chore: import upstream snapshot with attribution

2026-07-13 13:38:23 +08:00
commit 2725b63d23
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+# Python Machine Learning by Sebastian Raschka, Packt Publishing Ltd. 2015
+# Code Repository: https://github.com/rasbt/python-machine-learning-book
+# Code License: MIT License
+
+import numpy as np
+import sys
+
+
+class NeuralNetMLP(object):
+    """ Feedforward neural network / Multi-layer perceptron classifier.
+
+    Parameters
+    ------------
+    n_hidden : int (default: 30)
+        Number of hidden units.
+    l2 : float (default: 0.)
+        Lambda value for L2-regularization.
+        No regularization if l2=0. (default)
+    epochs : int (default: 100)
+        Number of passes over the training set.
+    eta : float (default: 0.001)
+        Learning rate.
+    shuffle : bool (default: True)
+        Shuffles training data every epoch if True to prevent circles.
+    minibatche_size : int (default: 1)
+        Number of training samples per minibatch.
+    seed : int (default: None)
+        Random seed for initializing weights and shuffling.
+
+    Attributes
+    -----------
+    eval_ : dict
+      Dictionary collecting the cost, training accuracy,
+      and validation accuracy for each epoch during training.
+
+    """
+    def __init__(self, n_hidden=30,
+                 l2=0., epochs=100, eta=0.001,
+                 shuffle=True, minibatch_size=1, seed=None):
+
+        self.random = np.random.RandomState(seed)
+        self.n_hidden = n_hidden
+        self.l2 = l2
+        self.epochs = epochs
+        self.eta = eta
+        self.shuffle = shuffle
+        self.minibatch_size = minibatch_size
+
+    def _onehot(self, y, n_classes):
+        """Encode labels into one-hot representation
+
+        Parameters
+        ------------
+        y : array, shape = [n_samples]
+            Target values.
+
+        Returns
+        -----------
+        onehot : array, shape = (n_samples, n_labels)
+
+        """
+        onehot = np.zeros((n_classes, y.shape[0].astype(int)))
+        for idx, val in enumerate(y):
+            onehot[val, idx] = 1.
+        return onehot.T
+
+    def _sigmoid(self, z):
+        """Compute logistic function (sigmoid)"""
+        return 1. / (1. + np.exp(-np.clip(z, -250, 250)))
+
+    def _forward(self, X):
+        """Compute forward propagation step"""
+
+        # step 1: net input of hidden layer
+        # [n_samples, n_features] dot [n_features, n_hidden]
+        # -> [n_samples, n_hidden]
+        z_h = np.dot(X, self.w_h) + self.b_h
+
+        # step 2: activation of hidden layer
+        a_h = self._sigmoid(z_h)
+
+        # step 3: net input of output layer
+        # [n_samples, n_hidden] dot [n_hidden, n_classlabels]
+        # -> [n_samples, n_classlabels]
+
+        z_out = np.dot(a_h, self.w_out) + self.b_out
+
+        # step 4: activation output layer
+        a_out = self._sigmoid(z_out)
+
+        return z_h, a_h, z_out, a_out
+
+    def _compute_cost(self, y_enc, output):
+        """Compute cost function.
+
+        Parameters
+        ----------
+        y_enc : array, shape = (n_samples, n_labels)
+            one-hot encoded class labels.
+        output : array, shape = [n_samples, n_output_units]
+            Activation of the output layer (forward propagation)
+
+        Returns
+        ---------
+        cost : float
+            Regularized cost
+
+        """
+        L2_term = (self.l2 *
+                   (np.sum(self.w_h ** 2.) +
+                    np.sum(self.w_out ** 2.)))
+
+        term1 = -y_enc * (np.log(output))
+        term2 = (1. - y_enc) * np.log(1. - output)
+        cost = np.sum(term1 - term2) + L2_term
+        return cost
+
+    def predict(self, X):
+        """Predict class labels
+
+        Parameters
+        -----------
+        X : array, shape = [n_samples, n_features]
+            Input layer with original features.
+
+        Returns:
+        ----------
+        y_pred : array, shape = [n_samples]
+            Predicted class labels.
+
+        """
+        z_h, a_h, z_out, a_out = self._forward(X)
+        y_pred = np.argmax(z_out, axis=1)
+        return y_pred
+
+    def fit(self, X_train, y_train, X_valid, y_valid):
+        """ Learn weights from training data.
+
+        Parameters
+        -----------
+        X_train : array, shape = [n_samples, n_features]
+            Input layer with original features.
+        y_train : array, shape = [n_samples]
+            Target class labels.
+        X_valid : array, shape = [n_samples, n_features]
+            Sample features for validation during training
+        y_valid : array, shape = [n_samples]
+            Sample labels for validation during training
+
+        Returns:
+        ----------
+        self
+
+        """
+        n_output = np.unique(y_train).shape[0]  # number of class labels
+        n_features = X_train.shape[1]
+
+        ########################
+        # Weight initialization
+        ########################
+
+        # weights for input -> hidden
+        self.b_h = np.zeros(self.n_hidden)
+        self.w_h = self.random.normal(loc=0.0, scale=0.1,
+                                      size=(n_features, self.n_hidden))
+
+        # weights for hidden -> output
+        self.b_out = np.zeros(n_output)
+        self.w_out = self.random.normal(loc=0.0, scale=0.1,
+                                        size=(self.n_hidden, n_output))
+
+        epoch_strlen = len(str(self.epochs))  # for progress formatting
+        self.eval_ = {'cost': [], 'train_acc': [], 'valid_acc': []}
+
+        y_train_enc = self._onehot(y_train, n_output)
+
+        # iterate over training epochs
+        for i in range(self.epochs):
+
+            # iterate over minibatches
+            indices = np.arange(X_train.shape[0])
+
+            if self.shuffle:
+                self.random.shuffle(indices)
+
+            for start_idx in range(0, indices.shape[0] - self.minibatch_size +
+                                   1, self.minibatch_size):
+                batch_idx = indices[start_idx:start_idx + self.minibatch_size]
+
+                # forward propagation
+                z_h, a_h, z_out, a_out = self._forward(X_train[batch_idx])
+
+                ##################
+                # Backpropagation
+                ##################
+
+                # [n_samples, n_classlabels]
+                sigma_out = a_out - y_train_enc[batch_idx]
+
+                # [n_samples, n_hidden]
+                sigmoid_derivative_h = a_h * (1. - a_h)
+
+                # [n_samples, n_classlabels] dot [n_classlabels, n_hidden]
+                # -> [n_samples, n_hidden]
+                sigma_h = (np.dot(sigma_out, self.w_out.T) *
+                           sigmoid_derivative_h)
+
+                # [n_features, n_samples] dot [n_samples, n_hidden]
+                # -> [n_features, n_hidden]
+                grad_w_h = np.dot(X_train[batch_idx].T, sigma_h)
+                grad_b_h = np.sum(sigma_h, axis=0)
+
+                # [n_hidden, n_samples] dot [n_samples, n_classlabels]
+                # -> [n_hidden, n_classlabels]
+                grad_w_out = np.dot(a_h.T, sigma_out)
+                grad_b_out = np.sum(sigma_out, axis=0)
+
+                # Regularization and weight updates
+                delta_w_h = (grad_w_h + self.l2*self.w_h)
+                delta_b_h = grad_b_h  # bias is not regularized
+                self.w_h -= self.eta * delta_w_h
+                self.b_h -= self.eta * delta_b_h
+
+                delta_w_out = (grad_w_out + self.l2*self.w_out)
+                delta_b_out = grad_b_out  # bias is not regularized
+                self.w_out -= self.eta * delta_w_out
+                self.b_out -= self.eta * delta_b_out
+
+            #############
+            # Evaluation
+            #############
+
+            # Evaluation after each epoch during training
+            z_h, a_h, z_out, a_out = self._forward(X_train)
+            cost = self._compute_cost(y_enc=y_train_enc,
+                                      output=a_out)
+
+            y_train_pred = self.predict(X_train)
+            y_valid_pred = self.predict(X_valid)
+
+            train_acc = ((np.sum(y_train == y_train_pred)).astype(np.float) /
+                         X_train.shape[0])
+            valid_acc = ((np.sum(y_valid == y_valid_pred)).astype(np.float) /
+                         X_valid.shape[0])
+
+            sys.stderr.write('\r%0*d/%d | Cost: %.2f '
+                             '| Train/Valid Acc.: %.2f%%/%.2f%% ' %
+                             (epoch_strlen, i+1, self.epochs, cost,
+                              train_acc*100, valid_acc*100))
+            sys.stderr.flush()
+
+            self.eval_['cost'].append(cost)
+            self.eval_['train_acc'].append(train_acc)
+            self.eval_['valid_acc'].append(valid_acc)
+
+        return self