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import json
import os
from pathlib import Path
from typing import Optional, Union, Iterable, List
import matplotlib
import numpy as np
import torch
from pytorch_lightning.callbacks import ModelCheckpoint
import shutil
def freeze_model_weights(model: torch.nn.Module) -> None:
for param in model.parameters():
param.requires_grad = False
# def init_attention_from_tf_idf(batch, tf_idf, vectorizer, token_vectors,):
# features = vectorizer.get_feature_names()
#
# all_relevant_tokens = []
# for j, sample in enumerate(batch["tokens"]):
#
# global_sample_ind = train_dataloader.dataset.data.id.tolist().index(batch["sample_ids"][j])
# tf_idf_sample = tf_idf[global_sample_ind]
# relevant_tokens_sample = []
# for k in range(batch["input_ids"].shape[1]):
# if k < len(sample):
# token = sample[k]
# if token in features:
# token_ind = features.index(token)
# if token_ind in tf_idf_sample.indices:
# tf_idf_ind = np.where(tf_idf_sample.indices == token_ind)[0][0]
# token_value = tf_idf_sample.data[tf_idf_ind]
# if token_value > 0.05:
# relevant_tokens_sample.append(1)
# continue
# relevant_tokens_sample.append(0)
# all_relevant_tokens.append(relevant_tokens_sample)
#
# all_relevant_tokens = torch.tensor(all_relevant_tokens)
# if self.use_cuda:
# all_relevant_tokens = all_relevant_tokens.cuda()
#
# relevant_tokens = torch.einsum('ik,ikl->ikl', all_relevant_tokens, token_vectors)
#
# mean_over_relevant_tokens = relevant_tokens.mean(dim=1)
#
# # get tensor of shape batch_size x num_classes x dim
# masked_att_vectors_per_sample = torch.einsum('ik,il->ilk', mean_over_relevant_tokens,
# target_tensors)
#
# # sum into one vector per prototype. shape: num_classes x dim
# sum_att_per_prototype = torch.add(sum_att_per_prototype, masked_att_vectors_per_sample.sum(dim=0)
# .detach())
#
# n_att_per_prototype += target_tensors.sum(dim=0).detach()
def attention_mask_from_tokens(masks, token_list):
mask_patterns = [["chief", "complaint", ":"],
["present", "illness", ":"],
["medical", "history", ":"],
["medication", "on", "admission", ":"],
["allergies", ":"],
["physical", "exam", ":"],
["family", "history", ":"],
["social", "history", ":"],
["[CLS]"],
["[SEP]"],
]
for i, tokens in enumerate(token_list):
for j, token in enumerate(tokens):
for pattern in mask_patterns:
if pattern == tokens[j:j + len(pattern)]:
masks[i, j:j + len(pattern)] = 0
return masks
def get_bert_vectors_per_sample(batch, bert, use_cuda, linear=None):
input_ids = batch["input_ids"]
attention_mask = batch["attention_masks"]
token_type_ids = batch["token_type_ids"]
if use_cuda:
input_ids = input_ids.cuda()
attention_mask = attention_mask.cuda()
token_type_ids = token_type_ids.cuda()
output = bert(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
if linear is not None:
if use_cuda:
linear = linear.cuda()
token_vectors = linear(output.last_hidden_state)
else:
token_vectors = output.last_hidden_state
mean_over_tokens = token_vectors.mean(dim=1)
return mean_over_tokens, token_vectors
def get_attended_vector_per_sample(batch, bert, use_cuda, linear=None):
input_ids = batch["input_ids"]
attention_mask = batch["attention_masks"]
token_type_ids = batch["token_type_ids"]
if use_cuda:
input_ids = input_ids.cuda()
attention_mask = attention_mask.cuda()
token_type_ids = token_type_ids.cuda()
output = bert(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
if linear is not None:
if use_cuda:
linear = linear.cuda()
token_vectors = linear(output.last_hidden_state)
else:
token_vectors = output.last_hidden_state
mean_over_tokens = token_vectors.mean(dim=1)
return mean_over_tokens, token_vectors
def pad_batch_samples(batch_samples: Iterable, num_tokens: int) -> List:
padded_samples = []
for sample in batch_samples:
missing_tokens = num_tokens - len(sample)
tokens_to_append = ["[PAD]"] * missing_tokens
padded_samples += sample + tokens_to_append
return padded_samples
class ProjectorCallback(ModelCheckpoint):
def __init__(
self,
train_dataloader,
project_n_batches=-1, # -1 means project all batches
dirpath: Optional[Union[str, Path]] = None,
filename: Optional[str] = None,
monitor: Optional[str] = None,
verbose: bool = False,
save_last: Optional[bool] = None,
save_top_k: Optional[int] = None,
save_weights_only: bool = False,
mode: str = "auto",
period: int = 1,
prefix: str = ""
):
super().__init__(dirpath=dirpath, filename=filename, monitor=monitor, verbose=verbose, save_last=save_last,
save_top_k=save_top_k, save_weights_only=save_weights_only, mode=mode, period=period,
prefix=prefix)
self.train_dataloader = train_dataloader
self.project_n_batches = project_n_batches
def on_validation_end(self, trainer, pl_module):
"""
After each validation step, save the learned token and prototype embeddings for analysis in the Projector.
"""
super().on_validation_end(trainer, pl_module)
with torch.no_grad():
all_vectors = []
metadata = []
for i, batch in enumerate(self.train_dataloader):
_, _, batch_features = pl_module(batch, return_metadata=True)
targets = batch["targets"]
features = batch_features[0]
tokens = batch_features[1]
prototype_vectors = batch_features[2]
batch_size = features.shape[0]
window_len = features.shape[1]
for sample_i in range(batch_size):
for window_i in range(window_len):
window_vector = features[sample_i][window_i]
window_tokens = tokens[sample_i * window_len + window_i]
if window_tokens == "[PAD]" or window_tokens == "[SEP]":
continue
all_vectors.append(window_vector)
metadata.append([window_tokens, targets[sample_i]])
if ["PROTO_0", 0] not in metadata:
for j, vector in enumerate(prototype_vectors):
prototype_class = int(j // pl_module.prototypes_per_class)
all_vectors.append(vector.squeeze())
metadata.append([f"PROTO_{prototype_class}", prototype_class])
if self.project_n_batches != -1 and i >= self.project_n_batches - 1:
break
trainer.logger.experiment.add_embedding(torch.stack(all_vectors), metadata, global_step=trainer.global_step,
metadata_header=["tokens", "target"])
delete_intermediate_embeddings(trainer.logger.experiment.log_dir, trainer.global_step)
def delete_intermediate_embeddings(log_dir, current_step):
dir_content = os.listdir(log_dir)
for file_or_dir in dir_content:
try:
file_as_integer = int(file_or_dir)
abs_path = os.path.join(log_dir, file_or_dir)
if os.path.isdir(abs_path) and file_as_integer != current_step and file_as_integer != 0:
remove_dir(abs_path)
except:
continue
embedding_config = """embeddings {{
tensor_name: "default:{embedding_id}"
metadata_path: "{embedding_id}/default/metadata.tsv"
tensor_path: "{embedding_id}/default/tensors.tsv"\n}}"""
config_text = embedding_config.format(embedding_id="00000") + "\n" + \
embedding_config.format(embedding_id=f"{current_step:05}")
with open(os.path.join(log_dir, "projector_config.pbtxt"), "w") as config_file_write:
config_file_write.write(config_text)
def remove_dir(path):
try:
shutil.rmtree(path)
print(f"delete dir {path}")
except OSError as e:
print("Error: %s : %s" % (path, e.strerror))
def load_eval_buckets(eval_bucket_path):
buckets = None
if eval_bucket_path is not None:
with open(eval_bucket_path) as bucket_file:
buckets = json.load(bucket_file)
return buckets
def build_heatmaps(case_tokens, token_scores, tint="red", amplifier=8):
heatmap_per_prototype = []
for prototype_scores in token_scores:
template = '<span style="color: black; background-color: {}">{}</span>'
heatmap_string = ''
for word, color in zip(case_tokens, prototype_scores):
color = min(1, color * amplifier)
if tint == "red":
hex_color = matplotlib.colors.rgb2hex([1, 1 - color, 1 - color])
elif tint == "blue":
hex_color = matplotlib.colors.rgb2hex([1 - color, 1 - color, 1])
else:
hex_color = matplotlib.colors.rgb2hex([1 - color, 1, 1 - color])
if "##" not in word:
heatmap_string += ' '
word_string = word
else:
word_string = word.replace("##", "")
heatmap_string += template.format(hex_color, word_string)
heatmap_per_prototype.append(heatmap_string)
return heatmap_per_prototype
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