Feat: added classification methods
Feat: added mapping to pattern-only method Chore: re-organized prediction to be within mapping folders
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__pycache__
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*.html
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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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data_path = f'../data_preprocess/exports/preprocessed_data.csv'
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df_pre = pd.read_csv(data_path, skipinitialspace=True)
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# %%
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# this should be >0 if we are using abbreviations processed data
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desc_list = df_pre['tag_description'].to_list()
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# %%
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[ elem for elem in desc_list if isinstance(elem, float)]
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##########################################
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# %%
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fold = 1
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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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# 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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# name = f"<NAME>{row['tag_name']}<NAME>"
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desc = f"<DESC>{row['tag_description']}<DESC>"
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# element = f"{name}{desc}"
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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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retriever_train = Retriever(train_data, checkpoint_path)
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retriever_train.make_mean_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.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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checkpoint_directory = "../train/mapping_pattern"
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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 = 5
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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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error_thing_df.index
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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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test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
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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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print("*" * 80)
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print("idx:", select_idx)
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print(training_data_pattern_list)
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print(test_data_pattern_list)
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print(predicted_test_data)
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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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find_back_element_with_print(2884)
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# %%
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def find_back_element(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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test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
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# print(training_data_pattern_list)
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# print(test_data_pattern_list)
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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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find_back_element(2884)
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# %%
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# for error thing
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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)
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pattern_in_train.append(result)
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# %%
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sum(pattern_in_train)/len(pattern_in_train)
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###
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# for error property
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# %%
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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(select_idx)
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pattern_in_train.append(result)
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# %%
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sum(pattern_in_train)/len(pattern_in_train)
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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)
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print("25th, 50th, 75th Percentiles:", percentiles)
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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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# %%
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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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# %%
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import matplotlib.pyplot as plt
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# Sample data
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list1 = similarity_thing_scores
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list2 = similarity_property_scores
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list3 = similarity_correct_scores
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# Plot histograms
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bins = 50
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plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=True)
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plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=True)
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plt.hist(list3, bins=bins, alpha=0.5, label='List 3', density=True)
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# Labels and legend
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plt.xlabel('Value')
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plt.ylabel('Frequency')
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plt.legend(loc='upper right')
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plt.title('Histograms of Three Lists')
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# Show plot
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plt.show()
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###########################################
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# %%
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# why do similarities of 97% still map correctly?
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score_array = np.array(similarity_correct_scores)
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# %%
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sum(score_array < 0.95)
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# %%
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correct_df[score_array < 0.95]['tag_description'].index.to_list()
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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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data_path = f'../data_preprocess/exports/preprocessed_data.csv'
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df_pre = pd.read_csv(data_path, skipinitialspace=True)
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# %%
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# this should be >0 if we are using abbreviations processed data
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desc_list = df_pre['tag_description'].to_list()
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# %%
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[ elem for elem in desc_list if isinstance(elem, float)]
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##########################################
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# %%
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fold = 1
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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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# 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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# name = f"<NAME>{row['tag_name']}<NAME>"
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desc = f"<DESC>{row['tag_description']}<DESC>"
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# element = f"{name}{desc}"
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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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retriever_train = Retriever(train_data, checkpoint_path)
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retriever_train.make_mean_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.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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checkpoint_directory = "../train/mapping_pattern"
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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 = 5
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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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error_thing_df.index
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####################################################
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# special find-back code
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# %%
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select_idx = 2884
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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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print(top_k_indices)
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print(top_k_values)
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# %%
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train_df.iloc[top_k_indices[0]]
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# %%
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test_df[test_df.index == select_idx]
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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)
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print("25th, 50th, 75th Percentiles:", percentiles)
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|
||||
|
||||
# %%
|
||||
##########################################
|
||||
# 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()
|
||||
# %%
|
|
@ -0,0 +1,75 @@
|
|||
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
|
||||
|
|
@ -32,7 +32,11 @@ df = pd.read_csv(file_path)
|
|||
# %%
|
||||
# Replace abbreviations
|
||||
print("running substitution")
|
||||
tag_descriptions = df['tag_description'].fillna("N/A")
|
||||
df['tag_description']= df['tag_description'].fillna("NOVALUE")
|
||||
# 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)
|
||||
# print("Descriptions after replacement:", replaced_descriptions)
|
||||
|
||||
|
@ -40,4 +44,3 @@ replaced_descriptions = replace_abbreviations(tag_descriptions, replacement_dict
|
|||
df["tag_description"] = replaced_descriptions
|
||||
df.to_csv("../exports/preprocessed_data.csv", index=False)
|
||||
print("file saved")
|
||||
# %%
|
||||
|
|
|
@ -101,6 +101,8 @@ 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")
|
||||
|
||||
|
@ -128,7 +130,6 @@ 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
|
||||
|
|
|
@ -0,0 +1 @@
|
|||
__pycache__
|
|
@ -0,0 +1,448 @@
|
|||
# %%
|
||||
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}')
|
||||
|
||||
|
||||
# %%
|
|
@ -0,0 +1,75 @@
|
|||
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
|
||||
|
|
@ -0,0 +1 @@
|
|||
__pycache__
|
|
@ -0,0 +1,450 @@
|
|||
# %%
|
||||
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}')
|
||||
|
||||
|
||||
# %%
|
|
@ -0,0 +1,75 @@
|
|||
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
|
||||
|
|
@ -4,16 +4,16 @@ import os
|
|||
import glob
|
||||
from inference import Inference
|
||||
|
||||
checkpoint_directory = '../../train/baseline'
|
||||
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"
|
||||
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"
|
||||
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']
|
|
@ -0,0 +1,2 @@
|
|||
checkpoint*
|
||||
tensorboard-log/
|
|
@ -0,0 +1,2 @@
|
|||
__pycache__
|
||||
exports/
|
|
@ -0,0 +1,162 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
|
||||
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
|
|
@ -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_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)
|
|
@ -0,0 +1,195 @@
|
|||
# %%
|
||||
|
||||
# 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)
|
||||
|
Loading…
Reference in New Issue