changed time_cutoff option

CHANGELOG.md

@@ -5,7 +5,7 @@
 * Improves support for datamodules with multiple test sets. Generalises this to support GO and FOLD. Also adds multiple seq ID.-based splits for GO. [#72](https://github.com/a-r-j/ProteinWorkshop/pull/72)
 * Add redownload checks for already downloaded datasets and harmonise pdb download interface [#86](https://github.com/a-r-j/ProteinWorkshop/pull/86)
 * Remove remaining errors from PDB dataset change
-* Add option to create pdb datasets with sequence-based splits [#88](https://github.com/a-r-j/ProteinWorkshop/pull/88)
+* Add option to create pdb datasets with sequence-based splits [#88](https://github.com/a-r-j/ProteinWorkshop/pull/88) as well as time-based splits [#89](https://github.com/a-r-j/ProteinWorkshop/pull/89)
 
 ### Models
 

proteinworkshop/config/dataset/pdb.yaml

@@ -10,10 +10,10 @@ datamodule:
 
   pdb_dataset:
     _target_: "proteinworkshop.datasets.pdb_dataset.PDBData"
-    fraction: 1.0 # Fraction of dataset to use
+    fraction: 0.01 # Fraction of dataset to use
     molecule_type: "protein" # Type of molecule for which to select
     experiment_types: ["diffraction", "NMR", "EM", "other"] # All experiment types
-    max_length: 1000 # Exclude polypeptides greater than length 1000
+    max_length: 150 # Exclude polypeptides greater than length 1000
     min_length: 10 # Exclude peptides of length 10
     oligomeric_min: 1 # Include only monomeric proteins
     oligomeric_max: 5 # Include up to 5-meric proteins 
@@ -24,6 +24,9 @@ datamodule:
     remove_non_standard_residues: True # Include only proteins containing standard amino acid residues
     remove_pdb_unavailable: True # Include only proteins that are available to download
     train_val_test: [0.8, 0.1, 0.1] # Cross-validation ratios to use for train, val, and test splits
-    split_type: "sequence_similarity" # Split sequences by sequence similarity clustering, other option is "random"
-    split_sequence_similiarity: 0.3 # Clustering at 30% sequence similarity (argument is ignored if split_type="random")
+    split_type: "sequence_similarity" # Split sequences by sequence similarity clustering, other options are "random" and "time_cutoff"
+    split_sequence_similiarity: 0.3 # Clustering at 30% sequence similarity (argument is ignored if split_type!="sequence_similarity")
     overwrite_sequence_clusters: False # Previous clusterings at same sequence similarity are reused and not overwritten
+    split_time_frames: null # Time-cutoffs for train, val and test set (argument is ignored if split_type!="time_cutoff") - e.g., ["2020-01-01", "2021-01-01", "2023-03-01"]
+
+

proteinworkshop/datasets/pdb_dataset.py

@@ -2,6 +2,7 @@
 
 import hydra
 import omegaconf
+import numpy as np
 import os
 import pandas as pd
 import pathlib
@@ -30,9 +31,11 @@ def __init__(
         remove_non_standard_residues: bool,
         remove_pdb_unavailable: bool,
         train_val_test: List[float],
-        split_type: Literal["sequence_similarity", "random"],
-        split_sequence_similiarity: int,
-        overwrite_sequence_clusters: bool
+        split_type: Literal["sequence_similarity", "time_cutoff", "random"] = "random",
+        split_sequence_similiarity: Optional[int] = None,
+        overwrite_sequence_clusters: Optional[bool] = False,
+        split_time_frames: Optional[List[str]] = None,
+
     ):
         self.fraction = fraction
         self.molecule_type = molecule_type
@@ -52,6 +55,11 @@ def __init__(
         self.split_type = split_type
         self.split_sequence_similarity = split_sequence_similiarity
         self.overwrite_sequence_clusters = overwrite_sequence_clusters
+        if self.split_type == "time_cutoff":
+            try:
+                self.split_time_frames = [np.datetime64(date) for date in split_time_frames]
+            except:
+                raise TypeError(f"{split_time_frames} does not contain valid dates for np.datetime64 format")
         self.splits = ["train", "val", "test"]
 
     def create_dataset(self):
@@ -128,9 +136,15 @@ def create_dataset(self):
         elif self.split_type == "sequence_similarity":
             log.info(f"Splitting dataset via sequence-similarity split into {self.train_val_test}...")
             log.info(f"Using {self.split_sequence_similarity} sequence similarity for split")
-            pdb_manager.cluster(min_seq_id=self.split_sequence_similarity, update=True)
-            splits = pdb_manager.split_clusters(
-                pdb_manager.df, update=True, overwrite = self.overwrite_sequence_clusters)
+            pdb_manager.cluster(min_seq_id=self.split_sequence_similarity, update=True,
+                                 overwrite = self.overwrite_sequence_clusters)
+            splits = pdb_manager.split_clusters(pdb_manager.df, update=True)
+
+        elif self.split_type == "time_cutoff":
+            log.info(f"Splitting dataset via time_cutoff split into {self.train_val_test}...")
+            log.info(f"Using {self.split_time_frames} dates for split")
+            pdb_manager.split_time_frames = self.split_time_frames
+            splits = pdb_manager.split_by_deposition_date(df=pdb_manager.df, update=True)
 
         log.info(splits["train"])
         return splits