Remove torch dependency from data.py

Author: Max Keller

.travis.yml

@@ -22,7 +22,7 @@ after_success:
 matrix:
   include:
     install:
-      - pip install numpy==1.20 scikit-learn==0.18 scipy==0.18
+      - pip install numpy==1.20 scikit-learn==0.18 scipy==0.18 torch==1.8.1
       - pip install codecov
       - pip install coverage
       - pip install .

modAL/utils/data.py

@@ -3,7 +3,6 @@
 import numpy as np
 import pandas as pd
 import scipy.sparse as sp
-import torch
 
 modALinput = Union[sp.csr_matrix, pd.DataFrame, np.ndarray, list]
 
@@ -26,8 +25,6 @@ def data_vstack(blocks: Sequence[modALinput]) -> modALinput:
         return np.concatenate(blocks)
     elif isinstance(blocks[0], list):
         return np.concatenate(blocks).tolist()
-    elif torch.is_tensor(blocks[0]):
-        return torch.cat(blocks)
 
     raise TypeError('%s datatype is not supported' % type(blocks[0]))
 
@@ -50,8 +47,6 @@ def data_hstack(blocks: Sequence[modALinput]) -> modALinput:
         return np.hstack(blocks)
     elif isinstance(blocks[0], list):
         return np.hstack(blocks).tolist()
-    elif torch.is_tensor(blocks[0]):
-        return torch.cat(blocks, dim=1)
 
     TypeError('%s datatype is not supported' % type(blocks[0]))
 
@@ -65,8 +60,6 @@ def add_row(X: modALinput, row: modALinput):
     row]    """
     if isinstance(X, np.ndarray):
         return np.vstack((X, row))
-    elif torch.is_tensor(X):
-        return torch.cat((X, row))
     elif isinstance(X, list):
         return np.vstack((X, row)).tolist()
 
@@ -107,8 +100,6 @@ def retrieve_rows(X: modALinput,
         return X_return
     elif isinstance(X, np.ndarray):
         return X[I]
-    elif torch.is_tensor(X):
-        return X[I]
 
     raise TypeError('%s datatype is not supported' % type(X))
 
@@ -128,9 +119,6 @@ def drop_rows(X: modALinput,
         return np.delete(X, I, axis=0)
     elif isinstance(X, list):
         return np.delete(X, I, axis=0).tolist()
-    elif torch.is_tensor(X):
-        return X[[True if row not in I else False
-                  for row in range(X.size(0))]]
 
     raise TypeError('%s datatype is not supported' % type(X))
 
@@ -149,8 +137,8 @@ def enumerate_data(X: modALinput):
         return enumerate(X.tocsr())
     elif isinstance(X, pd.DataFrame):
         return X.iterrows()
-    elif isinstance(X, np.ndarray) or isinstance(X, list) or torch.is_tensor(X):
-        # numpy arrays, torch tensors and lists can readily be enumerated
+    elif isinstance(X, np.ndarray) or isinstance(X, list):
+        # numpy arrays and lists can readily be enumerated
         return enumerate(X)
 
     raise TypeError('%s datatype is not supported' % type(X))
@@ -165,7 +153,5 @@ def data_shape(X: modALinput):
         return X.shape
     elif isinstance(X, list):
         return np.array(X).shape
-    elif torch.is_tensor(X):
-        return tuple(X.size())
 
     raise TypeError('%s datatype is not supported' % type(X))

rtd_requirements.txt

@@ -5,4 +5,3 @@ ipykernel
 nbsphinx
 pandas
 skorch
-torch

setup.py

@@ -11,5 +11,5 @@
     packages=['modAL', 'modAL.models', 'modAL.utils'],
     classifiers=['Development Status :: 4 - Beta'],
     install_requires=['numpy==1.20.0', 'scikit-learn>=0.18',
-                      'scipy>=0.18', 'pandas>=1.1.0', 'skorch==0.9.0', 'torch>=1.8.1'],
+                      'scipy>=0.18', 'pandas>=1.1.0', 'skorch==0.9.0'],
 )

tests/core_tests.py

@@ -181,12 +181,6 @@ def test_data_vstack(self):
                 self.assertEqual((modAL.utils.data.data_vstack(
                     (a, b)) != sp.vstack((a, b))).sum(), 0)
 
-            # pytorch tensors
-            a, b = torch.randn(n_samples, n_features), torch.randn(
-                n_samples, n_features)
-            self.assertTrue(
-                torch.equal(modAL.utils.data.data_vstack((a, b)), torch.cat((a, b))))
-
             # lists
             a, b = np.random.rand(n_samples, n_features).tolist(), np.random.rand(
                 n_samples, n_features).tolist()