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