Feat: implement selection for pattern-mapping
Feat: added error analysis for BERT find-back Feat: added direct mapping with unit Feat: added BERT for classification using description only
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__pycache__
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exports
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# %%
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import pandas as pd
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from utils import Retriever, cosine_similarity_chunked
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import os
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import glob
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import numpy as np
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def analysis_for_fold(fold):
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file_object = open(f'exports/output_{fold}.txt', 'w')
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# %%
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data_path = f'../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv'
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df = pd.read_csv(data_path, skipinitialspace=True)
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# %%
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# subset to mdm
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df = df[df['MDM']]
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thing_condition = df['p_thing'] == df['thing_pattern']
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error_thing_df = df[~thing_condition][['tag_description', 'thing_pattern','p_thing']]
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property_condition = df['p_property'] == df['property_pattern']
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error_property_df = df[~property_condition][['tag_description', 'property_pattern','p_property']]
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correct_df = df[thing_condition & property_condition][['tag_description', 'property_pattern', 'p_property']]
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test_df = df
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# %%
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print("Number of errors related to 'thing'", len(error_thing_df), file=file_object)
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print("Number of errors related to 'property'", len(error_property_df), file=file_object)
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# %%
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# thing_df.to_html('thing_errors.html')
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# property_df.to_html('property_errors.html')
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##########################################
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# what we need now is understand why the model is making these mispredictions
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# import train data and test data
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# %%
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class Embedder():
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input_df: pd.DataFrame
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fold: int
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def __init__(self, input_df):
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self.input_df = input_df
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def make_embedding(self, checkpoint_path):
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def generate_input_list(df):
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input_list = []
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for _, row in df.iterrows():
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desc = f"<DESC>{row['tag_description']}<DESC>"
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unit = f"<UNIT>{row['unit']}<UNIT>"
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element = f"{desc}{unit}"
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input_list.append(element)
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return input_list
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# prepare reference embed
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train_data = list(generate_input_list(self.input_df))
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# Define the directory and the pattern
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retriever_train = Retriever(train_data, checkpoint_path)
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retriever_train.make_embedding(batch_size=64)
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return retriever_train.embeddings.to('cpu')
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# %%
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data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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checkpoint_directory = "../../train/classification_bert"
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directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
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# Use glob to find matching paths
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# path is usually checkpoint_fold_1/checkpoint-<step number>
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# we are guaranteed to save only 1 checkpoint from training
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pattern = 'checkpoint-*'
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checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
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train_embedder = Embedder(input_df=train_df)
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train_embeds = train_embedder.make_embedding(checkpoint_path)
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test_embedder = Embedder(input_df=test_df)
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test_embeds = test_embedder.make_embedding(checkpoint_path)
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# %%
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# test embeds are inputs since we are looking back at train data
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cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
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# %%
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# the following function takes in a full cos_sim_matrix
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# condition_source: boolean selectors of the source embedding
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# condition_target: boolean selectors of the target embedding
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def find_closest(cos_sim_matrix, condition_source, condition_target):
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# subset_matrix = cos_sim_matrix[condition_source]
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# except we are subsetting 2D matrix (row, column)
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subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
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# we select top k here
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# Get the indices of the top 5 maximum values along axis 1
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top_k = 3
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top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
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# note that top_k_indices is a nested list because of the 2d nature of the matrix
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# the result is flipped
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top_k_indices[0] = top_k_indices[0][::-1]
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# Get the values of the top 5 maximum scores
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top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
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return top_k_indices, top_k_values
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####################################################
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# special find-back code
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# %%
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def find_back_element_with_print(select_idx):
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condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
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condition_target = np.ones(train_embeds.shape[0], dtype=bool)
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top_k_indices, top_k_values = find_closest(
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cos_sim_matrix=cos_sim_matrix,
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condition_source=condition_source,
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condition_target=condition_target)
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training_data_pattern_list = train_df.iloc[top_k_indices[0]]['pattern'].to_list()
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training_desc_list = train_df.iloc[top_k_indices[0]]['tag_description'].to_list()
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test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
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test_desc_list = test_df[test_df.index == select_idx]['tag_description'].to_list()
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test_ship_id = test_df[test_df.index == select_idx]['ships_idx'].to_list()[0]
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predicted_test_data = test_df[test_df.index == select_idx]['p_thing'] + ' ' + test_df[test_df.index == select_idx]['p_property']
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predicted_test_data = predicted_test_data.to_list()[0]
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print("*" * 20, file=file_object)
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print("idx:", select_idx, file=file_object)
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print("train desc", training_desc_list, file=file_object)
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print("train thing+property", training_data_pattern_list, file=file_object)
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print("test desc", test_desc_list, file=file_object)
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print("test thing+property", test_data_pattern_list, file=file_object)
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print("predicted thing+property", predicted_test_data, file=file_object)
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print("ships idx", test_ship_id, file=file_object)
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print("score:", top_k_values[0], file=file_object)
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test_pattern = test_data_pattern_list[0]
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find_back_list = [ test_pattern in pattern for pattern in training_data_pattern_list ]
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if sum(find_back_list) > 0:
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return True
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else:
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return False
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# %%
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# for error thing
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print('\n', file=file_object)
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print('*' * 80, file=file_object)
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print('Error analysis for thing errors', file=file_object)
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pattern_in_train = []
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for select_idx in error_thing_df.index:
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result = find_back_element_with_print(select_idx)
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print("status:", result, file=file_object)
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pattern_in_train.append(result)
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proportion_in_train = sum(pattern_in_train)/len(pattern_in_train)
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print('\n', file=file_object)
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print('*' * 80, file=file_object)
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print("Proportion of entries found in training data", proportion_in_train, file=file_object)
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# for error property
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# %%
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print('\n', file=file_object)
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print('*' * 80, file=file_object)
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print('Error analysis for property errors', file=file_object)
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pattern_in_train = []
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for select_idx in error_property_df.index:
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result = find_back_element_with_print(select_idx)
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print("status:", result, file=file_object)
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pattern_in_train.append(result)
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proportion_in_train = sum(pattern_in_train)/len(pattern_in_train)
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print('\n', file=file_object)
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print('*' * 80, file=file_object)
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print("Proportion of entries found in training data", proportion_in_train, file=file_object)
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####################################################
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# %%
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# make function to compute similarity of closest retrieved result
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def compute_similarity(select_idx):
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condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
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condition_target = np.ones(train_embeds.shape[0], dtype=bool)
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top_k_indices, top_k_values = find_closest(
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cos_sim_matrix=cos_sim_matrix,
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condition_source=condition_source,
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condition_target=condition_target)
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return np.mean(top_k_values[0])
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# %%
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def print_summary(similarity_scores):
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# Convert list to numpy array for additional stats
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np_array = np.array(similarity_scores)
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# Get stats
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mean_value = np.mean(np_array)
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percentiles = np.percentile(np_array, [25, 50, 75]) # 25th, 50th, and 75th percentiles
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# Display numpy results
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print("Mean:", mean_value, file=file_object)
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print("25th, 50th, 75th Percentiles:", percentiles, file=file_object)
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# %%
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##########################################
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# Analyze the degree of similarity differences between correct and incorrect results
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print('\n', file=file_object)
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print("*" * 80, file=file_object)
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print("This section analyzes the similarity statistics for the error and correct groups", file=file_object)
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# %%
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# compute similarity scores for all values in error_thing_df
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similarity_thing_scores = []
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for idx in error_thing_df.index:
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similarity_thing_scores.append(compute_similarity(idx))
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print_summary(similarity_thing_scores)
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# %%
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similarity_property_scores = []
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for idx in error_property_df.index:
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similarity_property_scores.append(compute_similarity(idx))
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print_summary(similarity_property_scores)
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# %%
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similarity_correct_scores = []
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for idx in correct_df.index:
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similarity_correct_scores.append(compute_similarity(idx))
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print_summary(similarity_correct_scores)
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file_object.close()
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for fold in [1,2,3,4,5]:
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print(f"running for fold {fold}")
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analysis_for_fold(fold)
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output.csv
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# we want to see if there are clear rules to filling numbers in the pattern
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# format
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# %%
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# %%
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import pandas as pd
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# from utils import Retriever, cosine_similarity_chunked
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import os
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import glob
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import numpy as np
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# %%
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fold = 5
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data_path = f'../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv'
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test_df = pd.read_csv(data_path, skipinitialspace=True)
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data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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# %%
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data_path = '../../data_import/exports/data_mapping_mdm.csv'
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# data_path = '../../data_preprocess/exports/preprocessed_data.csv'
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df = pd.read_csv(data_path, skipinitialspace=True)
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mdm_list = sorted(list((set(df['pattern']))))
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# %%
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symbol_pattern_list = [elem for elem in mdm_list if '#' in elem]
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# %%
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symbol_pattern_list
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# %%
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len(symbol_pattern_list)
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# %%
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idx = 22
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print(symbol_pattern_list[idx])
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condition1 = df['pattern'] == symbol_pattern_list[idx]
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subset_df = df[df['pattern'] == symbol_pattern_list[idx]]
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ship = list(set(subset_df['ships_idx']))
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print(ship)
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# %%
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subset_df[['thing', 'property', 'tag_name', 'tag_description', 'ships_idx']].to_csv('output.csv')
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# %%
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ship_idx = 10
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condition2 = df['ships_idx'] == ship_idx
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subset_df = df[condition1 & condition2]
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subset_df
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# %%
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@ -37,6 +37,9 @@ desc_replacement_dict = {
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r'\bOUTL\.\b': 'OUTLET',
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r'\boutlet\b\b': 'OUTLET',
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r'\bOUTLET\b\b': 'OUTLET',
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# bunker tank
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r'\bBK\b': 'BUNKER',
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r'\bTK\b': 'TANK',
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# pressure
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r'\bPRESS\b\b': 'PRESSURE',
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r'\bPRESS\.\b': 'PRESSURE',
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@ -100,12 +103,13 @@ desc_replacement_dict = {
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r'\bHT\b\b': 'HIGH TEMPERATURE',
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# auxiliary boiler
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# replace these first before replacing AUXILIARY only
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r'\bAUX\.BOILER\b\b': 'AUXILIARY BOILER',
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r'\bAUX\. BOILER\b\b': 'AUXILIARY BOILER',
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r'\bAUX BLR\b\b': 'AUXILIARY BOILER',
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r'\bAUX\.BOILER\b': 'AUXILIARY BOILER',
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r'\bAUX\. BOILER\b': 'AUXILIARY BOILER',
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r'\bAUX BLR\b': 'AUXILIARY BOILER',
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r'\bAUX\.\b': 'AUXILIARY ',
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# composite boiler
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r'\bCOMP\. BOILER\b\b': 'COMPOSITE BOILER',
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r'\bCOMP\.BOILER\b\b': 'COMPOSITE BOILER',
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r'\bCOMP BOILER\b\b': 'COMPOSITE BOILER',
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r'\bWIND\.\b': 'WINDING',
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r'\bWINDING\b\b': 'WINDING',
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@ -127,6 +131,8 @@ desc_replacement_dict = {
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r'\bTURBOCHARGER\b\b': 'TURBOCHARGER',
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# misc spelling errors
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r'\bOPERATOIN\b': 'OPERATION',
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# wrongly attached terms
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r'BOILERMGO': 'BOILER MGO',
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# additional standardizing replacement
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# replace # followed by a number with NO
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r'#(?=\d)\b': 'NO',
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__pycache__
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exports
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# %%
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import pandas as pd
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import os
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import glob
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
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import numpy as np
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from utils import BertEmbedder, cosine_similarity_chunked
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# %%
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# directory for checkpoints
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checkpoint_directory = '../../train/mapping_pattern'
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fold = 5
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# import test data
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data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv"
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df = pd.read_csv(data_path, skipinitialspace=True)
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# get target data
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data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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# processing to help with selection later
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# %%
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df['p_pattern'] = df['p_thing'] + " " + df['p_property']
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# %%
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# obtain the full mdm_list
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data_path = '../../data_import/exports/data_mapping_mdm.csv'
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full_df = pd.read_csv(data_path, skipinitialspace=True)
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full_mdm_pattern_list = sorted(list((set(full_df['pattern']))))
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# %%
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# we have to split into per-ship analysis
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ships_list = sorted(list(set(df['ships_idx'])))
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# %%
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# for ship_idx in ships_list:
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ship_idx = 1009 # choose an example ship
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ship_df = df[df['ships_idx'] == ship_idx].reset_index(drop=True)
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class Embedder():
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input_df: pd.DataFrame
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fold: int
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def __init__(self, input_df):
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self.input_df = input_df
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def make_embedding(self, checkpoint_path):
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def generate_input_list(df):
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input_list = []
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for _, row in df.iterrows():
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desc = f"{row['tag_description']}"
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element = f"{desc}"
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input_list.append(element)
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return input_list
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# prepare reference embed
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train_data = list(generate_input_list(self.input_df))
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# Define the directory and the pattern
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embedder = BertEmbedder(train_data, checkpoint_path)
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embedder.make_embedding(batch_size=64)
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return embedder.embeddings.to('cpu')
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# %%
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data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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checkpoint_directory = "../../train/classification_bert"
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directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
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# Use glob to find matching paths
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# path is usually checkpoint_fold_1/checkpoint-<step number>
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# we are guaranteed to save only 1 checkpoint from training
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pattern = 'checkpoint-*'
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checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
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train_embedder = Embedder(input_df=train_df)
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train_embeds = train_embedder.make_embedding(checkpoint_path)
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test_embedder = Embedder(input_df=ship_df)
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test_embeds = test_embedder.make_embedding(checkpoint_path)
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# %%
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||||
# 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 general idea:
|
||||
# step 1: keep only pattern generations that belong to mdm list
|
||||
# -> this removes totally wrong datasets that mapped to totally wrong things
|
||||
# step 2: loop through the mdm list and isolate data in both train and test that
|
||||
# belong to the same pattern class
|
||||
# -> this is more tricky, because we have non-mdm mapping to correct classes
|
||||
# -> so we have to find which candidate is most similar to the training data
|
||||
|
||||
# it is very tricky to keep track of classification across multiple stages so we
|
||||
# will use a boolean answer list
|
||||
|
||||
# %%
|
||||
answer_list = np.ones(len(ship_df), dtype=bool)
|
||||
|
||||
##########################################
|
||||
# %%
|
||||
# STEP 1
|
||||
# we want to loop through the the ship_df and find which ones match our full_mdm_list
|
||||
pattern_match_mask = ship_df['p_pattern'].apply(lambda x: x in full_mdm_pattern_list).to_numpy()
|
||||
# we assign only those that are False to our answer list
|
||||
# right now the 2 arrays are basically equal
|
||||
answer_list[~pattern_match_mask] = False
|
||||
|
||||
# %% TEMP
|
||||
print('proportion belonging to mdm classes', sum(pattern_match_mask)/len(pattern_match_mask))
|
||||
|
||||
# %% TEMP
|
||||
y_true = ship_df['MDM'].to_list()
|
||||
y_pred = pattern_match_mask
|
||||
|
||||
# Compute metrics
|
||||
accuracy = accuracy_score(y_true, y_pred)
|
||||
print(f'Accuracy: {accuracy:.5f}')
|
||||
|
||||
# we can see that the accuracy is not good
|
||||
# %%
|
||||
#########################################
|
||||
# STEP 2
|
||||
# we want to go through each mdm class label
|
||||
# but we do not want to make subsets of dataframes
|
||||
# we will make heavy use of boolean masks
|
||||
|
||||
# we want to identify per-ship mdm classes
|
||||
ship_mdm_classes = sorted(set(ship_df['p_pattern'][pattern_match_mask].to_list()))
|
||||
|
||||
# %%
|
||||
len(ship_mdm_classes)
|
||||
|
||||
# %%
|
||||
for idx,select_class in enumerate(ship_mdm_classes):
|
||||
print(idx, len(ship_df[ship_df['p_pattern'] == select_class]))
|
||||
|
||||
# %%
|
||||
select_class = ship_mdm_classes[22]
|
||||
sample_df = ship_df[ship_df['p_pattern'] == select_class]
|
||||
|
||||
# %%
|
||||
# we need to set all idx of chosen entries as False in answer_list
|
||||
selected_idx_list = sample_df.index.to_list()
|
||||
answer_list[selected_idx_list] = False
|
||||
|
||||
# %%
|
||||
# because we have variants of a tag_description, we cannot choose 1 from the
|
||||
# given candidates we have to first group the candidates, and then choose which
|
||||
# group is most similar
|
||||
|
||||
# %%
|
||||
from fuzzywuzzy import fuzz
|
||||
|
||||
# the purpose of this function is to group the strings that are similar to each other
|
||||
# we need to form related groups of inputs
|
||||
def group_similar_strings(obj_list, threshold=80):
|
||||
groups = []
|
||||
processed_strings = set() # To keep track of already grouped strings
|
||||
|
||||
for obj in obj_list:
|
||||
# tuple is (idx, string)
|
||||
if obj in processed_strings:
|
||||
continue
|
||||
|
||||
# Find all strings similar to the current string above the threshold
|
||||
similar_strings = [s for s in obj_list if s[1] != obj[1] and fuzz.ratio(obj[1], s[1]) >= threshold]
|
||||
|
||||
# Add the original string to the similar group
|
||||
similar_group = [obj] + similar_strings
|
||||
|
||||
# Mark all similar strings as processed
|
||||
processed_strings.update(similar_group)
|
||||
|
||||
# Add the group to the list of groups
|
||||
groups.append(similar_group)
|
||||
|
||||
return groups
|
||||
|
||||
# Example usage
|
||||
string_list = sample_df['tag_description'].to_list()
|
||||
index_list = sample_df.index.to_list()
|
||||
obj_list = list(zip(index_list, string_list))
|
||||
groups = group_similar_strings(obj_list, threshold=90)
|
||||
print(groups)
|
||||
|
||||
# %%
|
||||
# this function takes in groups of related terms and create candidate entries
|
||||
def make_candidates(groups):
|
||||
candidates = []
|
||||
for group in groups:
|
||||
first_tuple = group[0]
|
||||
# string_of_tuple = first_tuple[1]
|
||||
id_of_tuple = first_tuple[0]
|
||||
candidates.append(id_of_tuple)
|
||||
return candidates
|
||||
|
||||
# %%
|
||||
test_candidates = make_candidates(groups)
|
||||
test_candidates_mask = np.zeros(len(ship_df), dtype=bool)
|
||||
test_candidates_mask[test_candidates] = True
|
||||
|
||||
# %%
|
||||
train_candidates_mask = (train_df['pattern'] == select_class).to_numpy()
|
||||
|
||||
# %%
|
||||
# we need to make the cos_sim_matrix
|
||||
# for that, we need to generate the embeddings of the ship_df (test embedding)
|
||||
# and the train_df (train embeddin)
|
||||
|
||||
# we then use the selection function using the given mask to choose the most
|
||||
# appropriate candidate
|
||||
|
||||
# the selection function takes in the full cos_sim_matrix then subsets the
|
||||
# matrix according to the test_candidates_mask and train_candidates_mask that we
|
||||
# give it
|
||||
# it returns the most likely source candidate index and score among the source
|
||||
# candidate list - aka it returns a local idx
|
||||
def selection(cos_sim_matrix, source_mask, target_mask):
|
||||
# subset_matrix = cos_sim_matrix[condition_source]
|
||||
# except we are subsetting 2D matrix (row, column)
|
||||
subset_matrix = cos_sim_matrix[np.ix_(source_mask, target_mask)]
|
||||
# we select top-k here
|
||||
# Get the indices of the top-k maximum values along axis 1
|
||||
top_k = 1
|
||||
top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
|
||||
|
||||
# Get the values of the top 5 maximum scores
|
||||
top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
|
||||
|
||||
# Calculate the average of the top-k scores along axis 1
|
||||
y_scores = np.mean(top_k_values, axis=1)
|
||||
max_idx = np.argmax(y_scores)
|
||||
max_score = y_scores[max_idx]
|
||||
|
||||
return max_idx, max_score
|
||||
|
||||
|
||||
# %%
|
||||
max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask)
|
||||
|
||||
# %%
|
||||
# after obtaining best group, we set all candidates of the group as True
|
||||
chosen_group = groups[max_idx]
|
||||
chosen_idx = [tuple[0] for tuple in chosen_group]
|
||||
|
||||
# %%
|
||||
# before doing this, we have to use the max_score and evaluate if its close enough
|
||||
THRESHOLD = 0.8
|
||||
if max_score > THRESHOLD:
|
||||
answer_list[chosen_idx] = True
|
||||
|
||||
# %%
|
||||
|
|
@ -0,0 +1,407 @@
|
|||
# %%
|
||||
import pandas as pd
|
||||
import os
|
||||
import glob
|
||||
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
|
||||
import numpy as np
|
||||
from utils import BertEmbedder, cosine_similarity_chunked
|
||||
from fuzzywuzzy import fuzz
|
||||
|
||||
##################
|
||||
# global parameters
|
||||
DIAGNOSTIC = True
|
||||
THRESHOLD = 0.90
|
||||
FUZZY_SIM_THRESHOLD=90
|
||||
checkpoint_directory = "../../train/classification_bert"
|
||||
|
||||
###################
|
||||
# %%
|
||||
# helper functions
|
||||
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():
|
||||
desc = f"{row['tag_description']}"
|
||||
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
|
||||
embedder = BertEmbedder(train_data, checkpoint_path)
|
||||
embedder.make_embedding(batch_size=64)
|
||||
return embedder.embeddings.to('cpu')
|
||||
|
||||
|
||||
|
||||
# the purpose of this function is to group the strings that are similar to each other
|
||||
# we need to form related groups of inputs
|
||||
def group_similar_strings(obj_list, threshold=80):
|
||||
groups = []
|
||||
processed_strings = set() # To keep track of already grouped strings
|
||||
|
||||
for obj in obj_list:
|
||||
# tuple is (idx, string)
|
||||
if obj in processed_strings:
|
||||
continue
|
||||
# Find all strings similar to the current string above the threshold
|
||||
similar_strings = [s for s in obj_list if s[1] != obj[1] and fuzz.ratio(obj[1], s[1]) >= threshold]
|
||||
# Add the original string to the similar group
|
||||
similar_group = [obj] + similar_strings
|
||||
# Mark all similar strings as processed
|
||||
processed_strings.update(similar_group)
|
||||
# Add the group to the list of groups
|
||||
groups.append(similar_group)
|
||||
return groups
|
||||
|
||||
# this function takes in groups of related terms and create candidate entries
|
||||
def make_candidates(groups):
|
||||
candidates = []
|
||||
for group in groups:
|
||||
first_tuple = group[0]
|
||||
# string_of_tuple = first_tuple[1]
|
||||
id_of_tuple = first_tuple[0]
|
||||
candidates.append(id_of_tuple)
|
||||
return candidates
|
||||
|
||||
|
||||
# the selection function takes in the full cos_sim_matrix then subsets the
|
||||
# matrix according to the test_candidates_mask and train_candidates_mask that we
|
||||
# give it
|
||||
# it returns the most likely source candidate index and score among the source
|
||||
# candidate list - aka it returns a local idx
|
||||
def selection(cos_sim_matrix, source_mask, target_mask, file_object=None):
|
||||
# subset_matrix = cos_sim_matrix[condition_source]
|
||||
# except we are subsetting 2D matrix (row, column)
|
||||
subset_matrix = cos_sim_matrix[np.ix_(source_mask, target_mask)]
|
||||
# we select top-k here
|
||||
# Get the indices of the top-k maximum values along axis 1
|
||||
top_k = 1
|
||||
top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
|
||||
|
||||
# Get the values of the top 5 maximum scores
|
||||
top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
|
||||
|
||||
# Calculate the average of the top-k scores along axis 1
|
||||
y_scores = np.mean(top_k_values, axis=1)
|
||||
max_idx = np.argmax(y_scores)
|
||||
max_score = y_scores[max_idx]
|
||||
|
||||
if DIAGNOSTIC and (file_object is not None):
|
||||
print('all scores:', file=file_object)
|
||||
print(y_scores, file=file_object)
|
||||
|
||||
return max_idx, max_score
|
||||
|
||||
|
||||
|
||||
####################
|
||||
# global level
|
||||
# %%
|
||||
# obtain the full mdm_list
|
||||
data_path = '../../data_import/exports/data_mapping_mdm.csv'
|
||||
full_df = pd.read_csv(data_path, skipinitialspace=True)
|
||||
full_mdm_pattern_list = sorted(list((set(full_df['pattern']))))
|
||||
|
||||
|
||||
#####################
|
||||
# fold level
|
||||
|
||||
def run_selection(fold):
|
||||
|
||||
file_object = open(f'exports/output_{fold}.txt', 'w')
|
||||
# set the fold
|
||||
# import test data
|
||||
data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv"
|
||||
df = pd.read_csv(data_path, skipinitialspace=True)
|
||||
df['p_pattern'] = df['p_thing'] + " " + df['p_property']
|
||||
|
||||
# get target data
|
||||
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
|
||||
train_df = pd.read_csv(data_path, skipinitialspace=True)
|
||||
|
||||
# generate your embeddings
|
||||
# checkpoint_directory defined at global level
|
||||
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]
|
||||
|
||||
# we can generate the train embeddings once and re-use for every ship
|
||||
train_embedder = Embedder(input_df=train_df)
|
||||
train_embeds = train_embedder.make_embedding(checkpoint_path)
|
||||
|
||||
|
||||
# create global_answer array
|
||||
# the purpose of this array is to track the classification state at the global
|
||||
# level
|
||||
global_answer = np.zeros(len(df), dtype=bool)
|
||||
|
||||
global_sim = np.zeros(len(df))
|
||||
|
||||
#############################
|
||||
# ship level
|
||||
# we have to split into per-ship analysis
|
||||
ships_list = sorted(list(set(df['ships_idx'])))
|
||||
print(ships_list)
|
||||
for ship_idx in ships_list:
|
||||
# ship_idx = 1001 # choose an example ship
|
||||
|
||||
print(ship_idx, file=file_object) # print selected ship
|
||||
ship_df = df[df['ships_idx'] == ship_idx]
|
||||
# required to map local ship_answer array to global_answer array
|
||||
map_local_index_to_global_index = ship_df.index.to_numpy()
|
||||
ship_df = df[df['ships_idx'] == ship_idx].reset_index(drop=True)
|
||||
|
||||
# generate new embeddings for each ship
|
||||
test_embedder = Embedder(input_df=ship_df)
|
||||
test_embeds = test_embedder.make_embedding(checkpoint_path)
|
||||
|
||||
# generate the cosine sim matrix
|
||||
cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
|
||||
|
||||
##############################
|
||||
# selection level
|
||||
# The general idea:
|
||||
# step 1: keep only pattern generations that belong to mdm list
|
||||
# -> this removes totally wrong datasets that mapped to totally wrong things
|
||||
# step 2: loop through the mdm list and isolate data in both train and test that
|
||||
# belong to the same pattern class
|
||||
# -> this is more tricky, because we have non-mdm mapping to correct classes
|
||||
# -> so we have to find which candidate is most similar to the training data
|
||||
|
||||
# it is very tricky to keep track of classification across multiple stages so we
|
||||
# will use a boolean answer list
|
||||
|
||||
# initialize the local answer list
|
||||
ship_answer_list = np.ones(len(ship_df), dtype=bool)
|
||||
|
||||
ship_answer_sim = np.ones(len(ship_df))
|
||||
|
||||
###########
|
||||
# STEP 1
|
||||
# we want to loop through the generated class labels and find which ones match
|
||||
# our pattern list
|
||||
|
||||
pattern_match_mask = ship_df['p_pattern'].apply(lambda x: x in full_mdm_pattern_list).to_numpy()
|
||||
# we assign only those that are False to our answer list
|
||||
# right now the 2 arrays are basically equal
|
||||
ship_answer_list[~pattern_match_mask] = False
|
||||
|
||||
###########
|
||||
# STEP 2
|
||||
# we now go through each class found in our generated set
|
||||
|
||||
# we want to identify per-ship mdm classes
|
||||
ship_predicted_classes = sorted(set(ship_df['p_pattern'][pattern_match_mask].to_list()))
|
||||
|
||||
# this function performs the selection given a class
|
||||
# it takes in the cos_sim_matrix
|
||||
# it returns the selection by mutating the answer_list
|
||||
# it sets all relevant idxs to False initially, then sets the selected values to True
|
||||
def selection_for_class(select_class, cos_sim_matrix, answer_list, score_list):
|
||||
|
||||
# separate the global variable from function variable
|
||||
answer_list = answer_list.copy()
|
||||
score_list = score_list.copy()
|
||||
sample_df = ship_df[ship_df['p_pattern'] == select_class]
|
||||
|
||||
# we need to set all idx of chosen entries as False in answer_list
|
||||
selected_idx_list = sample_df.index.to_list()
|
||||
answer_list[selected_idx_list] = False
|
||||
|
||||
# basic assumption check
|
||||
|
||||
# group related inputs by description similarity
|
||||
string_list = sample_df['tag_description'].to_list()
|
||||
index_list = sample_df.index.to_list()
|
||||
obj_list = list(zip(index_list, string_list))
|
||||
# groups is a list of list, where each list is composed of a
|
||||
# (idx, string) tuple
|
||||
groups = group_similar_strings(obj_list, threshold=FUZZY_SIM_THRESHOLD)
|
||||
|
||||
if DIAGNOSTIC:
|
||||
print('*' * 10, file=file_object)
|
||||
print(select_class, file=file_object)
|
||||
print('candidate groups', file=file_object)
|
||||
print(groups, file=file_object)
|
||||
|
||||
# generate the masking arrays for both test and train embeddings
|
||||
# we select a tuple from each group, and use that as a candidate for selection
|
||||
test_candidates = make_candidates(groups)
|
||||
test_candidates_mask = np.zeros(len(ship_df), dtype=bool)
|
||||
test_candidates_mask[test_candidates] = True
|
||||
# we make candidates to compare against in the data sharing the same class
|
||||
train_candidates_mask = (train_df['pattern'] == select_class).to_numpy()
|
||||
|
||||
# perform selection
|
||||
# it returns the group index that is most likely
|
||||
max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask, file_object)
|
||||
|
||||
# consolidate all idx's in the same group
|
||||
chosen_group = groups[max_idx]
|
||||
chosen_idx_list = [tuple[0] for tuple in chosen_group]
|
||||
|
||||
if DIAGNOSTIC:
|
||||
print('chosen group', file=file_object)
|
||||
print(chosen_group, file=file_object)
|
||||
|
||||
# before doing this, we have to use the max_score and evaluate if its close enough
|
||||
if max_score > THRESHOLD:
|
||||
answer_list[chosen_idx_list] = True
|
||||
if DIAGNOSTIC:
|
||||
print('max score', file=file_object)
|
||||
print(max_score, file=file_object)
|
||||
print('accepted', file=file_object)
|
||||
else:
|
||||
if DIAGNOSTIC:
|
||||
print('max score', file=file_object)
|
||||
print(max_score, file=file_object)
|
||||
print('rejected', file=file_object)
|
||||
|
||||
|
||||
|
||||
|
||||
# for analysis
|
||||
test_candidates_mask = np.ones(len(ship_df), dtype=bool)
|
||||
_, every_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask, None)
|
||||
|
||||
# write the score for every idx of this class
|
||||
score_list[selected_idx_list] = every_score
|
||||
|
||||
return answer_list, score_list
|
||||
|
||||
|
||||
# we choose one mdm class
|
||||
for select_class in ship_predicted_classes:
|
||||
ship_answer_list, ship_answer_sim = selection_for_class(select_class, cos_sim_matrix, ship_answer_list, ship_answer_sim)
|
||||
|
||||
# we want to write back to global_answer
|
||||
# first we convert local indices to global indices
|
||||
local_indices = np.where(ship_answer_list)[0]
|
||||
global_indices = map_local_index_to_global_index[local_indices]
|
||||
global_answer[global_indices] = True
|
||||
|
||||
# similarity score
|
||||
global_sim[map_local_index_to_global_index] = ship_answer_sim
|
||||
|
||||
if DIAGNOSTIC:
|
||||
# evaluation at per-ship level
|
||||
y_true = ship_df['MDM'].to_list()
|
||||
y_pred = ship_answer_list
|
||||
|
||||
# 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:.5f}', file=file_object)
|
||||
print(f'F1 Score: {f1:.5f}', file=file_object)
|
||||
print(f'Precision: {precision:.5f}', file=file_object)
|
||||
print(f'Recall: {recall:.5f}', file=file_object)
|
||||
|
||||
|
||||
|
||||
y_true = df['MDM'].to_list()
|
||||
y_pred = global_answer
|
||||
|
||||
|
||||
# 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:.5f}')
|
||||
print(f'F1 Score: {f1:.5f}')
|
||||
print(f'Precision: {precision:.5f}')
|
||||
print(f'Recall: {recall:.5f}')
|
||||
|
||||
|
||||
file_object.close()
|
||||
|
||||
return global_answer, global_sim
|
||||
|
||||
# %%
|
||||
for fold in [1]:
|
||||
print(f'Perform selection for fold {fold}')
|
||||
global_answer, global_sim = run_selection(fold)
|
||||
|
||||
# %%
|
||||
data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv"
|
||||
df = pd.read_csv(data_path, skipinitialspace=True)
|
||||
df['p_pattern'] = df['p_thing'] + " " + df['p_property']
|
||||
df['score'] = global_sim
|
||||
|
||||
# %%
|
||||
# %%
|
||||
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)
|
||||
|
||||
# %%
|
||||
# analysis of non-mdm in predicted
|
||||
df_selected = df[global_answer]
|
||||
df_selected[~df_selected['MDM']]
|
||||
|
||||
in_scores = df_selected[df_selected['MDM']]['score'].to_numpy()
|
||||
print_summary(in_scores)
|
||||
|
||||
# %%
|
||||
# analysis of mdm in non-predicted
|
||||
df_selected = df[~global_answer]
|
||||
# df_selected
|
||||
df_selected[df_selected['MDM']]
|
||||
|
||||
|
||||
# %%
|
||||
out_scores = df_selected[df_selected['MDM']]['score'].to_numpy()
|
||||
|
||||
print_summary(out_scores)
|
||||
|
||||
|
||||
# %%
|
||||
import matplotlib.pyplot as plt
|
||||
# Sample data
|
||||
list1 = in_scores
|
||||
list2 = out_scores
|
||||
|
||||
# Plot histograms
|
||||
bins = 20
|
||||
plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=False)
|
||||
plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=False)
|
||||
|
||||
# Labels and legend
|
||||
plt.xlabel('Value')
|
||||
plt.ylabel('Frequency')
|
||||
plt.legend(loc='upper right')
|
||||
plt.title('Histograms of Three Lists')
|
||||
|
||||
# Show plot
|
||||
plt.show()
|
||||
|
||||
|
||||
# %%
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
import torch
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoModelForSequenceClassification,
|
||||
DataCollatorWithPadding,
|
||||
)
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
|
||||
class BertEmbedder:
|
||||
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(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
|
||||
|
||||
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
# device = "cpu"
|
||||
model.to(self.device)
|
||||
self.model = model.eval()
|
||||
|
||||
|
||||
def make_embedding(self, batch_size=64):
|
||||
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=64)
|
||||
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(input_ids, attention_mask=attention_mask, output_hidden_states=True)
|
||||
# get last layer
|
||||
embeddings = encoder_outputs.hidden_states[-1]
|
||||
# get cls token embedding
|
||||
cls_embeddings = embeddings[:, 0, :] # Shape: (batch_size, hidden_size)
|
||||
all_embeddings.append(cls_embeddings)
|
||||
|
||||
# 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=1024):
|
||||
device = 'cuda'
|
||||
batch1_size = batch1.size(0)
|
||||
batch2_size = batch2.size(0)
|
||||
batch2.to(device)
|
||||
|
||||
# Prepare an empty tensor to store results
|
||||
cos_sim = torch.empty(batch1_size, batch2_size, device=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
|
||||
|
||||
batch1_chunk.to(device)
|
||||
# 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)
|
||||
batch2_norms = batch2.norm(dim=1, keepdim=True)
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
# Compute cosine similarity by matrix multiplication and normalizing
|
||||
sim_chunk = torch.mm(batch1_chunk, batch2.T) / (batch1_chunk.norm(dim=1, keepdim=True) * batch2_norms.T + 1e-8)
|
||||
|
||||
# Store the results in the appropriate part of the final tensor
|
||||
cos_sim[i:i + chunk_size] = sim_chunk
|
||||
|
||||
return cos_sim
|
||||
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
checkpoint*
|
||||
tensorboard-log
|
||||
|
|
@ -0,0 +1,228 @@
|
|||
# %%
|
||||
|
||||
# from datasets import load_from_disk
|
||||
import os
|
||||
import glob
|
||||
|
||||
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 torch.utils.data import DataLoader
|
||||
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoModelForSequenceClassification,
|
||||
DataCollatorWithPadding,
|
||||
)
|
||||
import evaluate
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
# import matplotlib.pyplot as plt
|
||||
from datasets import Dataset, DatasetDict
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
torch.set_float32_matmul_precision('high')
|
||||
|
||||
# %%
|
||||
|
||||
# we need to create the mdm_list
|
||||
# import the full mdm-only file
|
||||
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']))))
|
||||
|
||||
# %%
|
||||
id2label = {}
|
||||
label2id = {}
|
||||
for idx, val in enumerate(mdm_list):
|
||||
id2label[idx] = val
|
||||
label2id[val] = idx
|
||||
|
||||
# %%
|
||||
|
||||
# outputs a list of dictionaries
|
||||
# processes dataframe into lists of dictionaries
|
||||
# each element maps input to output
|
||||
# input: tag_description
|
||||
# output: class label
|
||||
def process_df_to_dict(df, mdm_list):
|
||||
output_list = []
|
||||
for _, row in df.iterrows():
|
||||
desc = f"<DESC>{row['tag_description']}<DESC>"
|
||||
unit = f"<UNIT>{row['unit']}<UNIT>"
|
||||
|
||||
pattern = row['pattern']
|
||||
try:
|
||||
index = mdm_list.index(pattern)
|
||||
except ValueError:
|
||||
index = -1
|
||||
element = {
|
||||
'text' : f"{desc}{unit}",
|
||||
'label': index,
|
||||
}
|
||||
output_list.append(element)
|
||||
|
||||
return output_list
|
||||
|
||||
|
||||
def create_dataset(fold, mdm_list):
|
||||
data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
|
||||
test_df = pd.read_csv(data_path, skipinitialspace=True)
|
||||
# we only use the mdm subset
|
||||
test_df = test_df[test_df['MDM']].reset_index(drop=True)
|
||||
|
||||
test_dataset = Dataset.from_list(process_df_to_dict(test_df, mdm_list))
|
||||
|
||||
return test_dataset
|
||||
|
||||
|
||||
# %%
|
||||
|
||||
# function to perform training for a given fold
|
||||
def test(fold):
|
||||
|
||||
test_dataset = create_dataset(fold, mdm_list)
|
||||
|
||||
# prepare tokenizer
|
||||
|
||||
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-*'
|
||||
model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0]
|
||||
|
||||
tokenizer = AutoTokenizer.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})
|
||||
|
||||
# %%
|
||||
# compute max token length
|
||||
max_length = 0
|
||||
for sample in test_dataset['text']:
|
||||
# Tokenize the sample and get the length
|
||||
input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"]
|
||||
length = len(input_ids)
|
||||
|
||||
# Update max_length if this sample is longer
|
||||
if length > max_length:
|
||||
max_length = length
|
||||
|
||||
print(max_length)
|
||||
|
||||
# %%
|
||||
|
||||
max_length = 64
|
||||
|
||||
# given a dataset entry, run it through the tokenizer
|
||||
def preprocess_function(example):
|
||||
input = example['text']
|
||||
# text_target sets the corresponding label to inputs
|
||||
# there is no need to create a separate 'labels'
|
||||
model_inputs = tokenizer(
|
||||
input,
|
||||
max_length=max_length,
|
||||
# truncation=True,
|
||||
padding='max_length'
|
||||
)
|
||||
return model_inputs
|
||||
|
||||
# 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=8,
|
||||
remove_columns="text",
|
||||
)
|
||||
|
||||
|
||||
datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
|
||||
|
||||
# %% temp
|
||||
# tokenized_datasets['train'].rename_columns()
|
||||
|
||||
# %%
|
||||
# create data collator
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length")
|
||||
|
||||
# %%
|
||||
# compute metrics
|
||||
# metric = evaluate.load("accuracy")
|
||||
#
|
||||
#
|
||||
# def compute_metrics(eval_preds):
|
||||
# preds, labels = eval_preds
|
||||
# preds = np.argmax(preds, axis=1)
|
||||
# return metric.compute(predictions=preds, references=labels)
|
||||
|
||||
model = AutoModelForSequenceClassification.from_pretrained(
|
||||
model_checkpoint,
|
||||
num_labels=len(mdm_list),
|
||||
id2label=id2label,
|
||||
label2id=label2id)
|
||||
# important! after extending tokens vocab
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
model = model.eval()
|
||||
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
model.to(device)
|
||||
|
||||
pred_labels = []
|
||||
actual_labels = []
|
||||
|
||||
|
||||
BATCH_SIZE = 64
|
||||
dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False)
|
||||
for batch in tqdm(dataloader):
|
||||
# Inference in batches
|
||||
input_ids = batch['input_ids']
|
||||
attention_mask = batch['attention_mask']
|
||||
# save labels too
|
||||
actual_labels.extend(batch['label'])
|
||||
|
||||
|
||||
# Move to GPU if available
|
||||
input_ids = input_ids.to(device)
|
||||
attention_mask = attention_mask.to(device)
|
||||
|
||||
# Perform inference
|
||||
with torch.no_grad():
|
||||
logits = model(
|
||||
input_ids,
|
||||
attention_mask).logits
|
||||
predicted_class_ids = logits.argmax(dim=1).to("cpu")
|
||||
pred_labels.extend(predicted_class_ids)
|
||||
|
||||
pred_labels = [tensor.item() for tensor in pred_labels]
|
||||
|
||||
|
||||
# %%
|
||||
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
|
||||
y_true = actual_labels
|
||||
y_pred = pred_labels
|
||||
|
||||
# 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:.5f}')
|
||||
print(f'F1 Score: {f1:.5f}')
|
||||
print(f'Precision: {precision:.5f}')
|
||||
print(f'Recall: {recall:.5f}')
|
||||
|
||||
|
||||
# %%
|
||||
for fold in [1,2,3,4,5]:
|
||||
test(fold)
|
||||
|
|
@ -0,0 +1,209 @@
|
|||
# %%
|
||||
|
||||
# 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 (
|
||||
AutoTokenizer,
|
||||
AutoModelForSequenceClassification,
|
||||
DataCollatorWithPadding,
|
||||
Trainer,
|
||||
EarlyStoppingCallback,
|
||||
TrainingArguments
|
||||
)
|
||||
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')
|
||||
|
||||
# %%
|
||||
|
||||
# we need to create the mdm_list
|
||||
# import the full mdm-only file
|
||||
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']))))
|
||||
|
||||
# %%
|
||||
id2label = {}
|
||||
label2id = {}
|
||||
for idx, val in enumerate(mdm_list):
|
||||
id2label[idx] = val
|
||||
label2id[val] = idx
|
||||
|
||||
# %%
|
||||
|
||||
# outputs a list of dictionaries
|
||||
# processes dataframe into lists of dictionaries
|
||||
# each element maps input to output
|
||||
# input: tag_description
|
||||
# output: class label
|
||||
def process_df_to_dict(df, mdm_list):
|
||||
output_list = []
|
||||
for _, row in df.iterrows():
|
||||
desc = f"{row['tag_description']}"
|
||||
pattern = row['pattern']
|
||||
try:
|
||||
index = mdm_list.index(pattern)
|
||||
except ValueError:
|
||||
index = -1
|
||||
element = {
|
||||
'text' : f"{desc}",
|
||||
'label': index,
|
||||
}
|
||||
output_list.append(element)
|
||||
|
||||
return output_list
|
||||
|
||||
|
||||
def create_split_dataset(fold, mdm_list):
|
||||
# train
|
||||
data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.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, mdm_list)),
|
||||
'validation' : Dataset.from_list(process_df_to_dict(validation_df, mdm_list)),
|
||||
})
|
||||
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, mdm_list)
|
||||
|
||||
# prepare tokenizer
|
||||
|
||||
model_checkpoint = "distilbert/distilbert-base-uncased"
|
||||
tokenizer = AutoTokenizer.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['text']
|
||||
# text_target sets the corresponding label to inputs
|
||||
# there is no need to create a separate 'labels'
|
||||
model_inputs = tokenizer(
|
||||
input,
|
||||
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="text",
|
||||
)
|
||||
|
||||
# %% temp
|
||||
# tokenized_datasets['train'].rename_columns()
|
||||
|
||||
# %%
|
||||
# create data collator
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
|
||||
|
||||
# %%
|
||||
# compute metrics
|
||||
metric = evaluate.load("accuracy")
|
||||
|
||||
|
||||
def compute_metrics(eval_preds):
|
||||
preds, labels = eval_preds
|
||||
preds = np.argmax(preds, axis=1)
|
||||
return metric.compute(predictions=preds, references=labels)
|
||||
|
||||
# %%
|
||||
# create id2label and label2id
|
||||
|
||||
|
||||
# %%
|
||||
model = AutoModelForSequenceClassification.from_pretrained(
|
||||
model_checkpoint,
|
||||
num_labels=len(mdm_list),
|
||||
id2label=id2label,
|
||||
label2id=label2id)
|
||||
# important! after extending tokens vocab
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# model = torch.compile(model, backend="inductor", dynamic=True)
|
||||
|
||||
|
||||
# %%
|
||||
# Trainer
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir=f"{save_path}",
|
||||
# eval_strategy="epoch",
|
||||
eval_strategy="no",
|
||||
logging_dir="tensorboard-log",
|
||||
logging_strategy="epoch",
|
||||
# save_strategy="epoch",
|
||||
load_best_model_at_end=False,
|
||||
learning_rate=1e-5,
|
||||
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=80,
|
||||
bf16=True,
|
||||
push_to_hub=False,
|
||||
remove_unused_columns=False,
|
||||
)
|
||||
|
||||
|
||||
trainer = Trainer(
|
||||
model,
|
||||
training_args,
|
||||
train_dataset=tokenized_datasets["train"],
|
||||
eval_dataset=tokenized_datasets["validation"],
|
||||
tokenizer=tokenizer,
|
||||
data_collator=data_collator,
|
||||
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)
|
||||
|
||||
|
||||
# %%
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
checkpoint*
|
||||
tensorboard-log/
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
__pycache__
|
||||
exports/
|
||||
|
|
@ -0,0 +1,168 @@
|
|||
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 = []
|
||||
for _, row in df.iterrows():
|
||||
desc = f"<DESC>{row['tag_description']}<DESC>"
|
||||
unit = f"<UNIT>{row['unit']}<UNIT>"
|
||||
element = {
|
||||
'input' : f"{desc}{unit}",
|
||||
'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
|
||||
}
|
||||
output_list.append(element)
|
||||
|
||||
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
|
||||
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
|
||||
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']
|
||||
condition_correct_property = df['p_property'] == df['property']
|
||||
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)
|
||||
|
|
@ -0,0 +1,197 @@
|
|||
# %%
|
||||
|
||||
# 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>"
|
||||
unit = f"<UNIT>{row['unit']}<UNIT>"
|
||||
element = {
|
||||
'input' : f"{desc}{unit}",
|
||||
'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<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_all.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",
|
||||
eval_strategy="no",
|
||||
logging_dir="tensorboard-log",
|
||||
logging_strategy="epoch",
|
||||
# save_strategy="epoch",
|
||||
load_best_model_at_end=False,
|
||||
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=40,
|
||||
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)
|
||||
|
||||
Loading…
Reference in New Issue