Revert "Feature streamline cv training"

deeprvat/cv_utils.py

@@ -59,9 +59,11 @@ def spread_config(
     cv_path = f"{config_template['cv_path']}/{n_folds}_fold"
     for module in data_modules:
         config = copy.deepcopy(config_template)
-        sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
-        logger.info(f"setting sample file {sample_file}")
-        config["sample_file"] = sample_file
+        data_slots = DATA_SLOT_DICT[module]
+        for data_slot in data_slots:
+            sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
+            logger.info(f"setting sample file {sample_file}")
+            config[data_slot]["dataset_config"]["sample_file"] = sample_file
 
         if (module == "deeprvat") | (module == "deeprvat_pretrained"):
             logger.info("Writing baseline directories")
@@ -89,7 +91,8 @@ def generate_test_config(input_config, out_file, fold, n_folds):
     split = "test"
     sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
     logger.info(f"setting sample file {sample_file}")
-    config["sample_file"] = sample_file
+    for data_slot in DATA_SLOT_DICT["deeprvat"]:
+        config[data_slot]["dataset_config"]["sample_file"] = sample_file
     with open(out_file, "w") as f:
         yaml.dump(config, f)
 

deeprvat/data/dense_gt.py

@@ -362,7 +362,7 @@ def setup_phenotypes(
         # account for the fact that genotypes.h5 and phenotype_df can have different
         # orders of their samples
         self.index_map_geno, _ = get_matched_sample_indices(
-            samples_gt.astype(str), self.samples.astype(str)
+            samples_gt.astype(int), self.samples.astype(int)
         )
         # get_matched_sample_indices is a much, much faster implementation of the code below
         # self.index_map_geno = [np.where(samples_gt.astype(int) == i) for i in self.samples.astype(int)]
@@ -614,20 +614,13 @@ def setup_variants(
                     "Annotation dataframe has inconsistent allele frequency values"
                 )
             variants_with_af = safe_merge(
-                variants[["id"]].reset_index(drop=True), af_annotation, how="left"
+                variants[["id"]].reset_index(drop=True), af_annotation
             )
             assert np.all(
                 variants_with_af["id"].to_numpy() == variants["id"].to_numpy()
             )
-            af_isna = variants_with_af[af_col].isna()
-            if af_isna.sum() > 0:
-                logger.warning(
-                    f"Dropping {af_isna.sum()} variants missing from annotation dataframe"
-                )
-            mask = (
-                (~af_isna)
-                & (variants_with_af[af_col] >= af_threshold)
-                & (variants_with_af[af_col] <= 1 - af_threshold)
+            mask = (variants_with_af[af_col] >= af_threshold) & (
+                variants_with_af[af_col] <= 1 - af_threshold
             )
             mask = mask.to_numpy()
             del variants_with_af
@@ -938,10 +931,11 @@ def get_metadata(self) -> Dict[str, Any]:
         result = {
             "variant_metadata": self.variants[
                 ["id", "common_variant_mask", "rare_variant_mask", "matrix_index"]
-            ],
-            "samples": self.samples,
+            ]
         }
         if self.use_rare_variants:
             if hasattr(self.rare_embedding, "get_metadata"):
-                result["rare_embedding_metadata"] = self.rare_embedding.get_metadata()
+                result.update(
+                    {"rare_embedding_metadata": self.rare_embedding.get_metadata()}
+                )
         return result

deeprvat/deeprvat/associate.py

@@ -7,7 +7,7 @@
 import sys
 from pathlib import Path
 from pprint import pprint
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple
 
 import click
 import dask.dataframe as dd
@@ -115,7 +115,9 @@ def cli():
 
 def make_dataset_(
     config: Dict,
+    debug: bool = False,
     data_key="data",
+    samples: Optional[List[int]] = None,
 ) -> Dataset:
     """
     Create a dataset based on the configuration.
@@ -147,17 +149,29 @@ def make_dataset_(
             **copy.deepcopy(data_config["dataset_config"]),
         )
 
+        restrict_samples = config.get("restrict_samples", None)
+        if debug:
+            logger.info("Debug flag set; Using only 1000 samples")
+            ds = Subset(ds, range(1_000))
+        elif samples is not None:
+            ds = Subset(ds, samples)
+        elif restrict_samples is not None:
+            ds = Subset(ds, range(restrict_samples))
+
     return ds
 
 
 @cli.command()
+@click.option("--debug", is_flag=True)
 @click.option("--data-key", type=str, default="data")
-@click.argument("config-file", type=click.Path(exists=True, path_type=Path))
-@click.argument("out-file", type=click.Path(path_type=Path))
-def make_dataset(data_key: str, config_file: Path, out_file: Path):
+@click.argument("config-file", type=click.Path(exists=True))
+@click.argument("out-file", type=click.Path())
+def make_dataset(debug: bool, data_key: str, config_file: str, out_file: str):
     """
     Create a dataset based on the provided configuration and save to a pickle file.
 
+    :param debug: Flag for debugging.
+    :type debug: bool
     :param data_key: Key for dataset configuration in the config dictionary, defaults to "data".
     :type data_key: str
     :param config_file: Path to the configuration file.
@@ -169,7 +183,7 @@ def make_dataset(data_key: str, config_file: Path, out_file: Path):
     with open(config_file) as f:
         config = yaml.safe_load(f)
 
-    ds = make_dataset_(config, data_key=data_key)
+    ds = make_dataset_(config, debug=debug, data_key=data_key)
 
     with open(out_file, "wb") as f:
         pickle.dump(ds, f)
@@ -222,8 +236,6 @@ def compute_burdens_(
     .. note::
         Checkpoint models all corresponding to the same repeat are averaged for that repeat.
     """
-    logger.setLevel(logging.INFO)
-
     if not skip_burdens:
         logger.info("agg_models[*][*].reverse:")
         pprint(
@@ -235,38 +247,10 @@ def compute_burdens_(
 
     data_config = config["data"]
 
-    if "sample_file" in config:
-        sample_file = Path(config["sample_file"])
-        logger.info(f"Using samples from {sample_file}")
-        if sample_file.suffix == ".pkl":
-            with open(sample_file, "rb") as f:
-                sample_ids = np.array(pickle.load(f))
-        elif sample_file.suffix == ".zarr":
-            sample_ids = zarr.load(sample_file)
-        elif sample_file.suffix == ".npy":
-            sample_ids = np.load(sample_file)
-        else:
-            raise ValueError("Unknown file type for sample_file")
-        ds_samples = ds.get_metadata()["samples"]
-        sample_indices = np.where(
-            np.isin(ds_samples.astype(str), sample_ids.astype(str))
-        )[0]
-        if debug:
-            sample_indices = sample_indices[:1000]
-    elif debug:
-        sample_indices = np.arange(min(1000, len(ds)))
-    else:
-        sample_indices = np.arange(len(ds))
-
-    logger.info(
-        f"Computing gene impairment for {sample_indices.shape[0]} samples: {sample_indices}"
-    )
-    ds = Subset(ds, sample_indices)
-
-    ds_full = ds.dataset  # if isinstance(ds, Subset) else ds
+    ds_full = ds.dataset if isinstance(ds, Subset) else ds
     collate_fn = getattr(ds_full, "collate_fn", None)
     n_total_samples = len(ds)
-    ds_full.rare_embedding.skip_embedding = skip_burdens
+    ds.rare_embedding.skip_embedding = skip_burdens
 
     if chunk is not None:
         if n_chunks is None:
@@ -919,7 +903,7 @@ def compute_burdens(
         with open(dataset_file, "rb") as f:
             dataset = pickle.load(f)
     else:
-        dataset = make_dataset_(data_config)
+        dataset = make_dataset_(config)
 
     if torch.cuda.is_available():
         logger.info("Using GPU")