format

netam/common.py

@@ -160,7 +160,9 @@ def find_least_used_cuda_gpu(default_value):
         gpu_to_use = default_value
     else:
         gpu_to_use = utilization.index(min(utilization))
-    print(f"Picking GPU {gpu_to_use}; default {default_value}; utilization: {utilization}")
+    print(
+        f"Picking GPU {gpu_to_use}; default {default_value}; utilization: {utilization}"
+    )
     return gpu_to_use
 
 
@@ -169,6 +171,7 @@ def pick_device(jobid):
     Pick a device for PyTorch to use. If CUDA is available, use the least used
     GPU, and if all are idle use a GPU based on the jobid.
     """
+
     # check that CUDA is usable
     def check_CUDA():
         try:

netam/dnsm.py

@@ -264,35 +264,32 @@ def to(self, device):
         self.all_subs_probs = self.all_subs_probs.to(device)
 
 
-def train_test_datasets_of_pcp_df(pcp_df, train_frac=0.8, branch_length_multiplier=5.0):
-    nt_parents = pcp_df["parent"].reset_index(drop=True)
-    nt_children = pcp_df["child"].reset_index(drop=True)
-    rates = pcp_df["rates"].reset_index(drop=True)
-    subs_probs = pcp_df["subs_probs"].reset_index(drop=True)
-
-    train_len = int(train_frac * len(nt_parents))
-    train_parents, val_parents = nt_parents[:train_len], nt_parents[train_len:]
-    train_children, val_children = nt_children[:train_len], nt_children[train_len:]
-    train_rates, val_rates = rates[:train_len], rates[train_len:]
-    train_subs_probs, val_subs_probs = (
-        subs_probs[:train_len],
-        subs_probs[train_len:],
+def dataset_of_pcp_df(pcp_df, branch_length_multiplier=5.0):
+    return DNSMDataset.from_data(
+        pcp_df["parents"],
+        pcp_df["children"],
+        pcp_df["rates"],
+        pcp_df["subs_probs"],
+        branch_length_multiplier=branch_length_multiplier,
     )
-    val_dataset = DNSMDataset.from_data(
-        val_parents,
-        val_children,
-        val_rates,
-        val_subs_probs,
+
+
+def train_val_datasets_of_pcp_df(pcp_df, branch_length_multiplier=5.0):
+    """
+    Perform a train-val split based on a "in_train" column.
+    """
+    train_df = pcp_df[pcp_df["in_train"]].reset_index(drop=True)
+    val_df = pcp_df[~pcp_df["in_train"]].reset_index(drop=True)
+
+    val_dataset = dataset_of_pcp_df(
+        val_df,
         branch_length_multiplier=branch_length_multiplier,
     )
-    if train_frac == 0.0:
+    if len(train_df) == 0:
         return None, val_dataset
     # else:
-    train_dataset = DNSMDataset.from_data(
-        train_parents,
-        train_children,
-        train_rates,
-        train_subs_probs,
+    train_dataset = dataset_of_pcp_df(
+        train_df,
         branch_length_multiplier=branch_length_multiplier,
     )
     return train_dataset, val_dataset