Made mol prop dataloader more modular and

adaptable, added tests.

Untitled.ipynb

@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c5871038-462e-4001-9555-668be272e460",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gauche.dataloader import MolPropLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2232be68-ac1a-46c3-85db-9a4901693ccc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/c/Users/lklar/OneDrive - Nexus365/SABS/DPhil/Publications/gauche/gauche/gauche/dataloader/mol_prop.py\n",
+      "/mnt/c/Users/lklar/OneDrive - Nexus365/SABS/DPhil/Publications/gauche/gauche/data/property_prediction/Photoswitch.csv\n",
+      "Found invalid labels [nan nan nan nan nan nan nan nan nan nan nan nan nan] at indices [41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 158]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dl = MolPropLoader()\n",
+    "dl.load_benchmark(\"Photoswitch\")\n",
+    "dl.featurize(\"molecular_graphs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d8896c0-9e74-414c-a92b-6d009f2f6b3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
+       "       [0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
+       "       [0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0.],\n",
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+       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0.],\n",
+       "       [0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "nx.to_numpy_array(dl.features[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "36a65b60-7a4c-4aac-bfdc-3121c5d5b0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Graph' object has no attribute 'adjacency_matrix'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dl\u001b[38;5;241m.\u001b[39mfeatures[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39madjacency_matrix\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Graph' object has no attribute 'adjacency_matrix'"
+     ]
+    }
+   ],
+   "source": [
+    "adjacency_matrixdl.features[0].adjacency_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95720543-2790-4471-9e76-003f2522b169",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gauche",
+   "language": "python",
+   "name": "gauche"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

gauche/dataloader/dataloader.py

@@ -3,6 +3,7 @@
 type validation and feature extraction functionalities.
 """
 
+from typing import Optional
 from abc import ABCMeta, abstractmethod
 
 from sklearn.model_selection import train_test_split
@@ -34,27 +35,36 @@ def labels(self, value):
         raise NotImplementedError
 
     @abstractmethod
-    def validate(self, drop=True):
-        """Checks whether the loaded data is a valid instance of the specified
-        data type, potentially dropping invalid entries.
+    def validate(
+        self, drop: Optional[bool] = True, canonicalize: Optional[bool] = True
+    ):
+        """Checks whether the loaded data is a valid instance
+        of the specified data type, optionally dropping invalid
+        entries and standardizing the remaining ones.
 
         :param drop:  whether to drop invalid entries
         :type drop: bool
+        :param canonicalize: whether to standardize the data
+        :type canonicalize: bool
         """
         raise NotImplementedError
 
     @abstractmethod
-    def featurize(self, representation):
+    def featurize(self, representation: str, **kwargs):
         """Transforms the features to the specified representation (in-place).
 
         :param representation: desired feature format
         :type representation: str
-
+        :param kwargs: additional keyword arguments for the representation function
+        :type kwargs: dict
         """
         raise NotImplementedError
 
     def split_and_scale(
-        self, test_size=0.2, scale_labels=True, scale_features=False
+        self,
+        test_size: int = 0.2,
+        scale_labels: bool = True,
+        scale_features: bool = False,
     ):
         """Splits the data into training and testing sets.