fix merge conflict

Author: sfluegel05

.github/workflows/test.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
@@ -24,7 +24,7 @@ jobs:
           python -m pip install --upgrade pip
           python -m pip install --upgrade pip setuptools wheel
           python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-          python -m pip install -e .
+          python -m pip install -e .[dev]
 
       - name: Display Python & Installed Packages
         run: |

chebai/loggers/custom.py

@@ -2,7 +2,6 @@
 from datetime import datetime
 from typing import List, Literal, Optional, Union
 
-import wandb
 from lightning.fabric.utilities.types import _PATH
 from lightning.pytorch.callbacks import ModelCheckpoint
 from lightning.pytorch.loggers import WandbLogger
@@ -105,6 +104,8 @@ def set_fold(self, fold: int) -> None:
         Args:
             fold (int): Cross-validation fold number.
         """
+        import wandb
+
         if fold != self._fold:
             self._fold = fold
             # Start new experiment

chebai/loss/bce_weighted.py

@@ -5,7 +5,6 @@
 
 from chebai.preprocessing.datasets.base import XYBaseDataModule
 from chebai.preprocessing.datasets.chebi import _ChEBIDataExtractor
-from chebai.preprocessing.datasets.pubchem import LabeledUnlabeledMixed
 
 
 class BCEWeighted(torch.nn.BCEWithLogitsLoss):
@@ -27,6 +26,8 @@ def __init__(
         data_extractor: Optional[XYBaseDataModule] = None,
         **kwargs,
     ):
+        from chebai.preprocessing.datasets.pubchem import LabeledUnlabeledMixed
+
         self.beta = beta
         if isinstance(data_extractor, LabeledUnlabeledMixed):
             data_extractor = data_extractor.labeled

chebai/loss/semantic.py

@@ -2,14 +2,16 @@
 import math
 import os
 import pickle
-from typing import List, Literal, Union
+from typing import TYPE_CHECKING, List, Literal, Union
 
 import torch
 
 from chebai.loss.bce_weighted import BCEWeighted
 from chebai.preprocessing.datasets.base import XYBaseDataModule
 from chebai.preprocessing.datasets.chebi import ChEBIOver100, _ChEBIDataExtractor
-from chebai.preprocessing.datasets.pubchem import LabeledUnlabeledMixed
+
+if TYPE_CHECKING:
+    from chebai.preprocessing.datasets.pubchem import LabeledUnlabeledMixed
 
 
 class ImplicationLoss(torch.nn.Module):
@@ -68,6 +70,8 @@ def __init__(
         multiply_with_base_loss: bool = True,
         no_grads: bool = False,
     ):
+        from chebai.preprocessing.datasets.pubchem import LabeledUnlabeledMixed
+
         super().__init__()
         # automatically choose labeled subset for implication filter in case of mixed dataset
         if isinstance(data_extractor, LabeledUnlabeledMixed):
@@ -338,7 +342,7 @@ class DisjointLoss(ImplicationLoss):
     def __init__(
         self,
         path_to_disjointness: str,
-        data_extractor: Union[_ChEBIDataExtractor, LabeledUnlabeledMixed],
+        data_extractor: Union[_ChEBIDataExtractor, "LabeledUnlabeledMixed"],
         base_loss: torch.nn.Module = None,
         disjoint_loss_weight: float = 100,
         **kwargs,

chebai/models/ffn.py

@@ -11,7 +11,6 @@ class FFN(ChebaiBaseNet):
 
     def __init__(
         self,
-        input_size: int,
         hidden_layers: List[int] = [
             1024,
         ],
@@ -20,7 +19,7 @@ def __init__(
         super().__init__(**kwargs)
 
         layers = []
-        current_layer_input_size = input_size
+        current_layer_input_size = self.input_dim
         for hidden_dim in hidden_layers:
             layers.append(MLPBlock(current_layer_input_size, hidden_dim))
             layers.append(Residual(MLPBlock(hidden_dim, hidden_dim)))

chebai/preprocessing/datasets/base.py

@@ -1,24 +1,21 @@
 import os
 import random
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Generator, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple, Union
 
 import lightning as pl
-import networkx as nx
 import pandas as pd
 import torch
 import tqdm
-from iterstrat.ml_stratifiers import (
-    MultilabelStratifiedKFold,
-    MultilabelStratifiedShuffleSplit,
-)
 from lightning.pytorch.core.datamodule import LightningDataModule
 from lightning_utilities.core.rank_zero import rank_zero_info
-from sklearn.model_selection import StratifiedShuffleSplit
 from torch.utils.data import DataLoader
 
 from chebai.preprocessing import reader as dr
 
+if TYPE_CHECKING:
+    import networkx as nx
+
 
 class XYBaseDataModule(LightningDataModule):
     """
@@ -822,7 +819,7 @@ def _download_required_data(self) -> str:
         pass
 
     @abstractmethod
-    def _extract_class_hierarchy(self, data_path: str) -> nx.DiGraph:
+    def _extract_class_hierarchy(self, data_path: str) -> "nx.DiGraph":
         """
         Extracts the class hierarchy from the data.
         Constructs a directed graph (DiGraph) using NetworkX, where nodes are annotated with fields/terms from
@@ -837,7 +834,7 @@ def _extract_class_hierarchy(self, data_path: str) -> nx.DiGraph:
         pass
 
     @abstractmethod
-    def _graph_to_raw_dataset(self, graph: nx.DiGraph) -> pd.DataFrame:
+    def _graph_to_raw_dataset(self, graph: "nx.DiGraph") -> pd.DataFrame:
         """
         Converts the graph to a raw dataset.
         Uses the graph created by `_extract_class_hierarchy` method to extract the
@@ -852,7 +849,7 @@ def _graph_to_raw_dataset(self, graph: nx.DiGraph) -> pd.DataFrame:
         pass
 
     @abstractmethod
-    def select_classes(self, g: nx.DiGraph, *args, **kwargs) -> List:
+    def select_classes(self, g: "nx.DiGraph", *args, **kwargs) -> List:
         """
         Selects classes from the dataset based on a specified criteria.
 
@@ -1027,6 +1024,9 @@ def get_test_split(
         Raises:
             ValueError: If the DataFrame does not contain a column named "labels".
         """
+        from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit
+        from sklearn.model_selection import StratifiedShuffleSplit
+
         print("Get test data split")
 
         labels_list = df["labels"].tolist()
@@ -1064,6 +1064,12 @@ def get_train_val_splits_given_test(
                 and validation DataFrames. The keys are the names of the train and validation sets, and the values
                 are the corresponding DataFrames.
         """
+        from iterstrat.ml_stratifiers import (
+            MultilabelStratifiedKFold,
+            MultilabelStratifiedShuffleSplit,
+        )
+        from sklearn.model_selection import StratifiedShuffleSplit
+
         print("Split dataset into train / val with given test set")
 
         test_ids = test_df["ident"].tolist()

chebai/preprocessing/datasets/chebi.py

@@ -13,17 +13,28 @@
 import pickle
 from abc import ABC
 from collections import OrderedDict
-from typing import Any, Dict, Generator, List, Literal, Optional, Tuple, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Generator,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Union,
+)
 
-import fastobo
-import networkx as nx
 import pandas as pd
-import requests
 import torch
 
 from chebai.preprocessing import reader as dr
 from chebai.preprocessing.datasets.base import XYBaseDataModule, _DynamicDataset
 
+if TYPE_CHECKING:
+    import fastobo
+    import networkx as nx
+
 # exclude some entities from the dataset because the violate disjointness axioms
 CHEBI_BLACKLIST = [
     194026,
@@ -214,6 +225,8 @@ def _load_chebi(self, version: int) -> str:
         Returns:
             str: The file path of the loaded ChEBI ontology.
         """
+        import requests
+
         chebi_name = self.raw_file_names_dict["chebi"]
         chebi_path = os.path.join(self.raw_dir, chebi_name)
         if not os.path.isfile(chebi_path):
@@ -225,7 +238,7 @@ def _load_chebi(self, version: int) -> str:
             open(chebi_path, "wb").write(r.content)
         return chebi_path
 
-    def _extract_class_hierarchy(self, data_path: str) -> nx.DiGraph:
+    def _extract_class_hierarchy(self, data_path: str) -> "nx.DiGraph":
         """
         Extracts the class hierarchy from the ChEBI ontology.
         Constructs a directed graph (DiGraph) using NetworkX, where nodes are annotated with fields/terms from
@@ -237,6 +250,9 @@ def _extract_class_hierarchy(self, data_path: str) -> nx.DiGraph:
         Returns:
             nx.DiGraph: The class hierarchy.
         """
+        import fastobo
+        import networkx as nx
+
         with open(data_path, encoding="utf-8") as chebi:
             chebi = "\n".join(line for line in chebi if not line.startswith("xref:"))
 
@@ -266,7 +282,7 @@ def _extract_class_hierarchy(self, data_path: str) -> nx.DiGraph:
         print("Compute transitive closure")
         return nx.transitive_closure_dag(g)
 
-    def _graph_to_raw_dataset(self, g: nx.DiGraph) -> pd.DataFrame:
+    def _graph_to_raw_dataset(self, g: "nx.DiGraph") -> pd.DataFrame:
         """
         Converts the graph to a raw dataset.
         Uses the graph created by `_extract_class_hierarchy` method to extract the
@@ -278,6 +294,8 @@ def _graph_to_raw_dataset(self, g: nx.DiGraph) -> pd.DataFrame:
         Returns:
             pd.DataFrame: The raw dataset created from the graph.
         """
+        import networkx as nx
+
         smiles = nx.get_node_attributes(g, "smiles")
         names = nx.get_node_attributes(g, "name")
 
@@ -590,7 +608,7 @@ def _name(self) -> str:
         """
         return f"ChEBI{self.THRESHOLD}"
 
-    def select_classes(self, g: nx.DiGraph, *args, **kwargs) -> List:
+    def select_classes(self, g: "nx.DiGraph", *args, **kwargs) -> List:
         """
         Selects classes from the ChEBI dataset based on the number of successors meeting a specified threshold.
 
@@ -615,6 +633,8 @@ def select_classes(self, g: nx.DiGraph, *args, **kwargs) -> List:
             - The `THRESHOLD` attribute should be defined in the subclass of this class.
             - Nodes without a 'smiles' attribute are ignored in the successor count.
         """
+        import networkx as nx
+
         smiles = nx.get_node_attributes(g, "smiles")
         nodes = list(
             sorted(
@@ -753,7 +773,7 @@ def processed_dir_main(self) -> str:
             "processed",
         )
 
-    def _extract_class_hierarchy(self, chebi_path: str) -> nx.DiGraph:
+    def _extract_class_hierarchy(self, chebi_path: str) -> "nx.DiGraph":
         """
         Extracts a subset of ChEBI based on subclasses of the top class ID.
 
@@ -791,8 +811,10 @@ def _extract_class_hierarchy(self, chebi_path: str) -> nx.DiGraph:
         )
         return g
 
-    def select_classes(self, g: nx.DiGraph, *args, **kwargs) -> List:
+    def select_classes(self, g: "nx.DiGraph", *args, **kwargs) -> List:
         """Only selects classes that meet the threshold AND are subclasses of the top class ID (including itself)."""
+        import networkx as nx
+
         smiles = nx.get_node_attributes(g, "smiles")
         nodes = list(
             sorted(
@@ -868,7 +890,7 @@ def chebi_to_int(s: str) -> int:
     return int(s[s.index(":") + 1 :])
 
 
-def term_callback(doc: fastobo.term.TermFrame) -> Union[Dict, bool]:
+def term_callback(doc: "fastobo.term.TermFrame") -> Union[Dict, bool]:
     """
     Extracts information from a ChEBI term document.
     This function takes a ChEBI term document as input and extracts relevant information such as the term ID, parents,
@@ -885,6 +907,8 @@ def term_callback(doc: fastobo.term.TermFrame) -> Union[Dict, bool]:
     - "name": The name of the ChEBI term.
     - "smiles": The SMILES string associated with the ChEBI term, if available.
     """
+    import fastobo
+
     parts = set()
     parents = []
     name = None

chebai/preprocessing/reader.py

@@ -5,11 +5,8 @@
 from itertools import islice
 from typing import Any, Dict, List, Optional
 
-import deepsmiles
-import selfies as sf
 from pysmiles.read_smiles import _tokenize
 from rdkit import Chem
-from transformers import RobertaTokenizerFast
 
 from chebai.preprocessing.collate import DefaultCollator, RaggedCollator
 
@@ -224,6 +221,8 @@ class DeepChemDataReader(ChemDataReader):
     """
 
     def __init__(self, *args, **kwargs):
+        import deepsmiles
+
         super().__init__(*args, **kwargs)
         self.converter = deepsmiles.Converter(rings=True, branches=True)
         self.error_count = 0
@@ -298,6 +297,8 @@ def __init__(
         vsize: int = 4000,
         **kwargs,
     ):
+        from transformers import RobertaTokenizerFast
+
         super().__init__(*args, **kwargs)
         self.tokenizer = RobertaTokenizerFast.from_pretrained(
             data_path, max_len=max_len
@@ -331,6 +332,8 @@ def __init__(
         vsize: int = 4000,
         **kwargs,
     ):
+        import selfies as sf
+
         super().__init__(*args, **kwargs)
         self.error_count = 0
         sf.set_semantic_constraints("hypervalent")
@@ -342,6 +345,8 @@ def name(cls) -> str:
 
     def _read_data(self, raw_data: str) -> Optional[List[int]]:
         """Read and tokenize raw data using SELFIES."""
+        import selfies as sf
+
         try:
             tokenized = sf.split_selfies(sf.encoder(raw_data.strip(), strict=True))
             tokenized = [self._get_token_index(v) for v in tokenized]

chebai/preprocessing/structures.py

@@ -1,8 +1,10 @@
-from typing import Any, Tuple, Union
+from typing import TYPE_CHECKING, Any, Tuple, Union
 
-import networkx as nx
 import torch
 
+if TYPE_CHECKING:
+    import networkx as nx
+
 
 class XYData(torch.utils.data.Dataset):
     """
@@ -119,7 +121,7 @@ class XYMolData(XYData):
         kwargs: Additional fields to store in the dataset.
     """
 
-    def to_x(self, device: torch.device) -> Tuple[nx.Graph, ...]:
+    def to_x(self, device: torch.device) -> Tuple["nx.Graph", ...]:
         """
         Moves the node attributes of the molecular graphs to the specified device.
 
@@ -129,6 +131,8 @@ def to_x(self, device: torch.device) -> Tuple[nx.Graph, ...]:
         Returns:
             A tuple of molecular graphs with node attributes on the specified device.
         """
+        import networkx as nx
+
         l_ = []
         for g in self.x:
             graph = g.copy()