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Author: Mayurji

Day-17-K-Medoids/utility.py

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+import torch
+from sklearn.metrics import accuracy_score
+import numpy as np
+
+class SquareLoss:
+    def __init__(self):
+        pass
+
+    def loss(self, y, y_pred):
+        return 0.5 * torch.pow((y - y_pred), 2)
+
+    def gradient(self, y, y_pred):
+        return -(y - y_pred)
+
+class CrossEntropy:
+    def __init__(self):
+        pass
+
+    def loss(self, y, p):
+        p = torch.clip(p, 1e-15, 1 - 1e-15)
+        return - y * torch.log(p) - (1 - y) * torch.log(1 - p)
+
+    def accuracy(self, y, p):
+        return accuracy_score(torch.argmax(y, dim=1), torch.argmax(p, dim=1))
+
+    def gradient(self, y, p):
+        p = torch.clip(p, 1e-15, 1 - 1e-15)
+        return -(y/p) + (1-y) / (1-p)
+
+def euclidean_distance(x1, x2):
+    """
+    :param x1: input tensor
+    :param x2: input tensor
+    :return: distance between tensors
+    """
+
+    return torch.cdist(x1.unsqueeze(0), x2.unsqueeze(0))
+
+def to_categorical(X, n_columns=None):
+    if not n_columns:
+        n_columns = torch.amax(X) + 1
+    one_hot = torch.zeros((X.shape[0], n_columns))
+    one_hot[torch.arange(X.shape[0])] = 1
+    return one_hot
+
+def mean_squared_error(y_true, y_pred):
+    mse = torch.mean(torch.pow(y_true - y_pred, 2))
+    return mse
+
+def divide_on_feature(X, feature_i, threshold):
+
+    split_func = None
+    if isinstance(threshold, int) or isinstance(threshold, float):
+        split_func = lambda sample: sample[feature_i] >= threshold
+    else:
+        split_func = lambda sample: sample[feature_i] == threshold
+
+
+    X_1 = torch.tensor([sample.numpy() for sample in X if split_func(sample)])
+    X_2 = torch.tensor([sample.numpy() for sample in X if not split_func(sample)])
+
+    return np.array([X_1.numpy(), X_2.numpy()], dtype='object')
+
+def calculate_variance(X):
+    mean = torch.ones(X.shape) * torch.mean(X, dim=0)
+    n_samples = X.shape[0]
+    variance = (1/ n_samples) * torch.diag(torch.mm((X-mean).T, (X-mean)))
+    return variance