来存储函数返回的无用值# 将数据分成训练集和验证集  9:1ratio = 0.9train_len = int(len(X_train) * ratio)# X_train = X_train[:train_len]# Y_train = Y_train[:train_len]# X_dev = X_train[train_len:]# Y_dev = Y_train[train_len:]X_train0 = X_trainY_train0 = Y_trainX_train = X_train0[:train_len]Y_train = Y_train0[:train_len]X_dev = X_train0[train_len:]Y_dev = Y_train0[train_len:]#train_size = X_train.shape[0]dev_size = X_dev.shape[0]test_size = X_test.shape[0]data_dim = X_train.shape[1]# print('Size of training set: {}'.format(train_size))# print('Size of development set: {}'.format(dev_size))# print('Size of testing set: {}'.format(test_size))# print('Dimension of data: {}'.format(data_dim))#### Size of training set: 48830# Size of development set: 5426# Size of testing set: 27622# imension of data: 510###def _shuffle(X, Y):    # This function shuffles two equal-length List/array, X and Y, together.    randomize = np.arange(len(X))    np.random.shuffle(randomize)    return (X[randomize], Y[randomize])def _sigmoID(z):    # sigmoID function can be used to calculate probability    # to avoID overflow, min/max value is set    return np.clip(1.0 / (1.0 + np.exp(-z)), 1e-8, 1 - 1e-8)def _f(X, w, b):    # This is the logistic regression function, parameterized by w and b    #    # Arguements:    #     X: input data, shape = [batch_size, data_dimension]    #     w: weight vector, shape = [data_dimension, ]    #     b: bias, scalar    # Output:  numpy.matmul 函数返回两个数组的矩阵乘积    #     predicted probability of each row of X being positively labeled, shape = [batch_size, ]    return _sigmoID(np.matmul(X, w) + b)def _predict(X, w, b):    # This function returns a truth value prediction for each row of X    # by rounding the result of logistic regression function.    # return np.round(_f(X,w,b)).astype(np.int)  #原则：对于浮点型数据，四舍六入，正好一半就搞到偶数，和文中说的不太一样 修改    # return 1 if _f(X, w, b) >= 0.5 else 0    f = _f(X, w, b)    f[f >= 0.5] = 1    f[f < 0.5] = 0    return fdef _accuracy(Y_pred, Y_label):    # this function calculate presiction accuracy    acc = 1 - np.mean(np.abs(Y_pred - Y_label))    # acc = 1 - np.abs(Y_pred - Y_label)    return accdef _cross_entropy_loss(Y_pred, Y_label):    # This function computes the cross entropy.    #    # Arguements:    #     y_pred: probabiListic predictions, float vector    #     Y_label: ground truth labels, bool vector    # Output:    #     cross entropy, scalar    cross_entropy = -np.dot(Y_label, np.log(Y_pred)) - np.dot((1 - Y_label), np.log(1 - Y_pred))    return cross_entropydef _gradIEnt(X, Y_label, w, b):    # This function computes the gradIEnt of cross entropy loss with respect to weight w and bias b.    y_pred = _f(X, w, b)    pred_error = Y_label - y_pred    w_grad = -np.sum(pred_error * X.T, 1)    b_grad = -np.sum(pred_error)    return w_grad, b_grad# 初始化权重w和b 都为0w = np.zeros((data_dim,))b = np.zeros((1,))# 训练时的超参数max_iter = 20batch_size = 8learning_rate = 0.05# 保存每个iteration的loss和accuracy,方便画图train_loss = []dev_loss = []train_acc = []dev_acc = []# 累计参数更新的次数step = 1# 迭代训练for epoch in range(max_iter):    # 在每个epoch开始时，随机打散训练数据    X_train, Y_train = _shuffle(X_train, Y_train)    # Mini-batch训练    for IDx in range(int(np.floor(train_size / batch_size))):        X = X_train[IDx * batch_size:(IDx + 1) * batch_size]        Y = Y_train[IDx * batch_size:(IDx + 1) * batch_size]        # calculate gradIEnt        # 学习率随着时间衰减        w_grad, b_grad = _gradIEnt(X, Y, w, b)        w = w - learning_rate / np.sqrt(step) * w_grad        b = b - learning_rate / np.sqrt(step) * b_grad        #        step += 1    # 计算训练集合测试集的loss和accuracy    # Y_train_pred = _predict(X_train, w, b)    # for i in range(len(Y_train_pred)):    #     train_acc.append(_accuracy(Y_train_pred[i], Y_train[i]))    #     train_loss.append(_cross_entropy_loss(Y_train_pred[i], Y_train[i]) / train_size)    # Y_dev_pred = _predict(X_dev, w, b)    # for i in range(len(Y_dev_pred)):    #     dev_acc.append(_accuracy(Y_dev_pred[i], Y_dev[i]))    #     dev_loss.append(_cross_entropy_loss(Y_dev_pred[i], Y_dev[i]) / dev_size)    y_train_pred = _f(X_train, w, b)    # Y_train_pred = np.round(y_train_pred)    Y_train_pred = _predict(X_train, w, b)    train_acc.append(_accuracy(Y_train_pred, Y_train))    train_loss.append(_cross_entropy_loss(y_train_pred, Y_train) / train_size)    y_dev_pred = _f(X_dev, w, b)    # Y_dev_pred = np.round(y_dev_pred)    Y_dev_pred = _predict(X_dev, w, b)    dev_acc.append(_accuracy(Y_dev_pred, Y_dev))    dev_loss.append(_cross_entropy_loss(y_dev_pred, Y_dev) / dev_size)print('Training loss: {}'.format(train_loss[-1]))print('Development loss: {}'.format(dev_loss[-1]))print('Training accuracy: {}'.format(train_acc[-1]))print('Development accuracy: {}'.format(dev_acc[-1]))print('weight_hw2.npy', w)# Loss curveplt.plot(train_loss)plt.plot(dev_loss)plt.Title("Loss")plt.legend(['train', 'dev'])plt.show()# accuracy curveplt.plot(train_acc)plt.plot(dev_acc)plt.Title("Accuracy")plt.legend(['train', 'dev'])plt.show()

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