Merge pull request #19 from bioinf-jku/dev

Fix numerical issues and efficiency issues

.github/workflows/test_dev.yml

@@ -0,0 +1,40 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Tests (dev)
+
+on:
+  push:
+    branches: [ "dev" ]
+  pull_request:
+    branches: [ "dev" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        python -m pip install -e .
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest

.github/workflows/test_master.yml

@@ -0,0 +1,40 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Tests (master)
+
+on:
+  push:
+    branches: [ "master"]
+  pull_request:
+    branches: [ "master"]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        python -m pip install -e .
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest

README.md

@@ -1,15 +1,27 @@
 # Fréchet ChemNet Distance
+![PyPI](https://img.shields.io/pypi/v/fcd)
+![Tests (master)](https://github.com/bioinf-jku/fcd/actions/workflows/test_master.yml/badge.svg?branch=dev)
+![Tests (dev)](https://github.com/bioinf-jku/fcd/actions/workflows/test_dev.yml/badge.svg?branch=dev)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/fcd)
+![GitHub release (latest by date)](https://img.shields.io/github/v/release/bioinf-jku/fcd)
+![GitHub release date](https://img.shields.io/github/release-date/bioinf-jku/fcd)
+![GitHub](https://img.shields.io/github/license/bioinf-jku/fcd)
+
 
 Code for the paper "Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery"
 [JCIM](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00234) /
 [ArXiv](https://arxiv.org/abs/1803.09518)
 
 
 ## Installation
-You can install the FCD using
+You can install FCD using
 ```
 pip install fcd
 ```
+or run the example notebook on Google Colab  <a href="https://colab.research.google.com/github/bioinf-jku/FCD/blob/master/example.ipynb">
+   <img src="https://colab.research.google.com/assets/colab-badge.svg">
+</a>.
+
 
 # Requirements
 ```

example.ipynb

@@ -1,21 +1,31 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install fcd"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "from rdkit import RDLogger                                                                                                                                                               \n",
+    "from rdkit import RDLogger\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from fcd import get_fcd, load_ref_model,canonical_smiles, get_predictions, calculate_frechet_distance\n",
+    "from fcd import get_fcd, load_ref_model, canonical_smiles, get_predictions, calculate_frechet_distance\n",
     "\n",
-    "RDLogger.DisableLog('rdApp.*')\n",
+    "RDLogger.DisableLog(\"rdApp.*\")\n",
     "\n",
     "np.random.seed(0)\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"]= '0' #set gpu"
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"  # set gpu"
    ]
   },
   {
@@ -44,8 +54,10 @@
     "model = load_ref_model()\n",
     "\n",
     "# Load generated molecules\n",
-    "gen_mol_file = \"generated_smiles/LSTM_Segler.smi\" #input file which contains one generated SMILES per line\n",
-    "gen_mol = pd.read_csv(gen_mol_file,header=None)[0] #IMPORTANT: take at least 10000 molecules as FCD can vary with sample size \n",
+    "gen_mol_file = \"generated_smiles/LSTM_Segler.smi\"  # input file which contains one generated SMILES per line\n",
+    "gen_mol = pd.read_csv(gen_mol_file, header=None)[\n",
+    "    0\n",
+    "]  # IMPORTANT: take at least 10000 molecules as FCD can vary with sample size\n",
     "sample1 = np.random.choice(gen_mol, 10000, replace=False)\n",
     "sample2 = np.random.choice(gen_mol, 10000, replace=False)\n",
     "\n",
@@ -82,7 +94,7 @@
     }
    ],
    "source": [
-    "#get CHEBMLNET activations of generated molecules \n",
+    "# get CHEBMLNET activations of generated molecules\n",
     "act1 = get_predictions(model, can_sample1)\n",
     "act2 = get_predictions(model, can_sample2)\n",
     "\n",
@@ -92,13 +104,9 @@
     "mu2 = np.mean(act2, axis=0)\n",
     "sigma2 = np.cov(act2.T)\n",
     "\n",
-    "fcd_score = calculate_frechet_distance(\n",
-    "    mu1=mu1,\n",
-    "    mu2=mu2, \n",
-    "    sigma1=sigma1,\n",
-    "    sigma2=sigma2)\n",
+    "fcd_score = calculate_frechet_distance(mu1=mu1, mu2=mu2, sigma1=sigma1, sigma2=sigma2)\n",
     "\n",
-    "print('FCD: ',fcd_score)"
+    "print(\"FCD: \", fcd_score)"
    ]
   },
   {
@@ -123,7 +131,7 @@
     "\"\"\"if you don't need to store the activations you can also take a shortcut.\"\"\"\n",
     "fcd_score = get_fcd(can_sample1, can_sample2, model)\n",
     "\n",
-    "print('FCD: ',fcd_score)"
+    "print(\"FCD: \", fcd_score)"
    ]
   },
   {
@@ -147,7 +155,7 @@
    "source": [
     "\"\"\"This is what happens if you do not canonicalize the smiles\"\"\"\n",
     "fcd_score = get_fcd(can_sample1, sample2, model)\n",
-    "print('FCD: ',fcd_score)"
+    "print(\"FCD: \", fcd_score)"
    ]
   }
  ],

fcd/__init__.py

@@ -1,4 +1,14 @@
-from .fcd import get_fcd, get_predictions, load_ref_model
-from .utils import calculate_frechet_distance, canonical_smiles
+# ruff: noqa: F401
 
-__version__ = "1.2"
+from fcd.fcd import get_fcd, get_predictions, load_ref_model
+from fcd.utils import calculate_frechet_distance, canonical_smiles
+
+__all__ = [
+    "get_fcd",
+    "get_predictions",
+    "load_ref_model",
+    "calculate_frechet_distance",
+    "canonical_smiles",
+]
+
+__version__ = "1.2.1"

fcd/fcd.py

@@ -9,7 +9,7 @@
 from torch import nn
 from torch.utils.data import DataLoader
 
-from .utils import (
+from fcd.utils import (
     SmilesDataset,
     calculate_frechet_distance,
     load_imported_model,
@@ -31,6 +31,8 @@ def load_ref_model(model_path: Optional[str] = None):
     if model_path is None:
         chemnet_model_filename = "ChemNet_v0.13_pretrained.pt"
         model_bytes = pkgutil.get_data("fcd", chemnet_model_filename)
+        if model_bytes is None:
+            raise FileNotFoundError(f"Could not find model file {chemnet_model_filename}")
 
         tmpdir = tempfile.TemporaryDirectory()
         model_path = os.path.join(tmpdir.name, chemnet_model_filename)
@@ -48,7 +50,7 @@ def get_predictions(
     smiles_list: List[str],
     batch_size: int = 128,
     n_jobs: int = 1,
-    device: str = "cpu",
+    device: Optional[str] = None,
 ) -> np.ndarray:
     """Calculate Chemnet activations
 
@@ -65,46 +67,55 @@ def get_predictions(
     if len(smiles_list) == 0:
         return np.zeros((0, 512))
 
-    dataloader = DataLoader(
-        SmilesDataset(smiles_list), batch_size=batch_size, num_workers=n_jobs
-    )
+    dataloader = DataLoader(SmilesDataset(smiles_list), batch_size=batch_size, num_workers=n_jobs)
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
     with todevice(model, device), torch.no_grad():
         chemnet_activations = []
         for batch in dataloader:
             chemnet_activations.append(
-                model(batch.transpose(1, 2).float().to(device))
-                .to("cpu")
-                .detach()
-                .numpy()
+                model(batch.transpose(1, 2).float().to(device)).to("cpu").detach().numpy().astype(np.float32)
             )
     return np.row_stack(chemnet_activations)
 
 
-def get_fcd(smiles1: List[str], smiles2: List[str], model: nn.Module = None) -> float:
+def get_fcd(smiles1: List[str], smiles2: List[str], model: Optional[nn.Module] = None, device=None) -> float:
     """Calculate FCD between two sets of Smiles
 
     Args:
-        smiles1 (List[str]): First set of smiles
-        smiles2 (List[str]): Second set of smiles
+        smiles1 (List[str]): First set of SMILES.
+        smiles2 (List[str]): Second set of SMILES.
         model (nn.Module, optional): The model to use. Loads default model if None.
+        device: The device to use for computation.
 
     Returns:
-        float: The FCD score
+        float: The FCD score.
+
+    Raises:
+        ValueError: If the input SMILES lists are empty.
+
+    Example:
+        >>> smiles1 = ['CCO', 'CCN']
+        >>> smiles2 = ['CCO', 'CCC']
+        >>> fcd_score = get_fcd(smiles1, smiles2)
     """
+    if not smiles1 or not smiles2:
+        raise ValueError("Input SMILES lists cannot be empty.")
+
     if model is None:
         model = load_ref_model()
 
-    act1 = get_predictions(model, smiles1)
-    act2 = get_predictions(model, smiles2)
+    act1 = get_predictions(model, smiles1, device=device)
+    act2 = get_predictions(model, smiles2, device=device)
 
     mu1 = np.mean(act1, axis=0)
     sigma1 = np.cov(act1.T)
 
     mu2 = np.mean(act2, axis=0)
     sigma2 = np.cov(act2.T)
 
-    fcd_score = calculate_frechet_distance(
-        mu1=mu1, mu2=mu2, sigma1=sigma1, sigma2=sigma2
-    )
+    fcd_score = calculate_frechet_distance(mu1=mu1, mu2=mu2, sigma1=sigma1, sigma2=sigma2)
 
     return fcd_score