Implement transforms for normalization

requirements.txt

@@ -1,4 +1,5 @@
 tikzplotlib
 torch
+sklearn
 pytest
 json5

src/util/transform.py

@@ -0,0 +1,72 @@
+import torch
+from torch import nn
+
+from sklearn import preprocessing
+
+class Transform(nn.Module):
+    """
+    Base class to normalize observations and actions for network.
+    """
+
+    def __init__(self):
+        super(Transform, self).__init__()
+        # self.fit(X)
+
+    def fit(self, X):
+        """
+        Fit transformer to X
+        Args:
+            X (torch.tensor): (B, N) tensor of B data points with N features
+        """
+        raise NotImplementedError('Please implement fit()')
+
+    def transform(self, X):
+        """
+        Transform X. fit() has to be called first
+        Args:
+            X (torch.tensor): (B, N) tensor where N has to be the same as during fit()
+        """
+        raise NotImplementedError('Please implement transform()')
+
+    def inverse_transform(self, X):
+        """
+        Inverse transformation
+        Args:
+            X (torch.tensor): (B, N) tensor
+        """
+        raise NotImplementedError('Please implement inverse_transform()')
+
+    def forward(self, X):
+        return self.transform(X)
+
+class SciKitTransform(Transform):
+    """
+    Wrappers around scikit-learn transforms
+    """
+    def __init__(self, tf):
+        self.tf = tf
+        super(SciKitTransform, self).__init__()
+
+    def fit(self, X):
+        self.tf.fit(X)
+
+    def transform(self, X):
+        return torch.tensor(self.tf.transform(X), dtype=torch.float)
+
+    def inverse_transform(self, X):
+        return torch.tensor(self.tf.inverse_transform(X), dtype=torch.float)
+
+class SciKitStandardScaler(SciKitNormalization):
+    """
+    Wrapper around scikit-learn's StandardScaler for standardizing each feature individually.
+    """
+    def __init__(self):
+        super(SciKitStandardScaler, self).__init__(preprocessing.StandardScaler())
+
+class SciKitMinMaxScaler(SciKitNormalization):
+    """
+    Wrapper around scikit-learn's MinMaxScaler for scaling features to [0, 1] individually.
+    """
+    def __init__(self):
+        super(SciKitMinMaxScaler, self).__init__(preprocessing.MinMaxScaler())
+