Finished the homework04.

2021-11-21 22:41:54 +08:00
parent 16de235b4c
commit 44f6362737
8 changed files with 233 additions and 14 deletions
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+'''
+Author: SJ2050
+Date: 2021-11-21 17:22:02
+LastEditTime: 2021-11-21 22:05:09
+Version: v0.0.1
+Description: Use softmax regression method to solve multiclass classification problems.
+Copyright © 2021 SJ2050
+'''
+
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_digits
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+from softmax_regression import SoftmaxRegression
+
+# load data
+digits = load_digits()
+x_train = digits.data[:-500]
+y_train = digits.target[:-500]
+softmax_reg = SoftmaxRegression()
+softmax_reg.train(x_train, y_train)
+
+# plot confusion matrix
+x_test = digits.data[-500:]
+y_test = digits.target[-500:]
+pred_train = softmax_reg.predict(x_train)
+pred_test = softmax_reg.predict(x_test)
+
+print(f'accuracy train = {accuracy_score(y_train, pred_train)}')
+print(f'accuracy test = {accuracy_score(y_test, pred_test)}')
+
+cm = confusion_matrix(y_test, pred_test)
+plt.matshow(cm)
+plt.title(u'Confusion Matrix')
+plt.colorbar()
+plt.ylabel(u'Groundtruth')
+plt.xlabel(u'Predict')
+plt.show()
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+'''
+Author: SJ2050
+Date: 2021-11-21 18:24:41
+LastEditTime: 2021-11-21 18:50:47
+Version: v0.0.1
+Description: Use sklearn to solve logistic regression problems.
+Copyright © 2021 SJ2050
+'''
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_digits
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+
+# load data
+digits = load_digits()
+x_train = digits.data[:-500]
+y_train = digits.target[:-500]
+
+log_reg=LogisticRegression()
+log_reg.fit(x_train, y_train)
+
+# plot confusion matrix
+x_test = digits.data[-500:]
+y_test = digits.target[-500:]
+pred_train = log_reg.predict(x_train)
+pred_test = log_reg.predict(x_test)
+
+print(f'accuracy train = {accuracy_score(y_train, pred_train)}')
+print(f'accuracy test = {accuracy_score(y_test, pred_test)}')
+
+cm = confusion_matrix(y_test, pred_test)
+plt.matshow(cm)
+plt.title(u'Confusion Matrix')
+plt.colorbar()
+plt.ylabel(u'Groundtruth')
+plt.xlabel(u'Predict')
+plt.show()
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+'''
+Author: SJ2050
+Date: 2021-11-21 17:06:31
+LastEditTime: 2021-11-21 22:29:52
+Version: v0.0.1
+Description: Softmax regerssion.
+Copyright © 2021 SJ2050
+'''
+import numpy as np
+
+def softmax(Z):
+  assert(len(Z.shape) == 2 and Z.shape[1] == 1, 'Z should be a column vector!')
+  Z_exp = np.exp(Z)
+  return Z_exp/Z_exp.sum(0, keepdims=True)
+
+class SoftmaxRegression():
+  def __init__(self):
+    self.is_trained = False
+    pass
+
+  def train(self, train_data, train_label, num_iterations=150, alpha=0.01):
+    self.train_data = train_data
+    self.train_label = train_label
+    self.classes = np.unique(self.train_label)
+    self.out_dim = len(self.classes)
+
+    train_data_num, self.inp_dim = np.shape(self.train_data)
+    self.weights = np.random.random((self.inp_dim, self.out_dim))
+    self.b = np.random.random((self.out_dim, 1))
+
+    y = lambda k, cls: 1 if k == cls else 0
+    weights_grad = [[] for i in range(self.out_dim)]
+    for j in range(num_iterations):
+      # print(f'iteration: {j}')
+      data_index = list(range(train_data_num))
+      for i in range(train_data_num):
+        rand_index = int(np.random.uniform(0, len(data_index)))
+        # x_vec = np.vstack(self.train_data[rand_index])
+        x_vec = self.train_data[rand_index].reshape(-1, 1)
+        softmax_values = softmax(np.dot(self.weights.T, x_vec)+self.b)[:, 0]
+        label =self.train_label[rand_index]
+        cls = np.argwhere(self.classes == label)[0][0]
+        error = lambda k: y(k, cls)-softmax_values[k]
+
+        for k in range(self.out_dim):
+          err = error(k)
+          # self.weights += np.pad(alpha*err*x_vec, ((0, 0), (k, self.out_dim-1-k)), \
+          #                                             'constant', constant_values=0)
+          weights_grad[k] = (alpha*err*x_vec)[:, 0]
+          # print(self.weights)
+          self.b[k, 0] += alpha*err
+        self.weights += np.array(weights_grad).T
+
+        del(data_index[rand_index])
+
+    self.is_trained = True
+
+
+  def predict(self, predict_data):
+    if self.is_trained:
+      predict_num = len(predict_data)
+      result = np.empty(predict_num)
+      for i in range(predict_num):
+        # x_vec = np.vstack(predict_data[i])
+        x_vec = predict_data[i].reshape(-1, 1)
+        result[i] = self.classes[np.argmax(softmax(np.dot(self.weights.T, x_vec)+self.b))]
+
+      return result
+    else:
+      print('Need training before predicting!!')
+
+if __name__ == '__main__':
+  # test binary classsfication
+  import matplotlib.pyplot as plt
+  import sklearn.datasets
+  from sklearn.metrics import accuracy_score
+
+  def plot_decision_boundary(predict_func, data, label):
+    """画出结果图
+    Args:
+        pred_func (callable): 预测函数
+        data (numpy.ndarray): 训练数据集合
+        label (numpy.ndarray): 训练数据标签
+    """
+    x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5
+    y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5
+    h = 0.01
+
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+
+    Z = predict_func(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)   #画出登高线并填充
+    plt.scatter(data[:, 0], data[:, 1], c=label, cmap=plt.cm.Spectral)
+    plt.show()
+
+  data, label = sklearn.datasets.make_moons(200, noise=0.30)
+  plt.scatter(data[:,0], data[:,1], c=label)
+  plt.title("Original Data")
+
+  softmax_reg = SoftmaxRegression()
+  softmax_reg.train(data, label, 200)
+  plot_decision_boundary(lambda x: softmax_reg.predict(x), data, label)
+  y_train = softmax_reg.predict(data)
+  print(f'accuracy train = {accuracy_score(label, y_train)}')