Feat: implement selection for pattern-mapping

Feat: added error analysis for BERT find-back
Feat: added direct mapping with unit
Feat: added BERT for classification using description only

analysis/bert/.gitignore

@@ -1 +1,2 @@
 __pycache__
+exports

analysis/bert/error_analysis.py

@@ -0,0 +1,252 @@
+# %%
+import pandas as pd
+from utils import Retriever, cosine_similarity_chunked
+import os
+import glob
+import numpy as np
+
+def analysis_for_fold(fold):
+    file_object = open(f'exports/output_{fold}.txt', 'w')
+
+    # %%
+    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
+
+    # %%
+    print("Number of errors related to 'thing'", len(error_thing_df), file=file_object)
+    print("Number of errors related to 'property'", len(error_property_df), file=file_object)
+
+    # %%
+    # 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():
+                    desc = f"<DESC>{row['tag_description']}<DESC>"
+                    unit = f"<UNIT>{row['unit']}<UNIT>"
+                    element = f"{desc}{unit}"
+                    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_embedding(batch_size=64)
+            return retriever_train.embeddings.to('cpu')
+
+
+
+    # %%
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    checkpoint_directory = "../../train/classification_bert"
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+    train_embedder = Embedder(input_df=train_df)
+    train_embeds = train_embedder.make_embedding(checkpoint_path)
+
+    test_embedder = Embedder(input_df=test_df)
+    test_embeds = test_embedder.make_embedding(checkpoint_path)
+
+
+
+    # %%
+    # test embeds are inputs since we are looking back at train data
+    cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
+
+    # %%
+    # the following function takes in a full cos_sim_matrix
+    # condition_source: boolean selectors of the source embedding
+    # condition_target: boolean selectors of the target embedding
+    def find_closest(cos_sim_matrix, condition_source, condition_target):
+        # subset_matrix = cos_sim_matrix[condition_source]
+        # except we are subsetting 2D matrix (row, column)
+        subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
+        # we select top k here
+        # Get the indices of the top 5 maximum values along axis 1
+        top_k = 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
+
+
+    ####################################################
+    # 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()
+        test_ship_id = test_df[test_df.index == select_idx]['ships_idx'].to_list()[0]
+        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("*" * 20, file=file_object)
+        print("idx:", select_idx, file=file_object)
+        print("train desc", training_desc_list, file=file_object)
+        print("train thing+property", training_data_pattern_list, file=file_object)
+        print("test desc", test_desc_list, file=file_object)
+        print("test thing+property", test_data_pattern_list, file=file_object)
+        print("predicted thing+property", predicted_test_data, file=file_object)
+        print("ships idx", test_ship_id, file=file_object)
+        print("score:", top_k_values[0], file=file_object)
+
+        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
+
+
+
+    # %%
+    # for error thing
+    print('\n', file=file_object)
+    print('*' * 80, file=file_object)
+    print('Error analysis for thing errors', file=file_object)
+    pattern_in_train = []
+    for select_idx in error_thing_df.index:
+        result = find_back_element_with_print(select_idx)
+        print("status:", result, file=file_object)
+        pattern_in_train.append(result)
+
+
+    proportion_in_train = sum(pattern_in_train)/len(pattern_in_train)
+
+    print('\n', file=file_object)
+    print('*' * 80, file=file_object)
+    print("Proportion of entries found in training data", proportion_in_train, file=file_object)
+
+    # for error property
+    # %%
+    print('\n', file=file_object)
+    print('*' * 80, file=file_object)
+    print('Error analysis for property errors', file=file_object)
+    pattern_in_train = []
+    for select_idx in error_property_df.index:
+        result = find_back_element_with_print(select_idx)
+        print("status:", result, file=file_object)
+        pattern_in_train.append(result)
+
+    proportion_in_train = sum(pattern_in_train)/len(pattern_in_train)
+
+    print('\n', file=file_object)
+    print('*' * 80, file=file_object)
+    print("Proportion of entries found in training data", proportion_in_train, file=file_object)
+
+    ####################################################
+
+    # %%
+    # 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, file=file_object)
+        print("25th, 50th, 75th Percentiles:", percentiles, file=file_object)
+
+
+    # %%
+    ##########################################
+    # Analyze the degree of similarity differences between correct and incorrect results
+
+    print('\n', file=file_object)
+    print("*" * 80, file=file_object)
+    print("This section analyzes the similarity statistics for the error and correct groups", file=file_object)
+    # %%
+    # 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)
+
+    file_object.close()
+
+for fold in [1,2,3,4,5]:
+    print(f"running for fold {fold}")
+    analysis_for_fold(fold)

analysis/pattern_filling/.gitignore

@@ -0,0 +1 @@
+output.csv

analysis/pattern_filling/analysis.py

@@ -0,0 +1,55 @@
+# we want to see if there are clear rules to filling numbers in the pattern
+# format
+
+# %%
+# %%
+import pandas as pd
+# from utils import Retriever, cosine_similarity_chunked
+import os
+import glob
+import numpy as np
+
+# %%
+fold = 5
+data_path = f'../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv'
+test_df = pd.read_csv(data_path, skipinitialspace=True)
+
+data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+
+
+# %%
+data_path = '../../data_import/exports/data_mapping_mdm.csv'
+# data_path = '../../data_preprocess/exports/preprocessed_data.csv'
+df = pd.read_csv(data_path, skipinitialspace=True)
+mdm_list = sorted(list((set(df['pattern']))))
+
+
+
+# %%
+symbol_pattern_list = [elem for elem in mdm_list if '#' in elem]
+
+# %%
+symbol_pattern_list
+
+# %%
+len(symbol_pattern_list)
+
+# %%
+idx = 22
+print(symbol_pattern_list[idx])
+condition1 = df['pattern'] == symbol_pattern_list[idx]
+subset_df = df[df['pattern'] == symbol_pattern_list[idx]]
+ship = list(set(subset_df['ships_idx']))
+print(ship)
+
+# %%
+subset_df[['thing', 'property', 'tag_name', 'tag_description', 'ships_idx']].to_csv('output.csv')
+# %%
+ship_idx = 10
+condition2 = df['ships_idx'] == ship_idx
+subset_df = df[condition1 & condition2]
+subset_df
+
+# %%

data_preprocess/abbreviations/replacement_dict.py

@@ -37,6 +37,9 @@ desc_replacement_dict = {
     r'\bOUTL\.\b': 'OUTLET',
     r'\boutlet\b\b': 'OUTLET',
     r'\bOUTLET\b\b': 'OUTLET',
+    # bunker tank
+    r'\bBK\b': 'BUNKER',
+    r'\bTK\b': 'TANK',
     # pressure
     r'\bPRESS\b\b': 'PRESSURE',
     r'\bPRESS\.\b': 'PRESSURE',
@@ -100,12 +103,13 @@ desc_replacement_dict = {
     r'\bHT\b\b': 'HIGH TEMPERATURE',
     # auxiliary boiler
     # replace these first before replacing AUXILIARY only
-    r'\bAUX\.BOILER\b\b': 'AUXILIARY BOILER',
+    r'\bAUX\.BOILER\b': 'AUXILIARY BOILER',
-    r'\bAUX\. BOILER\b\b': 'AUXILIARY BOILER',
+    r'\bAUX\. BOILER\b': 'AUXILIARY BOILER',
-    r'\bAUX BLR\b\b': 'AUXILIARY BOILER',
+    r'\bAUX BLR\b': 'AUXILIARY BOILER',
     r'\bAUX\.\b': 'AUXILIARY ',
     # composite boiler
     r'\bCOMP\. BOILER\b\b': 'COMPOSITE BOILER',
+    r'\bCOMP\.BOILER\b\b': 'COMPOSITE BOILER',
     r'\bCOMP BOILER\b\b': 'COMPOSITE BOILER',
     r'\bWIND\.\b': 'WINDING',
     r'\bWINDING\b\b': 'WINDING',
@@ -127,6 +131,8 @@ desc_replacement_dict = {
     r'\bTURBOCHARGER\b\b': 'TURBOCHARGER',
     # misc spelling errors
     r'\bOPERATOIN\b': 'OPERATION',
+    # wrongly attached terms
+    r'BOILERMGO': 'BOILER MGO',
     # additional standardizing replacement
     # replace # followed by a number with NO
     r'#(?=\d)\b': 'NO',

post_process/selection_with_pattern/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports

post_process/selection_with_pattern/run.py

@@ -0,0 +1,261 @@
+# %%
+import pandas as pd
+import os
+import glob
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+import numpy as np
+from utils import BertEmbedder, cosine_similarity_chunked
+
+
+# %%
+# directory for checkpoints
+checkpoint_directory =  '../../train/mapping_pattern'
+
+fold = 5
+# import test data
+data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.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
+
+# %%
+df['p_pattern'] = df['p_thing'] + " " + df['p_property']
+
+# %%
+# obtain the full mdm_list
+data_path = '../../data_import/exports/data_mapping_mdm.csv'
+full_df = pd.read_csv(data_path, skipinitialspace=True)
+full_mdm_pattern_list = sorted(list((set(full_df['pattern']))))
+
+# %%
+# we have to split into per-ship analysis
+ships_list = sorted(list(set(df['ships_idx'])))
+# %%
+# for ship_idx in ships_list:
+ship_idx = 1009  # choose an example ship
+ship_df = df[df['ships_idx'] == ship_idx].reset_index(drop=True)
+
+class Embedder():
+    input_df: pd.DataFrame
+    fold: int
+
+    def __init__(self, input_df):
+        self.input_df = input_df
+
+
+    def make_embedding(self, checkpoint_path):
+
+        def generate_input_list(df):
+            input_list = []
+            for _, row in df.iterrows():
+                desc = f"{row['tag_description']}"
+                element = f"{desc}"
+                input_list.append(element)
+            return input_list
+
+        # prepare reference embed
+        train_data = list(generate_input_list(self.input_df))
+        # Define the directory and the pattern
+        embedder = BertEmbedder(train_data, checkpoint_path)
+        embedder.make_embedding(batch_size=64)
+        return embedder.embeddings.to('cpu')
+
+
+
+# %%
+data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+checkpoint_directory = "../../train/classification_bert"
+directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
+# Use glob to find matching paths
+# path is usually checkpoint_fold_1/checkpoint-<step number>
+# we are guaranteed to save only 1 checkpoint from training
+pattern = 'checkpoint-*'
+checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+train_embedder = Embedder(input_df=train_df)
+train_embeds = train_embedder.make_embedding(checkpoint_path)
+
+test_embedder = Embedder(input_df=ship_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 general idea:
+# step 1: keep only pattern generations that belong to mdm list
+# -> this removes totally wrong datasets that mapped to totally wrong things
+# step 2: loop through the mdm list and isolate data in both train and test that
+# belong to the same pattern class
+# -> this is more tricky, because we have non-mdm mapping to correct classes
+# -> so we have to find which candidate is most similar to the training data
+
+# it is very tricky to keep track of classification across multiple stages so we
+# will use a boolean answer list
+
+# %%
+answer_list = np.ones(len(ship_df), dtype=bool)
+
+##########################################
+# %%
+# STEP 1
+# we want to loop through the the ship_df and find which ones match our full_mdm_list
+pattern_match_mask = ship_df['p_pattern'].apply(lambda x: x in full_mdm_pattern_list).to_numpy()
+# we assign only those that are False to our answer list
+# right now the 2 arrays are basically equal
+answer_list[~pattern_match_mask] = False
+
+# %% TEMP
+print('proportion belonging to mdm classes', sum(pattern_match_mask)/len(pattern_match_mask))
+
+# %% TEMP
+y_true = ship_df['MDM'].to_list()
+y_pred = pattern_match_mask
+
+# Compute metrics
+accuracy = accuracy_score(y_true, y_pred)
+print(f'Accuracy: {accuracy:.5f}')
+
+# we can see that the accuracy is not good
+# %%
+#########################################
+# STEP 2
+# we want to go through each mdm class label
+# but we do not want to make subsets of dataframes
+# we will make heavy use of boolean masks
+
+# we want to identify per-ship mdm classes
+ship_mdm_classes = sorted(set(ship_df['p_pattern'][pattern_match_mask].to_list()))
+
+# %%
+len(ship_mdm_classes)
+
+# %%
+for idx,select_class in enumerate(ship_mdm_classes):
+    print(idx, len(ship_df[ship_df['p_pattern'] == select_class]))
+
+# %%
+select_class = ship_mdm_classes[22]
+sample_df = ship_df[ship_df['p_pattern'] == select_class]
+
+# %%
+# we need to set all idx of chosen entries as False in answer_list
+selected_idx_list = sample_df.index.to_list()
+answer_list[selected_idx_list] = False
+
+# %%
+# because we have variants of a tag_description, we cannot choose 1 from the
+# given candidates we have to first group the candidates, and then choose which
+# group is most similar
+
+# %%
+from fuzzywuzzy import fuzz
+
+# the purpose of this function is to group the strings that are similar to each other
+# we need to form related groups of inputs
+def group_similar_strings(obj_list, threshold=80):
+    groups = []
+    processed_strings = set()  # To keep track of already grouped strings
+
+    for obj in obj_list:
+        # tuple is (idx, string)
+        if obj in processed_strings:
+            continue
+
+        # Find all strings similar to the current string above the threshold
+        similar_strings = [s for s in obj_list if s[1] != obj[1] and fuzz.ratio(obj[1], s[1]) >= threshold]
+        
+        # Add the original string to the similar group
+        similar_group = [obj] + similar_strings
+
+        # Mark all similar strings as processed
+        processed_strings.update(similar_group)
+        
+        # Add the group to the list of groups
+        groups.append(similar_group)
+    
+    return groups
+
+# Example usage
+string_list = sample_df['tag_description'].to_list()
+index_list = sample_df.index.to_list()
+obj_list = list(zip(index_list, string_list))
+groups = group_similar_strings(obj_list, threshold=90)
+print(groups)
+
+# %%
+# this function takes in groups of related terms and create candidate entries
+def make_candidates(groups):
+    candidates = []
+    for group in groups:
+        first_tuple = group[0]
+        # string_of_tuple = first_tuple[1]
+        id_of_tuple = first_tuple[0]
+        candidates.append(id_of_tuple)
+    return candidates
+
+# %%
+test_candidates = make_candidates(groups)
+test_candidates_mask = np.zeros(len(ship_df), dtype=bool)
+test_candidates_mask[test_candidates] = True
+
+# %%
+train_candidates_mask = (train_df['pattern'] == select_class).to_numpy()
+
+# %%
+# we need to make the cos_sim_matrix
+# for that, we need to generate the embeddings of the ship_df (test embedding)
+# and the train_df (train embeddin)
+
+# we then use the selection function using the given mask to choose the most
+# appropriate candidate
+
+# the selection function takes in the full cos_sim_matrix then subsets the
+# matrix according to the test_candidates_mask and train_candidates_mask that we
+# give it
+# it returns the most likely source candidate index and score among the source
+# candidate list - aka it returns a local idx
+def selection(cos_sim_matrix, source_mask, target_mask):
+    # subset_matrix = cos_sim_matrix[condition_source]
+    # except we are subsetting 2D matrix (row, column)
+    subset_matrix = cos_sim_matrix[np.ix_(source_mask, target_mask)]
+    # we select top-k here
+    # Get the indices of the top-k maximum values along axis 1
+    top_k = 1
+    top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:]  # Get indices of top k values
+    
+    # Get the values of the top 5 maximum scores
+    top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
+    
+    # Calculate the average of the top-k scores along axis 1
+    y_scores = np.mean(top_k_values, axis=1)
+    max_idx = np.argmax(y_scores)
+    max_score = y_scores[max_idx]
+
+    return max_idx, max_score
+
+
+# %%
+max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask)
+
+# %%
+# after obtaining best group, we set all candidates of the group as True
+chosen_group = groups[max_idx]
+chosen_idx = [tuple[0] for tuple in chosen_group]
+
+# %%
+# before doing this, we have to use the max_score and evaluate if its close enough
+THRESHOLD = 0.8
+if max_score > THRESHOLD:
+    answer_list[chosen_idx] = True
+
+# %%

post_process/selection_with_pattern/run_diagnostic.py

@@ -0,0 +1,407 @@
+# %%
+import pandas as pd
+import os
+import glob
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+import numpy as np
+from utils import BertEmbedder, cosine_similarity_chunked
+from fuzzywuzzy import fuzz
+
+##################
+# global parameters
+DIAGNOSTIC = True
+THRESHOLD = 0.90
+FUZZY_SIM_THRESHOLD=90
+checkpoint_directory = "../../train/classification_bert"
+
+###################
+# %%
+# helper functions
+class Embedder():
+    input_df: pd.DataFrame
+    fold: int
+
+    def __init__(self, input_df):
+        self.input_df = input_df
+
+
+    def make_embedding(self, checkpoint_path):
+
+        def generate_input_list(df):
+            input_list = []
+            for _, row in df.iterrows():
+                desc = f"{row['tag_description']}"
+                element = f"{desc}"
+                input_list.append(element)
+            return input_list
+
+        # prepare reference embed
+        train_data = list(generate_input_list(self.input_df))
+        # Define the directory and the pattern
+        embedder = BertEmbedder(train_data, checkpoint_path)
+        embedder.make_embedding(batch_size=64)
+        return embedder.embeddings.to('cpu')
+
+
+
+# the purpose of this function is to group the strings that are similar to each other
+# we need to form related groups of inputs
+def group_similar_strings(obj_list, threshold=80):
+    groups = []
+    processed_strings = set()  # To keep track of already grouped strings
+
+    for obj in obj_list:
+        # tuple is (idx, string)
+        if obj in processed_strings:
+            continue
+        # Find all strings similar to the current string above the threshold
+        similar_strings = [s for s in obj_list if s[1] != obj[1] and fuzz.ratio(obj[1], s[1]) >= threshold]
+        # Add the original string to the similar group
+        similar_group = [obj] + similar_strings
+        # Mark all similar strings as processed
+        processed_strings.update(similar_group)
+        # Add the group to the list of groups
+        groups.append(similar_group)
+    return groups
+
+# this function takes in groups of related terms and create candidate entries
+def make_candidates(groups):
+    candidates = []
+    for group in groups:
+        first_tuple = group[0]
+        # string_of_tuple = first_tuple[1]
+        id_of_tuple = first_tuple[0]
+        candidates.append(id_of_tuple)
+    return candidates
+
+
+# the selection function takes in the full cos_sim_matrix then subsets the
+# matrix according to the test_candidates_mask and train_candidates_mask that we
+# give it
+# it returns the most likely source candidate index and score among the source
+# candidate list - aka it returns a local idx
+def selection(cos_sim_matrix, source_mask, target_mask, file_object=None):
+    # subset_matrix = cos_sim_matrix[condition_source]
+    # except we are subsetting 2D matrix (row, column)
+    subset_matrix = cos_sim_matrix[np.ix_(source_mask, target_mask)]
+    # we select top-k here
+    # Get the indices of the top-k maximum values along axis 1
+    top_k = 1
+    top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:]  # Get indices of top k values
+    
+    # Get the values of the top 5 maximum scores
+    top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
+    
+    # Calculate the average of the top-k scores along axis 1
+    y_scores = np.mean(top_k_values, axis=1)
+    max_idx = np.argmax(y_scores)
+    max_score = y_scores[max_idx]
+    
+    if DIAGNOSTIC and (file_object is not None):
+        print('all scores:', file=file_object)
+        print(y_scores, file=file_object)
+
+    return max_idx, max_score
+
+
+
+####################
+# global level
+# %%
+# obtain the full mdm_list
+data_path = '../../data_import/exports/data_mapping_mdm.csv'
+full_df = pd.read_csv(data_path, skipinitialspace=True)
+full_mdm_pattern_list = sorted(list((set(full_df['pattern']))))
+
+
+#####################
+# fold level
+
+def run_selection(fold):
+
+    file_object = open(f'exports/output_{fold}.txt', 'w')
+    # set the fold
+    # import test data
+    data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df['p_pattern'] = df['p_thing'] + " " + df['p_property']
+
+    # get target data
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # generate your embeddings
+    # checkpoint_directory defined at global level
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+    # we can generate the train embeddings once and re-use for every ship
+    train_embedder = Embedder(input_df=train_df)
+    train_embeds = train_embedder.make_embedding(checkpoint_path)
+
+
+    # create global_answer array
+    # the purpose of this array is to track the classification state at the global
+    # level
+    global_answer = np.zeros(len(df), dtype=bool)
+
+    global_sim = np.zeros(len(df))
+
+    #############################
+    # ship level
+    # we have to split into per-ship analysis
+    ships_list = sorted(list(set(df['ships_idx'])))
+    print(ships_list)
+    for ship_idx in ships_list:
+        # ship_idx = 1001  # choose an example ship
+
+        print(ship_idx, file=file_object)  # print selected ship
+        ship_df = df[df['ships_idx'] == ship_idx]
+        # required to map local ship_answer array to global_answer array
+        map_local_index_to_global_index = ship_df.index.to_numpy()
+        ship_df = df[df['ships_idx'] == ship_idx].reset_index(drop=True)
+
+        # generate new embeddings for each ship
+        test_embedder = Embedder(input_df=ship_df)
+        test_embeds = test_embedder.make_embedding(checkpoint_path)
+
+        # generate the cosine sim matrix
+        cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
+
+        ##############################
+        # selection level
+        # The general idea:
+        # step 1: keep only pattern generations that belong to mdm list
+        # -> this removes totally wrong datasets that mapped to totally wrong things
+        # step 2: loop through the mdm list and isolate data in both train and test that
+        # belong to the same pattern class
+        # -> this is more tricky, because we have non-mdm mapping to correct classes
+        # -> so we have to find which candidate is most similar to the training data
+
+        # it is very tricky to keep track of classification across multiple stages so we
+        # will use a boolean answer list
+
+        # initialize the local answer list
+        ship_answer_list = np.ones(len(ship_df), dtype=bool)
+
+        ship_answer_sim = np.ones(len(ship_df))
+
+        ###########
+        # STEP 1
+        # we want to loop through the generated class labels and find which ones match
+        # our pattern list
+
+        pattern_match_mask = ship_df['p_pattern'].apply(lambda x: x in full_mdm_pattern_list).to_numpy()
+        # we assign only those that are False to our answer list
+        # right now the 2 arrays are basically equal
+        ship_answer_list[~pattern_match_mask] = False
+
+        ###########
+        # STEP 2
+        # we now go through each class found in our generated set
+
+        # we want to identify per-ship mdm classes
+        ship_predicted_classes = sorted(set(ship_df['p_pattern'][pattern_match_mask].to_list()))
+
+        # this function performs the selection given a class
+        # it takes in the cos_sim_matrix
+        # it returns the selection by mutating the answer_list
+        # it sets all relevant idxs to False initially, then sets the selected values to True
+        def selection_for_class(select_class, cos_sim_matrix, answer_list, score_list):
+
+            # separate the global variable from function variable
+            answer_list = answer_list.copy()
+            score_list = score_list.copy()
+            sample_df = ship_df[ship_df['p_pattern'] == select_class]
+
+            # we need to set all idx of chosen entries as False in answer_list
+            selected_idx_list = sample_df.index.to_list()
+            answer_list[selected_idx_list] = False
+
+            # basic assumption check
+
+            # group related inputs by description similarity
+            string_list = sample_df['tag_description'].to_list()
+            index_list = sample_df.index.to_list()
+            obj_list = list(zip(index_list, string_list))
+            # groups is a list of list, where each list is composed of a
+            # (idx, string) tuple
+            groups = group_similar_strings(obj_list, threshold=FUZZY_SIM_THRESHOLD)
+
+            if DIAGNOSTIC:
+                print('*' * 10, file=file_object)
+                print(select_class, file=file_object)
+                print('candidate groups', file=file_object)
+                print(groups, file=file_object)
+
+            # generate the masking arrays for both test and train embeddings
+            # we select a tuple from each group, and use that as a candidate for selection
+            test_candidates = make_candidates(groups)
+            test_candidates_mask = np.zeros(len(ship_df), dtype=bool)
+            test_candidates_mask[test_candidates] = True
+            # we make candidates to compare against in the data sharing the same class
+            train_candidates_mask = (train_df['pattern'] == select_class).to_numpy()
+
+            # perform selection
+            # it returns the group index that is most likely
+            max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask, file_object)
+
+            # consolidate all idx's in the same group
+            chosen_group = groups[max_idx]
+            chosen_idx_list = [tuple[0] for tuple in chosen_group]
+
+            if DIAGNOSTIC:
+                print('chosen group', file=file_object)
+                print(chosen_group, file=file_object)
+
+            # before doing this, we have to use the max_score and evaluate if its close enough
+            if max_score > THRESHOLD:
+                answer_list[chosen_idx_list] = True
+                if DIAGNOSTIC:
+                    print('max score', file=file_object)
+                    print(max_score, file=file_object)
+                    print('accepted', file=file_object)
+            else:
+                if DIAGNOSTIC:
+                    print('max score', file=file_object)
+                    print(max_score, file=file_object)
+                    print('rejected', file=file_object)
+
+
+
+
+            # for analysis
+            test_candidates_mask = np.ones(len(ship_df), dtype=bool)
+            _, every_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask, None)
+
+            # write the score for every idx of this class
+            score_list[selected_idx_list] = every_score
+
+            return answer_list, score_list
+
+
+        # we choose one mdm class
+        for select_class in ship_predicted_classes:
+            ship_answer_list, ship_answer_sim = selection_for_class(select_class, cos_sim_matrix, ship_answer_list, ship_answer_sim)
+
+        # we want to write back to global_answer
+        # first we convert local indices to global indices
+        local_indices = np.where(ship_answer_list)[0]
+        global_indices = map_local_index_to_global_index[local_indices]
+        global_answer[global_indices] = True
+
+        # similarity score
+        global_sim[map_local_index_to_global_index] = ship_answer_sim
+
+        if DIAGNOSTIC:
+            # evaluation at per-ship level
+            y_true = ship_df['MDM'].to_list()
+            y_pred = ship_answer_list
+
+            # Compute metrics
+            accuracy = accuracy_score(y_true, y_pred)
+            f1 = f1_score(y_true, y_pred, average='macro')
+            precision = precision_score(y_true, y_pred, average='macro')
+            recall = recall_score(y_true, y_pred, average='macro')
+
+            # Print the results
+            print(f'Accuracy: {accuracy:.5f}', file=file_object)
+            print(f'F1 Score: {f1:.5f}', file=file_object)
+            print(f'Precision: {precision:.5f}', file=file_object)
+            print(f'Recall: {recall:.5f}', file=file_object)
+
+
+
+    y_true = df['MDM'].to_list()
+    y_pred = global_answer
+
+
+    # Compute metrics
+    accuracy = accuracy_score(y_true, y_pred)
+    f1 = f1_score(y_true, y_pred, average='macro')
+    precision = precision_score(y_true, y_pred, average='macro')
+    recall = recall_score(y_true, y_pred, average='macro')
+
+    # Print the results
+    print(f'Accuracy: {accuracy:.5f}')
+    print(f'F1 Score: {f1:.5f}')
+    print(f'Precision: {precision:.5f}')
+    print(f'Recall: {recall:.5f}')
+
+
+    file_object.close()
+
+    return global_answer, global_sim
+
+# %%
+for fold in [1]:
+    print(f'Perform selection for fold {fold}')
+    global_answer, global_sim = run_selection(fold)
+
+# %%
+data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv"
+df = pd.read_csv(data_path, skipinitialspace=True)
+df['p_pattern'] = df['p_thing'] + " " + df['p_property']
+df['score'] = global_sim
+
+# %%
+# %%
+def print_summary(similarity_scores):
+    # Convert list to numpy array for additional stats
+    np_array = np.array(similarity_scores)
+
+    # Get stats
+    mean_value = np.mean(np_array)
+    percentiles = np.percentile(np_array, [25, 50, 75])  # 25th, 50th, and 75th percentiles
+
+    # Display numpy results
+    print("Mean:", mean_value)
+    print("25th, 50th, 75th Percentiles:", percentiles)
+
+# %%
+# analysis of non-mdm in predicted
+df_selected = df[global_answer]
+df_selected[~df_selected['MDM']]
+
+in_scores = df_selected[df_selected['MDM']]['score'].to_numpy()
+print_summary(in_scores)
+
+# %%
+# analysis of mdm in non-predicted
+df_selected = df[~global_answer]
+# df_selected
+df_selected[df_selected['MDM']]
+
+
+# %%
+out_scores = df_selected[df_selected['MDM']]['score'].to_numpy()
+
+print_summary(out_scores)
+
+
+# %%
+import matplotlib.pyplot as plt
+# Sample data
+list1 = in_scores
+list2 = out_scores
+
+# Plot histograms
+bins = 20
+plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=False)
+plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=False)
+
+# Labels and legend
+plt.xlabel('Value')
+plt.ylabel('Frequency')
+plt.legend(loc='upper right')
+plt.title('Histograms of Three Lists')
+
+# Show plot
+plt.show()
+
+
+# %%

post_process/selection_with_pattern/utils.py

@@ -0,0 +1,82 @@
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+)
+import torch.nn.functional as F
+
+
+
+class BertEmbedder:
+    def __init__(self, input_texts, model_checkpoint):
+        # we need to generate the embedding from list of input strings
+        self.embeddings = []
+        self.inputs = input_texts
+        model_checkpoint = model_checkpoint 
+        self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+
+        model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # device = "cpu"
+        model.to(self.device)
+        self.model = model.eval()
+
+
+    def make_embedding(self, batch_size=64):
+        all_embeddings = self.embeddings
+        input_texts = self.inputs
+
+        for i in range(0, len(input_texts), batch_size):
+            batch_texts = input_texts[i:i+batch_size]
+            # Tokenize the input text
+            inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=64)
+            input_ids = inputs.input_ids.to(self.device)
+            attention_mask = inputs.attention_mask.to(self.device)
+
+
+            # Pass the input through the encoder and retrieve the embeddings
+            with torch.no_grad():
+                encoder_outputs = self.model(input_ids, attention_mask=attention_mask, output_hidden_states=True)
+                # get last layer
+                embeddings = encoder_outputs.hidden_states[-1]
+                # get cls token embedding
+                cls_embeddings = embeddings[:, 0, :]  # Shape: (batch_size, hidden_size)
+                all_embeddings.append(cls_embeddings)
+        
+        # remove the batch list and makes a single large tensor, dim=0 increases row-wise
+        all_embeddings = torch.cat(all_embeddings, dim=0)
+
+        self.embeddings = all_embeddings
+
+def cosine_similarity_chunked(batch1, batch2, chunk_size=1024):
+    device = 'cuda'
+    batch1_size = batch1.size(0)
+    batch2_size = batch2.size(0)
+    batch2.to(device)
+    
+    # Prepare an empty tensor to store results
+    cos_sim = torch.empty(batch1_size, batch2_size, device=device)
+
+    # Process batch1 in chunks
+    for i in range(0, batch1_size, chunk_size):
+        batch1_chunk = batch1[i:i + chunk_size]  # Get chunk of batch1
+        
+        batch1_chunk.to(device)
+        # Expand batch1 chunk and entire batch2 for comparison
+        # batch1_chunk_exp = batch1_chunk.unsqueeze(1)  # Shape: (chunk_size, 1, seq_len)
+        # batch2_exp = batch2.unsqueeze(0)  # Shape: (1, batch2_size, seq_len)
+        batch2_norms = batch2.norm(dim=1, keepdim=True)
+
+        
+        # Compute cosine similarity for the chunk and store it in the final tensor
+        # cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
+
+        # Compute cosine similarity by matrix multiplication and normalizing
+        sim_chunk = torch.mm(batch1_chunk, batch2.T) / (batch1_chunk.norm(dim=1, keepdim=True) * batch2_norms.T + 1e-8)
+        
+        # Store the results in the appropriate part of the final tensor
+        cos_sim[i:i + chunk_size] = sim_chunk
+    
+    return cos_sim
+

train/classification_bert_desc/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/classification_bert_desc/classification_prediction/predict.py

@@ -0,0 +1,228 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from torch.utils.data import DataLoader
+
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+from tqdm import tqdm
+
+torch.set_float32_matmul_precision('high')
+
+# %%
+
+# we need to create the mdm_list
+# import the full mdm-only file
+data_path = '../../../data_import/exports/data_mapping_mdm.csv'
+full_df = pd.read_csv(data_path, skipinitialspace=True)
+mdm_list = sorted(list((set(full_df['pattern']))))
+
+# %%
+id2label = {}
+label2id = {}
+for idx, val in enumerate(mdm_list):
+    id2label[idx] = val
+    label2id[val] = idx
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df, mdm_list):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+
+        pattern = row['pattern']
+        try:
+            index = mdm_list.index(pattern)
+        except ValueError:
+            index = -1
+        element = {
+            'text' : f"{desc}{unit}",
+            'label': index,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_dataset(fold, mdm_list):
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+    # we only use the mdm subset
+    test_df = test_df[test_df['MDM']].reset_index(drop=True)
+
+    test_dataset = Dataset.from_list(process_df_to_dict(test_df, mdm_list))
+
+    return test_dataset
+
+
+# %%
+
+# function to perform training for a given fold
+def test(fold):
+
+    test_dataset = create_dataset(fold, mdm_list)
+
+    # prepare tokenizer
+
+    checkpoint_directory = f'../checkpoint_fold_{fold}'
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0]
+
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    # %%
+    # compute max token length
+    max_length = 0
+    for sample in test_dataset['text']:
+        # Tokenize the sample and get the length
+        input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"]
+        length = len(input_ids)
+        
+        # Update max_length if this sample is longer
+        if length > max_length:
+            max_length = length
+
+    print(max_length)
+
+    # %%
+
+    max_length = 64
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            # truncation=True,
+            padding='max_length'
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    datasets = test_dataset.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+
+    datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length")
+
+    # %%
+    # compute metrics
+    # metric = evaluate.load("accuracy")
+    # 
+    # 
+    # def compute_metrics(eval_preds):
+    #     preds, labels = eval_preds
+    #     preds = np.argmax(preds, axis=1)
+    #     return metric.compute(predictions=preds, references=labels)
+
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_checkpoint,
+        num_labels=len(mdm_list),
+        id2label=id2label,
+        label2id=label2id)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    model = model.eval()
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.to(device)
+
+    pred_labels = []
+    actual_labels = []
+
+
+    BATCH_SIZE = 64
+    dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False)
+    for batch in tqdm(dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            actual_labels.extend(batch['label'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                logits = model(
+                    input_ids,
+                    attention_mask).logits
+                predicted_class_ids = logits.argmax(dim=1).to("cpu")
+                pred_labels.extend(predicted_class_ids)
+
+    pred_labels = [tensor.item() for tensor in pred_labels]
+
+
+    # %%
+    from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+    y_true = actual_labels
+    y_pred = pred_labels
+
+    # Compute metrics
+    accuracy = accuracy_score(y_true, y_pred)
+    f1 = f1_score(y_true, y_pred, average='macro')
+    precision = precision_score(y_true, y_pred, average='macro')
+    recall = recall_score(y_true, y_pred, average='macro')
+
+    # Print the results
+    print(f'Accuracy: {accuracy:.5f}')
+    print(f'F1 Score: {f1:.5f}')
+    print(f'Precision: {precision:.5f}')
+    print(f'Recall: {recall:.5f}')
+
+
+# %%
+for fold in [1,2,3,4,5]:
+    test(fold)

train/classification_bert_desc/train.py

@@ -0,0 +1,209 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+    Trainer,
+    EarlyStoppingCallback,
+    TrainingArguments
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# %%
+
+# we need to create the mdm_list
+# import the full mdm-only file
+data_path = '../../data_import/exports/data_mapping_mdm.csv'
+full_df = pd.read_csv(data_path, skipinitialspace=True)
+mdm_list = sorted(list((set(full_df['pattern']))))
+
+# %%
+id2label = {}
+label2id = {}
+for idx, val in enumerate(mdm_list):
+    id2label[idx] = val
+    label2id[val] = idx
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df, mdm_list):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"{row['tag_description']}"
+        pattern = row['pattern']
+        try:
+            index = mdm_list.index(pattern)
+        except ValueError:
+            index = -1
+        element = {
+            'text' : f"{desc}",
+            'label': index,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold, mdm_list):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df, mdm_list)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df, mdm_list)),
+    })
+    return combined_data
+
+
+# %%
+
+# function to perform training for a given fold
+def train(fold):
+
+    save_path = f'checkpoint_fold_{fold}'
+    split_datasets = create_split_dataset(fold, mdm_list)
+
+    # prepare tokenizer
+
+    model_checkpoint = "distilbert/distilbert-base-uncased"
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            truncation=True,
+            padding=True
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    # %%
+    # compute metrics
+    metric = evaluate.load("accuracy")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        preds = np.argmax(preds, axis=1)
+        return metric.compute(predictions=preds, references=labels)
+
+    # %%
+    # create id2label and label2id
+
+
+    # %%
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_checkpoint,
+        num_labels=len(mdm_list),
+        id2label=id2label,
+        label2id=label2id)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # %%
+    # Trainer
+
+    training_args = TrainingArguments(
+        output_dir=f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-5,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=80,
+        bf16=True,
+        push_to_hub=False,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Trainer(
+        model,
+        training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+
+
+# %%

train/mapping_with_unit/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log/

train/mapping_with_unit/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/mapping_with_unit/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding='max_length',
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/mapping_with_unit/mapping_prediction/predict.py

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

train/mapping_with_unit/train.py

@@ -0,0 +1,197 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding=True
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 64
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+