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analysis/.gitignore vendored
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__pycache__
*.html

297
analysis/find_closest.py
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# %%
import pandas as pd
from utils import Retriever, cosine_similarity_chunked
import os
import glob
import numpy as np
# %%
data_path = f'../data_preprocess/exports/preprocessed_data.csv'
df_pre = pd.read_csv(data_path, skipinitialspace=True)
# %%
# this should be >0 if we are using abbreviations processed data
desc_list = df_pre['tag_description'].to_list()
# %%
[ elem for elem in desc_list if isinstance(elem, float)]
##########################################
# %%
fold = 1
data_path = f'../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv'
df = pd.read_csv(data_path, skipinitialspace=True)
# %%
# subset to mdm
df = df[df['MDM']]
thing_condition = df['p_thing'] == df['thing_pattern']
error_thing_df = df[~thing_condition][['tag_description', 'thing_pattern','p_thing']]
property_condition = df['p_property'] == df['property_pattern']
error_property_df = df[~property_condition][['tag_description', 'property_pattern','p_property']]
correct_df = df[thing_condition & property_condition][['tag_description', 'property_pattern', 'p_property']]
test_df = df
# %%
# thing_df.to_html('thing_errors.html')
# property_df.to_html('property_errors.html')
##########################################
# what we need now is understand why the model is making these mispredictions
# import train data and test data
# %%
class Embedder():
input_df: pd.DataFrame
fold: int
def __init__(self, input_df):
self.input_df = input_df
def make_embedding(self, checkpoint_path):
def generate_input_list(df):
input_list = []
for _, row in df.iterrows():
# name = f"<NAME>{row['tag_name']}<NAME>"
desc = f"<DESC>{row['tag_description']}<DESC>"
# element = f"{name}{desc}"
element = f"{desc}"
input_list.append(element)
return input_list
# prepare reference embed
train_data = list(generate_input_list(self.input_df))
# Define the directory and the pattern
retriever_train = Retriever(train_data, checkpoint_path)
retriever_train.make_mean_embedding(batch_size=64)
return retriever_train.embeddings.to('cpu')
# %%
data_path = f"../data_preprocess/exports/dataset/group_{fold}/train.csv"
train_df = pd.read_csv(data_path, skipinitialspace=True)
checkpoint_directory = "../train/mapping_pattern"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
train_embedder = Embedder(input_df=train_df)
train_embeds = train_embedder.make_embedding(checkpoint_path)
test_embedder = Embedder(input_df=test_df)
test_embeds = test_embedder.make_embedding(checkpoint_path)
# %%
# test embeds are inputs since we are looking back at train data
cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
# %%
# the following function takes in a full cos_sim_matrix
# condition_source: boolean selectors of the source embedding
# condition_target: boolean selectors of the target embedding
def find_closest(cos_sim_matrix, condition_source, condition_target):
# subset_matrix = cos_sim_matrix[condition_source]
# except we are subsetting 2D matrix (row, column)
subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
# we select top k here
# Get the indices of the top 5 maximum values along axis 1
top_k = 5
top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
# note that top_k_indices is a nested list because of the 2d nature of the matrix
# the result is flipped
top_k_indices[0] = top_k_indices[0][::-1]
# Get the values of the top 5 maximum scores
top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
return top_k_indices, top_k_values
# %%
error_thing_df.index
####################################################
# special find-back code
# %%
def find_back_element_with_print(select_idx):
condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
condition_target = np.ones(train_embeds.shape[0], dtype=bool)
top_k_indices, top_k_values = find_closest(
cos_sim_matrix=cos_sim_matrix,
condition_source=condition_source,
condition_target=condition_target)
training_data_pattern_list = train_df.iloc[top_k_indices[0]]['pattern'].to_list()
test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
predicted_test_data = test_df[test_df.index == select_idx]['p_thing'] + test_df[test_df.index == select_idx]['p_property']
print("*" * 80)
print("idx:", select_idx)
print(training_data_pattern_list)
print(test_data_pattern_list)
print(predicted_test_data)
test_pattern = test_data_pattern_list[0]
find_back_list = [ test_pattern in pattern for pattern in training_data_pattern_list ]
if sum(find_back_list) > 0:
return True
else:
return False
find_back_element_with_print(2884)
# %%
def find_back_element(select_idx):
condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
condition_target = np.ones(train_embeds.shape[0], dtype=bool)
top_k_indices, top_k_values = find_closest(
cos_sim_matrix=cos_sim_matrix,
condition_source=condition_source,
condition_target=condition_target)
training_data_pattern_list = train_df.iloc[top_k_indices[0]]['pattern'].to_list()
test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
# print(training_data_pattern_list)
# print(test_data_pattern_list)
test_pattern = test_data_pattern_list[0]
find_back_list = [ test_pattern in pattern for pattern in training_data_pattern_list ]
if sum(find_back_list) > 0:
return True
else:
return False
find_back_element(2884)
# %%
# for error thing
pattern_in_train = []
for select_idx in error_thing_df.index:
result = find_back_element_with_print(select_idx)
print("status:", result)
pattern_in_train.append(result)
# %%
sum(pattern_in_train)/len(pattern_in_train)
###
# for error property
# %%
pattern_in_train = []
for select_idx in error_property_df.index:
result = find_back_element(select_idx)
pattern_in_train.append(result)
# %%
sum(pattern_in_train)/len(pattern_in_train)
####################################################
# %%
# make function to compute similarity of closest retrieved result
def compute_similarity(select_idx):
condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
condition_target = np.ones(train_embeds.shape[0], dtype=bool)
top_k_indices, top_k_values = find_closest(
cos_sim_matrix=cos_sim_matrix,
condition_source=condition_source,
condition_target=condition_target)
return np.mean(top_k_values[0])
# %%
def print_summary(similarity_scores):
# Convert list to numpy array for additional stats
np_array = np.array(similarity_scores)
# Get stats
mean_value = np.mean(np_array)
percentiles = np.percentile(np_array, [25, 50, 75]) # 25th, 50th, and 75th percentiles
# Display numpy results
print("Mean:", mean_value)
print("25th, 50th, 75th Percentiles:", percentiles)
# %%
##########################################
# Analyze the degree of similarity differences between correct and incorrect results
# %%
# compute similarity scores for all values in error_thing_df
similarity_thing_scores = []
for idx in error_thing_df.index:
similarity_thing_scores.append(compute_similarity(idx))
print_summary(similarity_thing_scores)
# %%
similarity_property_scores = []
for idx in error_property_df.index:
similarity_property_scores.append(compute_similarity(idx))
print_summary(similarity_property_scores)
# %%
similarity_correct_scores = []
for idx in correct_df.index:
similarity_correct_scores.append(compute_similarity(idx))
print_summary(similarity_correct_scores)
# %%
import matplotlib.pyplot as plt
# Sample data
list1 = similarity_thing_scores
list2 = similarity_property_scores
list3 = similarity_correct_scores
# Plot histograms
bins = 50
plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=True)
plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=True)
plt.hist(list3, bins=bins, alpha=0.5, label='List 3', density=True)
# Labels and legend
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.legend(loc='upper right')
plt.title('Histograms of Three Lists')
# Show plot
plt.show()
###########################################
# %%
# why do similarities of 97% still map correctly?
score_array = np.array(similarity_correct_scores)
# %%
sum(score_array < 0.95)
# %%
correct_df[score_array < 0.95]['tag_description'].index.to_list()
# %%

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analysis/mapping_error.py
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# %%
import pandas as pd
from utils import Retriever, cosine_similarity_chunked
import os
import glob
import numpy as np
# %%
data_path = f'../data_preprocess/exports/preprocessed_data.csv'
df_pre = pd.read_csv(data_path, skipinitialspace=True)
# %%
# this should be >0 if we are using abbreviations processed data
desc_list = df_pre['tag_description'].to_list()
# %%
[ elem for elem in desc_list if isinstance(elem, float)]
##########################################
# %%
fold = 1
data_path = f'../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv'
df = pd.read_csv(data_path, skipinitialspace=True)
# %%
# subset to mdm
df = df[df['MDM']]
thing_condition = df['p_thing'] == df['thing_pattern']
error_thing_df = df[~thing_condition][['tag_description', 'thing_pattern','p_thing']]
property_condition = df['p_property'] == df['property_pattern']
error_property_df = df[~property_condition][['tag_description', 'property_pattern','p_property']]
correct_df = df[thing_condition & property_condition][['tag_description', 'property_pattern', 'p_property']]
test_df = df
# %%
# thing_df.to_html('thing_errors.html')
# property_df.to_html('property_errors.html')
##########################################
# what we need now is understand why the model is making these mispredictions
# import train data and test data
# %%
class Embedder():
input_df: pd.DataFrame
fold: int
def __init__(self, input_df):
self.input_df = input_df
def make_embedding(self, checkpoint_path):
def generate_input_list(df):
input_list = []
for _, row in df.iterrows():
# name = f"<NAME>{row['tag_name']}<NAME>"
desc = f"<DESC>{row['tag_description']}<DESC>"
# element = f"{name}{desc}"
element = f"{desc}"
input_list.append(element)
return input_list
# prepare reference embed
train_data = list(generate_input_list(self.input_df))
# Define the directory and the pattern
retriever_train = Retriever(train_data, checkpoint_path)
retriever_train.make_mean_embedding(batch_size=64)
return retriever_train.embeddings.to('cpu')
# %%
data_path = f"../data_preprocess/exports/dataset/group_{fold}/train.csv"
train_df = pd.read_csv(data_path, skipinitialspace=True)
checkpoint_directory = "../train/mapping_pattern"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
train_embedder = Embedder(input_df=train_df)
train_embeds = train_embedder.make_embedding(checkpoint_path)
test_embedder = Embedder(input_df=test_df)
test_embeds = test_embedder.make_embedding(checkpoint_path)
# %%
# test embeds are inputs since we are looking back at train data
cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
# %%
# the following function takes in a full cos_sim_matrix
# condition_source: boolean selectors of the source embedding
# condition_target: boolean selectors of the target embedding
def find_closest(cos_sim_matrix, condition_source, condition_target):
# subset_matrix = cos_sim_matrix[condition_source]
# except we are subsetting 2D matrix (row, column)
subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
# we select top k here
# Get the indices of the top 5 maximum values along axis 1
top_k = 5
top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
# note that top_k_indices is a nested list because of the 2d nature of the matrix
# the result is flipped
top_k_indices[0] = top_k_indices[0][::-1]
# Get the values of the top 5 maximum scores
top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
return top_k_indices, top_k_values
# %%
error_thing_df.index
####################################################
# special find-back code
# %%
select_idx = 2884
condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
condition_target = np.ones(train_embeds.shape[0], dtype=bool)
top_k_indices, top_k_values = find_closest(
cos_sim_matrix=cos_sim_matrix,
condition_source=condition_source,
condition_target=condition_target)
print(top_k_indices)
print(top_k_values)
# %%
train_df.iloc[top_k_indices[0]]
# %%
test_df[test_df.index == select_idx]
####################################################
# %%
# make function to compute similarity of closest retrieved result
def compute_similarity(select_idx):
condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
condition_target = np.ones(train_embeds.shape[0], dtype=bool)
top_k_indices, top_k_values = find_closest(
cos_sim_matrix=cos_sim_matrix,
condition_source=condition_source,
condition_target=condition_target)
return np.mean(top_k_values[0])
# %%
def print_summary(similarity_scores):
# Convert list to numpy array for additional stats
np_array = np.array(similarity_scores)
# Get stats
mean_value = np.mean(np_array)
percentiles = np.percentile(np_array, [25, 50, 75]) # 25th, 50th, and 75th percentiles
# Display numpy results
print("Mean:", mean_value)
print("25th, 50th, 75th Percentiles:", percentiles)
# %%
##########################################
# Analyze the degree of similarity differences between correct and incorrect results
# %%
# compute similarity scores for all values in error_thing_df
similarity_thing_scores = []
for idx in error_thing_df.index:
similarity_thing_scores.append(compute_similarity(idx))
print_summary(similarity_thing_scores)
# %%
similarity_property_scores = []
for idx in error_property_df.index:
similarity_property_scores.append(compute_similarity(idx))
print_summary(similarity_property_scores)
# %%
similarity_correct_scores = []
for idx in correct_df.index:
similarity_correct_scores.append(compute_similarity(idx))
print_summary(similarity_correct_scores)
# %%
import matplotlib.pyplot as plt
# Sample data
list1 = similarity_thing_scores
list2 = similarity_property_scores
list3 = similarity_correct_scores
# Plot histograms
bins = 50
plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=True)
plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=True)
plt.hist(list3, bins=bins, alpha=0.5, label='List 3', density=True)
# Labels and legend
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.legend(loc='upper right')
plt.title('Histograms of Three Lists')
# Show plot
plt.show()
###########################################
# %%
# why do similarities of 97% still map correctly?
score_array = np.array(similarity_correct_scores)
# %%
sum(score_array < 0.95)
# %%
correct_df[score_array < 0.95]['tag_description'].index.to_list()
# %%

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analysis/utils.py
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import torch
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
import torch.nn.functional as F
class Retriever:
def __init__(self, input_texts, model_checkpoint):
# we need to generate the embedding from list of input strings
self.embeddings = []
self.inputs = input_texts
model_checkpoint = model_checkpoint
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", return_tensors="pt", clean_up_tokenization_spaces=True)
# define additional special tokens
additional_special_tokens = ["<thing_start>", "<thing_end>", "<property_start>", "<property_end>", "<name>", "<desc>", "<sig>", "<unit>", "<data_type>"]
# add the additional special tokens to the tokenizer
self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
self.device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# device = "cpu"
model.to(self.device)
self.model = model.eval()
def make_mean_embedding(self, batch_size=32):
all_embeddings = self.embeddings
input_texts = self.inputs
for i in range(0, len(input_texts), batch_size):
batch_texts = input_texts[i:i+batch_size]
# Tokenize the input text
inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
input_ids = inputs.input_ids.to(self.device)
attention_mask = inputs.attention_mask.to(self.device)
# Pass the input through the encoder and retrieve the embeddings
with torch.no_grad():
encoder_outputs = self.model.encoder(input_ids, attention_mask=attention_mask)
embeddings = encoder_outputs.last_hidden_state
# Compute the mean pooling of the token embeddings
# mean_embedding = embeddings.mean(dim=1)
mean_embedding = (embeddings * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=1, keepdim=True)
all_embeddings.append(mean_embedding)
# remove the batch list and makes a single large tensor, dim=0 increases row-wise
all_embeddings = torch.cat(all_embeddings, dim=0)
self.embeddings = all_embeddings
def cosine_similarity_chunked(batch1, batch2, chunk_size=16):
batch1_size = batch1.size(0)
batch2_size = batch2.size(0)
# Prepare an empty tensor to store results
cos_sim = torch.empty(batch1_size, batch2_size, device=batch1.device)
# Process batch1 in chunks
for i in range(0, batch1_size, chunk_size):
batch1_chunk = batch1[i:i + chunk_size] # Get chunk of batch1
# Expand batch1 chunk and entire batch2 for comparison
batch1_chunk_exp = batch1_chunk.unsqueeze(1) # Shape: (chunk_size, 1, seq_len)
batch2_exp = batch2.unsqueeze(0) # Shape: (1, batch2_size, seq_len)
# Compute cosine similarity for the chunk and store it in the final tensor
cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
return cos_sim

7
data_preprocess/abbreviations/abbreviations_replacer.py
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@ -32,11 +32,7 @@ df = pd.read_csv(file_path)
# %% # %%
# Replace abbreviations # Replace abbreviations
print("running substitution") print("running substitution")
df['tag_description']= df['tag_description'].fillna("NOVALUE") tag_descriptions = df['tag_description'].fillna("N/A")
# Replace whitespace-only entries with "NOVALUE"
# note that "N/A" can be read as nan
df['tag_description'] = df['tag_description'].replace(r'^\s*$', 'NOVALUE', regex=True)
tag_descriptions = df['tag_description']
replaced_descriptions = replace_abbreviations(tag_descriptions, replacement_dict) replaced_descriptions = replace_abbreviations(tag_descriptions, replacement_dict)
# print("Descriptions after replacement:", replaced_descriptions) # print("Descriptions after replacement:", replaced_descriptions)
@ -44,3 +40,4 @@ replaced_descriptions = replace_abbreviations(tag_descriptions, replacement_dict
df["tag_description"] = replaced_descriptions df["tag_description"] = replaced_descriptions
df.to_csv("../exports/preprocessed_data.csv", index=False) df.to_csv("../exports/preprocessed_data.csv", index=False)
print("file saved") print("file saved")
# %%

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post_process/classification/train.py Normal file
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train/mapping_baseline/train.py → train/baseline/train.py
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__pycache__

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train/classification_all/train.py
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# %%
import pandas as pd
import numpy as np
from typing import List
from tqdm import tqdm
from utils import Retriever, cosine_similarity_chunked
import glob
import os
# %%
class Embedder():
input_df: pd.DataFrame
fold: int
def __init__(self, input_df):
self.input_df = input_df
def make_embedding(self, checkpoint_path):
def generate_input_list(df):
input_list = []
for _, row in df.iterrows():
# name = f"<NAME>{row['tag_name']}<NAME>"
desc = f"<DESC>{row['tag_description']}<DESC>"
# element = f"{name}{desc}"
element = f"{desc}"
input_list.append(element)
return input_list
# prepare reference embed
train_data = list(generate_input_list(self.input_df))
# Define the directory and the pattern
retriever_train = Retriever(train_data, checkpoint_path)
retriever_train.make_mean_embedding(batch_size=64)
return retriever_train.embeddings.to('cpu')
# %%
# input data
fold = 2
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
test_df = pd.read_csv(data_path, skipinitialspace=True)
ships_list = list(set(test_df['ships_idx']))
# %%
data_path = '../../data_preprocess/exports/preprocessed_data.csv'
full_df = pd.read_csv(data_path, skipinitialspace=True)
train_df = full_df[~full_df['ships_idx'].isin(ships_list)]
# %%
checkpoint_directory = "../../train/baseline"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
train_embedder = Embedder(input_df=train_df)
train_embeds = train_embedder.make_embedding(checkpoint_path)
# %%
train_embeds.shape
# %%
# now we need to generate the class labels
data_path = '../../data_import/exports/data_mapping_mdm.csv'
full_df = pd.read_csv(data_path, skipinitialspace=True)
mdm_list = sorted(list((set(full_df['pattern']))))
# %%
# based on the mdm_labels, we assign a value to the dataframe
def generate_labels(df, mdm_list):
label_list = []
for _, row in df.iterrows():
pattern = row['pattern']
try:
index = mdm_list.index(pattern)
label_list.append(index + 1)
except ValueError:
label_list.append(0)
return label_list
# %%
label_list = generate_labels(train_df, mdm_list)
# %%
from collections import Counter
frequency = Counter(label_list)
frequency
####################################################
# %%
# we can start classifying
# %%
import torch
import torch.nn as nn
import torch.optim as optim
torch.set_float32_matmul_precision('high')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define the neural network with non-linearity
class NeuralNet(nn.Module):
def __init__(self, input_dim, output_dim):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_dim, 512) # First layer (input to hidden)
self.relu = nn.ReLU() # Non-linearity
self.fc2 = nn.Linear(512, 256) # Output layer
self.fc3 = nn.Linear(256, output_dim)
def forward(self, x):
out = self.fc1(x) # Input to hidden
out = self.relu(out) # Apply non-linearity
out = self.fc2(out) # Hidden to output
out = self.relu(out)
out = self.fc3(out)
return out
# Example usage
input_dim = 512 # Example input dimension (adjust based on your mean embedding size)
output_dim = 203 # 202 classes + 1 out
model = NeuralNet(input_dim, output_dim)
model = torch.compile(model)
model = model.to(device)
# %%
from torch.utils.data import DataLoader, TensorDataset
# Example mean embeddings and labels (replace these with your actual data)
# mean_embeddings = torch.randn(1000, embedding_dim) # 1000 samples of embedding_dim size
mean_embeddings = train_embeds
# labels = torch.randint(0, 2, (1000,)) # Random binary labels (0 for OOD, 1 for ID)
train_labels = generate_labels(train_df, mdm_list)
labels = torch.tensor(train_labels)
# Create a dataset and DataLoader
dataset = TensorDataset(mean_embeddings, labels)
dataloader = DataLoader(dataset, batch_size=256, shuffle=True)
# %%
# Define loss function and optimizer
# criterion = nn.BCELoss() # Binary cross entropy loss
# criterion = nn.BCEWithLogitsLoss()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
# Define the scheduler
# Training loop
num_epochs = 200 # Adjust as needed
# Define the lambda function for linear decay
# It should return the multiplier for the learning rate (starts at 1.0 and goes to 0)
def linear_decay(epoch):
return 1 - epoch / num_epochs
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=linear_decay)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, targets in dataloader:
# Forward pass
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
# loss = criterion(outputs.squeeze(), targets.float().squeeze()) # Ensure the target is float
loss = criterion(outputs, targets)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
scheduler.step()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss / len(dataloader)}")
# %%
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
test_df = pd.read_csv(data_path, skipinitialspace=True)
checkpoint_directory = "../../train/baseline"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
test_embedder = Embedder(input_df=test_df)
test_embeds = test_embedder.make_embedding(checkpoint_path)
test_labels = generate_labels(test_df, mdm_list)
# %%
mean_embeddings = test_embeds
labels = torch.tensor(test_labels)
dataset = TensorDataset(mean_embeddings, labels)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False)
model.eval()
output_classes = []
output_probs = []
for inputs, _ in dataloader:
with torch.no_grad():
inputs = inputs.to(device)
logits = model(inputs)
probabilities = torch.softmax(logits, dim=1)
# predicted_classes = torch.argmax(probabilities, dim=1)
max_probabilities, predicted_classes = torch.max(probabilities, dim=1)
output_classes.extend(predicted_classes.to('cpu').numpy())
output_probs.extend(max_probabilities.to('cpu').numpy())
# %%
# evaluation
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
y_true = test_labels
y_pred = output_classes
# Compute metrics
accuracy = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, average='macro')
precision = precision_score(y_true, y_pred, average='macro')
recall = recall_score(y_true, y_pred, average='macro')
# Print the results
print(f'Accuracy: {accuracy:.2f}')
print(f'F1 Score: {f1:.2f}')
print(f'Precision: {precision:.2f}')
print(f'Recall: {recall:.2f}')
# %%

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import torch
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
import torch.nn.functional as F
class Retriever:
def __init__(self, input_texts, model_checkpoint):
# we need to generate the embedding from list of input strings
self.embeddings = []
self.inputs = input_texts
model_checkpoint = model_checkpoint
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", return_tensors="pt", clean_up_tokenization_spaces=True)
# define additional special tokens
additional_special_tokens = ["<thing_start>", "<thing_end>", "<property_start>", "<property_end>", "<name>", "<desc>", "<sig>", "<unit>", "<data_type>"]
# add the additional special tokens to the tokenizer
self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
self.device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# device = "cpu"
model.to(self.device)
self.model = model.eval()
def make_mean_embedding(self, batch_size=32):
all_embeddings = self.embeddings
input_texts = self.inputs
for i in range(0, len(input_texts), batch_size):
batch_texts = input_texts[i:i+batch_size]
# Tokenize the input text
inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
input_ids = inputs.input_ids.to(self.device)
attention_mask = inputs.attention_mask.to(self.device)
# Pass the input through the encoder and retrieve the embeddings
with torch.no_grad():
encoder_outputs = self.model.encoder(input_ids, attention_mask=attention_mask)
embeddings = encoder_outputs.last_hidden_state
# Compute the mean pooling of the token embeddings
# mean_embedding = embeddings.mean(dim=1)
mean_embedding = (embeddings * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=1, keepdim=True)
all_embeddings.append(mean_embedding)
# remove the batch list and makes a single large tensor, dim=0 increases row-wise
all_embeddings = torch.cat(all_embeddings, dim=0)
self.embeddings = all_embeddings
def cosine_similarity_chunked(batch1, batch2, chunk_size=16):
batch1_size = batch1.size(0)
batch2_size = batch2.size(0)
# Prepare an empty tensor to store results
cos_sim = torch.empty(batch1_size, batch2_size, device=batch1.device)
# Process batch1 in chunks
for i in range(0, batch1_size, chunk_size):
batch1_chunk = batch1[i:i + chunk_size] # Get chunk of batch1
# Expand batch1 chunk and entire batch2 for comparison
batch1_chunk_exp = batch1_chunk.unsqueeze(1) # Shape: (chunk_size, 1, seq_len)
batch2_exp = batch2.unsqueeze(0) # Shape: (1, batch2_size, seq_len)
# Compute cosine similarity for the chunk and store it in the final tensor
cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
return cos_sim

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# %%
import pandas as pd
import numpy as np
from typing import List
from tqdm import tqdm
from utils import Retriever, cosine_similarity_chunked
import glob
import os
# import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
import random
import math
# %%
class Embedder():
input_df: pd.DataFrame
fold: int
def __init__(self, input_df):
self.input_df = input_df
def make_embedding(self, checkpoint_path):
def generate_input_list(df):
input_list = []
for _, row in df.iterrows():
# name = f"<NAME>{row['tag_name']}<NAME>"
desc = f"<DESC>{row['tag_description']}<DESC>"
# element = f"{name}{desc}"
element = f"{desc}"
input_list.append(element)
return input_list
# prepare reference embed
train_data = list(generate_input_list(self.input_df))
# Define the directory and the pattern
retriever_train = Retriever(train_data, checkpoint_path)
retriever_train.make_mean_embedding(batch_size=64)
return retriever_train.embeddings.to('cpu')
# %%
# input data
fold = 1
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
test_df = pd.read_csv(data_path, skipinitialspace=True)
ships_list = list(set(test_df['ships_idx']))
# %%
data_path = '../../data_preprocess/exports/preprocessed_data.csv'
full_df = pd.read_csv(data_path, skipinitialspace=True)
train_df = full_df[~full_df['ships_idx'].isin(ships_list)]
# %%
checkpoint_directory = "../../train/baseline"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
train_embedder = Embedder(input_df=train_df)
train_embeds = train_embedder.make_embedding(checkpoint_path)
# %%
train_embeds.shape
# %%
# now we need to generate the class labels
data_path = '../../data_import/exports/data_mapping_mdm.csv'
full_df = pd.read_csv(data_path, skipinitialspace=True)
mdm_list = sorted(list((set(full_df['pattern']))))
# %%
# based on the mdm_labels, we assign a value to the dataframe
def generate_labels(df, mdm_list):
label_list = []
for _, row in df.iterrows():
pattern = row['pattern']
try:
index = mdm_list.index(pattern)
label_list.append(index + 1)
except ValueError:
label_list.append(0)
return label_list
# %%
label_list = generate_labels(train_df, mdm_list)
# # %%
# from collections import Counter
#
# frequency = Counter(label_list)
# frequency
####################################################
# %%
# we can start contrastive learning on a re-projection layer for the embedding
#################################################
# MARK: start collaborative filtering
# we need to create a batch where half are positive examples and the other half
# is negative examples
# we first need to test out how we can get the embeddings of each ship
# %%
label_tensor = torch.asarray(label_list)
def create_pairs(all_embeddings, labels, batch_size):
positive_pairs = []
negative_pairs = []
# find unique ships labels
unique_labels = torch.unique(labels)
embeddings_by_label = {}
for label in unique_labels:
embeddings_by_label[label.item()] = all_embeddings[labels == label]
# create positive pairs from the same ship
for _ in range(batch_size // 2):
label = random.choice(unique_labels)
label_embeddings = embeddings_by_label[label.item()]
# randomly select 2 embeddings from the same ship
if len(label_embeddings) >= 2: # ensure that we can choose
emb1, emb2 = random.sample(list(label_embeddings), 2)
positive_pairs.append((emb1, emb2, torch.tensor(1.0)))
# create negative pairs (from different ships)
for _ in range(batch_size // 2):
label1, label2 = random.sample(list(unique_labels), 2)
# select one embedding from each ship
emb1 = random.choice(embeddings_by_label[label1.item()])
emb2 = random.choice(embeddings_by_label[label2.item()])
negative_pairs.append((emb1, emb2, torch.tensor(0.0)))
pairs = positive_pairs + negative_pairs
# separate embeddings and labels for the batch
emb1_batch = torch.stack([pair[0] for pair in pairs])
emb2_batch = torch.stack([pair[1] for pair in pairs])
labels_batch = torch.stack([pair[2] for pair in pairs])
return emb1_batch, emb2_batch, labels_batch
# # %%
# # demo of batch creation
# emb1_batch, emb2_batch, labels = create_pairs(
# train_embed,
# ship_labels,
# 64
# )
# %%
# create model
class linear_map(nn.Module):
def __init__(self, input_dim, output_dim):
super(linear_map, self).__init__()
self.linear_1 = nn.Linear(input_dim, 512)
self.linear_2 = nn.Linear(512, output_dim)
self.relu = nn.ReLU() # Non-linearity
def forward(self, x):
x = self.linear_1(x)
x = self.relu(x)
x = self.linear_2(x)
return x
# %%
# the contrastive loss
# def contrastive_loss(embedding1, embedding2, label, margin=1.0):
# # calculate euclidean distance
# distance = F.pairwise_distance(embedding1, embedding2)
#
# # loss for positive pairs
# # label will select on positive examples
# positive_loss = label * torch.pow(distance, 2)
#
# # loss for negative pairs
# negative_loss = (1 - label) * torch.pow(torch.clamp(margin - distance, min=0), 2)
#
# loss = torch.mean(positive_loss + negative_loss)
# return loss
def contrastive_loss_cosine(embeddings1, embeddings2, label, margin=0.5):
"""
Compute the contrastive loss using cosine similarity.
Args:
- embeddings1: Tensor of embeddings for one set of pairs, shape (batch_size, embedding_size)
- embeddings2: Tensor of embeddings for the other set of pairs, shape (batch_size, embedding_size)
- label: Tensor of labels, 1 for positive pairs (same class), 0 for negative pairs (different class)
- margin: Margin for negative pairs (default 0.5)
Returns:
- loss: Contrastive loss based on cosine similarity
"""
# Cosine similarity between the two sets of embeddings
cosine_sim = F.cosine_similarity(embeddings1, embeddings2)
# For positive pairs, we want the cosine similarity to be close to 1
positive_loss = label * (1 - cosine_sim)
# For negative pairs, we want the cosine similarity to be lower than the margin
negative_loss = (1 - label) * F.relu(cosine_sim - margin)
# Combine the two losses
loss = positive_loss + negative_loss
# Return the average loss across the batch
return loss.mean()
# %%
# training loop
num_epochs = 50
batch_size = 512
train_data_size = train_embeds.shape[0]
output_dim = 512
learning_rate = 1e-5
steps_per_epoch = math.ceil(train_data_size / batch_size)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_float32_matmul_precision('high')
linear_model = linear_map(
input_dim=train_embeds.shape[-1],
output_dim=output_dim)
linear_model = torch.compile(linear_model)
linear_model.to(device)
optimizer = torch.optim.Adam(linear_model.parameters(), lr=learning_rate)
# %%
for epoch in tqdm(range(num_epochs)):
with tqdm(total=steps_per_epoch, desc=f"Epoch {epoch+1}/{num_epochs}") as pbar:
for _ in range(steps_per_epoch):
emb1_batch, emb2_batch, labels_batch = create_pairs(
train_embeds,
label_tensor,
batch_size
)
output1 = linear_model(emb1_batch.to(device))
output2 = linear_model(emb2_batch.to(device))
loss = contrastive_loss_cosine(output1, output2, labels_batch.to(device), margin=0.7)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# if epoch % 10 == 0:
# print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")
pbar.set_postfix({'loss': loss.item()})
pbar.update(1)
# %%
# apply the re-projection layer to achieve better classification
# new_embeds = for loop of model on old embeds
# we have to transform our previous embeddings into mapped embeddings
def predict_batch(embeds, model, batch_size):
output_list = []
with torch.no_grad():
for i in range(0, len(embeds), batch_size):
batch_embed = embeds[i:i+batch_size]
output = model(batch_embed.to(device))
output_list.append(output)
all_embeddings = torch.cat(output_list, dim=0)
return all_embeddings
train_remap_embeds = predict_batch(train_embeds, linear_model, 32)
####################################################
# %%
# we can start classifying
# %%
import torch
import torch.nn as nn
import torch.optim as optim
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define the neural network with non-linearity
class NeuralNet(nn.Module):
def __init__(self, input_dim, output_dim):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_dim, 512) # First layer (input to hidden)
self.relu = nn.ReLU() # Non-linearity
self.fc2 = nn.Linear(512, 256) # Output layer
self.fc3 = nn.Linear(256, output_dim)
def forward(self, x):
out = self.fc1(x) # Input to hidden
out = self.relu(out) # Apply non-linearity
out = self.fc2(out) # Hidden to output
out = self.relu(out)
out = self.fc3(out)
return out
# Example usage
input_dim = 512 # Example input dimension (adjust based on your mean embedding size)
output_dim = 203 # 202 classes + 1 out
model = NeuralNet(input_dim, output_dim)
model = torch.compile(model)
model = model.to(device)
torch.set_float32_matmul_precision('high')
# %%
from torch.utils.data import DataLoader, TensorDataset
# we use the re-projected embeds
mean_embeddings = train_remap_embeds
# labels = torch.randint(0, 2, (1000,)) # Random binary labels (0 for OOD, 1 for ID)
train_labels = generate_labels(train_df, mdm_list)
labels = torch.tensor(train_labels)
# Create a dataset and DataLoader
dataset = TensorDataset(mean_embeddings, labels)
dataloader = DataLoader(dataset, batch_size=256, shuffle=True)
# %%
# Define loss function and optimizer
# criterion = nn.BCELoss() # Binary cross entropy loss
# criterion = nn.BCEWithLogitsLoss()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
# Define the scheduler
# Training loop
num_epochs = 200 # Adjust as needed
# Define the lambda function for linear decay
# It should return the multiplier for the learning rate (starts at 1.0 and goes to 0)
def linear_decay(epoch):
return 1 - epoch / num_epochs
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=linear_decay)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, targets in dataloader:
# Forward pass
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
# loss = criterion(outputs.squeeze(), targets.float().squeeze()) # Ensure the target is float
loss = criterion(outputs, targets)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
scheduler.step()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss / len(dataloader)}")
# %%
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
test_df = pd.read_csv(data_path, skipinitialspace=True)
checkpoint_directory = "../../train/baseline"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
test_embedder = Embedder(input_df=test_df)
test_embeds = test_embedder.make_embedding(checkpoint_path)
test_remap_embeds = predict_batch(test_embeds, linear_model, 32)
test_labels = generate_labels(test_df, mdm_list)
# %%
mean_embeddings = test_remap_embeds
labels = torch.tensor(test_labels)
dataset = TensorDataset(mean_embeddings, labels)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False)
model.eval()
output_classes = []
output_probs = []
for inputs, _ in dataloader:
with torch.no_grad():
inputs = inputs.to(device)
logits = model(inputs)
probabilities = torch.softmax(logits, dim=1)
# predicted_classes = torch.argmax(probabilities, dim=1)
max_probabilities, predicted_classes = torch.max(probabilities, dim=1)
output_classes.extend(predicted_classes.to('cpu').numpy())
output_probs.extend(max_probabilities.to('cpu').numpy())
# %%
# evaluation
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
y_true = test_labels
y_pred = output_classes
# Compute metrics
accuracy = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, average='macro')
precision = precision_score(y_true, y_pred, average='macro')
recall = recall_score(y_true, y_pred, average='macro')
# Print the results
print(f'Accuracy: {accuracy:.2f}')
print(f'F1 Score: {f1:.2f}')
print(f'Precision: {precision:.2f}')
print(f'Recall: {recall:.2f}')
# %%

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import torch
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
import torch.nn.functional as F
class Retriever:
def __init__(self, input_texts, model_checkpoint):
# we need to generate the embedding from list of input strings
self.embeddings = []
self.inputs = input_texts
model_checkpoint = model_checkpoint
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", return_tensors="pt", clean_up_tokenization_spaces=True)
# define additional special tokens
additional_special_tokens = ["<thing_start>", "<thing_end>", "<property_start>", "<property_end>", "<name>", "<desc>", "<sig>", "<unit>", "<data_type>"]
# add the additional special tokens to the tokenizer
self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
self.device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# device = "cpu"
model.to(self.device)
self.model = model.eval()
def make_mean_embedding(self, batch_size=32):
all_embeddings = self.embeddings
input_texts = self.inputs
for i in range(0, len(input_texts), batch_size):
batch_texts = input_texts[i:i+batch_size]
# Tokenize the input text
inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
input_ids = inputs.input_ids.to(self.device)
attention_mask = inputs.attention_mask.to(self.device)
# Pass the input through the encoder and retrieve the embeddings
with torch.no_grad():
encoder_outputs = self.model.encoder(input_ids, attention_mask=attention_mask)
embeddings = encoder_outputs.last_hidden_state
# Compute the mean pooling of the token embeddings
# mean_embedding = embeddings.mean(dim=1)
mean_embedding = (embeddings * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=1, keepdim=True)
all_embeddings.append(mean_embedding)
# remove the batch list and makes a single large tensor, dim=0 increases row-wise
all_embeddings = torch.cat(all_embeddings, dim=0)
self.embeddings = all_embeddings
def cosine_similarity_chunked(batch1, batch2, chunk_size=16):
batch1_size = batch1.size(0)
batch2_size = batch2.size(0)
# Prepare an empty tensor to store results
cos_sim = torch.empty(batch1_size, batch2_size, device=batch1.device)
# Process batch1 in chunks
for i in range(0, batch1_size, chunk_size):
batch1_chunk = batch1[i:i + chunk_size] # Get chunk of batch1
# Expand batch1 chunk and entire batch2 for comparison
batch1_chunk_exp = batch1_chunk.unsqueeze(1) # Shape: (chunk_size, 1, seq_len)
batch2_exp = batch2.unsqueeze(0) # Shape: (1, batch2_size, seq_len)
# Compute cosine similarity for the chunk and store it in the final tensor
cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
return cos_sim

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# %%
import pandas as pd
import numpy as np
from typing import List
from tqdm import tqdm
from utils import Retriever, cosine_similarity_chunked
import glob
import os
# import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
import random
import math
# %%
class Embedder():
input_df: pd.DataFrame
fold: int
def __init__(self, input_df):
self.input_df = input_df
def make_embedding(self, checkpoint_path):
def generate_input_list(df):
input_list = []
for _, row in df.iterrows():
# name = f"<NAME>{row['tag_name']}<NAME>"
desc = f"<DESC>{row['tag_description']}<DESC>"
# element = f"{name}{desc}"
element = f"{desc}"
input_list.append(element)
return input_list
# prepare reference embed
train_data = list(generate_input_list(self.input_df))
# Define the directory and the pattern
retriever_train = Retriever(train_data, checkpoint_path)
retriever_train.make_mean_embedding(batch_size=64)
return retriever_train.embeddings.to('cpu')
# %%
# input data
fold = 1
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train.csv"
train_df = pd.read_csv(data_path, skipinitialspace=True)
# %%
checkpoint_directory = "../../train/baseline"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
train_embedder = Embedder(input_df=train_df)
train_embeds = train_embedder.make_embedding(checkpoint_path)
# %%
train_embeds.shape
# %%
# now we need to generate the class labels
data_path = '../../data_import/exports/data_mapping_mdm.csv'
full_df = pd.read_csv(data_path, skipinitialspace=True)
mdm_list = sorted(list((set(full_df['pattern']))))
# %%
# based on the mdm_labels, we assign a value to the dataframe
def generate_labels(df, mdm_list):
label_list = []
for _, row in df.iterrows():
pattern = row['pattern']
try:
index = mdm_list.index(pattern)
label_list.append(index)
except ValueError:
label_list.append(-1)
return label_list
# %%
label_list = generate_labels(train_df, mdm_list)
# # %%
# from collections import Counter
#
# frequency = Counter(label_list)
# frequency
####################################################
# %%
# we can start contrastive learning on a re-projection layer for the embedding
#################################################
# MARK: start collaborative filtering
# we need to create a batch where half are positive examples and the other half
# is negative examples
# we first need to test out how we can get the embeddings of each ship
# %%
label_tensor = torch.asarray(label_list)
def create_pairs(all_embeddings, labels, batch_size):
positive_pairs = []
negative_pairs = []
# find unique ships labels
unique_labels = torch.unique(labels)
embeddings_by_label = {}
for label in unique_labels:
embeddings_by_label[label.item()] = all_embeddings[labels == label]
# create positive pairs from the same ship
for _ in range(batch_size // 2):
label = random.choice(unique_labels)
label_embeddings = embeddings_by_label[label.item()]
# randomly select 2 embeddings from the same ship
if len(label_embeddings) >= 2: # ensure that we can choose
emb1, emb2 = random.sample(list(label_embeddings), 2)
positive_pairs.append((emb1, emb2, torch.tensor(1.0)))
# create negative pairs (from different ships)
for _ in range(batch_size // 2):
label1, label2 = random.sample(list(unique_labels), 2)
# select one embedding from each ship
emb1 = random.choice(embeddings_by_label[label1.item()])
emb2 = random.choice(embeddings_by_label[label2.item()])
negative_pairs.append((emb1, emb2, torch.tensor(0.0)))
pairs = positive_pairs + negative_pairs
# separate embeddings and labels for the batch
emb1_batch = torch.stack([pair[0] for pair in pairs])
emb2_batch = torch.stack([pair[1] for pair in pairs])
labels_batch = torch.stack([pair[2] for pair in pairs])
return emb1_batch, emb2_batch, labels_batch
# %%
# create model
class linear_map(nn.Module):
def __init__(self, input_dim, output_dim):
super(linear_map, self).__init__()
self.linear_1 = nn.Linear(input_dim, output_dim)
# self.linear_2 = nn.Linear(512, output_dim)
# self.relu = nn.ReLU() # Non-linearity
def forward(self, x):
x = self.linear_1(x)
# x = self.relu(x)
# x = self.linear_2(x)
return x
# %%
# the contrastive loss
# def contrastive_loss(embedding1, embedding2, label, margin=1.0):
# # calculate euclidean distance
# distance = F.pairwise_distance(embedding1, embedding2)
#
# # loss for positive pairs
# # label will select on positive examples
# positive_loss = label * torch.pow(distance, 2)
#
# # loss for negative pairs
# negative_loss = (1 - label) * torch.pow(torch.clamp(margin - distance, min=0), 2)
#
# loss = torch.mean(positive_loss + negative_loss)
# return loss
def contrastive_loss_cosine(embeddings1, embeddings2, label, margin=0.5):
"""
Compute the contrastive loss using cosine similarity.
Args:
- embeddings1: Tensor of embeddings for one set of pairs, shape (batch_size, embedding_size)
- embeddings2: Tensor of embeddings for the other set of pairs, shape (batch_size, embedding_size)
- label: Tensor of labels, 1 for positive pairs (same class), 0 for negative pairs (different class)
- margin: Margin for negative pairs (default 0.5)
Returns:
- loss: Contrastive loss based on cosine similarity
"""
# Cosine similarity between the two sets of embeddings
cosine_sim = F.cosine_similarity(embeddings1, embeddings2)
# For positive pairs, we want the cosine similarity to be close to 1
positive_loss = label * (1 - cosine_sim)
# For negative pairs, we want the cosine similarity to be lower than the margin
negative_loss = (1 - label) * F.relu(cosine_sim - margin)
# Combine the two losses
loss = positive_loss + negative_loss
# Return the average loss across the batch
return loss.mean()
# %%
# training loop
num_epochs = 50
batch_size = 256
train_data_size = train_embeds.shape[0]
output_dim = 512
learning_rate = 2e-6
steps_per_epoch = math.ceil(train_data_size / batch_size)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_float32_matmul_precision('high')
linear_model = linear_map(
input_dim=train_embeds.shape[-1],
output_dim=output_dim)
linear_model = torch.compile(linear_model)
linear_model.to(device)
optimizer = torch.optim.Adam(linear_model.parameters(), lr=learning_rate)
# Define the lambda function for linear decay
# It should return the multiplier for the learning rate (starts at 1.0 and goes to 0)
def linear_decay(epoch):
return 1 - epoch / num_epochs
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=linear_decay)
# %%
for epoch in tqdm(range(num_epochs)):
with tqdm(total=steps_per_epoch, desc=f"Epoch {epoch+1}/{num_epochs}") as pbar:
for _ in range(steps_per_epoch):
emb1_batch, emb2_batch, labels_batch = create_pairs(
train_embeds,
label_tensor,
batch_size
)
output1 = linear_model(emb1_batch.to(device))
output2 = linear_model(emb2_batch.to(device))
loss = contrastive_loss_cosine(output1, output2, labels_batch.to(device), margin=0.7)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
# if epoch % 10 == 0:
# print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")
pbar.set_postfix({'loss': loss.item()})
pbar.update(1)
# %%
# apply the re-projection layer to achieve better classification
# new_embeds = for loop of model on old embeds
# we have to transform our previous embeddings into mapped embeddings
def predict_batch(embeds, model, batch_size):
output_list = []
with torch.no_grad():
for i in range(0, len(embeds), batch_size):
batch_embed = embeds[i:i+batch_size]
output = model(batch_embed.to(device))
output_list.append(output)
all_embeddings = torch.cat(output_list, dim=0)
return all_embeddings
train_remap_embeds = predict_batch(train_embeds, linear_model, 32)
####################################################
# %%
# we can start classifying
# %%
import torch
import torch.nn as nn
import torch.optim as optim
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define the neural network with non-linearity
class NeuralNet(nn.Module):
def __init__(self, input_dim, output_dim):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_dim, 512) # First layer (input to hidden)
self.relu = nn.ReLU() # Non-linearity
self.fc2 = nn.Linear(512, 256) # Output layer
self.fc3 = nn.Linear(256, output_dim)
def forward(self, x):
out = self.fc1(x) # Input to hidden
out = self.relu(out) # Apply non-linearity
out = self.fc2(out) # Hidden to output
out = self.relu(out)
out = self.fc3(out)
return out
# Example usage
input_dim = 512 # Example input dimension (adjust based on your mean embedding size)
output_dim = 202 # 202 classes
model = NeuralNet(input_dim, output_dim)
model = torch.compile(model)
model = model.to(device)
torch.set_float32_matmul_precision('high')
# %%
from torch.utils.data import DataLoader, TensorDataset
# we use the re-projected embeds
mean_embeddings = train_remap_embeds
# mean_embeddings = train_embeds
# labels = torch.randint(0, 2, (1000,)) # Random binary labels (0 for OOD, 1 for ID)
train_labels = generate_labels(train_df, mdm_list)
labels = torch.tensor(train_labels)
# Create a dataset and DataLoader
dataset = TensorDataset(mean_embeddings, labels)
dataloader = DataLoader(dataset, batch_size=256, shuffle=True)
# %%
# Define loss function and optimizer
# criterion = nn.BCELoss() # Binary cross entropy loss
# criterion = nn.BCEWithLogitsLoss()
criterion = nn.CrossEntropyLoss()
learning_rate = 1e-3
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Define the scheduler
# Training loop
num_epochs = 800 # Adjust as needed
# Define the lambda function for linear decay
# It should return the multiplier for the learning rate (starts at 1.0 and goes to 0)
def linear_decay(epoch):
return 1 - epoch / num_epochs
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=linear_decay)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, targets in dataloader:
# Forward pass
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
# loss = criterion(outputs.squeeze(), targets.float().squeeze()) # Ensure the target is float
loss = criterion(outputs, targets)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
scheduler.step()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss / len(dataloader)}")
# %%
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
test_df = pd.read_csv(data_path, skipinitialspace=True)
test_df = test_df[test_df['MDM']].reset_index(drop=True)
checkpoint_directory = "../../train/baseline"
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
test_embedder = Embedder(input_df=test_df)
test_embeds = test_embedder.make_embedding(checkpoint_path)
test_remap_embeds = predict_batch(test_embeds, linear_model, 32)
test_labels = generate_labels(test_df, mdm_list)
# %%
# mean_embeddings = test_embeds
mean_embeddings = test_remap_embeds
labels = torch.tensor(test_labels)
dataset = TensorDataset(mean_embeddings, labels)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False)
model.eval()
output_classes = []
output_probs = []
for inputs, _ in dataloader:
with torch.no_grad():
inputs = inputs.to(device)
logits = model(inputs)
probabilities = torch.softmax(logits, dim=1)
# predicted_classes = torch.argmax(probabilities, dim=1)
max_probabilities, predicted_classes = torch.max(probabilities, dim=1)
output_classes.extend(predicted_classes.to('cpu').numpy())
output_probs.extend(max_probabilities.to('cpu').numpy())
# %%
# evaluation
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
y_true = test_labels
y_pred = output_classes
# Compute metrics
accuracy = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, average='macro')
precision = precision_score(y_true, y_pred, average='macro')
recall = recall_score(y_true, y_pred, average='macro')
# Print the results
print(f'Accuracy: {accuracy:.2f}')
print(f'F1 Score: {f1:.2f}')
print(f'Precision: {precision:.2f}')
print(f'Recall: {recall:.2f}')
# %%

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train/classification_mdm_with_contrastive/utils.py
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import torch
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
import torch.nn.functional as F
class Retriever:
def __init__(self, input_texts, model_checkpoint):
# we need to generate the embedding from list of input strings
self.embeddings = []
self.inputs = input_texts
model_checkpoint = model_checkpoint
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", return_tensors="pt", clean_up_tokenization_spaces=True)
# define additional special tokens
additional_special_tokens = ["<thing_start>", "<thing_end>", "<property_start>", "<property_end>", "<name>", "<desc>", "<sig>", "<unit>", "<data_type>"]
# add the additional special tokens to the tokenizer
self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
self.device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# device = "cpu"
model.to(self.device)
self.model = model.eval()
def make_mean_embedding(self, batch_size=32):
all_embeddings = self.embeddings
input_texts = self.inputs
for i in range(0, len(input_texts), batch_size):
batch_texts = input_texts[i:i+batch_size]
# Tokenize the input text
inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
input_ids = inputs.input_ids.to(self.device)
attention_mask = inputs.attention_mask.to(self.device)
# Pass the input through the encoder and retrieve the embeddings
with torch.no_grad():
encoder_outputs = self.model.encoder(input_ids, attention_mask=attention_mask)
embeddings = encoder_outputs.last_hidden_state
# Compute the mean pooling of the token embeddings
# mean_embedding = embeddings.mean(dim=1)
mean_embedding = (embeddings * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=1, keepdim=True)
all_embeddings.append(mean_embedding)
# remove the batch list and makes a single large tensor, dim=0 increases row-wise
all_embeddings = torch.cat(all_embeddings, dim=0)
self.embeddings = all_embeddings
def cosine_similarity_chunked(batch1, batch2, chunk_size=16):
batch1_size = batch1.size(0)
batch2_size = batch2.size(0)
# Prepare an empty tensor to store results
cos_sim = torch.empty(batch1_size, batch2_size, device=batch1.device)
# Process batch1 in chunks
for i in range(0, batch1_size, chunk_size):
batch1_chunk = batch1[i:i + chunk_size] # Get chunk of batch1
# Expand batch1 chunk and entire batch2 for comparison
batch1_chunk_exp = batch1_chunk.unsqueeze(1) # Shape: (chunk_size, 1, seq_len)
batch2_exp = batch2.unsqueeze(0) # Shape: (1, batch2_size, seq_len)
# Compute cosine similarity for the chunk and store it in the final tensor
cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
return cos_sim

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train/mapping_baseline/mapping_prediction/.gitignore → train/mapping/.gitignore vendored
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0
train/mapping_baseline/mapping_prediction/inference.py → train/mapping/inference.py
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train/mapping_baseline/mapping_prediction/output.txt → train/mapping/output.txt
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6
train/mapping_baseline/mapping_prediction/predict.py → train/mapping/predict.py
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@ -4,16 +4,16 @@ import os
import glob import glob
from inference import Inference from inference import Inference
checkpoint_directory = '../' checkpoint_directory = '../../train/baseline'
def infer_and_select(fold): def infer_and_select(fold):
print(f"Inference for fold {fold}") print(f"Inference for fold {fold}")
# import test data # import test data
data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv" data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
df = pd.read_csv(data_path, skipinitialspace=True) df = pd.read_csv(data_path, skipinitialspace=True)
# get target data # get target data
data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv" data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
train_df = pd.read_csv(data_path, skipinitialspace=True) train_df = pd.read_csv(data_path, skipinitialspace=True)
# processing to help with selection later # processing to help with selection later
train_df['thing_property'] = train_df['thing'] + " " + train_df['property'] train_df['thing_property'] = train_df['thing'] + " " + train_df['property']

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train/mapping_pattern/.gitignore vendored
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checkpoint*
tensorboard-log/

2
train/mapping_pattern/mapping_prediction/.gitignore vendored
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@ -1,2 +0,0 @@
__pycache__
exports/

162
train/mapping_pattern/mapping_prediction/inference.py
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import torch
from torch.utils.data import DataLoader
from transformers import (
T5TokenizerFast,
AutoModelForSeq2SeqLM,
)
import os
from tqdm import tqdm
from datasets import Dataset
import numpy as np
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
class Inference():
tokenizer: T5TokenizerFast
model: torch.nn.Module
dataloader: DataLoader
def __init__(self, checkpoint_path):
self._create_tokenizer()
self._load_model(checkpoint_path)
def _create_tokenizer(self):
# %%
# load tokenizer
self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
# Define additional special tokens
additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
# Add the additional special tokens to the tokenizer
self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
def _load_model(self, checkpoint_path: str):
# load model
# Define the directory and the pattern
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
model = torch.compile(model)
# set model to eval
self.model = model.eval()
def prepare_dataloader(self, input_df, batch_size, max_length):
"""
*arguments*
- input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
- batch_size: the batch size of dataloader output
- max_length: length of tokenizer output
"""
print("preparing dataloader")
# convert each dataframe row into a dictionary
# outputs a list of dictionaries
def _process_df(df):
output_list = [{
'input': f"<DESC>{row['tag_description']}<DESC>",
'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
} for _, row in df.iterrows()]
return output_list
def _preprocess_function(example):
input = example['input']
target = example['output']
# text_target sets the corresponding label to inputs
# there is no need to create a separate 'labels'
model_inputs = self.tokenizer(
input,
text_target=target,
max_length=max_length,
return_tensors="pt",
padding='max_length',
truncation=True,
)
return model_inputs
test_dataset = Dataset.from_list(_process_df(input_df))
# map maps function to each "row" in the dataset
# aka the data in the immediate nesting
datasets = test_dataset.map(
_preprocess_function,
batched=True,
num_proc=1,
remove_columns=test_dataset.column_names,
)
# datasets = _preprocess_function(test_dataset)
datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
# create dataloader
self.dataloader = DataLoader(datasets, batch_size=batch_size)
def generate(self):
device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
MAX_GENERATE_LENGTH = 128
pred_generations = []
pred_labels = []
print("start generation")
for batch in tqdm(self.dataloader):
# Inference in batches
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
# save labels too
pred_labels.extend(batch['labels'])
# Move to GPU if available
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
self.model.to(device)
# Perform inference
with torch.no_grad():
outputs = self.model.generate(input_ids,
attention_mask=attention_mask,
max_length=MAX_GENERATE_LENGTH)
# Decode the output and print the results
pred_generations.extend(outputs.to("cpu"))
# %%
# extract sequence and decode
def extract_seq(tokens, start_value, end_value):
if start_value not in tokens or end_value not in tokens:
return None # Or handle this case according to your requirements
start_id = np.where(tokens == start_value)[0][0]
end_id = np.where(tokens == end_value)[0][0]
return tokens[start_id+1:end_id]
def process_tensor_output(tokens):
thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
p_thing = None
p_property = None
if (thing_seq is not None):
p_thing = self.tokenizer.decode(thing_seq, skip_special_tokens=False)
if (property_seq is not None):
p_property = self.tokenizer.decode(property_seq, skip_special_tokens=False)
return p_thing, p_property
# decode prediction labels
def decode_preds(tokens_list):
thing_prediction_list = []
property_prediction_list = []
for tokens in tokens_list:
p_thing, p_property = process_tensor_output(tokens)
thing_prediction_list.append(p_thing)
property_prediction_list.append(p_property)
return thing_prediction_list, property_prediction_list
thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
return thing_prediction_list, property_prediction_list

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train/mapping_pattern/mapping_prediction/output.txt
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@ -1,6 +0,0 @@
Accuracy for fold 1: 0.943208707998107
Accuracy for fold 2: 0.9214953271028037
Accuracy for fold 3: 0.9728915662650602
Accuracy for fold 4: 0.967174119885823
Accuracy for fold 5: 0.9097572148419606

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train/mapping_pattern/mapping_prediction/predict.py
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@ -1,71 +0,0 @@
import pandas as pd
import os
import glob
from inference import Inference
checkpoint_directory = '../'
def infer_and_select(fold):
print(f"Inference for fold {fold}")
# import test data
data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
df = pd.read_csv(data_path, skipinitialspace=True)
# get target data
data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
train_df = pd.read_csv(data_path, skipinitialspace=True)
# processing to help with selection later
train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
##########################################
# run inference
# checkpoint
# Use glob to find matching paths
directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
# Use glob to find matching paths
# path is usually checkpoint_fold_1/checkpoint-<step number>
# we are guaranteed to save only 1 checkpoint from training
pattern = 'checkpoint-*'
checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
infer = Inference(checkpoint_path)
infer.prepare_dataloader(df, batch_size=256, max_length=128)
thing_prediction_list, property_prediction_list = infer.generate()
# add labels too
# thing_actual_list, property_actual_list = decode_preds(pred_labels)
# Convert the list to a Pandas DataFrame
df_out = pd.DataFrame({
'p_thing': thing_prediction_list,
'p_property': property_prediction_list
})
# df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
# df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
df = pd.concat([df, df_out], axis=1)
# we can save the t5 generation output here
df.to_csv(f"exports/result_group_{fold}.csv", index=False)
# here we want to evaluate mapping accuracy within the valid in mdm data only
in_mdm = df['MDM']
condition_correct_thing = df['p_thing'] == df['thing_pattern']
condition_correct_property = df['p_property'] == df['property_pattern']
prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
# write output to file output.txt
with open("output.txt", "a") as f:
print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
###########################################
# Execute for all folds
# reset file before writing to it
with open("output.txt", "w") as f:
print('', file=f)
for fold in [1,2,3,4,5]:
infer_and_select(fold)

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train/mapping_pattern/train.py
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@ -1,195 +0,0 @@
# %%
# from datasets import load_from_disk
import os
os.environ['NCCL_P2P_DISABLE'] = '1'
os.environ['NCCL_IB_DISABLE'] = '1'
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
import torch
from transformers import (
T5TokenizerFast,
AutoModelForSeq2SeqLM,
DataCollatorForSeq2Seq,
Seq2SeqTrainer,
EarlyStoppingCallback,
Seq2SeqTrainingArguments
)
import evaluate
import numpy as np
import pandas as pd
# import matplotlib.pyplot as plt
from datasets import Dataset, DatasetDict
torch.set_float32_matmul_precision('high')
# outputs a list of dictionaries
def process_df_to_dict(df):
output_list = []
for _, row in df.iterrows():
desc = f"<DESC>{row['tag_description']}<DESC>"
element = {
'input' : f"{desc}",
'output': f"<THING_START>{row['thing_pattern']}<THING_END><PROPERTY_START>{row['property_pattern']}<PROPERTY_END>",
}
output_list.append(element)
return output_list
def create_split_dataset(fold):
# train
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train.csv"
train_df = pd.read_csv(data_path, skipinitialspace=True)
# valid
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
validation_df = pd.read_csv(data_path, skipinitialspace=True)
combined_data = DatasetDict({
'train': Dataset.from_list(process_df_to_dict(train_df)),
'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
})
return combined_data
# function to perform training for a given fold
def train(fold):
save_path = f'checkpoint_fold_{fold}'
split_datasets = create_split_dataset(fold)
# prepare tokenizer
model_checkpoint = "t5-small"
tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
# Define additional special tokens
additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
# Add the additional special tokens to the tokenizer
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
max_length = 120
# given a dataset entry, run it through the tokenizer
def preprocess_function(example):
input = example['input']
target = example['output']
# text_target sets the corresponding label to inputs
# there is no need to create a separate 'labels'
model_inputs = tokenizer(
input,
text_target=target,
max_length=max_length,
truncation=True,
padding=True
)
return model_inputs
# map maps function to each "row" in the dataset
# aka the data in the immediate nesting
tokenized_datasets = split_datasets.map(
preprocess_function,
batched=True,
num_proc=8,
remove_columns=split_datasets["train"].column_names,
)
# https://github.com/huggingface/transformers/pull/28414
# model_checkpoint = "google/t5-efficient-tiny"
# device_map set to auto to force it to load contiguous weights
# model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
# important! after extending tokens vocab
model.resize_token_embeddings(len(tokenizer))
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
metric = evaluate.load("sacrebleu")
def compute_metrics(eval_preds):
preds, labels = eval_preds
# In case the model returns more than the prediction logits
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds,
skip_special_tokens=False)
# Replace -100s in the labels as we can't decode them
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels,
skip_special_tokens=False)
# Remove <PAD> tokens from decoded predictions and labels
decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
# Some simple post-processing
# decoded_preds = [pred.strip() for pred in decoded_preds]
# decoded_labels = [[label.strip()] for label in decoded_labels]
# print(decoded_preds, decoded_labels)
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
return {"bleu": result["score"]}
# Generation Config
# from transformers import GenerationConfig
gen_config = model.generation_config
gen_config.max_length = 64
# compile
# model = torch.compile(model, backend="inductor", dynamic=True)
# Trainer
args = Seq2SeqTrainingArguments(
f"{save_path}",
eval_strategy="epoch",
logging_dir="tensorboard-log",
logging_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
learning_rate=1e-3,
per_device_train_batch_size=64,
per_device_eval_batch_size=64,
auto_find_batch_size=False,
ddp_find_unused_parameters=False,
weight_decay=0.01,
save_total_limit=1,
num_train_epochs=20,
predict_with_generate=True,
bf16=True,
push_to_hub=False,
generation_config=gen_config,
remove_unused_columns=False,
)
trainer = Seq2SeqTrainer(
model,
args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
# callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
)
# uncomment to load training from checkpoint
# checkpoint_path = 'default_40_1/checkpoint-5600'
# trainer.train(resume_from_checkpoint=checkpoint_path)
trainer.train()
# execute training
for fold in [1,2,3,4,5]:
print(fold)
train(fold)