diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 1fb98abf..7e4658e6 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -60,6 +60,8 @@ jobs: run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html - name: Install BLAST run: sudo apt install ncbi-blast+ + - name: Install notebook utils + run: pip install nbformat - name: Install Graphein run: pip install -e . - name: Install Extras diff --git a/graphein/ml/datasets/torch_geometric_dataset.py b/graphein/ml/datasets/torch_geometric_dataset.py index 5de6f9eb..5bb395ec 100644 --- a/graphein/ml/datasets/torch_geometric_dataset.py +++ b/graphein/ml/datasets/torch_geometric_dataset.py @@ -44,9 +44,9 @@ def __init__( pdb_paths: Optional[List[str]] = None, pdb_codes: Optional[List[str]] = None, uniprot_ids: Optional[List[str]] = None, - graph_label_map: Optional[Dict[str, torch.Tensor]] = None, - node_label_map: Optional[Dict[str, torch.Tensor]] = None, - chain_selection_map: Optional[Dict[str, List[str]]] = None, + graph_labels: Optional[List[torch.Tensor]] = None, + node_labels: Optional[List[torch.Tensor]] = None, + chain_selections: Optional[List[str]] = None, graphein_config: ProteinGraphConfig = ProteinGraphConfig(), graph_format_convertor: GraphFormatConvertor = GraphFormatConvertor( src_format="nx", dst_format="pyg" @@ -130,47 +130,53 @@ def __init__( self.pdb_codes = ( [pdb.lower() for pdb in pdb_codes] if pdb_codes is not None - else None + else [] ) self.uniprot_ids = ( [up.upper() for up in uniprot_ids] if uniprot_ids is not None - else None + else [] + ) + self.pdb_paths = ( + pdb_paths if pdb_paths is not None + else [] ) self.pdb_paths = pdb_paths - if self.pdb_paths is None: - if self.pdb_codes and self.uniprot_ids: - self.structures = self.pdb_codes + self.uniprot_ids - elif self.pdb_codes: - self.structures = pdb_codes - elif self.uniprot_ids: - self.structures = uniprot_ids - # Use local saved pdb_files instead of download or move them to self.root/raw dir - else: - if isinstance(self.pdb_paths, list): - self.structures = [ + # make sure root path is unique + if self.pdb_paths: + # add pdb_paths' name into self.structure + self.pdb_paths_name = [ os.path.splitext(os.path.split(pdb_path)[-1])[0] for pdb_path in self.pdb_paths ] - self.pdb_path, _ = os.path.split(self.pdb_paths[0]) - - if self.pdb_codes and self.uniprot_ids: - self.structures = self.pdb_codes + self.uniprot_ids - elif self.pdb_codes: - self.structures = pdb_codes - elif self.uniprot_ids: - self.structures = uniprot_ids - self.af_version = af_version + # if root pdb_path is not unique raise error since we will save all pdb into this root pdb_path and take it as the self.raw_dir + if len(set([os.path.split(pdb_path)[0] for pdb_path in pdb_paths])) != 1: + raise ValueError("pdb_paths should have only one root path not so much!") + else: + self.pdb_paths_name = [] + + self.structures = list(set(self.pdb_codes + self.uniprot_ids + self.pdb_paths_name)) # remove some pdb_codes is in pdb_path and loaded repeately + + # Labels & Chains + if graph_labels is not None: + self.graph_label_map = dict(enumerate(graph_labels)) + else: + self.graph_label_map = None + + if node_labels is not None: + self.node_label_map = dict(enumerate(node_labels)) + else: + self.node_label_map = None + if chain_selections is not None: + self.chain_selection_map = dict(enumerate(chain_selections)) + else: + self.chain_selection_map = None + self.validate_input() self.bad_pdbs: List[ str ] = [] # list of pdb codes that failed to download - # Labels & Chains - self.graph_label_map = graph_label_map - self.node_label_map = node_label_map - self.chain_selection_map = chain_selection_map - # Configs self.config = graphein_config self.graph_format_convertor = graph_format_convertor @@ -178,6 +184,7 @@ def __init__( self.pdb_transform = pdb_transform self.num_cores = num_cores self.af_version = af_version + super().__init__( root, transform=transform, @@ -200,10 +207,36 @@ def processed_file_names(self) -> List[str]: @property def raw_dir(self) -> str: if self.pdb_paths is not None: - return self.pdb_path # replace raw dir with user local pdb_path + # replace raw dir with user local pdb_path; so pdb_paths should be located in the same place + self.pdb_path, _ = os.path.split(self.pdb_paths[0]) + return self.pdb_path else: return os.path.join(self.root, "raw") + def validate_input(self): + if self.graph_label_map is not None: + assert len(self.structures) == len( + self.graph_label_map + ), "Number of proteins and graph labels must match" + if self.node_label_map is not None: + assert len(self.structures) == len( + self.node_label_map + ), "Number of proteins and node labels must match" + if self.chain_selection_map is not None: + print(len(self.structures)) + print(len(self.chain_selection_map)) + assert len(self.structures) == len( + self.chain_selection_map + ), "Number of proteins and chain selections must match" + assert len( + { + f"{pdb}_{chain}" + for pdb, chain in zip( + self.structures, self.chain_selection_map + ) + } + ) == len(self.structures), "Duplicate protein/chain combinations" + def download(self): """Download the PDB files from RCSB or Alphafold.""" self.config.pdb_dir = Path(self.raw_dir) @@ -225,6 +258,7 @@ def download(self): for pdb in set(self.pdb_codes) if not os.path.exists(Path(self.raw_dir) / f"{pdb}.pdb") ] + print("downloading uniprotids") if self.uniprot_ids: [ download_alphafold_structure( @@ -237,6 +271,7 @@ def download(self): ] def __len__(self) -> int: + """Returns length of data set (number of structures and chain split).""" return len(self.structures) def transform_pdbs(self): @@ -252,6 +287,7 @@ def transform_pdbs(self): def process(self): """Process structures into PyG format and save to disk.""" # Read data into huge `Data` list. + print(self.structures) structure_files = [ f"{self.raw_dir}/{pdb}.pdb" for pdb in self.structures ] @@ -330,12 +366,9 @@ def __init__( pdb_paths: Optional[List[str]] = None, pdb_codes: Optional[List[str]] = None, uniprot_ids: Optional[List[str]] = None, - # graph_label_map: Optional[Dict[str, int]] = None, graph_labels: Optional[List[torch.Tensor]] = None, node_labels: Optional[List[torch.Tensor]] = None, chain_selections: Optional[List[str]] = None, - # node_label_map: Optional[Dict[str, int]] = None, - # chain_selection_map: Optional[Dict[str, List[str]]] = None, graphein_config: ProteinGraphConfig = ProteinGraphConfig(), graph_format_convertor: GraphFormatConvertor = GraphFormatConvertor( src_format="nx", dst_format="pyg" @@ -364,14 +397,12 @@ def __init__( :param uniprot_ids: List of Uniprot IDs to download and parse from Alphafold Database. Defaults to ``None``. :type uniprot_ids: Optional[List[str]], optional - :param graph_label_map: Dictionary mapping PDB/Uniprot IDs to - graph-level labels. Defaults to ``None``. - :type graph_label_map: Optional[Dict[str, Tensor]], optional - :param node_label_map: Dictionary mapping PDB/Uniprot IDs to node-level - labels. Defaults to ``None``. - :type node_label_map: Optional[Dict[str, torch.Tensor]], optional - :param chain_selection_map: Dictionary mapping, defaults to ``None``. - :type chain_selection_map: Optional[Dict[str, List[str]]], optional + :param graph_labels: List mapping to self.structures by index to graph-level labels. Defaults to ``None``. + :type graph_labels: Optional[List[torch.Tensor]], optional + :param node_labels: List mapping to self.structures by index to node-level labels. Defaults to ``None``. + :type node_labels: Optional[List[torch.Tensor]], optional + :param chain_selections: List mapping to self.structures by index to chain selection, defaults to ``None``. + :type chain_selections: Optional[List[str]], optional :param graphein_config: Protein graph construction config, defaults to ``ProteinGraphConfig()``. :type graphein_config: ProteinGraphConfig, optional @@ -412,34 +443,36 @@ def __init__( self.pdb_codes = ( [pdb.lower() for pdb in pdb_codes] if pdb_codes is not None - else None + else [] ) self.uniprot_ids = ( [up.upper() for up in uniprot_ids] if uniprot_ids is not None - else None + else [] ) - self.pdb_paths = pdb_paths - if self.pdb_paths is None: - if self.pdb_codes and self.uniprot_ids: - self.structures = self.pdb_codes + self.uniprot_ids - elif self.pdb_codes: - self.structures = pdb_codes - elif self.uniprot_ids: - self.structures = uniprot_ids - # Use local saved pdb_files instead of download or move them to self.root/raw dir - else: - if isinstance(self.pdb_paths, list): - self.structures = [ + self.pdb_paths = ( + pdb_paths if pdb_paths is not None + else [] + ) + + # make sure root path is unique + if self.pdb_paths: + # add pdb_paths' name into self.structure + self.pdb_paths_name = [ os.path.splitext(os.path.split(pdb_path)[-1])[0] for pdb_path in self.pdb_paths ] - self.pdb_path, _ = os.path.split(self.pdb_paths[0]) - - # Labels & Chains + # if root pdb_path is not unique raise error since we will save all pdb into this root pdb_path and take it as the self.raw_dir + if len(set([os.path.split(pdb_path)[0] for pdb_path in pdb_paths])) != 1: + raise ValueError("pdb_paths should have only one root path not so much!") + else: + self.pdb_paths_name = [] + self.structures = list(set(self.pdb_codes + self.uniprot_ids + self.pdb_paths_name)) # remove some pdb_codes is in pdb_path and loaded repeately + self.examples: Dict[int, str] = dict(enumerate(self.structures)) + # Labels & Chains if graph_labels is not None: self.graph_label_map = dict(enumerate(graph_labels)) else: @@ -460,9 +493,9 @@ def __init__( # Configs self.config = graphein_config self.graph_format_convertor = graph_format_convertor - self.num_cores = num_cores - self.pdb_transform = pdb_transform self.graph_transformation_funcs = graph_transformation_funcs + self.pdb_transform = pdb_transform + self.num_cores = num_cores self.af_version = af_version super().__init__( root, @@ -492,8 +525,10 @@ def processed_file_names(self) -> List[str]: @property def raw_dir(self) -> str: - if self.pdb_paths is not None: - return self.pdb_path # replace raw dir with user local pdb_path + if self.pdb_paths: + # replace raw dir with user local pdb_path; so pdb_paths should be located in the same place + self.pdb_path, _ = os.path.split(self.pdb_paths[0]) + return self.pdb_path else: return os.path.join(self.root, "raw") @@ -610,7 +645,6 @@ def divide_chunks(l: List[str], n: int = 2) -> Generator: ) if self.graph_transformation_funcs is not None: graphs = [self.transform_graphein_graphs(g) for g in graphs] - # Convert to PyTorch Geometric Data graphs = [self.graph_format_convertor(g) for g in graphs] diff --git a/graphein/ml/transform.py b/graphein/ml/transform.py new file mode 100644 index 00000000..b612433a --- /dev/null +++ b/graphein/ml/transform.py @@ -0,0 +1,65 @@ +"""Dataset loader transform Functions for loading set""" +# Graphein +# Author: xutingfeng +# License: MIT + +# Project Website: https://github.com/a-r-j/graphein +# Code Repository: https://github.com/a-r-j/graphein + +from torch_geometric.transforms import BaseTransform +from typing import Callable, Dict, Generator, List, Optional, Union + +import torch +import torch_geometric + + +def attr2Tensor(data:torch_geometric.data.Data, attr:str, dtype=torch.float32)->torch_geometric.data.Data: + + """ + ``attr2Tensor`` Change specific attribution ``attr`` of ``torch_geometric.data.Data`` to Tensor. Assume these ``attr`` are [``np.array``, ``np.array``]. If the array's shape is ``(dim,)`` will be reshaped as ``(1, dim)``. Finally the shape of the array will be ``(O_1, dim1, dim2, ...)`` or ``(1, dim)``, and will be concated at axis=0 or dim=0. + + :param data: Data to change it's attribution + :type data: torch_geometric.data.Data + :param attr: The name of attribution + :type attr: str + :param dtype: Dtype of new attr, defaults to torch.float32 + :type dtype: _type_, optional + :raises ValueError: Passing the correct name of attribution + :return: torch_geometric.data.Data with specific attr Tensor + :rtype: torch_geometric.data.Data + """ + if not hasattr(data, attr): + raise ValueError(f"doesn't have an attr:{attr}") + attr_data = getattr(data, attr) + + if isinstance(attr_data, list): + attr_data = [torch.tensor(i, dtype=dtype).unsqueeze(0) if len(i.shape) == 1 else torch.tensor(i, dtype=dtype) for i in attr_data ] + + attr_data = torch.concat(attr_data, 0) + setattr(data, attr, attr_data) + + +class Reshape_Attr2Tensor(BaseTransform): + """ + Reshape_Attr2Tensor Convert ``Data.attr``, like ``[np.array, np.array]`` to ``torch.Tensor`` + """ + def __init__(self, attr:Union[str, List]) -> None: + self.attr = attr + + def __call__(self, data:torch_geometric.data.Data)->torch_geometric.data.Data: + """ + :param data: Convert attr of ``torch_geometric.data.Data`` to ``torch.Tensor`` + :type data: torch_geometric.data.Data + """ + def _attr2Tensor_recursive(x, attr:Union[str, List]): + if isinstance(attr, str): + attr2Tensor(x, attr) + return x + elif isinstance(attr, list): # multiple attr will be done by this + for i in attr: + x = _attr2Tensor_recursive(x, i) + return x + else: + raise ValueError(f"attr name or list of attr name") + _attr2Tensor_recursive(data, self.attr) + return data diff --git a/graphein/ml/visualisation.py b/graphein/ml/visualisation.py index 711cb50a..690a5390 100644 --- a/graphein/ml/visualisation.py +++ b/graphein/ml/visualisation.py @@ -109,15 +109,15 @@ def plot_pyg_data( d["colour"] = float(edge_colour_tensor[i]) return plotly_protein_structure_graph( - nx_graph, - plot_title, - figsize, - node_alpha, - node_size_min, - node_size_multiplier, - label_node_ids, - node_colour_map, - edge_color_map, - colour_nodes_by if node_colour_tensor is None else "colour", - colour_edges_by if edge_colour_tensor is None else "colour", - ) + G = nx_graph, + plot_title = plot_title, + figsize = figsize, + node_alpha = node_alpha, + node_size_min = node_size_min, + node_size_multiplier = node_size_multiplier, + label_node_ids = label_node_ids, + node_colour_map = node_colour_map, + edge_color_map = edge_color_map, + colour_nodes_by = colour_nodes_by if node_colour_tensor is None else "colour", + colour_edges_by = colour_edges_by if edge_colour_tensor is None else "colour", + ) \ No newline at end of file diff --git a/graphein/protein/utils.py b/graphein/protein/utils.py index 5b1f8d37..3cc56901 100644 --- a/graphein/protein/utils.py +++ b/graphein/protein/utils.py @@ -272,8 +272,9 @@ def download_alphafold_structure( query_url = f"{BASE_URL}AF-{uniprot_id}-F1-model_v{version}.cif" if pdb: query_url = f"{BASE_URL}AF-{uniprot_id}-F1-model_v{version}.pdb" + structure_filename = wget.download(query_url, out=out_dir) - + if rename: extension = ".pdb" if pdb else ".cif" os.rename( diff --git a/notebooks/dataloader_tutorial.ipynb b/notebooks/dataloader_tutorial.ipynb index 3b5dcfd8..cd5a694f 100644 --- a/notebooks/dataloader_tutorial.ipynb +++ b/notebooks/dataloader_tutorial.ipynb @@ -185,8 +185,8 @@ } ], "source": [ - "# import sys\n", - "# sys.path.append('../') # add system path for python\n", + "import sys\n", + "sys.path.append('../') # add system path for python\n", "\n", "import os \n", "from graphein.protein.config import ProteinGraphConfig\n", @@ -227,6 +227,22954 @@ " break" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, if you want load both local and download data, you can try like below:\n", + "\n", + "Just specific `pdb_paths`and`pdb_codes` and `uniprot_ids`🦞\n", + "\n", + "And it works doooooooooo~😃" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append('../')\n", + "\n", + "from graphein.ml.datasets import ProteinGraphDataset\n", + "\n", + "import os \n", + "import os.path as osp \n", + "local_dir = \"../tests/protein/test_data\"\n", + "pdb_paths = [osp.join(local_dir, pdb_file) for pdb_file in os.listdir(local_dir) if pdb_file.endswith(\".pdb\")]\n", + "\n", + "ds = ProteinGraphDataset(root = \"../tests/protein/test_data/ProteinGraphDataset\",\n", + " pdb_paths=pdb_paths,\n", + " pdb_codes=[\"10gs\"],\n", + " uniprot_ids=[\"A0A6J1BG53\", \"A0A6P5Z5F7\"],\n", + " af_version=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataBatch(edge_index=[2, 951], node_id=[2], coords=[2], name=[2], dist_mat=[2], num_nodes=956, batch=[956], ptr=[3])\n", + "DataBatch(edge_index=[2, 430], node_id=[2], coords=[2], name=[2], dist_mat=[2], num_nodes=432, batch=[432], ptr=[3])\n", + "DataBatch(edge_index=[2, 1329], node_id=[2], coords=[2], name=[2], dist_mat=[2], num_nodes=1332, batch=[1332], ptr=[3])\n" + ] + } + ], + "source": [ + "# Create a dataloader from dataset and inspect a batch\n", + "from torch_geometric.loader import DataLoader\n", + "dl = DataLoader(ds, batch_size=2, shuffle=True, drop_last=True)\n", + "for i in dl:\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can plot `pyg Data` by this func `` as below:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": 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"", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Peptide backbone graph. Nodes coloured by degree." + }, + "width": 620 + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from graphein.ml.visualisation import plot_pyg_data\n", + "\n", + "p = plot_pyg_data(ds[0],\n", + " colour_nodes_by=\"degree\",\n", + " label_node_ids=False,\n", + " plot_title=\"Peptide backbone graph. Nodes coloured by degree.\",\n", + " node_size_multiplier=1 # set this lower or will happen some unknown bug~\n", + " )\n", + "\n", + "p.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transform to transform `Data`\n", + "\n", + "like `Reshape_Attr2Tensor`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append('../')\n", + "\n", + "from graphein.ml.datasets import ProteinGraphDataset\n", + "from graphein.protein.features.nodes.amino_acid import amino_acid_one_hot, meiler_embedding\n", + "from functools import partial\n", + "from graphein.protein.config import ProteinGraphConfig\n", + "from graphein.protein.edges.distance import add_distance_threshold\n", + "from graphein.ml.conversion import GraphFormatConvertor\n", + "import os \n", + "import os.path as osp \n", + "\n", + "from graphein.ml.transform import Reshape_Attr2Tensor\n", + "\n", + "params_to_change = {\"granularity\": \"centroids\", \n", + " \"node_metadata_functions\": [amino_acid_one_hot, meiler_embedding],\n", + " \"edge_construction_functions\": [partial(add_distance_threshold, long_interaction_threshold=0, threshold=15)]}\n", + "config = ProteinGraphConfig(**params_to_change)\n", + "\n", + "graph_format_convertor = GraphFormatConvertor(\n", + " src_format=\"nx\", dst_format=\"pyg\",\n", + " columns = [\n", + " \"edge_index\",\n", + " \"coords\",\n", + " \"dist_mat\",\n", + " \"name\",\n", + " \"node_id\",\n", + " \"amino_acid_one_hot\",\n", + " \"meiler\"\n", + " ]\n", + ")\n", + "\n", + "\n", + "local_dir = \"../tests/protein/test_data\"\n", + "pdb_paths = [osp.join(local_dir, pdb_file) for pdb_file in os.listdir(local_dir) if pdb_file.endswith(\".pdb\")]\n", + "\n", + "ds = ProteinGraphDataset(root = \"../tests/protein/test_data/ProteinGraphDataset_transforme_None\",\n", + " pdb_paths=pdb_paths,\n", + " pdb_codes=[\"10gs\"],\n", + " uniprot_ids=[\"A0A6J1BG53\", \"A0A6P5Z5F7\"],\n", + " graphein_config=config,\n", + " graph_format_convertor=graph_format_convertor,\n", + " # transform = Reshape_Attr2Tensor([\"amino_acid_one_hot\",\"meiler\"]),\n", + " af_version=3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A0A6P5Z5F7']\n", + "before using transform\n", + "Data(edge_index=[2, 24614], node_id=[916], coords=[1], amino_acid_one_hot=[916, 20], meiler=[916, 7], name=[1], dist_mat=[1], num_nodes=916)\n", + "after using transform\n", + "Data(edge_index=[2, 24614], node_id=[916], coords=[1], amino_acid_one_hot=[916, 20], meiler=[916, 7], name=[1], dist_mat=[1], num_nodes=916)\n" + ] + } + ], + "source": [ + "print(ds[0].name)\n", + "print(f\"before using transform\")\n", + "print(ds[0])\n", + "\n", + "ds.transform = Reshape_Attr2Tensor([\"amino_acid_one_hot\",\"meiler\"])\n", + "\n", + "print(f\"after using transform\")\n", + "print(ds[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataBatch(edge_index=[2, 32998], node_id=[2], coords=[2], amino_acid_one_hot=[1251, 20], meiler=[1251, 7], name=[2], dist_mat=[2], num_nodes=1251, batch=[1251], ptr=[3])\n", + "[['A0A6J1BG53'], ['A0A6P5Z5F7']]\n" + ] + } + ], + "source": [ + "from torch_geometric.loader import DataLoader\n", + "dl = DataLoader(ds, batch_size=2, shuffle=True, drop_last=True)\n", + "for i in dl:\n", + " print(i)\n", + " print(i.name)\n", + " break " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([574, 20])\n", + "torch.Size([574, 7])\n" + ] + }, + { + "data": { + "text/plain": [ + "Data(edge_index=[2, 10811], node_id=[574], coords=[1], amino_acid_one_hot=[574, 20], meiler=[574, 7], name=[1], dist_mat=[1], num_nodes=574)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -483,6 +23431,130 @@ " break" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, if you want load both local and download data, you can try like below:\n", + "\n", + "Just specific `pdb_paths`and`pdb_codes` and `uniprot_ids`🦞\n", + "\n", + "And it works doooooooooo~😃" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constructing Graphs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n" + ] + }, + { + "data": { + "application/json": { + "ascii": false, + "bar_format": null, + "colour": null, + "elapsed": 0.31999993324279785, + "initial": 0, + "n": 0, + "ncols": null, + "nrows": null, + "postfix": null, + "prefix": "", + "rate": null, + "total": 6, + "unit": "it", + "unit_divisor": 1000, + "unit_scale": false + }, + "application/vnd.jupyter.widget-view+json": { + "model_id": "2e482ec9f51a46519080251c961500cf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/6 [00:00