From 4cc3cafed364afec16238104a0a484165579eb7f Mon Sep 17 00:00:00 2001 From: Amrit K Date: Mon, 18 Mar 2024 11:01:22 -0400 Subject: [PATCH] Add some small fixes, sklearn to dependencies --- data/sequence.py | 4 +- lib/utils.py | 2 + models/baseline/Bi-LSTM.py | 10 +- models/big_bird_cehr/embeddings.py | 4 +- models/cehr_bert/embeddings.py | 15 +- models/cehr_bert/model.py | 46 ++-- poetry.lock | 323 +++++++++++++++++++---------- pyproject.toml | 1 + 8 files changed, 261 insertions(+), 144 deletions(-) diff --git a/data/sequence.py b/data/sequence.py index 21d63e8..32cb814 100644 --- a/data/sequence.py +++ b/data/sequence.py @@ -545,8 +545,8 @@ def _time_after_admission( encounter_index = encounter_row["encounter_ids"].index(encounter_id) start_time = encounter_row["starts"][encounter_index] start_time = parser.parse(start_time) - event_time = parser.parse(event_time) - elapsed_time = round((event_time - start_time).total_seconds() / 3600, 2) + event_time_ = parser.parse(event_time) + elapsed_time = round((event_time_ - start_time).total_seconds() / 3600, 2) elapsed_times.append(elapsed_time) row["elapsed_time"] = elapsed_times return row diff --git a/lib/utils.py b/lib/utils.py index 2c79ed1..3428f2d 100644 --- a/lib/utils.py +++ b/lib/utils.py @@ -37,6 +37,8 @@ def get_latest_checkpoint(checkpoint_dir: str) -> Any: list_of_files = glob.glob(join(checkpoint_dir, "last*.ckpt")) return max(list_of_files, key=os.path.getctime) if list_of_files else None + return None + def load_pretrain_data( data_dir: str, diff --git a/models/baseline/Bi-LSTM.py b/models/baseline/Bi-LSTM.py index ca0f08a..93739f6 100644 --- a/models/baseline/Bi-LSTM.py +++ b/models/baseline/Bi-LSTM.py @@ -21,15 +21,15 @@ from torch.utils.data import DataLoader, Dataset from tqdm import tqdm - -ROOT = "/fs01/home/afallah/odyssey/odyssey" -os.chdir(ROOT) - from models.big_bird_cehr.data import FinetuneDataset from models.big_bird_cehr.embeddings import Embeddings from models.big_bird_cehr.tokenizer import HuggingFaceConceptTokenizer +ROOT = "/fs01/home/afallah/odyssey/odyssey" +os.chdir(ROOT) + + DATA_ROOT = f"{ROOT}/data/slurm_data/512/one_month" DATA_PATH = f"{DATA_ROOT}/pretrain.parquet" FINE_TUNE_PATH = f"{DATA_ROOT}/fine_tune.parquet" @@ -162,7 +162,7 @@ def get_inputs_labels( @staticmethod def get_balanced_accuracy(outputs: torch.Tensor, labels: torch.Tensor) -> Any: - """Return the balanced accuracy metric by comparing outputs to labels""" + """Return the balanced accuracy metric by comparing outputs to labels.""" predictions = torch.round(sigmoid(outputs)) predictions = predictions.detach().cpu().numpy() labels = labels.detach().cpu().numpy() diff --git a/models/big_bird_cehr/embeddings.py b/models/big_bird_cehr/embeddings.py index f6e222f..f8920c6 100644 --- a/models/big_bird_cehr/embeddings.py +++ b/models/big_bird_cehr/embeddings.py @@ -271,8 +271,10 @@ def cache_input( visit_orders: torch.Tensor, visit_segments: torch.Tensor, ) -> None: - """Cache values for time_stamps, ages, visit_orders & visit_segments inside the class object. + """Cache values for time_stamps, ages, visit_orders & visit_segments. + These values will be used by the forward pass to change the final embedding. + """ self.time_stamps = time_stamps self.ages = ages diff --git a/models/cehr_bert/embeddings.py b/models/cehr_bert/embeddings.py index 6cc2ef8..ecd096b 100644 --- a/models/cehr_bert/embeddings.py +++ b/models/cehr_bert/embeddings.py @@ -1,3 +1,5 @@ +"""Embedding modules.""" + import math import torch @@ -19,7 +21,7 @@ def __init__(self, embedding_size: int, is_time_delta: bool = False): nn.init.xavier_uniform_(self.phi) def forward(self, time_stamps: torch.Tensor) -> torch.Tensor: - """Applies time embedding to the input time stamps.""" + """Apply time embedding to the input time stamps.""" if self.is_time_delta: # If the time_stamps represent time deltas, we calculate the deltas. # This is equivalent to the difference between consecutive elements. @@ -48,7 +50,7 @@ def __init__( self.embedding = nn.Embedding(self.visit_order_size, self.embedding_size) def forward(self, visit_segments: torch.Tensor) -> torch.Tensor: - """Applies visit embedding to the input visit segments.""" + """Apply visit embedding to the input visit segments.""" return self.embedding(visit_segments) @@ -69,7 +71,7 @@ def __init__( ) def forward(self, inputs: torch.Tensor) -> torch.Tensor: - """Applies concept embedding to the input concepts.""" + """Apply concept embedding to the input concepts.""" return self.embedding(inputs) @@ -95,7 +97,7 @@ def __init__(self, embedding_size, max_len=512): self.register_buffer("pe", pe) def forward(self, visit_orders: torch.Tensor) -> torch.Tensor: - """Applies positional embedding to the input visit orders.""" + """Apply positional embedding to the input visit orders.""" first_visit_concept_orders = visit_orders[:, 0:1] normalized_visit_orders = torch.clamp( visit_orders - first_visit_concept_orders, @@ -157,7 +159,7 @@ def forward( visit_orders: torch.Tensor, visit_segments: torch.Tensor, ) -> torch.Tensor: - """Applies embeddings to the input features.""" + """Apply embeddings to the input features.""" concept_embed = self.concept_embedding(concept_ids) type_embed = self.token_type_embeddings(type_ids) time_embed = self.time_embedding(time_stamps) @@ -169,6 +171,5 @@ def forward( embeddings = self.tanh(self.scale_back_concat_layer(embeddings)) embeddings = embeddings + type_embed + positional_embed + visit_segment_embed embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings + return self.dropout(embeddings) diff --git a/models/cehr_bert/model.py b/models/cehr_bert/model.py index ca5d080..a1334dd 100644 --- a/models/cehr_bert/model.py +++ b/models/cehr_bert/model.py @@ -1,3 +1,5 @@ +"""CEHR-BERT model.""" + from typing import Optional, Tuple, Union import pytorch_lightning as pl @@ -89,7 +91,7 @@ def __init__( self.post_init() def _init_weights(self, module) -> None: - """Initialize the weights""" + """Initialize the weights.""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.bert_config.initializer_range) if module.bias is not None: @@ -103,11 +105,12 @@ def _init_weights(self, module) -> None: module.weight.data.fill_(1.0) def post_init(self) -> None: + """Apply post initialization.""" self.apply(self._init_weights) def forward( self, - input: Tuple[ + input_: Tuple[ torch.Tensor, torch.Tensor, torch.Tensor, @@ -122,7 +125,7 @@ def forward( return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor, ...], MaskedLMOutput]: """Forward pass for the model.""" - concept_ids, type_ids, time_stamps, ages, visit_orders, visit_segments = input + concept_ids, type_ids, time_stamps, ages, visit_orders, visit_segments = input_ embedding_output = self.embeddings( concept_ids, type_ids, @@ -166,8 +169,8 @@ def forward( ) def training_step(self, batch, batch_idx) -> torch.Tensor: - """Training step.""" - input = ( + """Compute training step.""" + input_ = ( batch["concept_ids"], batch["type_ids"], batch["time_stamps"], @@ -178,7 +181,7 @@ def training_step(self, batch, batch_idx) -> torch.Tensor: labels = batch["labels"] attention_mask = batch["attention_mask"] loss = self( - input, + input_, attention_mask=attention_mask, labels=labels, return_dict=True, @@ -187,8 +190,8 @@ def training_step(self, batch, batch_idx) -> torch.Tensor: return loss def validation_step(self, batch, batch_idx) -> torch.Tensor: - """Validation step.""" - input = ( + """Compute validation step.""" + input_ = ( batch["concept_ids"], batch["type_ids"], batch["time_stamps"], @@ -199,7 +202,7 @@ def validation_step(self, batch, batch_idx) -> torch.Tensor: labels = batch["labels"] attention_mask = batch["attention_mask"] loss = self( - input, + input_, attention_mask=attention_mask, labels=labels, return_dict=True, @@ -281,11 +284,12 @@ def _init_weights(self, module): module.weight.data.fill_(1.0) def post_init(self): + """Apply post initialization.""" self.apply(self._init_weights) def forward( self, - input: Tuple[ + input_: Tuple[ torch.Tensor, torch.Tensor, torch.Tensor, @@ -301,9 +305,9 @@ def forward( ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutput]: """Forward pass for the model.""" if attention_mask is None: - attention_mask = torch.ones_like(input[0]) + attention_mask = torch.ones_like(input_[0]) outputs = self.pretrained_model( - input, + input_, attention_mask=attention_mask, output_attentions=True, output_hidden_states=True, @@ -332,8 +336,8 @@ def forward( ) def training_step(self, batch, batch_idx): - """Training step.""" - input = ( + """Compute training step.""" + input_ = ( batch["concept_ids"], batch["type_ids"], batch["time_stamps"], @@ -344,7 +348,7 @@ def training_step(self, batch, batch_idx): labels = batch["labels"] attention_mask = batch["attention_mask"] loss = self( - input, + input_, attention_mask=attention_mask, labels=labels, return_dict=True, @@ -353,8 +357,8 @@ def training_step(self, batch, batch_idx): return loss def validation_step(self, batch, batch_idx): - """Validation step.""" - input = ( + """Compute validation step.""" + input_ = ( batch["concept_ids"], batch["type_ids"], batch["time_stamps"], @@ -365,7 +369,7 @@ def validation_step(self, batch, batch_idx): labels = batch["labels"] attention_mask = batch["attention_mask"] loss = self( - input, + input_, attention_mask=attention_mask, labels=labels, return_dict=True, @@ -374,8 +378,8 @@ def validation_step(self, batch, batch_idx): return loss def test_step(self, batch, batch_idx): - """Test step.""" - input = ( + """Compute test step.""" + input_ = ( batch["concept_ids"], batch["type_ids"], batch["time_stamps"], @@ -386,7 +390,7 @@ def test_step(self, batch, batch_idx): labels = batch["labels"] attention_mask = batch["attention_mask"] outputs = self( - input, + input_, attention_mask=attention_mask, labels=labels, return_dict=True, diff --git a/poetry.lock b/poetry.lock 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