diff --git a/.gitignore b/.gitignore index e8060b3..715369f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,8 @@ lightning_logs/ notebooks/_*.ipynb - +wandb/ # vscode .vscode - # jupyter MANIFEST build @@ -157,3 +156,12 @@ venv.bak/ # mypy .mypy_cache/ +molexpress/**/*.ckpt +molexpress/**/*.pth +molexpress/**/*.txt +molexpress/**/*.csv +molexpress/**/*.zip + + + + diff --git a/molexpress/datasets/encoders.py b/molexpress/datasets/encoders.py index 76db488..be872bd 100644 --- a/molexpress/datasets/encoders.py +++ b/molexpress/datasets/encoders.py @@ -26,18 +26,38 @@ def __init__( def __call__(self, residues: list[types.Molecule | types.SMILES | types.InChI]) -> np.ndarray: residue_graphs = [] residue_sizes = [] + # print(residues) for residue in residues: - residue_graph, residue_size = self._encode_residue( - residue, self.node_encoder, self.edge_encoder - ) + try: + residue_graph, residue_size = self._encode_residue( + residue, self.node_encoder, self.edge_encoder + ) + # print("Printing residue graphs",residue_graph) + except Exception as e: + # print(residues) + print("Residues cannot be encoded properly") + # continue + residue_graphs.append(residue_graph) residue_sizes.append(residue_size) - + + #print(residues, residue_graphs) + # print(residue_graphs) disjoint_peptide_graph = self._merge_molecular_graphs(residue_graphs) - disjoint_peptide_graph["residue_size"] = np.array(residue_sizes) + + # print(disjoint_peptide_graph) + try: + disjoint_peptide_graph["residue_size"] = np.array(residue_sizes) + except Exception as e: + # print(disjoint_peptide_graph) + print("Cannot construct disjoint graph") + raise e + disjoint_peptide_graph["peptide_size"] = np.array([len(residues)], dtype="int32") return disjoint_peptide_graph + + @staticmethod @lru_cache(maxsize=None) def _encode_residue( @@ -96,10 +116,19 @@ def masked_collate_fn( """ disjoint_peptide_graphs = data + disjoint_peptide_batch_graph = PeptideGraphEncoder._merge_molecular_graphs( disjoint_peptide_graphs ) + disjoint_peptide_batch_graph["peptide_size"] = np.concatenate( + [g["residue_size"].shape[:1] for g in disjoint_peptide_graphs] + ).astype("int32") + disjoint_peptide_batch_graph["residue_size"] = np.concatenate( + [g["residue_size"] for g in disjoint_peptide_graphs] + ).astype("int32") + + # print(disjoint_peptide_batch_graph) node_state = disjoint_peptide_batch_graph['node_state'] node_mask = np.random.uniform(size=node_state.shape[0]) < node_masking_rate disjoint_peptide_batch_graph['node_loss_weight'] = np.copy(node_mask.astype(node_state.dtype)) @@ -117,37 +146,51 @@ def masked_collate_fn( mask_state[:, -1] = 1. disjoint_peptide_batch_graph['edge_state'] = np.where( edge_mask[:, None], mask_state, edge_state) - + # print(disjoint_peptide_batch_graph) + # residue_size = np.array([g["residue_size"] for g in molecular_graphs]) return disjoint_peptide_batch_graph @staticmethod def _merge_molecular_graphs( molecular_graphs: list[types.MolecularGraph], ) -> types.MolecularGraph: + # print(molecular_graphs) + # print([g for g in molecular_graphs]) + + num_nodes = np.array([g["node_state"].shape[0] for g in molecular_graphs]) disjoint_molecular_graph = {} - disjoint_molecular_graph["node_state"] = np.concatenate( - [g["node_state"] for g in molecular_graphs] - ) - - if "edge_state" in molecular_graphs[0]: - disjoint_molecular_graph["edge_state"] = np.concatenate( - [g["edge_state"] for g in molecular_graphs] + if len(molecular_graphs)>0: + disjoint_molecular_graph["node_state"] = np.concatenate( + [g["node_state"] for g in molecular_graphs] ) - edge_src = np.concatenate([graph["edge_src"] for graph in molecular_graphs]) - edge_dst = np.concatenate([graph["edge_dst"] for graph in molecular_graphs]) - num_edges = np.array([graph["edge_src"].shape[0] for graph in molecular_graphs]) - indices = np.repeat(range(len(molecular_graphs)), num_edges) - edge_incr = np.concatenate([[0], num_nodes[:-1]]) - edge_incr = np.take_along_axis(edge_incr, indices, axis=0) - - disjoint_molecular_graph["edge_src"] = edge_src + edge_incr - disjoint_molecular_graph["edge_dst"] = edge_dst + edge_incr - - return disjoint_molecular_graph + if "edge_state" in molecular_graphs[0]: + try: + disjoint_molecular_graph["edge_state"] = np.concatenate( + [g["edge_state"] for g in molecular_graphs] + ) + except ValueError as e: + + print("Error is due to the presence of structures without any bonds, usually these are ions / atoms") + print("Error during concatenation. Shapes of edge_state arrays:") + print([g["edge_state"].shape for g in molecular_graphs]) + + raise e + + edge_src = np.concatenate([graph["edge_src"] for graph in molecular_graphs]) + edge_dst = np.concatenate([graph["edge_dst"] for graph in molecular_graphs]) + num_edges = np.array([graph["edge_src"].shape[0] for graph in molecular_graphs]) + indices = np.repeat(range(len(molecular_graphs)), num_edges) + edge_incr = np.concatenate([[0], num_nodes[:-1]]) + edge_incr = np.take_along_axis(edge_incr, indices, axis=0) + + disjoint_molecular_graph["edge_src"] = edge_src + edge_incr + disjoint_molecular_graph["edge_dst"] = edge_dst + edge_incr + + return disjoint_molecular_graph class Composer: @@ -234,6 +277,7 @@ def __call__(self, molecule: types.Molecule) -> np.ndarray: } + class MolecularNodeEncoder: def __init__( self, @@ -244,9 +288,11 @@ def __init__( self.supports_masking = supports_masking def __call__(self, molecule: types.Molecule) -> np.ndarray: - node_encodings = np.stack([self.featurizer(atom) for atom in molecule.GetAtoms()], axis=0) + + node_encodings = np.stack([self.featurizer(atom) for atom in molecule.GetAtoms() if molecule ], axis=0) if self.supports_masking: node_encodings = np.pad(node_encodings, [(0, 0), (0, 1)]) return { "node_state": np.stack(node_encodings), } + diff --git a/molexpress/layers/gcn_conv.py b/molexpress/layers/gcn_conv.py index d85dc3b..5e83989 100644 --- a/molexpress/layers/gcn_conv.py +++ b/molexpress/layers/gcn_conv.py @@ -101,7 +101,7 @@ def call(self, inputs: types.MolecularGraph) -> types.MolecularGraph: if self.skip_connection: if self._transform_skip_connection: node_state = gnn_ops.transform(state=node_state, kernel=self.skip_connect_kernel) - node_state_updated += node_state + node_state_updated = node_state_updated + node_state if self.dropout_rate: node_state_updated = self.dropout(node_state_updated) diff --git a/molexpress/layers/gin_conv.py b/molexpress/layers/gin_conv.py index 77c35a0..8c59655 100644 --- a/molexpress/layers/gin_conv.py +++ b/molexpress/layers/gin_conv.py @@ -107,7 +107,7 @@ def call(self, inputs: types.MolecularGraph) -> types.MolecularGraph: edge_weight=edge_weight, ) - node_state_updated += (1 + self.epsilon) * node_state + node_state_updated = node_state_updated + (1 + self.epsilon) * node_state node_state_updated = gnn_ops.transform( state=node_state_updated, kernel=self.node_kernel_1, bias=self.node_bias_1 diff --git a/molexpress/ops/gnn_ops.py b/molexpress/ops/gnn_ops.py index 8e009dd..9afc409 100644 --- a/molexpress/ops/gnn_ops.py +++ b/molexpress/ops/gnn_ops.py @@ -33,7 +33,7 @@ def transform( # kernel.rank == 3 and state.rank == 2 state_transformed = keras.ops.einsum('ij,jkh->ikh', state, kernel) if bias is not None: - state_transformed += bias + state_transformed =state_transformed + bias return state_transformed def aggregate( @@ -71,13 +71,14 @@ def aggregate( edge_src = keras.ops.expand_dims(edge_src, axis=-1) edge_dst = keras.ops.expand_dims(edge_dst, axis=-1) + # print(edge_src.size(),node_state.size()) node_state_src = keras.ops.take_along_axis(node_state, edge_src, axis=0) if edge_weight is not None: node_state_src *= edge_weight if edge_state is not None: - node_state_src += edge_state + node_state_src = node_state_src + edge_state edge_dst = keras.ops.squeeze(edge_dst) diff --git a/molexpress/pretraining/canonicalise_smiles.py b/molexpress/pretraining/canonicalise_smiles.py new file mode 100644 index 0000000..dd9b30e --- /dev/null +++ b/molexpress/pretraining/canonicalise_smiles.py @@ -0,0 +1,62 @@ +import multiprocessing as mp +from rdkit import Chem +from tqdm import tqdm +import os + +# Function to canonicalize SMILES +def canonicalize_smiles(smiles): + mol = Chem.MolFromSmiles(smiles) # Convert SMILES to molecule object + if mol: # Check if molecule conversion was successful + return Chem.MolToSmiles(mol, canonical=True) # Return canonical SMILES + else: + return None + +# Process a chunk of SMILES +def process_chunk(smiles_chunk): + valid_smiles = [] + invalid_smiles = [] + for smiles in smiles_chunk: + canonical_smiles = canonicalize_smiles(smiles.strip()) + if canonical_smiles: + valid_smiles.append(canonical_smiles) + else: + invalid_smiles.append(smiles.strip()) + return valid_smiles, invalid_smiles + +# Read SMILES from input file and split them into chunks +def process_smiles_file(input_file, output_file, invalid_file, num_processes=4, chunk_size=100000): + # Get the total number of lines (SMILES strings) + total_lines = sum(1 for _ in open(input_file, 'r')) + + # Use multiprocessing to process the file in parallel + with open(input_file, 'r') as infile: + smiles_list = infile.readlines() + + # Split the smiles into chunks + smiles_chunks = [smiles_list[i:i + chunk_size] for i in range(0, len(smiles_list), chunk_size)] + + # Set up a multiprocessing pool + with mp.Pool(processes=num_processes) as pool: + # Process each chunk in parallel + results = list(tqdm(pool.imap(process_chunk, smiles_chunks), total=len(smiles_chunks))) + + # Gather results + valid_smiles = [] + invalid_smiles = [] + for valid, invalid in results: + valid_smiles.extend(valid) + invalid_smiles.extend(invalid) + + # Write the results to the output files + with open(output_file, 'w') as outfile: + outfile.write('\n'.join(valid_smiles) + '\n') + with open(invalid_file, 'w') as invalid_outfile: + invalid_outfile.write('\n'.join(invalid_smiles) + '\n') + +# Example usage +input_file = 'filtered_pubchem.txt' # Your input file containing SMILES strings +output_file = 'canon_filtered_pubchem.txt' # Output file for valid canonical SMILES +invalid_file = 'invalid_smiles.txt' # Output file for invalid SMILES + +# Adjust num_processes based on your machine's CPU cores, and tune chunk_size based on file size +process_smiles_file(input_file, output_file, invalid_file, num_processes=8, chunk_size=100000) diff --git a/molexpress/pretraining/debug_data.ipynb b/molexpress/pretraining/debug_data.ipynb new file mode 100644 index 0000000..485c3a0 --- /dev/null +++ b/molexpress/pretraining/debug_data.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\" \n", + "from functools import partial\n", + "from molexpress import layers\n", + "from molexpress.datasets import featurizers\n", + "from molexpress.datasets import encoders\n", + "from molexpress.ops.chem_ops import get_molecule\n", + "import torch\n", + "import pandas as pd \n", + "from tqdm import tqdm\n", + "\n", + "\n", + "class GraphNeuralNetwork(torch.nn.Module):\n", + " \n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.gcn1 = layers.GINConv(dim)\n", + " self.gcn2 = layers.GINConv(dim)\n", + " self.gcn3 = layers.GINConv(dim)\n", + " self.gcn4 = layers.GINConv(dim)\n", + " \n", + " def forward(self, x):\n", + " x = self.gcn1(x)\n", + " x = self.gcn2(x)\n", + " x = self.gcn3(x)\n", + " x = self.gcn4(x)\n", + " return x\n", + "\n", + "\n", + "class NodePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim) \n", + " \n", + " def forward(self, x):\n", + " x = self.linear1(x['node_state'])\n", + " x = torch.nn.functional.relu(x,inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + "\n", + "\n", + "class EdgePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim)\n", + " self.gather_incident = layers.GatherIncident()\n", + " \n", + " def forward(self, x):\n", + " x = self.gather_incident(x) # We do not use edge states but incident node states.\n", + " x = self.linear1(x)\n", + " x = torch.nn.functional.relu(x,inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + " \n", + "\n", + "atom_featurizers = [\n", + " featurizers.AtomType(vocab={'C', 'N', 'O'}),\n", + " featurizers.Hybridization(),\n", + "]\n", + "\n", + "bond_featurizers = [\n", + " featurizers.BondType(),\n", + " featurizers.Conjugated()\n", + "]\n", + "\n", + "peptide_graph_encoder = encoders.PeptideGraphEncoder(\n", + " atom_featurizers=atom_featurizers, \n", + " bond_featurizers=bond_featurizers,\n", + " self_loops=False, # self_loops True adds one feature dim to edge state\n", + " supports_masking=True, # supports_masking True adds one feature dim to node and edge state\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class Dataset(torch.utils.data.Dataset):\n", + " \n", + " def __init__(self, x):\n", + " self.x = x\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + " \n", + " def __getitem__(self, index):\n", + " graph = peptide_graph_encoder(self.x[index])\n", + " return graph\n", + "s\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"/home/harikrishnan/molexpress-main/molexpress/pretraining/filtered_pubchem.txt\",names=[\"smiles\"])\n", + "\n", + "dataset = data[\"smiles\"].apply(lambda x: [x]).to_list()[400000:]\n", + "# print(len(dataset))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "torch_dataset = Dataset(dataset)\n", + "\n", + "partial_collate_fn = partial(\n", + " peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.3, edge_masking_rate=0.3)\n", + "\n", + "dataset = torch.utils.data.DataLoader(\n", + " torch_dataset, batch_size=1024, collate_fn=partial_collate_fn,num_workers= 6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for ind, batch in enumerate(dataset):\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "molexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "undefined.undefined.undefined" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/molexpress/pretraining/encode_amino_acids-model-2.ipynb b/molexpress/pretraining/encode_amino_acids-model-2.ipynb new file mode 100644 index 0000000..6a13025 --- /dev/null +++ b/molexpress/pretraining/encode_amino_acids-model-2.ipynb @@ -0,0 +1,9186 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from rdkit import Chem" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "aa_smiles = {aa: Chem.MolToSmiles(Chem.MolFromFASTA(aa)) for aa in \"ACDEFGHIKLMNPQRSTVWY\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': 'C[C@H](N)C(=O)O',\n", + " 'C': 'N[C@@H](CS)C(=O)O',\n", + " 'D': 'N[C@@H](CC(=O)O)C(=O)O',\n", + " 'E': 'N[C@@H](CCC(=O)O)C(=O)O',\n", + " 'F': 'N[C@@H](Cc1ccccc1)C(=O)O',\n", + " 'G': 'NCC(=O)O',\n", + " 'H': 'N[C@@H](Cc1c[nH]cn1)C(=O)O',\n", + " 'I': 'CC[C@H](C)[C@H](N)C(=O)O',\n", + " 'K': 'NCCCC[C@H](N)C(=O)O',\n", + " 'L': 'CC(C)C[C@H](N)C(=O)O',\n", + " 'M': 'CSCC[C@H](N)C(=O)O',\n", + " 'N': 'NC(=O)C[C@H](N)C(=O)O',\n", + " 'P': 'O=C(O)[C@@H]1CCCN1',\n", + " 'Q': 'NC(=O)CC[C@H](N)C(=O)O',\n", + " 'R': 'N=C(N)NCCC[C@H](N)C(=O)O',\n", + " 'S': 'N[C@@H](CO)C(=O)O',\n", + " 'T': 'C[C@@H](O)[C@H](N)C(=O)O',\n", + " 'V': 'CC(C)[C@H](N)C(=O)O',\n", + " 'W': 'N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O',\n", + " 'Y': 'N[C@@H](Cc1ccc(O)cc1)C(=O)O'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aa_smiles" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# phosphorylated serine\n", + "aa_smiles[\"pS\"] = \"N[C@@H](COP(O)(O)=O)C(O)=O\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# import tensorflow as tf\n", + "# import torch\n", + "# import pytorch_lightning as lightning\n", + "# import numpy as np\n", + "\n", + "# import definitions\n", + "# import utils\n", + "\n", + "\n", + "# class PretrainedModel:\n", + "\n", + "# def __init__(self, model):\n", + "# self._model = model\n", + "# self._encoder = utils.get_encoder()\n", + "\n", + "# @classmethod\n", + "# def from_path(cls, path):\n", + "# return cls(tf.saved_model.load(path))\n", + "\n", + "# def __call__(self, smiles):\n", + "# encoded = self._encoder(smiles)\n", + "# return self._model(encoded).numpy()\n", + "\n", + "# # gnn = PretrainedModel.from_path(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/harikrishnan/miniconda3/envs/molexp/lib/python3.11/site-packages/pytorch_lightning/core/saving.py:195: Found keys that are not in the model state dict but in the checkpoint: ['graph_model.gcn1._torch_params.gin_conv/epsilon', 'graph_model.gcn1._torch_params.gin_conv/node_bias_1', 'graph_model.gcn1._torch_params.gin_conv/node_bias_2', 'graph_model.gcn1._torch_params.gin_conv/node_kernel_2', 'graph_model.gcn1._torch_params.gin_conv/special_edge_kernel', 'graph_model.gcn1._torch_params.gin_conv/special_node_kernel', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/beta', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/gamma', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/moving_mean', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/moving_variance', 'graph_model.gcn2._torch_params.gin_conv_1/epsilon', 'graph_model.gcn2._torch_params.gin_conv_1/node_bias_1', 'graph_model.gcn2._torch_params.gin_conv_1/node_bias_2', 'graph_model.gcn2._torch_params.gin_conv_1/node_kernel_2', 'graph_model.gcn2._torch_params.gin_conv_1/special_edge_kernel', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/beta', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/gamma', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/moving_mean', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/moving_variance', 'graph_model.gcn3._torch_params.gin_conv_2/epsilon', 'graph_model.gcn3._torch_params.gin_conv_2/node_bias_1', 'graph_model.gcn3._torch_params.gin_conv_2/node_bias_2', 'graph_model.gcn3._torch_params.gin_conv_2/node_kernel_2', 'graph_model.gcn3._torch_params.gin_conv_2/special_edge_kernel', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/beta', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/gamma', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/moving_mean', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/moving_variance', 'graph_model.gcn4._torch_params.gin_conv_3/epsilon', 'graph_model.gcn4._torch_params.gin_conv_3/node_bias_1', 'graph_model.gcn4._torch_params.gin_conv_3/node_bias_2', 'graph_model.gcn4._torch_params.gin_conv_3/node_kernel_2', 'graph_model.gcn4._torch_params.gin_conv_3/special_edge_kernel', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/beta', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/gamma', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/moving_mean', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/moving_variance']\n" + ] + }, + { + "data": { + "text/plain": [ + "GraphModelModule(\n", + " (graph_model): GraphNeuralNetwork(\n", + " (gcn1): \n", + " (gcn2): \n", + " (gcn3): \n", + " (gcn4): \n", + " (readout): \n", + " )\n", + " (node_pred_model): NodePrediction(\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (linear2): Linear(in_features=512, out_features=11, bias=True)\n", + " )\n", + " (edge_pred_model): EdgePrediction(\n", + " (linear1): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (linear2): Linear(in_features=1024, out_features=6, bias=True)\n", + " (gather_incident): \n", + " )\n", + " (loss_fn): BCELoss()\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\" \n", + "\n", + "from functools import partial\n", + "from molexpress import layers\n", + "from molexpress.datasets import featurizers\n", + "from molexpress.datasets import encoders\n", + "from molexpress.ops.chem_ops import get_molecule\n", + "import torch\n", + "import pandas as pd \n", + "import pytorch_lightning as pl\n", + "import wandb\n", + "from torch.utils.data import random_split, DataLoader\n", + "from functools import partial\n", + "from tqdm import tqdm\n", + "\n", + "\n", + "\n", + "\n", + "atom_featurizers = [\n", + " featurizers.AtomType(vocab={'C', 'N', 'O'}),\n", + " featurizers.Hybridization(),\n", + "]\n", + "\n", + "bond_featurizers = [\n", + " featurizers.BondType(),\n", + " featurizers.Conjugated()\n", + "]\n", + "\n", + "peptide_graph_encoder = encoders.PeptideGraphEncoder(\n", + " atom_featurizers=atom_featurizers, \n", + " bond_featurizers=bond_featurizers,\n", + " self_loops=False, # self_loops True adds one feature dim to edge state\n", + " supports_masking=True, # supports_masking True adds one feature dim to node and edge state\n", + ")\n", + "\n", + "# Graph Neural Network using PyTorch Lightning\n", + "class GraphNeuralNetwork(torch.nn.Module):\n", + " \n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.gcn1 = layers.GINConv(dim)\n", + " self.gcn2 = layers.GINConv(dim)\n", + " self.gcn3 = layers.GINConv(dim)\n", + " self.gcn4 = layers.GINConv(dim)\n", + " self.readout = layers.ResidueReadout()\n", + " self.mode = \"inference\"\n", + "\n", + " def forward(self, x):\n", + " x = self.gcn1(x)\n", + " x = self.gcn2(x)\n", + " x = self.gcn3(x)\n", + " x = self.gcn4(x)\n", + "\n", + " if self.mode == \"train\":\n", + " return x\n", + " elif self.mode == \"inference\":\n", + " return self.readout(x)\n", + "\n", + " def set_mode(self, mode):\n", + " self.mode = mode\n", + "\n", + "\n", + "class NodePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim) \n", + " \n", + " def forward(self, x):\n", + " x = self.linear1(x['node_state'])\n", + " x = torch.nn.functional.relu(x, inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + "\n", + "\n", + "class EdgePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim)\n", + " self.gather_incident = layers.GatherIncident()\n", + " \n", + " def forward(self, x):\n", + " x = self.gather_incident(x) # We do not use edge states but incident node states.\n", + " x = self.linear1(x)\n", + " x = torch.nn.functional.relu(x, inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + "\n", + "\n", + "class GraphDataset(torch.utils.data.Dataset):\n", + " \n", + " def __init__(self, x):\n", + " self.x = x\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + " \n", + " def __getitem__(self, index):\n", + " graph = peptide_graph_encoder(self.x[index])\n", + " return graph\n", + "\n", + "\n", + "class GraphModelModule(pl.LightningModule):\n", + " \n", + " def __init__(self, graph_model, node_pred_model, edge_pred_model, lr=1e-3):\n", + " super().__init__()\n", + " self.graph_model = graph_model\n", + " self.node_pred_model = node_pred_model\n", + " self.edge_pred_model = edge_pred_model\n", + " self.loss_fn = torch.nn.BCELoss(reduction='none')\n", + " self.lr = lr\n", + "\n", + " def forward(self, x):\n", + " graph = self.graph_model(x)\n", + " node_pred = self.node_pred_model(graph)\n", + " edge_pred = self.edge_pred_model(graph)\n", + " return node_pred, edge_pred\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " graph = self.graph_model(batch)\n", + " node_pred = self.node_pred_model(graph)\n", + " edge_pred = self.edge_pred_model(graph)\n", + " \n", + " node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight'])\n", + " edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight'])\n", + " loss = node_loss + edge_loss\n", + "\n", + " self.log(\"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True,batch_size = 64)\n", + " return loss\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " graph = self.graph_model(batch)\n", + " node_pred = self.node_pred_model(graph)\n", + " edge_pred = self.edge_pred_model(graph)\n", + " \n", + " node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight'])\n", + " edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight'])\n", + " loss = node_loss + edge_loss\n", + "\n", + " self.log(\"val_loss\", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True,batch_size = 64)\n", + " return loss\n", + "\n", + " \n", + " def configure_optimizers(self):\n", + " optimizer = torch.optim.AdamW(\n", + " list(self.graph_model.parameters()) + \n", + " list(self.node_pred_model.parameters()) + \n", + " list(self.edge_pred_model.parameters()), \n", + " lr=self.lr,weight_decay=1e-2\n", + " )\n", + " return optimizer\n", + "\n", + " def weighted_loss(self, pred, true, weight):\n", + " log = torch.sigmoid(pred) # Sigmoid() only with BCELoss\n", + " true = torch.from_numpy(true)\n", + " true = true.to(\"cuda\")\n", + " weight = torch.from_numpy(weight)\n", + " weight = weight.to(\"cuda\")\n", + " assert true.shape ==log.shape, f\"Expected the two inputs to have the same shape\"\n", + " \n", + " loss = self.loss_fn(log, true)\n", + " # print(true.get_device(),log.get_device(),loss.get_device(),)\n", + " w_loss = loss * weight[:, None] # weight[:, None] only with BCELoss\n", + " return torch.mean(w_loss)\n", + "\n", + "\n", + "graph_model = GraphNeuralNetwork(512).to('cuda')\n", + "node_pred_model = NodePrediction(512, 11).to('cuda')\n", + "edge_pred_model = EdgePrediction(512 * 2, 6).to('cuda')\n", + "\n", + "model = GraphModelModule.load_from_checkpoint(\n", + " \"/home/harikrishnan/Molexpress/molexpress/molexpress/pretraining/finetuned_model_adam_dim512.ckpt\",\n", + " graph_model=graph_model,\n", + " node_pred_model=node_pred_model,\n", + " edge_pred_model=edge_pred_model,\n", + " strict= False\n", + " )\n", + "model.to(\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GraphModelModule(\n", + " (graph_model): GraphNeuralNetwork(\n", + " (gcn1): \n", + " (gcn2): \n", + " (gcn3): \n", + " (gcn4): \n", + " (readout): \n", + " )\n", + " (node_pred_model): NodePrediction(\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (linear2): Linear(in_features=512, out_features=11, bias=True)\n", + " )\n", + " (edge_pred_model): EdgePrediction(\n", + " (linear1): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (linear2): Linear(in_features=1024, out_features=6, bias=True)\n", + " (gather_incident): \n", + " )\n", + " (loss_fn): BCELoss()\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['C[C@H](N)C(=O)O'],\n", + " ['N[C@@H](CS)C(=O)O'],\n", + " ['N[C@@H](CC(=O)O)C(=O)O'],\n", + " ['N[C@@H](CCC(=O)O)C(=O)O'],\n", + " ['N[C@@H](Cc1ccccc1)C(=O)O'],\n", + " ['NCC(=O)O'],\n", + " ['N[C@@H](Cc1c[nH]cn1)C(=O)O'],\n", + " ['CC[C@H](C)[C@H](N)C(=O)O'],\n", + " ['NCCCC[C@H](N)C(=O)O'],\n", + " ['CC(C)C[C@H](N)C(=O)O'],\n", + " ['CSCC[C@H](N)C(=O)O'],\n", + " ['NC(=O)C[C@H](N)C(=O)O'],\n", + " ['O=C(O)[C@@H]1CCCN1'],\n", + " ['NC(=O)CC[C@H](N)C(=O)O'],\n", + " ['N=C(N)NCCC[C@H](N)C(=O)O'],\n", + " ['N[C@@H](CO)C(=O)O'],\n", + " ['C[C@@H](O)[C@H](N)C(=O)O'],\n", + " ['CC(C)[C@H](N)C(=O)O'],\n", + " ['N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O'],\n", + " ['N[C@@H](Cc1ccc(O)cc1)C(=O)O'],\n", + " ['N[C@@H](COP(O)(O)=O)C(O)=O']]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inputs = list(aa_smiles.values())\n", + "inputs = [[elem] for elem in inputs]\n", + "inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# x_dummy = [\n", + "# ['Oc1cc(I)c(OC(F)(F)F)nc1I'], \n", + "# ['C(C(=O)O)N', 'CC(C(=O)O)N', 'C(C(=O)O)N'], \n", + "# ['CCCBCI']\n", + "# ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dataset_test = GraphDataset(inputs)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "partial_collate_fn = partial(\n", + " peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.25, edge_masking_rate=0.25)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "dataset =DataLoader(dataset_test,collate_fn=partial_collate_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# for elem in dataset:\n", + "# print(elem)\n", + "# break\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# class Dataset(torch.utils.data.Dataset):\n", + " \n", + "# def __init__(self, x):\n", + "# self.x = x\n", + "\n", + "# def __len__(self):\n", + "# return len(self.x)\n", + " \n", + "# def __getitem__(self, index):\n", + "# graph = peptide_graph_encoder(self.x[index])\n", + "# return graph\n", + " \n", + "# torch_dataset = Dataset(inputs)\n", + "\n", + "# dataset = torch.utils.data.DataLoader(\n", + "# torch_dataset, )" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# model.eval()\n", + "# with torch.no_grad():\n", + "# for ind, elem in enumerate(dataset):\n", + "# graph = graph_model(elem)\n", + "# node_pred = node_pred_model(graph)\n", + "# edge_pred = edge_pred_model(graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "graph_reps_all = []\n", + "model.eval()\n", + "with torch.no_grad():\n", + " for ind,batch in enumerate(dataset):\n", + " graph_reps = model.graph_model(batch)\n", + " # print(graph_reps?)\n", + " graph_reps_all.append(graph_reps)\n", + " # print(graph_reps)\n", + " # print(ind)\n", + " # batch_node_reps, batch_edge_reps = model(batch)\n", + " # node_reps.append(batch_node_reps)\n", + " # edge_reps.append(batch_edge_reps)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[ -6.2551, 8.3638, 9.7031, -4.1468, -2.1490, -2.8389, -1.5568,\n", + " 1.6912, 1.5505, -3.7785, -4.2274, -0.8554, 3.7264, -2.5869,\n", + " -2.5697, 4.3157, -14.0192, 3.4509, 14.2025, 0.7293, -2.8139,\n", + " 2.0298, -2.4157, 2.9880, -1.1684, 2.4771, 1.5591, -3.9168,\n", + " 0.1142, 5.0364, 1.5014, -4.7372, -7.8844, 1.2570, 7.8266,\n", + " 9.4623, 8.9782, 2.3461, -2.5154, -8.9568, -10.6012, -15.6100,\n", + " 14.5857, 1.3797, -3.5496, -4.7696, 2.0219, -2.0027, 5.3208,\n", + " 5.7937, 8.0777, -0.4348, -9.4335, -6.1019, -4.4290, 1.0823,\n", + " 0.0471, 2.1792, 11.7566, 0.5727, 7.8521, 6.1646, 2.9441,\n", + " 10.5012, 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1.79315624e+01],\n", + " dtype=float32)]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph_reps_nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/harikrishnan/miniconda3/envs/molexp/lib/python3.11/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.clustermap(\n", + " graph_reps_nodes,\n", + " row_cluster=True,\n", + " col_cluster=False,\n", + " yticklabels=list(aa_smiles.keys())\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'aa_encoding_molformer' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[35], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# aa_encoding_molformer = np.load(\"aa_embeddings_molformer.npy\")\u001b[39;00m\n\u001b[1;32m 3\u001b[0m sns\u001b[38;5;241m.\u001b[39mclustermap(\n\u001b[0;32m----> 4\u001b[0m \u001b[43maa_encoding_molformer\u001b[49m,\n\u001b[1;32m 5\u001b[0m row_cluster\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 6\u001b[0m col_cluster\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 7\u001b[0m yticklabels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(aa_smiles\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 8\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'aa_encoding_molformer' is not defined" + ] + } + ], + "source": [ + "# aa_encoding_molformer = np.load(\"aa_embeddings_molformer.npy\")\n", + "\n", + "sns.clustermap(\n", + " aa_encoding_molformer,\n", + " row_cluster=True,\n", + " col_cluster=False,\n", + " yticklabels=list(aa_smiles.keys())\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['A',\n", + " 'C',\n", + " 'D',\n", + " 'E',\n", + " 'F',\n", + " 'G',\n", + " 'H',\n", + " 'I',\n", + " 'K',\n", + " 'L',\n", + " 'M',\n", + " 'N',\n", + " 'P',\n", + " 'Q',\n", + " 'R',\n", + " 'S',\n", + " 'T',\n", + " 'V',\n", + " 'W',\n", + " 'Y',\n", + " 'pS']" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(aa_smiles.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# sns.heatmap(pd.DataFrame(aa_encoding_molformer).T.corr(), ax=axes[0])\n", + "# axes[0].set_title(\"Molformer\")\n", + "# axes[0].set_xticklabels(list(aa_smiles.keys())[:-1])\n", + "# axes[0].set_yticklabels(list(aa_smiles.keys())[:-1])\n", + "\n", + "sns.heatmap(pd.DataFrame(graph_reps_nodes).T.corr(), ax=axes[1])\n", + "axes[1].set_title(\"MolExpress\")\n", + "axes[1].set_xticklabels(list(aa_smiles.keys()))\n", + "axes[1].set_yticklabels(list(aa_smiles.keys()))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sns.kdeplot(pd.DataFrame(aa_encoding_molformer).T.corr().values.flatten(), fill=True, label='Molformer')\n", + "sns.kdeplot(pd.DataFrame(graph_reps_nodes).T.corr().values.flatten(), fill=True, label='MolExpress')\n", + "plt.xlabel('Correlation between amino acid embeddings')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "import plotly.express as px\n", + "import pandas as pd\n", + "from pyteomics.mass import std_aa_mass" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "colors = {\n", + " \"A\": \"aliphatic\",\n", + " \"C\": \"sulfur-containing\",\n", + " \"D\": \"acidic\",\n", + " \"E\": \"acidic\",\n", + " \"F\": \"aromatic\",\n", + " \"G\": \"aliphatic\",\n", + " \"H\": \"basic\",\n", + " \"I\": \"aliphatic\",\n", + " \"K\": \"basic\",\n", + " \"L\": \"aliphatic\",\n", + " \"M\": \"sulfur-containing\",\n", + " \"N\": \"amidic\",\n", + " \"P\": \"aliphatic\",\n", + " \"Q\": \"amidic\",\n", + " \"R\": \"basic\",\n", + " \"S\": \"hydroxylic\",\n", + " \"T\": \"hydroxylic\",\n", + " \"V\": \"aliphatic\",\n", + " \"W\": \"aromatic\",\n", + " \"Y\": \"aromatic\",\n", + " \"pS\": \"phosphorylated\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File 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sizes_normalized.min() + 10\n", + "sizes_normalized = sizes_normalized / sizes_normalized.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/molexp/lib/python3.11/site-packages/IPython/core/formatters.py:925\u001b[0m, in \u001b[0;36mIPythonDisplayFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 923\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 925\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/molexp/lib/python3.11/site-packages/plotly/basedatatypes.py:832\u001b[0m, in \u001b[0;36mBaseFigure._ipython_display_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pio\u001b[38;5;241m.\u001b[39mrenderers\u001b[38;5;241m.\u001b[39mrender_on_display \u001b[38;5;129;01mand\u001b[39;00m pio\u001b[38;5;241m.\u001b[39mrenderers\u001b[38;5;241m.\u001b[39mdefault:\n\u001b[0;32m--> 832\u001b[0m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 834\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n", + "File \u001b[0;32m~/miniconda3/envs/molexp/lib/python3.11/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", + "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "color=aliphatic
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'br>text=%{text}'),\n", + " 'legendgroup': 'phosphorylated',\n", + " 'marker': {'color': '#B6E880',\n", + " 'size': array([0.86278399]),\n", + " 'sizemode': 'area',\n", + " 'sizeref': 0.0011111111111111111,\n", + " 'symbol': 'circle'},\n", + " 'mode': 'markers+text',\n", + " 'name': 'phosphorylated',\n", + " 'orientation': 'v',\n", + " 'showlegend': True,\n", + " 'text': array(['pS'], dtype=object),\n", + " 'type': 'scatter',\n", + " 'x': array([35.77560276]),\n", + " 'xaxis': 'x',\n", + " 'y': array([-0.98644032]),\n", + " 'yaxis': 'y'}],\n", + " 'layout': {'font': {'size': 16},\n", + " 'legend': {'itemsizing': 'constant', 'title': {'text': 'color'}, 'tracegroupgap': 0},\n", + " 'margin': {'t': 60},\n", + " 'template': '...',\n", + " 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'x'}},\n", + " 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'y'}}}\n", + "})" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca_components_last = PCA(n_components=2).fit_transform(graph_reps_nodes)\n", + "\n", + "fig = px.scatter(\n", + " x=pca_components_last[:, 0],\n", + " y=pca_components_last[:, 1],\n", + " size=sizes_normalized,\n", + " text=list(aa_smiles.keys()),\n", + " color=[colors[aa] for aa in aa_smiles.keys()],\n", + " size_max=30,\n", + ")\n", + "fig.update_layout(\n", + " font=dict(\n", + " size=16 # Set the font size to 16\n", + " )\n", + ")\n", + "# fig.update_traces(textposition='bottom right')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(encodings_list[-1], index=list(aa_smiles.keys())).to_csv(\"aa_encodings.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import csv" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "aa_encodings_csv = {}\n", + "\n", + "with open(\"aa_encodings.csv\", \"rt\") as f:\n", + " for row in csv.reader(f):\n", + " if row[0]:\n", + " aa_encodings_csv[row[0]] = np.array(row[1:], dtype=float)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y', 'pS', 'pT', 'pY'])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aa_encodings_csv.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aa_encodings_csv['G'].shape" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "molexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/molexpress/pretraining/finetune_lightning.py b/molexpress/pretraining/finetune_lightning.py new file mode 100644 index 0000000..685e924 --- /dev/null +++ b/molexpress/pretraining/finetune_lightning.py @@ -0,0 +1,228 @@ +import os +os.environ["KERAS_BACKEND"] = "torch" + +from functools import partial +from molexpress import layers +from molexpress.datasets import featurizers +from molexpress.datasets import encoders +from molexpress.ops.chem_ops import get_molecule +import torch +import pandas as pd +import pytorch_lightning as pl +import wandb +from torch.utils.data import random_split, DataLoader +from functools import partial +from tqdm import tqdm + + + +# Initialize Weights & Biases +wandb.init(project="ms2dip_gnn_finetune") + +atom_featurizers = [ + featurizers.AtomType(vocab={'C', 'N', 'O'}), + featurizers.Hybridization(), +] + +bond_featurizers = [ + featurizers.BondType(), + featurizers.Conjugated() +] + +peptide_graph_encoder = encoders.PeptideGraphEncoder( + atom_featurizers=atom_featurizers, + bond_featurizers=bond_featurizers, + self_loops=False, # self_loops True adds one feature dim to edge state + supports_masking=True, # supports_masking True adds one feature dim to node and edge state +) + +# Graph Neural Network using PyTorch Lightning +class GraphNeuralNetwork(torch.nn.Module): + + def __init__(self, dim): + super().__init__() + self.gcn1 = layers.GINConv(dim) + self.gcn2 = layers.GINConv(dim) + self.gcn3 = layers.GINConv(dim) + self.gcn4 = layers.GINConv(dim) + + def forward(self, x): + x = self.gcn1(x) + x = self.gcn2(x) + x = self.gcn3(x) + x = self.gcn4(x) + return x + + +class NodePrediction(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + self.linear1 = torch.nn.Linear(input_dim, input_dim) + self.linear2 = torch.nn.Linear(input_dim, output_dim) + + def forward(self, x): + x = self.linear1(x['node_state']) + x = torch.nn.functional.relu(x, inplace=False) + x = self.linear2(x) + return x + + +class EdgePrediction(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + self.linear1 = torch.nn.Linear(input_dim, input_dim) + self.linear2 = torch.nn.Linear(input_dim, output_dim) + self.gather_incident = layers.GatherIncident() + + def forward(self, x): + x = self.gather_incident(x) # We do not use edge states but incident node states. + x = self.linear1(x) + x = torch.nn.functional.relu(x, inplace=False) + x = self.linear2(x) + return x + + +class GraphDataset(torch.utils.data.Dataset): + + def __init__(self, x): + self.x = x + + def __len__(self): + return len(self.x) + + def __getitem__(self, index): + graph = peptide_graph_encoder(self.x[index]) + return graph + + +class GraphModelModule(pl.LightningModule): + + def __init__(self, graph_model, node_pred_model, edge_pred_model, lr=1e-3): + super().__init__() + self.graph_model = graph_model + self.node_pred_model = node_pred_model + self.edge_pred_model = edge_pred_model + self.loss_fn = torch.nn.BCELoss(reduction='none') + self.lr = lr + + def forward(self, x): + graph = self.graph_model(x) + node_pred = self.node_pred_model(graph) + edge_pred = self.edge_pred_model(graph) + return node_pred, edge_pred + + def training_step(self, batch, batch_idx): + graph = self.graph_model(batch) + node_pred = self.node_pred_model(graph) + edge_pred = self.edge_pred_model(graph) + + node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight']) + edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight']) + loss = node_loss + edge_loss + + self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True,batch_size = 64) + return loss + + def validation_step(self, batch, batch_idx): + graph = self.graph_model(batch) + node_pred = self.node_pred_model(graph) + edge_pred = self.edge_pred_model(graph) + + node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight']) + edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight']) + loss = node_loss + edge_loss + + self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True,batch_size = 64) + return loss + + + def configure_optimizers(self): + optimizer = torch.optim.AdamW( + list(self.graph_model.parameters()) + + list(self.node_pred_model.parameters()) + + list(self.edge_pred_model.parameters()), + lr=self.lr,weight_decay=1e-2 + ) + return optimizer + + def weighted_loss(self, pred, true, weight): + log = torch.sigmoid(pred) # Sigmoid() only with BCELoss + true = torch.from_numpy(true) + true = true.to("cuda") + weight = torch.from_numpy(weight) + weight = weight.to("cuda") + assert true.shape ==log.shape, f"Expected the two inputs to have the same shape" + + loss = self.loss_fn(log, true) + # print(true.get_device(),log.get_device(),loss.get_device(),) + w_loss = loss * weight[:, None] # weight[:, None] only with BCELoss + return torch.mean(w_loss) + + + + +graph_model = GraphNeuralNetwork(512) +node_pred_model = NodePrediction(512, 11) +edge_pred_model = EdgePrediction(512 * 2, 6) + +model = GraphModelModule.load_from_checkpoint( + "model_checkpoint_adam_dim512.ckpt", + graph_model=graph_model, + node_pred_model=node_pred_model, + edge_pred_model=edge_pred_model, + strict= False + ) + +# model = model.load_from_checkpoint("model_checkpoint_shuffled_adamW.ckpt") +# print(model) + + +data = pd.read_csv("smiles_finetune.csv",names=["smiles"]) +data = data.sample(frac=1) +dataset = data["smiles"].apply(lambda x: [x]).to_list() + + +dataset = GraphDataset(dataset) + +# Validation split and DataLoaders +train_size = int(0.8 * len(dataset)) +val_size = len(dataset) - train_size +train_dataset, val_dataset = random_split(dataset, [train_size, val_size],generator= torch.Generator().manual_seed(42)) + + +batch_size = 64 +partial_collate_fn = partial( + peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.3, edge_masking_rate=0.3 +) + +train_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=partial_collate_fn) +val_loader = DataLoader(val_dataset, batch_size=batch_size, collate_fn=partial_collate_fn,) + +# Logging with Weights & Biases +wandb_logger = pl.loggers.WandbLogger() + +# Training with PyTorch Lightning Trainer +trainer = pl.Trainer( + max_epochs=250, + logger=wandb_logger, + accelerator="gpu", + devices="auto", + log_every_n_steps=1 +) + +trainer.fit(model, train_loader, val_loader) + +# Save model checkpoint +trainer.save_checkpoint("finetuned_model_adam_dim512.ckpt") + + + + + + + + + + diff --git a/molexpress/pretraining/model_debug.ipynb b/molexpress/pretraining/model_debug.ipynb new file mode 100644 index 0000000..74abb20 --- /dev/null +++ b/molexpress/pretraining/model_debug.ipynb @@ -0,0 +1,2739 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\" \n", + "\n", + "from functools import partial\n", + "from molexpress import layers\n", + "from molexpress.datasets import featurizers\n", + "from molexpress.datasets import encoders\n", + "from molexpress.ops.chem_ops import get_molecule\n", + "import torch\n", + "import pandas as pd \n", + "import pytorch_lightning as pl\n", + "import wandb\n", + "from torch.utils.data import random_split, DataLoader\n", + "from functools import partial\n", + "from tqdm import tqdm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "atom_featurizers = [\n", + " featurizers.AtomType(vocab={'C', 'N', 'O'}),\n", + " featurizers.Hybridization(),\n", + "]\n", + "\n", + "bond_featurizers = [\n", + " featurizers.BondType(),\n", + " featurizers.Conjugated()\n", + "]\n", + "\n", + "peptide_graph_encoder = encoders.PeptideGraphEncoder(\n", + " atom_featurizers=atom_featurizers, \n", + " bond_featurizers=bond_featurizers,\n", + " self_loops=False, # self_loops True adds one feature dim to edge state\n", + " supports_masking=True, # supports_masking True adds one feature dim to node and edge state\n", + ")\n", + "\n", + "# Graph Neural Network using PyTorch Lightning\n", + "class GraphNeuralNetwork(torch.nn.Module):\n", + " \n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.gcn1 = layers.GINConv(dim)\n", + " self.gcn2 = layers.GINConv(dim)\n", + " self.gcn3 = layers.GINConv(dim)\n", + " self.gcn4 = layers.GINConv(dim)\n", + " \n", + " def forward(self, x):\n", + " x = self.gcn1(x)\n", + " x = self.gcn2(x)\n", + " x = self.gcn3(x)\n", + " x = self.gcn4(x)\n", + " return x\n", + "\n", + "\n", + "class NodePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim) \n", + " \n", + " def forward(self, x):\n", + " x = self.linear1(x['node_state'])\n", + " x = torch.nn.functional.relu(x, inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + "\n", + "\n", + "class EdgePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim)\n", + " self.gather_incident = layers.GatherIncident()\n", + " \n", + " def forward(self, x):\n", + " x = self.gather_incident(x) # We do not use edge states but incident node states.\n", + " x = self.linear1(x)\n", + " x = torch.nn.functional.relu(x, inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + "\n", + "\n", + "class GraphDataset(torch.utils.data.Dataset):\n", + " \n", + " def __init__(self, x):\n", + " self.x = x\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + " \n", + " def __getitem__(self, index):\n", + " graph = peptide_graph_encoder(self.x[index])\n", + " return graph\n", + "\n", + "\n", + "class GraphModelModule(pl.LightningModule):\n", + " \n", + " def __init__(self, graph_model, node_pred_model, edge_pred_model, lr=0.001):\n", + " super().__init__()\n", + " self.graph_model = graph_model\n", + " self.node_pred_model = node_pred_model\n", + " self.edge_pred_model = edge_pred_model\n", + " self.loss_fn = torch.nn.BCELoss(reduction='none')\n", + " self.lr = lr\n", + "\n", + " def forward(self, x):\n", + " graph = self.graph_model(x)\n", + " node_pred = self.node_pred_model(graph)\n", + " edge_pred = self.edge_pred_model(graph)\n", + " return node_pred, edge_pred\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " graph = self.graph_model(batch)\n", + " node_pred = self.node_pred_model(graph)\n", + " edge_pred = self.edge_pred_model(graph)\n", + " \n", + " node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight'])\n", + " edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight'])\n", + " loss = node_loss + edge_loss\n", + "\n", + " self.log(\"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True,batch_size = 1024)\n", + " return loss\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " graph = self.graph_model(batch)\n", + " node_pred = self.node_pred_model(graph)\n", + " edge_pred = self.edge_pred_model(graph)\n", + " \n", + " node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight'])\n", + " edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight'])\n", + " loss = node_loss + edge_loss\n", + "\n", + " self.log(\"val_loss\", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True,batch_size = 1024)\n", + " return loss\n", + "\n", + " \n", + " def configure_optimizers(self):\n", + " optimizer = torch.optim.AdamW(\n", + " list(self.graph_model.parameters()) + \n", + " list(self.node_pred_model.parameters()) + \n", + " list(self.edge_pred_model.parameters()), \n", + " lr=self.lr,weight_decay=0.001\n", + " )\n", + " return optimizer\n", + "\n", + " def weighted_loss(self, pred, true, weight):\n", + " log = torch.sigmoid(pred) # Sigmoid() only with BCELoss\n", + " true = torch.from_numpy(true)\n", + " true = true.to(\"cuda\")\n", + " weight = torch.from_numpy(weight)\n", + " weight = weight.to(\"cuda\")\n", + " assert true.shape ==log.shape, f\"Expected the two inputs to have the same shape\"\n", + " \n", + " loss = self.loss_fn(log, true)\n", + " # print(true.get_device(),log.get_device(),loss.get_device(),)\n", + " w_loss = loss * weight[:, None] # weight[:, None] only with BCELoss\n", + " return torch.mean(w_loss)\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3479419/3684643767.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " state_dict = torch.load(\"model_checkpoint_shuffled_adamW.ckpt\")\n" + ] + } + ], + "source": [ + "# checkpoint = torch.load(\"model_checkpoint_shuffled_adamW.ckpt\")\n", + "# state_dict = checkpoint[\"state_dict\"]\n", + "state_dict = torch.load(\"model_checkpoint_shuffled_adamW.ckpt\")\n", + "remove_prefix = 'module.'\n", + "state_dict = {k[len(remove_prefix):] if k.startswith(remove_prefix) else k: v for k, v in state_dict.items()}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'epoch': 250,\n", + " 'global_step': 976750,\n", + " 'pytorch-lightning_version': '2.4.0',\n", + " 'state_dict': OrderedDict([('graph_model.gcn1._torch_params.gin_conv/epsilon',\n", + " tensor(0.)),\n", + " ('graph_model.gcn1._torch_params.gin_conv/node_bias_1',\n", + " tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 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7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 4.7819e-40, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.9397e-12, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 2.1280e-25, 7.0065e-43, 1.8664e-33, 7.0065e-43, 2.0579e-13,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 4.1159e-17,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 2.3161e-17, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 5.8009e-33, 7.0065e-43, 1.3966e-35, 7.0065e-43, 2.8226e-18,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 3.8018e-18,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 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tensor([1.0524e-08, 4.9897e-09, 5.3540e-09, 5.8432e-14, 4.4196e-10, 2.5763e-08,\n", + " 2.5760e-08, 1.6365e-12, 8.5178e-13, 7.5661e-16, 4.1419e-20])},\n", + " 4: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([[ 5.6052e-45, -5.6052e-45, 5.6052e-45, ..., -5.6052e-45,\n", + " -5.6052e-45, 5.6052e-45],\n", + " [-1.1101e-05, 9.8121e-07, 2.9708e-05, ..., -1.2992e-05,\n", + " 2.2465e-05, 9.0885e-06],\n", + " [ 5.6052e-45, -5.6052e-45, 5.6052e-45, ..., -5.6052e-45,\n", + " -5.6052e-45, 5.6052e-45],\n", + " ...,\n", + " [-4.8661e-07, -2.3081e-07, 1.4022e-06, ..., -3.2180e-07,\n", + " 5.6968e-07, -3.1639e-08],\n", + " [ 1.1479e-06, 1.9021e-08, -2.0937e-06, ..., 4.1902e-07,\n", + " -8.8990e-07, -6.6745e-07],\n", + " [ 5.6052e-45, 5.6052e-45, 5.6052e-45, ..., 5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45]]),\n", + " 'exp_avg_sq': tensor([[1.3239e-17, 1.7065e-18, 8.2356e-18, ..., 1.2518e-17, 9.7533e-19,\n", + " 2.8298e-18],\n", + " [1.0345e-09, 2.2182e-11, 5.4162e-09, ..., 1.8417e-10, 1.1382e-09,\n", + " 1.0771e-10],\n", + " [3.2938e-15, 2.8326e-16, 1.3191e-15, ..., 2.6708e-15, 3.0524e-16,\n", + " 8.1639e-16],\n", + " ...,\n", + " [2.5715e-10, 3.0915e-12, 1.1694e-09, ..., 2.0329e-11, 7.3083e-11,\n", + " 1.9264e-11],\n", + " [9.7886e-11, 3.9782e-12, 5.0143e-10, ..., 8.8220e-11, 1.9291e-10,\n", + " 7.7402e-11],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, ..., 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43]])},\n", + " 5: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([ 5.6052e-45, 1.8041e-07, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, -5.6052e-45, 5.6052e-45, -5.6052e-45,\n", + " -8.5349e-08, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, -5.6052e-45, -5.6052e-45, 5.6052e-45, -4.9372e-07,\n", + " -5.5349e-08, 5.7325e-08, 5.6052e-45, -2.5042e-08, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, 5.6052e-45, -4.1172e-09, 5.6052e-45,\n", + " 5.6052e-45, 6.5402e-19, 5.6052e-45, 1.5800e-08, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 1.4007e-08, -5.6052e-45, -3.5471e-11,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, -2.9334e-07, 5.6052e-45,\n", + " -5.6052e-45, 9.0142e-08, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " 5.6052e-45, 6.1661e-09, -1.4047e-09, 5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, 3.0368e-08, -2.3611e-08, 5.6052e-45]),\n", + " 'exp_avg_sq': tensor([3.9782e-20, 8.2026e-14, 8.8558e-18, 8.6194e-25, 7.0065e-43, 9.9180e-24,\n", + " 4.2658e-24, 7.0065e-43, 9.3822e-18, 7.0065e-43, 6.9845e-14, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.5135e-25, 1.2700e-18, 1.1213e-26, 2.3167e-18,\n", + " 1.8426e-18, 7.4592e-13, 1.5475e-13, 3.8257e-14, 7.0065e-43, 8.2530e-15,\n", + " 4.7286e-18, 3.5580e-19, 2.2513e-18, 7.0065e-43, 1.5346e-26, 1.9785e-26,\n", + " 4.1502e-28, 2.7974e-19, 8.0933e-18, 5.0977e-14, 7.0065e-43, 7.0065e-43,\n", + " 6.2833e-20, 7.0065e-43, 2.1331e-14, 7.0065e-43, 7.0065e-43, 5.7493e-19,\n", + " 1.0528e-13, 7.3148e-43, 6.6216e-16, 7.0065e-43, 7.0065e-43, 1.0892e-18,\n", + " 2.6959e-13, 7.0065e-43, 1.1102e-23, 2.2029e-13, 7.0065e-43, 2.0254e-24,\n", + " 7.0065e-43, 7.0065e-43, 3.3569e-14, 1.1114e-14, 7.5220e-19, 7.0065e-43,\n", + " 7.0065e-43, 1.1148e-13, 1.2003e-13, 7.0065e-43])},\n", + " 6: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([[ 5.6052e-45, 3.3556e-05, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 3.3241e-08, 5.6052e-45, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 3.2360e-09,\n", + " 2.2322e-06, 5.2387e-13, 5.6052e-45, 4.8632e-05, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 9.8744e-13, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, 8.0612e-05, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 2.0102e-06, 5.6052e-45, 5.1275e-19,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.0549e-12, 5.6052e-45,\n", + " 5.6052e-45, 5.0807e-06, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, -5.7810e-06, 1.5450e-13, 5.6052e-45, 5.6052e-45,\n", + " -5.6052e-45, 3.1455e-17, 8.4185e-11, 5.6052e-45],\n", + " [-5.6052e-45, 8.8618e-06, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, -5.6052e-45, 5.6052e-45,\n", + " -1.3146e-05, 5.6052e-45, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, 5.6052e-45, -3.9843e-06,\n", + " 2.5728e-06, 1.4413e-05, 5.6052e-45, -5.6858e-05, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, -5.6052e-45, -5.6052e-45, -1.2129e-08, 5.6052e-45,\n", + " 5.6052e-45, 5.0134e-17, 5.6052e-45, -2.5084e-04, 5.6052e-45,\n", + " 5.6052e-45, -5.6052e-45, -1.0696e-05, -5.6052e-45, 2.6065e-09,\n", + " 5.6052e-45, -5.6052e-45, -5.6052e-45, -1.0246e-05, -5.6052e-45,\n", + " 5.6052e-45, -1.8523e-05, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 8.9634e-06, 3.8360e-08, 5.6052e-45, -5.6052e-45,\n", + " 5.6052e-45, 1.5101e-07, 3.4154e-07, 5.6052e-45],\n", + " [ 5.6052e-45, -3.0580e-05, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, -5.6052e-45, 5.6052e-45, -5.6052e-45,\n", + " 1.2850e-05, 5.6052e-45, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, 5.6052e-45, -5.6052e-45, 3.9060e-06,\n", + " 2.1332e-05, -1.6070e-05, -5.6052e-45, 6.4626e-05, -5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, 5.6052e-45, 5.6052e-45, 8.3181e-09, -5.6052e-45,\n", + " 5.6052e-45, -2.2227e-17, 5.6052e-45, 3.4148e-04, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 2.1284e-05, 5.6052e-45, -4.7932e-09,\n", + " -5.6052e-45, -5.6052e-45, 5.6052e-45, 1.4786e-05, 5.6052e-45,\n", + " -5.6052e-45, 1.3784e-05, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, 8.9554e-06, -8.8389e-08, -5.6052e-45, 5.6052e-45,\n", + " -5.6052e-45, -3.4573e-07, 6.9691e-07, -5.6052e-45],\n", + " [ 5.6052e-45, 7.0406e-07, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 1.1076e-07, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.0094e-08,\n", + " 8.6420e-07, -1.4177e-09, 5.6052e-45, 7.2364e-07, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.7730e-11, 5.6052e-45,\n", + " -5.6052e-45, 6.9307e-23, 5.6052e-45, 1.2946e-06, 5.6052e-45,\n", + " -5.6052e-45, 5.6052e-45, 2.3588e-07, 5.6052e-45, 1.4108e-14,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 3.9871e-08, 5.6052e-45,\n", + " 5.6052e-45, 2.7708e-07, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 3.6186e-07, 1.1564e-08, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 1.3617e-08, 6.6809e-08, 5.6052e-45],\n", + " [ 5.6052e-45, 4.1676e-05, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " -1.4203e-05, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, -2.5175e-06,\n", + " -1.5378e-05, -2.7282e-06, 5.6052e-45, -2.4261e-05, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, -6.1842e-09, 5.6052e-45,\n", + " 5.6052e-45, 9.7621e-20, -5.6052e-45, 1.3160e-04, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -8.5719e-06, -5.6052e-45, 6.3702e-11,\n", + " 5.6052e-45, -5.6052e-45, 5.6052e-45, -1.1215e-05, -5.6052e-45,\n", + " 5.6052e-45, 8.0116e-06, 5.6052e-45, 5.6052e-45, -5.6052e-45,\n", + " 5.6052e-45, 2.3219e-05, 1.5883e-08, 5.6052e-45, -5.6052e-45,\n", + " 5.6052e-45, 2.8650e-06, 7.2885e-07, 5.6052e-45],\n", + " [ 5.6052e-45, 7.6669e-16, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 2.9234e-16, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 2.0595e-17,\n", + " 1.1229e-13, 1.2232e-18, 5.6052e-45, 1.1584e-12, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.0046e-18, 5.6052e-45,\n", + " 5.6052e-45, 3.1147e-32, 5.6052e-45, 7.2249e-14, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 3.3980e-15, 5.6052e-45, 1.3256e-22,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 3.6977e-16, 5.6052e-45,\n", + " 5.6052e-45, 2.7678e-15, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 2.0807e-14, 1.2079e-19, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 2.4905e-17, 1.1016e-15, 5.6052e-45]]),\n", + " 'exp_avg_sq': tensor([[7.0065e-43, 1.3230e-08, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 3.5713e-12, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 1.3848e-12, 4.5278e-08, 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3.4281e-06, 4.8132e-05,\n", + " 2.0356e-10]),\n", + " 'exp_avg_sq': tensor([7.7730e-08, 6.5253e-09, 6.8881e-08, 2.8943e-10, 8.4027e-08, 4.1460e-20])}},\n", + " 'param_groups': [{'lr': 0.001,\n", + " 'betas': (0.9, 0.999),\n", + " 'eps': 1e-08,\n", + " 'weight_decay': 0.001,\n", + " 'amsgrad': False,\n", + " 'foreach': None,\n", + " 'maximize': False,\n", + " 'capturable': False,\n", + " 'differentiable': False,\n", + " 'fused': None,\n", + " 'params': [0, 1, 2, 3, 4, 5, 6, 7]}]}],\n", + " 'lr_schedulers': []}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'epoch': 250,\n", + " 'global_step': 976750,\n", + " 'pytorch-lightning_version': '2.4.0',\n", + " 'state_dict': OrderedDict([('graph_model.gcn1._torch_params.gin_conv/epsilon',\n", + " tensor(0.)),\n", + " 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5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 4.3503e-12, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 2.4216e-13,\n", + " 5.6052e-45, 5.6052e-45],\n", + " [ 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.2445e-15, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 4.2211e-14, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.0868e-15,\n", + " 5.6052e-45, 5.6052e-45],\n", + " [ 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 3.7431e-16, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 7.0272e-15, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.9932e-16,\n", + " 5.6052e-45, 5.6052e-45]]),\n", + " 'exp_avg_sq': tensor([[7.0065e-43, 7.0065e-43, 7.0065e-43, 5.9791e-26, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.1678e-08, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 4.0563e-14, 7.0065e-43, 1.6545e-21, 7.0065e-43, 4.0356e-09,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 9.5929e-08,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 8.7300e-28, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 3.7354e-09, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 6.5349e-15, 7.0065e-43, 1.8113e-21, 7.0065e-43, 2.0003e-09,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 2.1819e-09,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 5.0566e-28, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 4.9947e-09, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 3.7183e-15, 7.0065e-43, 6.2211e-25, 7.0065e-43, 1.5070e-09,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 1.6569e-09,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.9732e-20, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 4.9013e-34, 7.0065e-43, 1.6393e-37, 7.0065e-43, 2.5914e-19,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 1.1386e-18,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 3.4632e-30, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.8207e-09, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 6.3509e-16, 7.0065e-43, 1.9886e-26, 7.0065e-43, 1.8616e-10,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 4.7639e-09,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 8.5113e-26, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.7668e-09, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 1.8176e-13, 7.0065e-43, 8.8253e-23, 7.0065e-43, 3.7600e-09,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 3.3472e-09,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 8.6340e-26, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.8134e-09, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 2.2789e-13, 7.0065e-43, 8.3609e-23, 7.0065e-43, 3.4394e-09,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 3.2589e-09,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 4.7819e-40, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.9397e-12, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 2.1280e-25, 7.0065e-43, 1.8664e-33, 7.0065e-43, 2.0579e-13,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 4.1159e-17,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 2.3161e-17, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 5.8009e-33, 7.0065e-43, 1.3966e-35, 7.0065e-43, 2.8226e-18,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 3.8018e-18,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.3599e-22, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.5819e-37, 7.0065e-43, 7.9636e-40, 7.0065e-43, 1.4377e-23,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.2332e-24,\n", + " 7.0065e-43, 7.0065e-43],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.7916e-26, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 2.8739e-38, 7.0065e-43, 2.8620e-41, 7.0065e-43, 1.2132e-25,\n", + " 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 7.0065e-43, 1.3958e-25,\n", + " 7.0065e-43, 7.0065e-43]])},\n", + " 3: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([ 2.2533e-05, -1.6586e-05, -1.1877e-06, 3.8174e-08, 6.5359e-06,\n", + " -2.8613e-05, 2.2861e-05, 3.3443e-07, 3.5424e-08, 1.5194e-09,\n", + " 2.0350e-10]),\n", + " 'exp_avg_sq': tensor([1.0524e-08, 4.9897e-09, 5.3540e-09, 5.8432e-14, 4.4196e-10, 2.5763e-08,\n", + " 2.5760e-08, 1.6365e-12, 8.5178e-13, 7.5661e-16, 4.1419e-20])},\n", + " 4: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([[ 5.6052e-45, -5.6052e-45, 5.6052e-45, ..., -5.6052e-45,\n", + " -5.6052e-45, 5.6052e-45],\n", + " [-1.1101e-05, 9.8121e-07, 2.9708e-05, ..., -1.2992e-05,\n", + " 2.2465e-05, 9.0885e-06],\n", + " [ 5.6052e-45, -5.6052e-45, 5.6052e-45, ..., -5.6052e-45,\n", + " -5.6052e-45, 5.6052e-45],\n", + " ...,\n", + " [-4.8661e-07, -2.3081e-07, 1.4022e-06, ..., -3.2180e-07,\n", + " 5.6968e-07, -3.1639e-08],\n", + " [ 1.1479e-06, 1.9021e-08, -2.0937e-06, ..., 4.1902e-07,\n", + " -8.8990e-07, -6.6745e-07],\n", + " [ 5.6052e-45, 5.6052e-45, 5.6052e-45, ..., 5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45]]),\n", + " 'exp_avg_sq': tensor([[1.3239e-17, 1.7065e-18, 8.2356e-18, ..., 1.2518e-17, 9.7533e-19,\n", + " 2.8298e-18],\n", + " [1.0345e-09, 2.2182e-11, 5.4162e-09, ..., 1.8417e-10, 1.1382e-09,\n", + " 1.0771e-10],\n", + " [3.2938e-15, 2.8326e-16, 1.3191e-15, ..., 2.6708e-15, 3.0524e-16,\n", + " 8.1639e-16],\n", + " ...,\n", + " [2.5715e-10, 3.0915e-12, 1.1694e-09, ..., 2.0329e-11, 7.3083e-11,\n", + " 1.9264e-11],\n", + " [9.7886e-11, 3.9782e-12, 5.0143e-10, ..., 8.8220e-11, 1.9291e-10,\n", + " 7.7402e-11],\n", + " [7.0065e-43, 7.0065e-43, 7.0065e-43, ..., 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43]])},\n", + " 5: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([ 5.6052e-45, 1.8041e-07, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, -5.6052e-45, 5.6052e-45, -5.6052e-45,\n", + " -8.5349e-08, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, -5.6052e-45, -5.6052e-45, 5.6052e-45, -4.9372e-07,\n", + " -5.5349e-08, 5.7325e-08, 5.6052e-45, -2.5042e-08, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " -5.6052e-45, -5.6052e-45, 5.6052e-45, -4.1172e-09, 5.6052e-45,\n", + " 5.6052e-45, 6.5402e-19, 5.6052e-45, 1.5800e-08, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 1.4007e-08, -5.6052e-45, -3.5471e-11,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, -2.9334e-07, 5.6052e-45,\n", + " -5.6052e-45, 9.0142e-08, 5.6052e-45, -5.6052e-45, -5.6052e-45,\n", + " 5.6052e-45, 6.1661e-09, -1.4047e-09, 5.6052e-45, -5.6052e-45,\n", + " -5.6052e-45, 3.0368e-08, -2.3611e-08, 5.6052e-45]),\n", + " 'exp_avg_sq': tensor([3.9782e-20, 8.2026e-14, 8.8558e-18, 8.6194e-25, 7.0065e-43, 9.9180e-24,\n", + " 4.2658e-24, 7.0065e-43, 9.3822e-18, 7.0065e-43, 6.9845e-14, 7.0065e-43,\n", + " 7.0065e-43, 7.0065e-43, 1.5135e-25, 1.2700e-18, 1.1213e-26, 2.3167e-18,\n", + " 1.8426e-18, 7.4592e-13, 1.5475e-13, 3.8257e-14, 7.0065e-43, 8.2530e-15,\n", + " 4.7286e-18, 3.5580e-19, 2.2513e-18, 7.0065e-43, 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9.8744e-13, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, 8.0612e-05, -5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 2.0102e-06, 5.6052e-45, 5.1275e-19,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, 1.0549e-12, 5.6052e-45,\n", + " 5.6052e-45, 5.0807e-06, 5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, -5.7810e-06, 1.5450e-13, 5.6052e-45, 5.6052e-45,\n", + " -5.6052e-45, 3.1455e-17, 8.4185e-11, 5.6052e-45],\n", + " [-5.6052e-45, 8.8618e-06, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, 5.6052e-45, -5.6052e-45, 5.6052e-45,\n", + " -1.3146e-05, 5.6052e-45, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, 5.6052e-45, -3.9843e-06,\n", + " 2.5728e-06, 1.4413e-05, 5.6052e-45, -5.6858e-05, 5.6052e-45,\n", + " 5.6052e-45, 5.6052e-45, -5.6052e-45, 5.6052e-45, 5.6052e-45,\n", + " 5.6052e-45, -5.6052e-45, -5.6052e-45, -1.2129e-08, 5.6052e-45,\n", + " 5.6052e-45, 5.0134e-17, 5.6052e-45, -2.5084e-04, 5.6052e-45,\n", + " 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2.4024e-35, 7.0065e-43, 7.0065e-43,\n", + " 7.0065e-43, 1.2378e-31, 4.1263e-29, 7.0065e-43]])},\n", + " 7: {'step': tensor(976750.),\n", + " 'exp_avg': tensor([ 2.4195e-05, -1.1909e-05, -1.7214e-05, 3.4281e-06, 4.8132e-05,\n", + " 2.0356e-10]),\n", + " 'exp_avg_sq': tensor([7.7730e-08, 6.5253e-09, 6.8881e-08, 2.8943e-10, 8.4027e-08, 4.1460e-20])}},\n", + " 'param_groups': [{'lr': 0.001,\n", + " 'betas': (0.9, 0.999),\n", + " 'eps': 1e-08,\n", + " 'weight_decay': 0.001,\n", + " 'amsgrad': False,\n", + " 'foreach': None,\n", + " 'maximize': False,\n", + " 'capturable': False,\n", + " 'differentiable': False,\n", + " 'fused': None,\n", + " 'params': [0, 1, 2, 3, 4, 5, 6, 7]}]}],\n", + " 'lr_schedulers': []}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/harikrishnan/miniconda3/envs/molexp/lib/python3.11/site-packages/pytorch_lightning/core/saving.py:195: Found keys that are not in the model state dict but in the checkpoint: ['graph_model.gcn1._torch_params.gin_conv/epsilon', 'graph_model.gcn1._torch_params.gin_conv/node_bias_1', 'graph_model.gcn1._torch_params.gin_conv/node_bias_2', 'graph_model.gcn1._torch_params.gin_conv/node_kernel_2', 'graph_model.gcn1._torch_params.gin_conv/special_edge_kernel', 'graph_model.gcn1._torch_params.gin_conv/special_node_kernel', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/beta', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/gamma', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/moving_mean', 'graph_model.gcn1.normalize._torch_params.gin_conv/batch_normalization/moving_variance', 'graph_model.gcn2._torch_params.gin_conv_1/epsilon', 'graph_model.gcn2._torch_params.gin_conv_1/node_bias_1', 'graph_model.gcn2._torch_params.gin_conv_1/node_bias_2', 'graph_model.gcn2._torch_params.gin_conv_1/node_kernel_2', 'graph_model.gcn2._torch_params.gin_conv_1/special_edge_kernel', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/beta', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/gamma', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/moving_mean', 'graph_model.gcn2.normalize._torch_params.gin_conv_1/batch_normalization_1/moving_variance', 'graph_model.gcn3._torch_params.gin_conv_2/epsilon', 'graph_model.gcn3._torch_params.gin_conv_2/node_bias_1', 'graph_model.gcn3._torch_params.gin_conv_2/node_bias_2', 'graph_model.gcn3._torch_params.gin_conv_2/node_kernel_2', 'graph_model.gcn3._torch_params.gin_conv_2/special_edge_kernel', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/beta', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/gamma', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/moving_mean', 'graph_model.gcn3.normalize._torch_params.gin_conv_2/batch_normalization_2/moving_variance', 'graph_model.gcn4._torch_params.gin_conv_3/epsilon', 'graph_model.gcn4._torch_params.gin_conv_3/node_bias_1', 'graph_model.gcn4._torch_params.gin_conv_3/node_bias_2', 'graph_model.gcn4._torch_params.gin_conv_3/node_kernel_2', 'graph_model.gcn4._torch_params.gin_conv_3/special_edge_kernel', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/beta', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/gamma', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/moving_mean', 'graph_model.gcn4.normalize._torch_params.gin_conv_3/batch_normalization_3/moving_variance']\n" + ] + } + ], + "source": [ + "graph_model = GraphNeuralNetwork(32)\n", + "node_pred_model = NodePrediction(32, 11)\n", + "edge_pred_model = EdgePrediction(32 * 2, 6)\n", + "\n", + "model = GraphModelModule.load_from_checkpoint(\n", + " \"model_checkpoint_shuffled_adamW.ckpt\",\n", + " graph_model=graph_model,\n", + " node_pred_model=node_pred_model,\n", + " edge_pred_model=edge_pred_model,\n", + " strict=False\n", + " )\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['C12=C3C(=CC=C1C=CC4=C2C=CC=C4)C=CC=C3'],\n", + " ['C[C@H](NC(C)=O)C(O)=O'],\n", + " ['Nc1nc2n(cnc2c(=O)[nH]1)[C@@H]1O[C@H](COP([O-])(=O)OP([O-])(=O)OP([O-])(=O)OP([O-])(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)n2cnc3c2nc(N)[nH]c3=O)[C@@H](O)[C@H]1O'],\n", + " ['[O-]P([O-])(=O)OC[C@H](N-*)C(-*)=O'],\n", + " ['COc1ccc(CC=C)cc1OC'],\n", + " ['COC(=O)[C@@H](N)CC(C)C'],\n", + " ['[H]C(*)=O'],\n", + " ['N[C@@H](Cc1ccc(O)c(O)c1)C(O)=O'],\n", + " ['C[S@@](=O)CC[C@H]([NH3+])C([O-])=O'],\n", + " ['NCCc1c[nH]cn1'],\n", + " ['NC(Cc1ccc(O)c(c1)-c1cc(CC(N)C(O)=O)ccc1O)C(O)=O'],\n", + " 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['O([C@H]1[C@@H]([C@H]([C@@H](CO1)O[C@H]2[C@@H]([C@H]([C@H]([C@H](O2)CO)O)O[C@H]3[C@@H]([C@H]([C@H]([C@H](O3)CO)O)O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)C(=O)[O-])O[C@H]5[C@@H]([C@H]([C@@H]([C@H](O5)CO)O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)C(=O)[O-])O[C@H]7[C@@H]([C@H]([C@@H]([C@H](O7)CO)O)O)NC(C)=O)O)O)NC(C)=O)O)O)O)O)O)O)C[C@@H](C(*)=O)N*'],\n", + " ['C[N+](C)(C)CCCC[C@H](NC([*])=O)C(=O)N[*]'],\n", + " ['CC[C@H](C)[C@@H]1N(C)C(=O)C([C@@H](C)CC)N(C)C(=O)[C@H](CC(O)=O)N(C)C(=O)[C@@H](NC(=O)[C@H](C(C)C)N(C)C(=O)[C@@H]2CCCCN2C(=O)[C@@H](C)OC(=O)[C@H](Cc2ccc(OC)cc2)NC(=O)[C@H](C(C)C)N(C)C(=O)CNC1=O)C(C)C'],\n", + " ['NCCCOP(=O)(NC(=O)[C@@H](N)CC([O-])=O)OC[C@H]1O[C@H]([C@H](O)[C@@H]1O)n1cnc2c(N)ncnc12'],\n", + " ['N[C@@H](CSSC[C@H](N)C([O-])=O)C([O-])=O'],\n", + " ['*-N[C@@H](CCCCNC(=O)CCCC[C@@H]1CCSS1)C(-*)=O'],\n", + " ['*C([C@@H](N*)CSC(=O)CCCCCCCCCCCCCCC)=O'],\n", + " ['[H][C@]12CS[C@@H](CCCCC(=O)NCCCC[C@H](N)C(O)=O)[C@@]1([H])NC(=O)N2'],\n", + " ['CCCC(=O)CC(=O)CCC'],\n", + " ['CC(=O)N[C@@H](CCCC[NH3+])C([O-])=O'],\n", + " ['CN(C)[C@@H](Cc1c[nH]cn1)C(O)=O'],\n", + " ['[NH3+][C@H](Cc1ccc(O)cc1)C([O-])=O'],\n", + " ['O[C@H](COP(=O)([O-])*)[C@H](CC1SC[C@H](N1)C(*)=O)O*'],\n", + " ['C([C@H]([C@H](OC(CCCCCCC)=O)C)N*)(*)=O'],\n", + " ['CC(C)=CCC\\\\C(C)=C\\\\CC\\\\C(C)=C\\\\Cc1c(O)c2ccc(O)cc2oc1=O'],\n", + " ['N[C@@H](CC(O)C(O)=O)C(O)=O'],\n", + " ['COc1ccccc1'],\n", + " ['P(O)(O)(=O)N\\\\C(=N\\\\CCCC(N)C(O)=O)\\\\N'],\n", + " ['CCC(N)=O'],\n", + " ['[C@H](CC(C)C)(NC(=O)C)C(O)=O'],\n", + " ['C[C@@H](O)CN'],\n", + " ['NC(=O)NCCCC[C@H]([NH3+])C([O-])=O'],\n", + " ['[H][C@@]12N[C@@H](C[C@]1(C\\\\C=C(/C)CC\\\\C=C(/C)CCC=C(C)C)c1ccccc1N2)C(O)=O'],\n", + " ['Oc1c(Cl)cc2c(oc3c([As]4SCCS4)c(O)c(Cl)cc3c2=O)c1[As]1SCCS1'],\n", + " ['CC(C)=C'],\n", + " ['[H]C(=O)N[C@@H](CCC(O)=O)C(O)=O'],\n", + " ['*[N+](=O)[O-]'],\n", + " ['[Na+].[NH3+]C(CCC([O-])=O)C([O-])=O'],\n", + " ['C=CCSSSCC=C'],\n", + " ['NC(Cc1ccccc1)C(O)=O'],\n", + " ['CC(C)C[C@H](NC(=O)OCc1ccccc1)C(O)=O'],\n", + " ['O1C2C3C(CCCC3(C)C)(C=4C(C2O)=CC(=C(OC)C4OC)C(C)C)C1=O'],\n", + " ['CC(=O)OC[C@H](N)C(O)=O'],\n", + " ['CSCC[C@H](NC(C)=O)C(O)=O'],\n", + " ['O([C@H]1[C@@H]([C@H]([C@@H](CO1)O[C@H]2[C@@H]([C@H]([C@H]([C@H](O2)CO)O)O[C@H]3[C@@H]([C@H]([C@H]([C@H](O3)CO)O)O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)C(=O)[O-])O[C@H]5[C@@H]([C@H]([C@@H]([C@H](O5)CO)O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)C(=O)[O-])O)O)O)NC(C)=O)O)O)O)O)O)O)C[C@@H](C(*)=O)N*'],\n", + " ['N(C[C@@H](CC[C@@H](C(*)=O)N*)O)=S(CC[C@@H](C(*)=O)N*)C'],\n", + " ['NC(CC(O)=O)C(O)=O'],\n", + " ['CCCCCCCCCCCCCCCCOC(=O)CCCCCCCCCCCCCCC'],\n", + " ['[NH3+][C@@H](CSC[C@H]([NH3+])C([O-])=O)C([O-])=O'],\n", + " ['Nc1ncnc2n(cnc12)[C@@H]1O[C@H](COP(O)(=O)OP(O)(=O)OP(O)(O)=O)C(=O)[C@H]1O'],\n", + " ['C(CC/C=C\\\\C[C@@H]([C@@H](/C=C/C=C/C=C\\\\C=C\\\\[C@@H](C/C=C\\\\CC)O)O)O)(O)=O'],\n", + " ['N[C@@H](CCC(=O)N[C@@H](CS)C(=O)NCC(=O)O)C(=O)O'],\n", + " ['CC[C@H]1OC(=O)[C@H](C)C(=O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@@H](C)C[C@@H](C)C(=O)\\\\C=C\\\\[C@H]1C'],\n", + " ['CNCCCC[C@H](N)C(O)=O'],\n", + " ['[C@@H]1(N2C3=C(C(=NC=N3)N)N=C2)O[C@H](COP(OP(OCC(C)([C@H](C(NCCC(NCCSC(=O)C4=CC=CN=C4)=O)=O)O)C)(=O)[O-])(=O)[O-])[C@H]([C@H]1O)OP([O-])([O-])=O'],\n", + " ['O=C(*)C(N*)=C'],\n", + " ['[H][C@]1(OC(O)(C[C@H](O)[C@H]1NC(C)=O)C(O)=O)[C@H](O)[C@H](O)CO'],\n", + " ['N[C@@H](CCCNC(N)=N)C(N)=O'],\n", + " ['NC(Cc1c[nH]cn1)C(O)=O'],\n", + " ['CNCCCCC(N)C(O)=O'],\n", + " ['CSC([C@H](N)C(O)=O)C(O)=O'],\n", + " ['Oc1ccc(Cc2nc3c(Cc4ccccc4)[nH]c(cn3c2=O)-c2ccc(O)cc2)cc1'],\n", + " ['CC(=CCC=C(C=C)C)C'],\n", + " ['[Br-].Cc1nc(sc1C)-[n+]1nc(nn1-c1ccccc1)-c1ccccc1']]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "data = pd.read_csv(\"/home/harikrishnan/molexpress-main/molexpress/pretraining/smiles_finetune.csv\",names=[\"smiles\"])\n", + "data = data.sample(frac=1)\n", + "dataset = data[\"smiles\"].apply(lambda x: [x]).to_list()\n", + "\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dataset = GraphDataset(dataset)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "test_elem = dataset.__getitem__(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "data = pd.read_csv(\"/home/harikrishnan/molexpress-main/molexpress/pretraining/canon_filtered_pubchem.txt\",names=[\"smiles\"])\n", + "# data = data.sample(frac=1)\n", + "dataset = data[\"smiles\"].apply(lambda x: [x]).to_list()\n", + "\n", + "\n", + "# Validation split and DataLoaders\n", + "train_size = int(0.8 * len(dataset))\n", + "val_size = len(dataset) - train_size\n", + "train_dataset, val_dataset = random_split(dataset, [train_size, val_size],generator= torch.Generator().manual_seed(42))\n", + "\n", + "\n", + "batch_size = 1024\n", + "partial_collate_fn = partial(\n", + " peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.3, edge_masking_rate=0.3\n", + ")\n", + "train_dataset, val_dataset = random_split(dataset, [train_size, val_size],generator= torch.Generator().manual_seed(42))\n", + "\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=partial_collate_fn)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mind\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/molexp/lib/python3.11/site-packages/torch/utils/data/dataloader.py:701\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 701\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[1;32m 705\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 706\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[1;32m 707\u001b[0m ):\n", + "File \u001b[0;32m~/miniconda3/envs/molexp/lib/python3.11/site-packages/torch/utils/data/dataloader.py:757\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 756\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 757\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 758\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 759\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", + "File \u001b[0;32m~/miniconda3/envs/molexp/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:55\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/molexpress-main/molexpress/datasets/encoders.py:388\u001b[0m, in \u001b[0;36mPeptideGraphEncoder.masked_collate_fn\u001b[0;34m(data, node_masking_rate, edge_masking_rate)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;124;03mMerge list of graphs into a single disjoint graph.\u001b[39;00m\n\u001b[1;32m 381\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 384\u001b[0m \n\u001b[1;32m 385\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 386\u001b[0m disjoint_peptide_graphs \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m--> 388\u001b[0m disjoint_peptide_batch_graph \u001b[38;5;241m=\u001b[39m \u001b[43mPeptideGraphEncoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_molecular_graphs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mdisjoint_peptide_graphs\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 392\u001b[0m node_state \u001b[38;5;241m=\u001b[39m disjoint_peptide_batch_graph[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnode_state\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 393\u001b[0m node_mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39muniform(size\u001b[38;5;241m=\u001b[39mnode_state\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m<\u001b[39m node_masking_rate\n", + "File \u001b[0;32m~/molexpress-main/molexpress/datasets/encoders.py:416\u001b[0m, in \u001b[0;36mPeptideGraphEncoder._merge_molecular_graphs\u001b[0;34m(molecular_graphs)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_merge_molecular_graphs\u001b[39m(\n\u001b[1;32m 414\u001b[0m molecular_graphs: \u001b[38;5;28mlist\u001b[39m[types\u001b[38;5;241m.\u001b[39mMolecularGraph],\n\u001b[1;32m 415\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m types\u001b[38;5;241m.\u001b[39mMolecularGraph:\n\u001b[0;32m--> 416\u001b[0m num_nodes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[43m[\u001b[49m\u001b[43mg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnode_state\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmolecular_graphs\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 418\u001b[0m disjoint_molecular_graph \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 420\u001b[0m disjoint_molecular_graph[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnode_state\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(\n\u001b[1;32m 421\u001b[0m [g[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnode_state\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m molecular_graphs]\n\u001b[1;32m 422\u001b[0m )\n", + "File \u001b[0;32m~/molexpress-main/molexpress/datasets/encoders.py:416\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_merge_molecular_graphs\u001b[39m(\n\u001b[1;32m 414\u001b[0m molecular_graphs: \u001b[38;5;28mlist\u001b[39m[types\u001b[38;5;241m.\u001b[39mMolecularGraph],\n\u001b[1;32m 415\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m types\u001b[38;5;241m.\u001b[39mMolecularGraph:\n\u001b[0;32m--> 416\u001b[0m num_nodes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[43mg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnode_state\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m molecular_graphs])\n\u001b[1;32m 418\u001b[0m disjoint_molecular_graph \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 420\u001b[0m disjoint_molecular_graph[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnode_state\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(\n\u001b[1;32m 421\u001b[0m [g[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnode_state\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m molecular_graphs]\n\u001b[1;32m 422\u001b[0m )\n", + "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + ] + } + ], + "source": [ + "\n", + "for ind, data in enumerate(train_loader):\n", + " print(data)\n", + " print(ind)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "molexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/molexpress/pretraining/notebook_pretrain.ipynb b/molexpress/pretraining/notebook_pretrain.ipynb new file mode 100644 index 0000000..df2a67c --- /dev/null +++ b/molexpress/pretraining/notebook_pretrain.ipynb @@ -0,0 +1,547 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\" \n", + "from functools import partial\n", + "from molexpress import layers\n", + "from molexpress.datasets import featurizers\n", + "from molexpress.datasets import encoders\n", + "from molexpress.ops.chem_ops import get_molecule\n", + "import torch\n", + "import pandas as pd \n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class GraphNeuralNetwork(torch.nn.Module):\n", + " \n", + " def __init__(self, dim):\n", + " super().__init__()\n", + " self.gcn1 = layers.GINConv(dim)\n", + " self.gcn2 = layers.GINConv(dim)\n", + " self.gcn3 = layers.GINConv(dim)\n", + " self.gcn4 = layers.GINConv(dim)\n", + " \n", + " def forward(self, x):\n", + " x = self.gcn1(x)\n", + " x = self.gcn2(x)\n", + " x = self.gcn3(x)\n", + " x = self.gcn4(x)\n", + " return x\n", + "\n", + "\n", + "class NodePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim) \n", + " \n", + " def forward(self, x):\n", + " x = self.linear1(x['node_state'])\n", + " x = torch.nn.functional.relu(x,inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + "\n", + "\n", + "class EdgePrediction(torch.nn.Module):\n", + " \n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + " self.linear1 = torch.nn.Linear(input_dim, input_dim) \n", + " self.linear2 = torch.nn.Linear(input_dim, output_dim)\n", + " self.gather_incident = layers.GatherIncident()\n", + " \n", + " def forward(self, x):\n", + " x = self.gather_incident(x) # We do not use edge states but incident node states.\n", + " x = self.linear1(x)\n", + " x = torch.nn.functional.relu(x,inplace=False)\n", + " x = self.linear2(x)\n", + " return x\n", + " \n", + "class Dataset(torch.utils.data.Dataset):\n", + " \n", + " def __init__(self, x):\n", + " self.x = x\n", + "\n", + " def __len__(self):\n", + " return len(self.x)\n", + " \n", + " def __getitem__(self, index):\n", + " graph = peptide_graph_encoder(self.x[index])\n", + " return graph\n", + "\n", + "atom_featurizers = [\n", + " featurizers.AtomType({'C', 'N', 'P', 'H', 'S', 'O'}),\n", + " featurizers.Hybridization(),\n", + "]\n", + "\n", + "bond_featurizers = [\n", + " featurizers.BondType(),\n", + " featurizers.Conjugated()\n", + "]\n", + "\n", + "peptide_graph_encoder = encoders.PeptideGraphEncoder(\n", + " atom_featurizers=atom_featurizers, \n", + " bond_featurizers=bond_featurizers,\n", + " self_loops=False, # self_loops True adds one feature dim to edge state\n", + " supports_masking=True, # supports_masking True adds one feature dim to node and edge state\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "data = pd.read_csv(r\"C:\\Users\\harik\\Desktop\\Doctoral_Project\\Molexpress\\molexpress-main\\molexpress\\pretraining\\canon_pubchem.txt\",header=None,names=[\"smiles\"])\n", + "\n", + "smile_dataset = data[\"smiles\"].apply(lambda x: [x]).to_list() \n", + "# print(dataset)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'node_state': array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]],\n", + " dtype=float32),\n", + " 'edge_state': array([[0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 1., 0., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 1., 0., 0., 1., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.]], dtype=float32),\n", + " 'edge_src': array([ 0, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7,\n", + " 8, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 13, 14, 14, 14,\n", + " 15, 16, 16, 17, 17, 18, 18, 18, 18, 19, 20, 20, 21, 21, 22, 22, 23,\n", + " 23, 24, 24]),\n", + " 'edge_dst': array([ 1, 0, 2, 8, 1, 3, 7, 2, 4, 3, 5, 4, 6, 5, 7, 2, 6,\n", + " 1, 9, 13, 8, 10, 9, 11, 10, 12, 11, 13, 8, 12, 14, 13, 15, 16,\n", + " 14, 14, 17, 16, 18, 17, 19, 20, 24, 18, 18, 21, 20, 22, 21, 23, 22,\n", + " 24, 18, 23]),\n", + " 'residue_size': array([25]),\n", + " 'peptide_size': array([1])}" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch_dataset[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "torch_dataset = Dataset(smile_dataset)\n", + "\n", + "partial_collate_fn = partial(\n", + " peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.25, edge_masking_rate=0.25)\n", + "\n", + "dataset = torch.utils.data.DataLoader(\n", + " torch_dataset, batch_size=1, collate_fn=partial_collate_fn,shuffle=False)\n", + "\n", + "\n", + "graph_model = GraphNeuralNetwork(32).to('cuda')\n", + "node_pred_model = NodePrediction(32, 14).to('cuda')\n", + "edge_pred_model = EdgePrediction(32 * 2, 6).to('cuda')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'node_state': array([[1., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 1.],\n", + " [0., 0., 0., ..., 0., 0., 1.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 1., ..., 0., 0., 0.]], dtype=float32), 'edge_state': array([[0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " ...,\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 1., 0.]], dtype=float32), 'edge_src': array([ 0, 1, 1, ..., 48, 49, 49]), 'edge_dst': array([ 1, 0, 2, ..., 43, 31, 34]), 'node_loss_weight': array([0., 1., 1., ..., 0., 0., 0.], dtype=float32), 'node_label': array([[1., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 1., ..., 0., 0., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 1., ..., 0., 0., 0.]], dtype=float32), 'edge_loss_weight': array([0., 0., 0., ..., 0., 1., 0.], dtype=float32), 'edge_label': array([[0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " ...,\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.]], dtype=float32)}\n" + ] + } + ], + "source": [ + "for elem in dataset:\n", + " print(elem)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "optimizer = torch.optim.SGD(\n", + " (\n", + " list(graph_model.parameters()) + \n", + " list(node_pred_model.parameters()) + \n", + " list(edge_pred_model.parameters())\n", + " ),\n", + " lr=0.001, momentum=0.5\n", + ")\n", + "loss_fn = torch.nn.BCELoss(reduction='none') # use BCELoss if node/edge label (initial node/edge state) is multi-hot.\n", + "# loss_fn = torch.nn.CrossEntropyLoss(reduction='none') # use CrossEntropyLoss if node/edge label is one-hot.\n", + "\n", + "def weighted_loss(pred, true, weight):\n", + " log = torch.sigmoid(pred) # Sigmoid() only with BCELoss\n", + " # print(log.shape)\n", + " # print(true.shape)\n", + " loss = loss_fn(log, true)\n", + " w_loss = loss * weight[:, None] # weight[:, None] only with BCELoss\n", + " # print(w_loss.shape)\n", + " # print(torch.mean(w_loss).shape)\n", + " # print(torch.mean(w_loss))\n", + " return torch.mean(w_loss,)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "graph = graph_model(test)\n", + "node_pred = node_pred_model(graph)\n", + "edge_pred = edge_pred_model(graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "node_loss = weighted_loss(node_pred, graph['node_label'], graph['node_loss_weight'])\n", + "edge_loss = weighted_loss(edge_pred, graph['edge_label'], graph['edge_loss_weight'])\n", + "\n", + "loss = node_loss + edge_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(19.9234, device='cuda:0', grad_fn=)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loss" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 0%| | 1/250 [01:14<5:10:49, 74.90s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error is here 5941\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 1%| | 2/250 [02:30<5:10:11, 75.05s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error is here 5941\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 1%| | 3/250 [03:45<5:09:43, 75.24s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error is here 5941\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 1%| | 3/250 [04:15<5:51:01, 85.27s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[47], line 16\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind, x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dataset):\n\u001b[1;32m---> 16\u001b[0m graph \u001b[38;5;241m=\u001b[39m \u001b[43mgraph_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 17\u001b[0m node_pred \u001b[38;5;241m=\u001b[39m node_pred_model(graph)\n\u001b[0;32m 18\u001b[0m edge_pred \u001b[38;5;241m=\u001b[39m edge_pred_model(graph)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "Cell \u001b[1;32mIn[13], line 14\u001b[0m, in \u001b[0;36mGraphNeuralNetwork.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 12\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgcn2(x)\n\u001b[0;32m 13\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgcn3(x)\n\u001b[1;32m---> 14\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgcn4\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\layers\\layer.py:899\u001b[0m, in \u001b[0;36mLayer.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 897\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 898\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 899\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 900\u001b[0m \u001b[38;5;66;03m# Change the layout for the layer output if needed.\u001b[39;00m\n\u001b[0;32m 901\u001b[0m \u001b[38;5;66;03m# This is useful for relayout intermediate tensor in the model\u001b[39;00m\n\u001b[0;32m 902\u001b[0m \u001b[38;5;66;03m# to achieve the optimal performance.\u001b[39;00m\n\u001b[0;32m 903\u001b[0m distribution \u001b[38;5;241m=\u001b[39m distribution_lib\u001b[38;5;241m.\u001b[39mdistribution()\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\backend\\torch\\layer.py:44\u001b[0m, in \u001b[0;36mTorchLayer.forward\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Operation\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\ops\\operation.py:46\u001b[0m, in \u001b[0;36mOperation.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 41\u001b[0m call_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall\n\u001b[0;32m 42\u001b[0m call_fn \u001b[38;5;241m=\u001b[39m traceback_utils\u001b[38;5;241m.\u001b[39minject_argument_info_in_traceback(\n\u001b[0;32m 43\u001b[0m call_fn,\n\u001b[0;32m 44\u001b[0m object_name\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.call()\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 45\u001b[0m )\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m call_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# Plain flow.\u001b[39;00m\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m any_symbolic_tensors(args, kwargs):\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:156\u001b[0m, in \u001b[0;36minject_argument_info_in_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m bound_signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 155\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 156\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(e, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_keras_call_info_injected\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 159\u001b[0m \u001b[38;5;66;03m# Only inject info for the innermost failing call\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\molexpress\\layers\\gin_conv.py:112\u001b[0m, in \u001b[0;36mGINConv.call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 102\u001b[0m node_state_updated \u001b[38;5;241m=\u001b[39m gnn_ops\u001b[38;5;241m.\u001b[39maggregate(\n\u001b[0;32m 103\u001b[0m node_state\u001b[38;5;241m=\u001b[39mnode_state,\n\u001b[0;32m 104\u001b[0m edge_src\u001b[38;5;241m=\u001b[39medge_src,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 107\u001b[0m edge_weight\u001b[38;5;241m=\u001b[39medge_weight,\n\u001b[0;32m 108\u001b[0m )\n\u001b[0;32m 110\u001b[0m node_state_updated \u001b[38;5;241m=\u001b[39m node_state_updated \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepsilon) \u001b[38;5;241m*\u001b[39m node_state\n\u001b[1;32m--> 112\u001b[0m node_state_updated \u001b[38;5;241m=\u001b[39m \u001b[43mgnn_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode_state_updated\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_kernel_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_bias_1\u001b[49m\n\u001b[0;32m 114\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalization:\n\u001b[0;32m 117\u001b[0m node_state_updated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalize(node_state_updated)\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\molexpress\\ops\\gnn_ops.py:36\u001b[0m, in \u001b[0;36mtransform\u001b[1;34m(state, kernel, bias)\u001b[0m\n\u001b[0;32m 34\u001b[0m state_transformed \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39meinsum(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mij,jkh->ikh\u001b[39m\u001b[38;5;124m'\u001b[39m, state, kernel)\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bias \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 36\u001b[0m state_transformed \u001b[38;5;241m=\u001b[39m\u001b[43mstate_transformed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\n\u001b[0;32m 37\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m state_transformed\n", + "File \u001b[1;32mc:\\Users\\harik\\miniconda3\\envs\\MHC\\lib\\site-packages\\keras\\src\\backend\\torch\\core.py:127\u001b[0m, in \u001b[0;36mVariable.__torch_function__\u001b[1;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[0;32m 122\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 123\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 124\u001b[0m key: value\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, KerasVariable) \u001b[38;5;28;01melse\u001b[39;00m value\n\u001b[0;32m 125\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 126\u001b[0m }\n\u001b[1;32m--> 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "log_file = \"training_log.txt\"\n", + "epochs = 250\n", + "error_log_file = \"error_log.txt\"\n", + "\n", + "with open(log_file, \"w\") as f:\n", + " f.write(\"Epoch,Loss\\n\") \n", + "\n", + "# Training loop\n", + "for epoch in tqdm(range(epochs), total=epochs, desc=\"Training Progress\"):\n", + " loss_sum = 0\n", + " optimizer.zero_grad()\n", + "\n", + " try:\n", + " for ind, x in enumerate(dataset):\n", + "\n", + " graph = graph_model(x)\n", + " node_pred = node_pred_model(graph)\n", + " edge_pred = edge_pred_model(graph)\n", + "\n", + " node_loss = weighted_loss(node_pred, graph['node_label'], graph['node_loss_weight'])\n", + " edge_loss = weighted_loss(edge_pred, graph['edge_label'], graph['edge_loss_weight'])\n", + "\n", + " loss = node_loss + edge_loss\n", + " loss.backward()\n", + "\n", + " loss_sum += loss.item()\n", + "\n", + " optimizer.step()\n", + "\n", + " # tqdm.write(f\"Epoch {epoch:<3} - Loss {loss_sum:.3f}\")\n", + "\n", + " except ValueError:\n", + " print(f\"Error is here {ind}\")\n", + "\n", + "\n", + " with open(log_file, \"a\") as f:\n", + " f.write(f\"{epoch},{loss_sum}\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MHC", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/molexpress/pretraining/pretrain_lightning.py b/molexpress/pretraining/pretrain_lightning.py new file mode 100644 index 0000000..6d355a2 --- /dev/null +++ b/molexpress/pretraining/pretrain_lightning.py @@ -0,0 +1,276 @@ +import os +os.environ["KERAS_BACKEND"] = "torch" + +from functools import partial +from molexpress import layers +from molexpress.datasets import featurizers +from molexpress.datasets import encoders +from molexpress.ops.chem_ops import get_molecule +import torch +import pandas as pd +import pytorch_lightning as pl +import wandb +from torch.utils.data import random_split, DataLoader +from functools import partial +from tqdm import tqdm +from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor +from torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingLR, OneCycleLR +from lightning.pytorch.tuner import Tuner + + +atom_featurizers = [ + featurizers.AtomType(vocab={'C', 'N', 'O'}), + featurizers.Hybridization(), +] + +bond_featurizers = [ + featurizers.BondType(), + featurizers.Conjugated() +] + +peptide_graph_encoder = encoders.PeptideGraphEncoder( + atom_featurizers=atom_featurizers, + bond_featurizers=bond_featurizers, + self_loops=False, # self_loops True adds one feature dim to edge state + supports_masking=True, # supports_masking True adds one feature dim to node and edge state +) + +# Graph Neural Network using PyTorch Lightning +class GraphNeuralNetwork(torch.nn.Module): + + def __init__(self, dim): + super().__init__() + self.gcn1 = layers.GINConv(dim) + self.gcn2 = layers.GINConv(dim) + self.gcn3 = layers.GINConv(dim) + self.gcn4 = layers.GINConv(dim) + self.gcn5 = layers.GINConv(dim) + self.gcn6 = layers.GINConv(dim) + self.readout = layers.ResidueReadout() + self.mode = "train" + + def forward(self, x): + x = self.gcn1(x) + x = self.gcn2(x) + x = self.gcn3(x) + x = self.gcn4(x) + x = self.gcn5(x) + x = self.gcn6(x) + if self.mode == "train": + return x + elif self.mode == "inference": + return self.readout(x) + + def set_mode(self, mode): + self.mode = mode + + +class NodePrediction(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + self.linear1 = torch.nn.Linear(input_dim, input_dim) + self.linear2 = torch.nn.Linear(input_dim, output_dim) + + def forward(self, x): + x = self.linear1(x['node_state']) + x = torch.nn.functional.relu(x, inplace=False) + x = self.linear2(x) + return x + + +class EdgePrediction(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + self.linear1 = torch.nn.Linear(input_dim, input_dim) + self.linear2 = torch.nn.Linear(input_dim, output_dim) + self.gather_incident = layers.GatherIncident() + + def forward(self, x): + x = self.gather_incident(x) # We do not use edge states but incident node states. + x = self.linear1(x) + x = torch.nn.functional.relu(x, inplace=False) + x = self.linear2(x) + return x + + +class GraphDataset(torch.utils.data.Dataset): + + def __init__(self, x): + self.x = x + + def __len__(self): + return len(self.x) + + def __getitem__(self, index): + graph = peptide_graph_encoder(self.x[index]) + return graph + + +class GraphModelModule(pl.LightningModule): + + def __init__(self, graph_model, node_pred_model, edge_pred_model, lr=0.001): + super().__init__() + self.graph_model = graph_model + self.node_pred_model = node_pred_model + self.edge_pred_model = edge_pred_model + self.loss_fn = torch.nn.BCELoss(reduction='none') + self.lr = lr + + def forward(self, x): + graph = self.graph_model(x) + node_pred = self.node_pred_model(graph) + edge_pred = self.edge_pred_model(graph) + return node_pred, edge_pred + + def training_step(self, batch, batch_idx): + graph = self.graph_model(batch) + node_pred = self.node_pred_model(graph) + edge_pred = self.edge_pred_model(graph) + + node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight']) + edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight']) + loss = node_loss + edge_loss + + self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True,batch_size = 1024) + return loss + + def validation_step(self, batch, batch_idx): + graph = self.graph_model(batch) + node_pred = self.node_pred_model(graph) + edge_pred = self.edge_pred_model(graph) + + node_loss = self.weighted_loss(node_pred, batch['node_label'], batch['node_loss_weight']) + edge_loss = self.weighted_loss(edge_pred, batch['edge_label'], batch['edge_loss_weight']) + loss = node_loss + edge_loss + + self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True,batch_size = 1024) + return loss + + + def configure_optimizers(self): + optimizer = torch.optim.Adam( + list(self.graph_model.parameters()) + + list(self.node_pred_model.parameters()) + + list(self.edge_pred_model.parameters()), + lr=self.lr, + ) + # schedulers = [ + # # ReduceLROnPlateau + # { + # 'scheduler': ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3, min_lr=1e-6), + # 'monitor': 'val_loss' + # }, + # # CosineAnnealingLR + # { + # 'scheduler': CosineAnnealingLR(optimizer, T_max=10, eta_min=1e-6), + # 'interval': 'epoch' + # }, + # # OneCycleLR + # { + # 'scheduler': OneCycleLR(optimizer, max_lr=self.lr, steps_per_epoch=len(train_loader), epochs=100), + # 'interval': 'step' + # } + # ] + + # return [optimizer], schedulers + return optimizer + + + def weighted_loss(self, pred, true, weight): + log = torch.sigmoid(pred) # Sigmoid() only with BCELoss + true = torch.from_numpy(true) + true = true.to("cuda") + weight = torch.from_numpy(weight) + weight = weight.to("cuda") + assert true.shape ==log.shape, f"Expected the two inputs to have the same shape" + + loss = self.loss_fn(log, true) + # print(true.get_device(),log.get_device(),loss.get_device(),) + w_loss = loss * weight[:, None] # weight[:, None] only with BCELoss + return torch.mean(w_loss) + + +# Model and Data Preparation +graph_model = GraphNeuralNetwork(1280,) +node_pred_model = NodePrediction(1280, 11) +edge_pred_model = EdgePrediction(1280 * 2, 6) + +data = pd.read_csv("/home/harikrishnan/Molexpress/molexpress/molexpress/pretraining/canon_filtered_pubchem.txt",names=["smiles"]) +data = data.sample(frac=1) +dataset = data["smiles"].apply(lambda x: [x]).to_list() + + +dataset = GraphDataset(dataset) + +# Validation split and DataLoaders +train_size = int(0.8 * len(dataset)) +val_size = len(dataset) - train_size +train_dataset, val_dataset = random_split(dataset, [train_size, val_size],generator= torch.Generator().manual_seed(42)) + + +batch_size = 1024 +partial_collate_fn = partial( + peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.3, edge_masking_rate=0.3 +) + +train_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=partial_collate_fn, num_workers=72,shuffle=True,) +val_loader = DataLoader(val_dataset, batch_size=batch_size, collate_fn=partial_collate_fn, num_workers=72,) + +# Initialize Lightning Module and Trainer +model = GraphModelModule(graph_model, node_pred_model, edge_pred_model, lr= 4.786300923226385e-05, ) + + +# Logging with Weights & Biases +wandb_logger = pl.loggers.WandbLogger() + +# Training with PyTorch Lightning Trainer +trainer = pl.Trainer( + max_epochs=25, + + # callbacks=[EarlyStopping(monitor="val_loss", mode="min",patience=5)], + logger=wandb_logger, + # gradient_clip_val=1.0, + accelerator="gpu", + devices="auto", + log_every_n_steps=1, + +) + +# tuner = Tuner(trainer) + +# tuner.lr_find(model) + +trainer.fit(model, train_loader, val_loader) + +# Run the learning rate finder without auto-reset +# lr_finder = tuner.lr_find(model, train_dataloaders=train_loader, ) + +# # Manually reload the initial state with strict=False + +# fig = lr_finder.plot(suggest=True) +# fig.savefig("lr_finder_plot.png") +# fig.show() + +# # Get the recommended learning rate +# suggested_lr = lr_finder.suggestion() +# print(f"Suggested learning rate: {suggested_lr}") + + +# # Save model checkpoint +trainer.save_checkpoint("model_checkpoint_6_layers_dim1280.ckpt") + + + + + + + + + + + + + + diff --git a/molexpress/pretraining/pretrain_script.py b/molexpress/pretraining/pretrain_script.py new file mode 100644 index 0000000..a6ac3f5 --- /dev/null +++ b/molexpress/pretraining/pretrain_script.py @@ -0,0 +1,221 @@ +import os +os.environ["KERAS_BACKEND"] = "torch" +from functools import partial +from molexpress import layers +from molexpress.datasets import featurizers +from molexpress.datasets import encoders +from molexpress.ops.chem_ops import get_molecule +import torch +import pandas as pd +from tqdm import tqdm + + +class GraphNeuralNetwork(torch.nn.Module): + + def __init__(self, dim): + super().__init__() + self.gcn1 = layers.GINConv(dim) + self.gcn2 = layers.GINConv(dim) + self.gcn3 = layers.GINConv(dim) + self.gcn4 = layers.GINConv(dim) + + def forward(self, x): + x = self.gcn1(x) + x = self.gcn2(x) + x = self.gcn3(x) + x = self.gcn4(x) + return x + + +class NodePrediction(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + self.linear1 = torch.nn.Linear(input_dim, input_dim) + self.linear2 = torch.nn.Linear(input_dim, output_dim) + + def forward(self, x): + x = self.linear1(x['node_state']) + x = torch.nn.functional.relu(x,inplace=False) + x = self.linear2(x) + return x + + +class EdgePrediction(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + self.linear1 = torch.nn.Linear(input_dim, input_dim) + self.linear2 = torch.nn.Linear(input_dim, output_dim) + self.gather_incident = layers.GatherIncident() + + def forward(self, x): + x = self.gather_incident(x) # We do not use edge states but incident node states. + x = self.linear1(x) + x = torch.nn.functional.relu(x,inplace=False) + x = self.linear2(x) + return x + + +atom_featurizers = [ + featurizers.AtomType(vocab={'C', 'N', 'O'}), + featurizers.Hybridization(), +] + +bond_featurizers = [ + featurizers.BondType(), + featurizers.Conjugated() +] + +peptide_graph_encoder = encoders.PeptideGraphEncoder( + atom_featurizers=atom_featurizers, + bond_featurizers=bond_featurizers, + self_loops=False, # self_loops True adds one feature dim to edge state + supports_masking=True, # supports_masking True adds one feature dim to node and edge state +) + +data = pd.read_csv("/home/harikrishnan/molexpress-main/molexpress/pretraining/canon_filtered_pubchem.txt",names=["smiles"]) + +dataset = data["smiles"].apply(lambda x: [x]).to_list() +# print(len(dataset)) + +class Dataset(torch.utils.data.Dataset): + + def __init__(self, x): + self.x = x + + def __len__(self): + return len(self.x) + + def __getitem__(self, index): + graph = peptide_graph_encoder(self.x[index]) + return graph + +torch_dataset = Dataset(dataset) + +batch_size = 1024 +partial_collate_fn = partial( + peptide_graph_encoder.masked_collate_fn, node_masking_rate=0.3, edge_masking_rate=0.3) + +dataset = torch.utils.data.DataLoader( + torch_dataset, batch_size=batch_size, collate_fn=partial_collate_fn,num_workers= 8) + + +graph_model = GraphNeuralNetwork(32).to('cuda') +node_pred_model = NodePrediction(32, 11).to('cuda') +edge_pred_model = EdgePrediction(32 * 2, 6).to('cuda') + + + +optimizer = torch.optim.SGD( + ( + list(graph_model.parameters()) + + list(node_pred_model.parameters()) + + list(edge_pred_model.parameters()) + ), + lr=0.001, momentum=0.5 +) +loss_fn = torch.nn.BCELoss(reduction='none') # use BCELoss if node/edge label (initial node/edge state) is multi-hot. +# loss_fn = torch.nn.CrossEntropyLoss(reduction='none') # use CrossEntropyLoss if node/edge label is one-hot. + +def weighted_loss(pred, true, weight): + log = torch.sigmoid(pred) # Sigmoid() only with BCELoss + loss = loss_fn(log, true) + w_loss = loss * weight[:, None] # weight[:, None] only with BCELoss + return torch.mean(w_loss,) + +log_file = "training_log.txt" +epochs = 250 +error_log_file = "error_log.txt" + +with open(log_file, "w") as f: + f.write("Epoch,Loss\n") + +# Training loop + +def train(): + + for epoch in tqdm(range(epochs), total=epochs, desc="Training Progress"): + loss_sum = 0 + optimizer.zero_grad() + + for ind, x in tqdm(enumerate(dataset),total=dataset.__len__()): + + + graph = graph_model(x) + + try: + node_pred = node_pred_model(graph) + + except Exception as e: + print(f"Issue is in batch number {ind+1}") + + raise e + + + try: + edge_pred = edge_pred_model(graph) + + except Exception as e: + print(f"Issue is in batch number {ind+1}") + + raise e + + + node_loss = weighted_loss(node_pred, graph['node_label'], graph['node_loss_weight']) + edge_loss = weighted_loss(edge_pred, graph['edge_label'], graph['edge_loss_weight']) + + loss = node_loss + edge_loss + loss.backward() + + loss_sum += loss.item() + + optimizer.step() + + # + # tqdm.write(f"Epoch {epoch:<3} - Loss {bat:.3f}") + + + with open(log_file, "a") as f: + f.write(f"{epoch},{loss_sum}\n") + + + # if epoch % 5 == 0: + # print(f"Epoch {epoch:<3} - Loss {loss_sum:.3f}") + + + + + torch.save({ + 'epoch': epochs, + 'model_state_dict': graph_model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': loss_sum, + }, 'model_checkpoint.pth') + + print("Training complete. Model saved to 'model_checkpoint.pth'.") + + + +if __name__ == "__main__": + train() + + + + + + + + + + + + + + + + + + + + diff --git a/molexpress/pretraining/test_remove_atoms.ipynb b/molexpress/pretraining/test_remove_atoms.ipynb new file mode 100644 index 0000000..f0f2931 --- /dev/null +++ b/molexpress/pretraining/test_remove_atoms.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "metals = ['Li', 'Na', 'K', 'Rb', 'Cs', 'Fr', 'Be', 'Mg', 'Ca', 'Sr', 'Ba', 'Ra', 'Sc', 'Ti', \n", + " 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', \n", + " 'Rh', 'Pd', 'Ag', 'Cd', 'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'La', \n", + " 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', \n", + " 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', 'Cf', 'Es', 'Fm', 'Md', 'No', 'Lr', \n", + " 'Al', 'Ga', 'In', 'Tl', 'Pb', 'Bi', 'Po']\n", + "\n", + "# Create a regex pattern that matches any of the metal elements\n", + "metal_pattern = re.compile(r'\\b(' + '|'.join(metals) + r')\\b')\n", + "\n", + "# Create a regex pattern to detect organic elements (C, H, O, N, P, S)\n", + "organic_pattern = re.compile(r'[CHONPS]')\n", + "\n", + "\n", + "with open('pubchem-10m.txt', 'r') as file:\n", + " smiles_data = file.readlines()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "filtered_smiles = []\n", + "removed_smiles = []\n", + "\n", + "# Filtering SMILES\n", + "for smiles in smiles_data:\n", + " smiles_str = smiles.strip()\n", + " \n", + " # Check if it contains any metal\n", + " if metal_pattern.search(smiles_str):\n", + " # If it also contains organic elements, keep it\n", + " if organic_pattern.search(smiles_str):\n", + " filtered_smiles.append(smiles_str) # Keep it\n", + " else:\n", + " removed_smiles.append(smiles_str) # Remove it\n", + " else:\n", + " filtered_smiles.append(smiles_str) # Keep it since no metal present\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "99" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "len(removed_smiles)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Br[Al](Br)c1ccccc1',\n", + " '[Ru+3]I',\n", + " '[W+2]',\n", + " 'I[Fe]I',\n", + " 'Fc1cc(F)cc([Bi](c2cc(F)cc(F)c2)c2cc(F)cc(F)c2)c1',\n", + " '[Te]=[Mo]=[Te]',\n", + " 'Fc1ccc([Bi](c2ccccc2)c2ccccc2)cc1',\n", + " '[Ta]',\n", + " 'F[Ta-](F)(F)(F)(Br)Br',\n", + " '[Tm]',\n", + " '[Ga]n1c2ccccc2nc1c1cc2ccccc2cn1',\n", + " '[Tl]',\n", + " 'Fc1cccc(F)c1[Al-](c1c(F)cccc1F)(c1c(F)cccc1F)c1c(F)cccc1F',\n", + " 'Br[Ir-3](Br)(Br)(Br)(Br)Br',\n", + " '[Ga]n1c2cccnc2nc1c1ccccn1',\n", + " 'Br[In-](Br)(Br)Br',\n", + " 'F[Th](F)F',\n", + " '[W]#[W]',\n", + " '[Al]n1c2ccccc2c2ccc3cccnc3c21',\n", + " 'F[Th]F',\n", + " 'F[W]',\n", + " 'F[Mn](F)(F)(F)(Br)Br',\n", + " '[Te]=[Tm]',\n", + " 'F[Eu]F',\n", + " 'I[Mn]I',\n", + " 'Brc1ccc([Bi](c2ccccc2)c2cccc3ccccc23)cc1',\n", + " '[Ag][At]',\n", + " 'c1ccc(c2ccc([Al](c3ccc(c4ccccc4)cc3)c3ccc(c4ccccc4)cc3)cc2)cc1',\n", + " 'B#[U]',\n", + " 'Fc1ccc([Ga](c2ccc(F)c(F)c2F)c2ccc(F)c(F)c2F)c(F)c1F',\n", + " 'c1coc([In](c2ccco2)c2ccco2)c1',\n", + " '[Ga]n1c2ccccc2nc1c1ccccn1',\n", + " 'Br[Gd](Br)Br',\n", + " '[Al]c1cc2ccccc2s1',\n", + " '[Fe]=[V]=[Fe]',\n", + " 'F[Am](F)F',\n", + " 'Br[Re]Br',\n", + " 'c1ccc([Ga](c2ccccc2)c2ccccc2)cc1',\n", + " 'Br[Ru](Br)Br',\n", + " 'Br[Al](Br)Br',\n", + " 'F[U](F)(F)(F)F',\n", + " 'Br[Ta](Br)(Br)Br',\n", + " 'c1ccc([Ga]c2ccccc2)cc1',\n", + " 'F[V](I)(I)I',\n", + " 'I[Mo](I)(I)I',\n", + " 'Fc1cc(F)c(F)c([Tl](c2c(F)c(F)cc(F)c2F)c2c(F)c(F)cc(F)c2F)c1F',\n", + " 'c1ccc([Al]c2ccccc2)cc1',\n", + " '[Fe]=[Bi+]',\n", + " 'I[In](c1ccccc1)c1ccccc1',\n", + " 'Fc1cc2c(cc1F)c1nc3nc(nc4c5cc(F)c(F)cc5c5nc6nc(nc2n1[Al]n54)-c1c(F)ccc(F)c1-6)-c1c(F)ccc(F)c1-3',\n", + " 'Fc1cc2c(cc1F)c1nc3nc(nc4c5cc(F)c(F)cc5c5nc6nc(cc2n1[Al](F)n54)-c1c(F)ccc(F)c1-6)-c1c(F)ccc(F)c1-3',\n", + " 'F[Tl]c1ccccc1',\n", + " 'Fc1ccc([Ga](F)F)c(F)c1F',\n", + " 'Br[Re](Br)#[Re](Br)Br',\n", + " 'c1ccc2c([Al+]c3cccc4ccccc34)cccc2c1',\n", + " 'c1csc([Tl]c2cccs2)c1',\n", + " '[Ba][Ba+]',\n", + " '[Ir+3]',\n", + " '[Ra][Ra][Ra][Ra]',\n", + " 'Br[Bi](Br)c1ccccc1',\n", + " 'I[Fe](I)I',\n", + " '[Rb][Rb]',\n", + " 'Br[Yb]Br',\n", + " 'Br[Mn-2](Br)(Br)Br',\n", + " 'Br[Mo](Br)(Br)Br',\n", + " 'Br[Au](Br)Br',\n", + " 'F[Zr](F)(F)F',\n", + " 'F[Eu](F)F',\n", + " 'Br[Ir-2](Br)(Br)(Br)(Br)Br',\n", + " '[Mn]=[Te]',\n", + " 'I[Zr](I)(I)I',\n", + " 'c1cncc([Al-](c2cccnc2)(c2cccnc2)c2cccnc2)c1',\n", + " 'F[Ru-2](F)(F)(F)F',\n", + " 'I[Zn]I',\n", + " '[U+3]',\n", + " 'Br[Y](Br)Br',\n", + " '[Al]n1c2ccccc2nc1c1nccc2ccccc21',\n", + " 'c1ccc(c2nc3ccccc3n2[Ga](n2c3ccccc3nc2c2ccccn2)n2c3ccccc3nc2c2ccccn2)nc1',\n", + " '[As]#[Gd]',\n", + " 'F[Ta](F)F',\n", + " 'Br[Rh+]Br',\n", + " '[Fe]=[Bi]',\n", + " 'I[U](I)(I)I',\n", + " 'Brc1ccc([Al](c2ccc(Br)cc2)c2ccc(Br)cc2)cc1',\n", + " 'I[Tl](I)I',\n", + " 'c1ccc2sc([Bi](c3cc4ccccc4s3)c3cc4ccccc4s3)cc2c1',\n", + " 'F[Ag]I',\n", + " 'Br[Fe+]Br',\n", + " 'I[Re](I)(I)I',\n", + " 'Br[W](Br)(Br)(Br)Br',\n", + " 'Fc1c(F)c(F)c([Al-](F)(c2c(F)c(F)c(F)c3c(F)c4c(F)c(F)c(F)c(F)c4c(F)c23)c2c(F)c(F)c(F)c3c(F)c4c(F)c(F)c(F)c(F)c4c(F)c23)c(F)c1F',\n", + " 'Fc1c(F)c(F)c([Al](Br)c2c(F)c(F)c(F)c(F)c2F)c(F)c1F',\n", + " 'Br[Re](Br)Br',\n", + " '[Tl]c1ccc(c2ccccc2)nc1',\n", + " '[Al]n1c2ccccc2nc1c1nc2cccnc2n1c1ccccc1',\n", + " 'Fc1c(F)c(F)c([Al](F)c2c(F)c(F)c(F)c(F)c2F)c(F)c1F',\n", + " 'F[Tl]F',\n", + " '[As]#[Tb]',\n", + " 'F[Yb](F)F']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "removed_smiles" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "import pandas as pd \n", + "\n", + "df = pd.DataFrame(filtered_smiles,)\n", + "df.to_csv(\"filtered_pubchem.txt\",index=False,header=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "molexp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "undefined.undefined.undefined" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}