Feat: added de_duplication post-processing method

post_process/binary_classifier/classification_prediction/.gitignore

@@ -0,0 +1,2 @@
+exports
+output.txt

post_process/binary_classifier/classification_prediction/combined_mapping_and_classification_analysis.py

@@ -0,0 +1,81 @@
+# %%
+import pandas as pd
+
+# following code computes final mapping + classification accuracy
+# %%
+def run(fold):
+    data_path = f'exports/result_group_{fold}.csv'
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    p_mdm = df['p_mdm']
+
+    data_path = f'../../../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv'
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    actual_mdm = df['MDM']
+
+    thing_correctness = df['thing'] == df['p_thing']
+    property_correctness = df['property'] == df['p_property']
+    answer = thing_correctness & property_correctness
+
+    # if is non-MDM -> then should be unmapped
+    # if is MDM -> then should be mapped correctly
+
+    # out of correctly predicted relevant data, how many are mapped correctly?
+    correct_positive_mdm_and_map = sum(p_mdm & actual_mdm & answer)
+
+    # number of correctly predicted non-relevant data
+    correct_negative_mdm = sum(~(p_mdm) & ~(actual_mdm))
+
+    overall_correct = (correct_positive_mdm_and_map + correct_negative_mdm)/len(actual_mdm)
+    print(overall_correct)
+# %%
+for fold in [1,2,3,4,5]:
+    run(fold)
+
+# %%
+# check for "duplicates" in each ship
+# we want to enforce a unique mapping
+fold = 1
+
+data_path = f'exports/result_group_{fold}.csv'
+df = pd.read_csv(data_path, skipinitialspace=True)
+
+# get predicted mdm labels
+p_mdm = df['p_mdm'].to_numpy()
+predicted_mdm_mask = p_mdm.astype(bool)
+
+# %%
+# get the mapped data
+data_path = f'../../../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv'
+df = pd.read_csv(data_path, skipinitialspace=True)
+df['mapping'] = df['p_thing'] + ' ' + df['p_property']
+
+
+# get ship list
+ship_list = sorted(list(set(df['ships_idx'])))
+
+# assign ship
+ship = ship_list[1]
+
+ship_boolean_mask = df['ships_idx'] == ship
+
+# isolate predicted mdm data of the ship
+ship_predicted_mdm_mask = predicted_mdm_mask & ship_boolean_mask
+
+mapping_list = df['mapping'][ship_predicted_mdm_mask].to_list()
+
+mapping_count = {}
+
+for mapping in mapping_list:
+    if mapping in mapping_count:
+        mapping_count[mapping] = mapping_count[mapping] + 1
+    else:
+        mapping_count[mapping] = 1
+
+# print the mapping count
+mapping_count
+
+# %%
+# we can take one of the elements that exceeded 1 mapping and check
+df_ship = df[ship_predicted_mdm_mask]
+df_ship[df_ship['mapping'] == 'GeneratorEngine2 RunningState']
+# %%

post_process/binary_classifier/classification_prediction/output.txt

@@ -1,31 +1,51 @@
 
 ********************************************************************************
 Fold: 1
-Accuracy: 0.95174
-F1 Score: 0.90912
-Precision: 0.91788
-Recall: 0.90092
+tp: 1808
+tn: 10692
+fp: 269
+fn: 305
+Accuracy: 0.95610
+F1 Score: 0.86301
+Precision: 0.87049
+Recall: 0.85566
 ********************************************************************************
 Fold: 2
-Accuracy: 0.95159
-F1 Score: 0.92593
-Precision: 0.91697
-Recall: 0.93574
+tp: 1932
+tn: 8304
+fp: 278
+fn: 208
+Accuracy: 0.95467
+F1 Score: 0.88828
+Precision: 0.87421
+Recall: 0.90280
 ********************************************************************************
 Fold: 3
-Accuracy: 0.95373
-F1 Score: 0.93021
-Precision: 0.91935
-Recall: 0.94233
+tp: 1789
+tn: 7613
+fp: 250
+fn: 203
+Accuracy: 0.95403
+F1 Score: 0.88762
+Precision: 0.87739
+Recall: 0.89809
 ********************************************************************************
 Fold: 4
-Accuracy: 0.96524
-F1 Score: 0.92902
-Precision: 0.91306
-Recall: 0.94702
+tp: 1967
+tn: 12929
+fp: 420
+fn: 135
+Accuracy: 0.96408
+F1 Score: 0.87636
+Precision: 0.82405
+Recall: 0.93578
 ********************************************************************************
 Fold: 5
-Accuracy: 0.95643
-F1 Score: 0.92319
-Precision: 0.91793
-Recall: 0.92869
+tp: 1915
+tn: 10381
+fp: 405
+fn: 268
+Accuracy: 0.94811
+F1 Score: 0.85054
+Precision: 0.82543
+Recall: 0.87723

post_process/binary_classifier/classification_prediction/predict.py

@@ -27,6 +27,9 @@ from tqdm import tqdm
 
 torch.set_float32_matmul_precision('high')
 
+
+BATCH_SIZE = 256
+
 # %%
 
 # %%
@@ -158,7 +161,6 @@ def test(fold):
     actual_labels = []
 
 
-    BATCH_SIZE = 64
     dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False)
     for batch in tqdm(dataloader):
             # Inference in batches
@@ -181,6 +183,17 @@ def test(fold):
                 pred_labels.extend(predicted_class_ids)
 
     pred_labels = [tensor.item() for tensor in pred_labels]
+    pred_labels = np.array(pred_labels, dtype=bool)
+
+    # append the mdm prediction to the test_df for analysis later
+    df_out = pd.DataFrame({
+        'p_mdm': pred_labels, 
+    })
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+    df_export = pd.concat([test_df, df_out], axis=1)
+    df_export.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
 
 
     # %%
@@ -190,15 +203,23 @@ def test(fold):
 
     # 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')
+    f1 = f1_score(y_true, y_pred)
+    precision = precision_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred)
+
+    cm = confusion_matrix(y_true, y_pred)
+    tn, fp, fn, tp = cm.ravel()
 
     with open("output.txt", "a") as f:
 
+
         print('*' * 80, file=f)
         print(f'Fold: {fold}', file=f)
         # Print the results
+        print(f"tp: {tp}", file=f)
+        print(f"tn: {tn}", file=f)
+        print(f"fp: {fp}", file=f)
+        print(f"fn: {fn}", file=f)
         print(f'Accuracy: {accuracy:.5f}', file=f)
         print(f'F1 Score: {f1:.5f}', file=f)
         print(f'Precision: {precision:.5f}', file=f)

post_process/binary_classifier/train.py

@@ -104,8 +104,8 @@ def train(fold):
 
     # prepare tokenizer
 
-    model_checkpoint = "distilbert/distilbert-base-uncased"
-    # model_checkpoint = 'google-bert/bert-base-uncased'
+    model_checkpoint = "distilbert/distilbert-base-cased"
+    # model_checkpoint = 'google-bert/bert-base-cased'
     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>"]
@@ -180,13 +180,13 @@ def train(fold):
         # 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,
+        per_device_train_batch_size=128,
+        per_device_eval_batch_size=128,
         auto_find_batch_size=False,
         ddp_find_unused_parameters=False,
         weight_decay=0.01,
         save_total_limit=1,
-        num_train_epochs=40,
+        num_train_epochs=80,
         bf16=True,
         push_to_hub=False,
         remove_unused_columns=False,

post_process/de_duplication/.gitignore

@@ -0,0 +1,2 @@
+output*
+__pycache__

post_process/de_duplication/run.py

@@ -0,0 +1,313 @@
+# %%
+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 T5Embedder, BertEmbedder, cosine_similarity_chunked
+from tqdm import tqdm
+
+##################
+# global parameters
+DIAGNOSTIC = False
+BATCH_SIZE = 1024
+
+###################
+# 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"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                name = f"<NAME>{row['tag_name']}<NAME>"
+                element = f"{desc}{unit}{name}"
+                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 = T5Embedder(train_data, checkpoint_path)
+        # embedder = BertEmbedder(train_data, checkpoint_path)
+        embedder.make_embedding(batch_size=BATCH_SIZE)
+        return embedder.embeddings
+
+
+
+
+# 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
+# we then map the local idx to the ship-level 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
+    # returns a potential 2d matrix of which columns have the highest values
+    # top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:]  # Get indices of top k values
+    # this partial sorts and ensures we care only top_k are correctly sorted
+    top_k_indices = np.argpartition(subset_matrix, -top_k, axis=1)[:, -top_k:]
+    
+    # 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]
+
+    # convert boolean to indices
+    condition_indices = np.where(source_mask)[0]
+    max_idx = condition_indices[max_idx]
+    
+
+    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_df['mapping'] = full_df['thing'] + ' ' + full_df['property']
+full_mdm_mapping_list = sorted(list((set(full_df['mapping']))))
+
+
+#####################
+# fold level
+
+def run_selection(fold):
+
+    # set the fold
+    # import test data
+    # data_path = f"../binary_classifier/classification_prediction/exports/result_group_{fold}.csv"
+    data_path = f"../similarity_classifier/exports/result_group_{fold}.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    predicted_mdm = df['p_mdm'].to_numpy().astype(bool)
+
+    data_path = f"../../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df['p_mdm'] = predicted_mdm
+    df['p_mapping'] = 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)
+    train_df['mapping'] = train_df['thing'] + " " + train_df['property']
+
+    # generate your embeddings
+    # checkpoint_directory defined at global level
+    # checkpoint_directory = "../../train/classification_bert_pattern_desc_unit"
+    checkpoint_directory = "../../train/mapping_t5_complete_desc_unit_name"
+    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)
+
+    # generate new embeddings for each ship
+    test_embedder = Embedder(input_df=df)
+    global_test_embeds = test_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)
+
+    #############################
+    # ship level
+    # we have to split into per-ship analysis
+    ships_list = sorted(list(set(df['ships_idx'])))
+
+    for ship_idx in tqdm(ships_list):
+        # 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()
+
+        # we want to subset the ship and only p_mdm values
+        ship_mask = df['ships_idx'] == ship_idx
+        p_mdm_mask = df['p_mdm']
+        map_local_index_to_global_index = np.where(ship_mask & p_mdm_mask)[0]
+        ship_df = df[ship_mask & p_mdm_mask].reset_index(drop=True)
+
+        # subset the test embeds
+        test_embeds = global_test_embeds[map_local_index_to_global_index]
+
+        # generate the cosine sim matrix for the ship level
+        cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=1024).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 to map answers back to the global answer list
+
+        # initialize the local answer list
+        ship_answer_list = np.ones(len(ship_df), dtype=bool)
+
+        ###########
+        # STEP 1A: ensure that the predicted mapping labels are valid
+        pattern_match_mask = ship_df['p_mapping'].apply(lambda x: x in full_mdm_mapping_list).to_numpy()
+        pattern_match_mask = pattern_match_mask.astype(bool)
+        # anything not in the pattern_match_mask are hallucinations
+        # this has the same effect as setting any wrong generations as non-mdm
+        ship_answer_list[~pattern_match_mask] = False
+
+        # # STEP 1B: subset our de-duplication to use only predicted_mdm labels
+        # p_mdm_mask = ship_df['p_mdm']
+        # # assign false to any non p_mdm entries
+        # ship_answer_list[~p_mdm_mask] = False
+        # # modify pattern_match_mask to remove any non p_mdm values
+        # pattern_match_mask = pattern_match_mask & p_mdm_mask
+
+        ###########
+        # 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_mapping'][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):
+
+            # create local copy of answer_list
+            ship_answer_list = answer_list.copy()
+            # sample_df = ship_df[ship_df['p_mapping'] == select_class]
+
+
+            # we need to set all idx of chosen entries as False in answer_list -> assume wrong by default
+            # selected_idx_list = sample_df.index.to_numpy()
+            selected_idx_list = np.where(ship_df['p_mapping'] == select_class)[0]
+
+            # basic assumption check
+
+            # 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_mask = ship_df['p_mapping'] == select_class
+            # we make candidates to compare against in the data sharing the same class
+            train_candidates_mask = train_df['mapping'] == select_class
+
+            if sum(train_candidates_mask) == 0:
+                # it can be the case that the mdm-valid mapping class is not found in training data
+                # print("not found in training data", select_class)
+                ship_answer_list[selected_idx_list] = False
+                return ship_answer_list
+
+            # perform selection
+            # max_idx is the id
+            max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask)
+
+
+            # set the duplicate entries to False
+            ship_answer_list[selected_idx_list] = False
+            # then only set the one unique chosen value as True
+            ship_answer_list[max_idx] = True
+
+            return ship_answer_list 
+
+        # we choose one mdm class
+        for select_class in ship_predicted_classes:
+            # this resulted in big improvement
+            if (sum(ship_df['p_mapping'] == select_class)) > 0:
+                ship_answer_list = selection_for_class(select_class, cos_sim_matrix, ship_answer_list)
+
+        # we want to write back to global_answer
+        # first we convert local indices to global indices
+        ship_local_indices = np.where(ship_answer_list)[0]
+        ship_global_indices = map_local_index_to_global_index[ship_local_indices]
+        global_answer[ship_global_indices] = True
+
+
+        if DIAGNOSTIC:
+            # evaluation at per-ship level
+            y_true = ship_df['MDM'].to_list()
+            y_pred = ship_answer_list
+            tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
+            print(f"tp: {tp}")
+            print(f"tn: {tn}")
+            print(f"fp: {fp}")
+            print(f"fn: {fn}")
+
+            # Compute metrics
+            accuracy = accuracy_score(y_true, y_pred)
+            f1 = f1_score(y_true, y_pred)
+            precision = precision_score(y_true, y_pred)
+            recall = recall_score(y_true, y_pred)
+
+            # Print the results
+            print(f'Accuracy: {accuracy:.5f}')
+            print(f'F1 Score: {f1:.5f}')
+            print(f'Precision: {precision:.5f}')
+            print(f'Recall: {recall:.5f}')
+
+
+
+    with open("output.txt", "a") as f:
+        print(80 * '*', file=f)
+        print(f'Statistics for fold {fold}', file=f)
+
+        y_true = df['MDM'].to_list()
+        y_pred = global_answer
+
+        tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
+        print(f"tp: {tp}", file=f)
+        print(f"tn: {tn}", file=f)
+        print(f"fp: {fp}", file=f)
+        print(f"fn: {fn}", file=f)
+
+        # compute metrics
+        accuracy = accuracy_score(y_true, y_pred)
+        f1 = f1_score(y_true, y_pred)
+        precision = precision_score(y_true, y_pred)
+        recall = recall_score(y_true, y_pred)
+
+        # print the results
+        print(f'accuracy: {accuracy:.5f}', file=f)
+        print(f'f1 score: {f1:.5f}', file=f)
+        print(f'Precision: {precision:.5f}', file=f)
+        print(f'Recall: {recall:.5f}', file=f)
+
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+# %%
+for fold in [1,2,3,4,5]:
+    print(f'Perform selection for fold {fold}')
+    run_selection(fold)
+
+
+# %%

post_process/de_duplication/utils.py

@@ -0,0 +1,132 @@
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    AutoModelForSeq2SeqLM,
+    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=120)
+            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
+
+class T5Embedder:
+    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_embedding(self, batch_size=128):
+        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=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
+

post_process/selection/.gitignore

@@ -1,2 +1 @@
-__pycache__
-output.txt
+__pycache__

post_process/selection/output.txt

@@ -0,0 +1,41 @@
+
+tp: 1738
+tn: 10744
+fp: 217
+fn: 375
+accuracy: 0.95472
+f1 score: 0.85447
+Precision: 0.88900
+Recall: 0.82253
+tp: 1794
+tn: 8302
+fp: 280
+fn: 346
+accuracy: 0.94162
+f1 score: 0.85145
+Precision: 0.86500
+Recall: 0.83832
+tp: 1755
+tn: 7598
+fp: 265
+fn: 237
+accuracy: 0.94906
+f1 score: 0.87488
+Precision: 0.86881
+Recall: 0.88102
+tp: 1911
+tn: 13079
+fp: 270
+fn: 191
+accuracy: 0.97016
+f1 score: 0.89237
+Precision: 0.87620
+Recall: 0.90913
+tp: 1826
+tn: 10540
+fp: 246
+fn: 357
+accuracy: 0.95350
+f1 score: 0.85828
+Precision: 0.88127
+Recall: 0.83646

post_process/selection/run.py

@@ -0,0 +1,299 @@
+# %%
+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 T5Embedder, BertEmbedder, cosine_similarity_chunked
+from tqdm import tqdm
+
+##################
+# global parameters
+DIAGNOSTIC = False
+THRESHOLD = 0.95
+BATCH_SIZE = 1024
+
+###################
+# 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"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                name = f"<NAME>{row['tag_name']}<NAME>"
+                element = f"{desc}{unit}{name}"
+                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 = T5Embedder(train_data, checkpoint_path)
+        # embedder = BertEmbedder(train_data, checkpoint_path)
+        embedder.make_embedding(batch_size=BATCH_SIZE)
+        return embedder.embeddings
+
+
+
+
+# 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
+# we then map the local idx to the ship-level 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
+    # returns a potential 2d matrix of which columns have the highest values
+    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]
+
+    # convert boolean to indices
+    condition_indices = np.where(source_mask)[0]
+    max_idx = condition_indices[max_idx]
+    
+
+    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_df['mapping'] = full_df['thing'] + ' ' + full_df['property']
+full_mdm_mapping_list = sorted(list((set(full_df['mapping']))))
+
+
+#####################
+# fold level
+
+def run_selection(fold):
+
+    # set the fold
+    # import test data
+    data_path = f"../../train/mapping_t5_complete_desc_unit_name/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['p_mapping'] = 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)
+    train_df['mapping'] = train_df['thing'] + " " + train_df['property']
+
+    # generate your embeddings
+    # checkpoint_directory defined at global level
+    # checkpoint_directory = "../../train/classification_bert_pattern_desc_unit"
+    checkpoint_directory = "../../train/mapping_t5_complete_desc_unit_name"
+    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)
+
+    # generate new embeddings for each ship
+    test_embedder = Embedder(input_df=df)
+    global_test_embeds = test_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)
+
+    #############################
+    # ship level
+    # we have to split into per-ship analysis
+    ships_list = sorted(list(set(df['ships_idx'])))
+
+    for ship_idx in tqdm(ships_list):
+        # 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()
+        map_local_index_to_global_index = np.where(df['ships_idx'] == ship_idx)[0]
+        ship_df = df[df['ships_idx'] == ship_idx].reset_index(drop=True)
+
+        # subset the test embeds
+        test_embeds = global_test_embeds[map_local_index_to_global_index]
+
+        # generate the cosine sim matrix for the ship level
+        cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=1024).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 to map answers back to the global answer list
+
+        # initialize the local answer list
+        ship_answer_list = np.ones(len(ship_df), dtype=bool)
+
+        ###########
+        # 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_mapping'].apply(lambda x: x in full_mdm_mapping_list).to_numpy()
+        pattern_match_mask = pattern_match_mask.astype(bool)
+        # anything not in the pattern_match_mask are hallucinations
+        # this has the same effect as setting any wrong generations as non-mdm
+        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_mapping'][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):
+
+            # create local copy of answer_list
+            ship_answer_list = answer_list.copy()
+            # sample_df = ship_df[ship_df['p_mapping'] == select_class]
+
+
+            # we need to set all idx of chosen entries as False in answer_list -> assume wrong by default
+            # selected_idx_list = sample_df.index.to_numpy()
+            selected_idx_list = np.where(ship_df['p_mapping'] == select_class)[0]
+
+            # basic assumption check
+
+            # 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_mask = ship_df['p_mapping'] == select_class
+            # we make candidates to compare against in the data sharing the same class
+            train_candidates_mask = train_df['mapping'] == select_class
+
+            if sum(train_candidates_mask) == 0:
+                # it can be the case that the mdm-valid mapping class is not found in training data
+                # print("not found in training data", select_class)
+                ship_answer_list[selected_idx_list] = False
+                return ship_answer_list
+
+            # perform selection
+            # max_idx is the id
+            max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask)
+
+
+            # set the duplicate entries to False
+            ship_answer_list[selected_idx_list] = False
+            # before doing this, we have to use the max_score and evaluate if its close enough
+            if max_score > THRESHOLD:
+                ship_answer_list[max_idx] = True
+
+            return ship_answer_list 
+
+        # we choose one mdm class
+        for select_class in ship_predicted_classes:
+            # this resulted in big improvement
+            if (sum(ship_df['p_mapping'] == select_class)) > 0:
+                ship_answer_list = selection_for_class(select_class, cos_sim_matrix, ship_answer_list)
+
+        # we want to write back to global_answer
+        # first we convert local indices to global indices
+        ship_local_indices = np.where(ship_answer_list)[0]
+        ship_global_indices = map_local_index_to_global_index[ship_local_indices]
+        global_answer[ship_global_indices] = True
+
+
+        if DIAGNOSTIC:
+            # evaluation at per-ship level
+            y_true = ship_df['MDM'].to_list()
+            y_pred = ship_answer_list
+            tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
+            print(f"tp: {tp}")
+            print(f"tn: {tn}")
+            print(f"fp: {fp}")
+            print(f"fn: {fn}")
+
+            # Compute metrics
+            accuracy = accuracy_score(y_true, y_pred)
+            f1 = f1_score(y_true, y_pred)
+            precision = precision_score(y_true, y_pred)
+            recall = recall_score(y_true, y_pred)
+
+            # Print the results
+            print(f'Accuracy: {accuracy:.5f}')
+            print(f'F1 Score: {f1:.5f}')
+            print(f'Precision: {precision:.5f}')
+            print(f'Recall: {recall:.5f}')
+
+
+
+    with open("output.txt", "a") as f:
+        print(80 * '*', file=f)
+        print(f'Statistics for fold {fold}', file=f)
+
+        y_true = df['MDM'].to_list()
+        y_pred = global_answer
+
+        tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
+        print(f"tp: {tp}", file=f)
+        print(f"tn: {tn}", file=f)
+        print(f"fp: {fp}", file=f)
+        print(f"fn: {fn}", file=f)
+
+        # compute metrics
+        accuracy = accuracy_score(y_true, y_pred)
+        f1 = f1_score(y_true, y_pred)
+        precision = precision_score(y_true, y_pred)
+        recall = recall_score(y_true, y_pred)
+
+        # print the results
+        print(f'accuracy: {accuracy:.5f}', file=f)
+        print(f'f1 score: {f1:.5f}', file=f)
+        print(f'Precision: {precision:.5f}', file=f)
+        print(f'Recall: {recall:.5f}', file=f)
+
+
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+
+# %%
+for fold in [1,2,3,4,5]:
+    print(f'Perform selection for fold {fold}')
+    run_selection(fold)

post_process/selection/utils.py

@@ -1,12 +1,56 @@
 import torch
-from tqdm import tqdm
-from transformers import AutoTokenizer
-from transformers import AutoModelForSeq2SeqLM
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    AutoModelForSeq2SeqLM,
+    DataCollatorWithPadding,
+)
 import torch.nn.functional as F
 
 
 
-class Retriever:
+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=120)
+            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
+
+class T5Embedder:
     def __init__(self, input_texts, model_checkpoint):
         # we need to generate the embedding from list of input strings
         self.embeddings = []
@@ -14,7 +58,7 @@ class Retriever:
         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>"]
+        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})
 
@@ -27,7 +71,7 @@ class Retriever:
 
 
 
-    def make_mean_embedding(self, batch_size=32):
+    def make_embedding(self, batch_size=128):
         all_embeddings = self.embeddings
         input_texts = self.inputs
 
@@ -54,6 +98,7 @@ class Retriever:
 
         self.embeddings = all_embeddings
 
+
 def cosine_similarity_chunked(batch1, batch2, chunk_size=1024):
     device = 'cuda'
     batch1_size = batch1.size(0)

post_process/selection_old/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+output.txt

post_process/selection_old/predict.py

@@ -1,13 +1,14 @@
+# %%
 import pandas as pd
 import os
 import glob
 
 # directory for checkpoints
-checkpoint_directory =  '../../train/mapping_with_unit'
+checkpoint_directory =  '../../train/mapping_t5_complete_desc_unit_name'
 
 def select(fold):
     # import test data
-    data_path = f"../../train/mapping_with_unit/mapping_prediction/exports/result_group_{fold}.csv"
+    data_path = f"../../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv"
     df = pd.read_csv(data_path, skipinitialspace=True)
 
     # get target data
@@ -91,3 +92,5 @@ with open("output.txt", "w") as f:
 
 for fold in [1,2,3,4,5]:
     select(fold)
+
+# %%

post_process/selection_old/selection.py

@@ -4,6 +4,12 @@ from typing import List
 from tqdm import tqdm
 from utils import Retriever, cosine_similarity_chunked
 
+
+# global parameters
+THRESHOLD = 0.95
+BATCH_SIZE = 512
+
+# 
 class Selector():
     input_df: pd.DataFrame
     reference_df: pd.DataFrame
@@ -22,10 +28,10 @@ class Selector():
         def generate_input_list(df):
             input_list = []
             for _, row in df.iterrows():
-                # name = f"<NAME>{row['tag_name']}<NAME>"
+                name = f"<NAME>{row['tag_name']}<NAME>"
                 desc = f"<DESC>{row['tag_description']}<DESC>"
-                # element = f"{name}{desc}"
-                element = f"{desc}"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = f"{name}{desc}{unit}"
                 input_list.append(element)
             return input_list
 
@@ -58,13 +64,13 @@ class Selector():
         train_data = list(generate_input_list(self.reference_df))
         # Define the directory and the pattern
         retriever_train = Retriever(train_data, checkpoint_path)
-        retriever_train.make_mean_embedding(batch_size=64)
+        retriever_train.make_mean_embedding(batch_size=BATCH_SIZE)
         train_embed = retriever_train.embeddings
 
         # take the inputs for df_sub
         test_data = list(generate_input_list(self.input_df))
         retriever_test = Retriever(test_data, checkpoint_path)
-        retriever_test.make_mean_embedding(batch_size=64)
+        retriever_test.make_mean_embedding(batch_size=BATCH_SIZE)
         test_embed = retriever_test.embeddings
 
 
@@ -75,7 +81,6 @@ class Selector():
         tn_accumulate = 0
         fp_accumulate = 0
         fn_accumulate = 0
-        THRESHOLD = 0.9
         for ship_idx in self.ships_list:
             print(ship_idx)
             # we select a ship and select only data exhibiting MDM pattern in the predictions
@@ -119,6 +124,7 @@ class Selector():
                     all_idx_list.append(max_idx)
                     similarity_score.append(max_score)
                     # implement thresholding
+                    print(max_score)
                     if max_score > THRESHOLD:
                         selected_idx_list.append(max_idx)
 

post_process/selection_old/utils.py

@@ -0,0 +1,87 @@
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM
+import torch.nn.functional as F
+
+BATCH_SIZE = 128
+
+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=BATCH_SIZE):
+        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=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
+

post_process/similarity_classifier/.gitignore

@@ -1 +1,3 @@
 __pycache__
+exports
+output.txt

post_process/similarity_classifier/output.txt

@@ -1,31 +1,31 @@
 
 Fold: 1
-Best threshold: 0.9775
-Accuracy: 0.92512
-F1 Score: 0.76313
-Precision: 0.78069
-Recall: 0.74633
+Best threshold: 0.9
+Accuracy: 0.89804
+F1 Score: 0.74986
+Precision: 0.62127
+Recall: 0.94558
 Fold: 2
-Best threshold: 0.9775
-Accuracy: 0.92054
-F1 Score: 0.81117
-Precision: 0.77150
-Recall: 0.85514
+Best threshold: 0.9
+Accuracy: 0.86719
+F1 Score: 0.73213
+Precision: 0.61272
+Recall: 0.90935
 Fold: 3
-Best threshold: 0.985
-Accuracy: 0.93201
-F1 Score: 0.83578
-Precision: 0.81657
-Recall: 0.85592
+Best threshold: 0.9
+Accuracy: 0.86941
+F1 Score: 0.74849
+Precision: 0.61280
+Recall: 0.96135
 Fold: 4
-Best threshold: 0.9924999999999999
-Accuracy: 0.95334
-F1 Score: 0.82722
-Precision: 0.83341
-Recall: 0.82112
+Best threshold: 0.9
+Accuracy: 0.86325
+F1 Score: 0.65826
+Precision: 0.49865
+Recall: 0.96813
 Fold: 5
-Best threshold: 0.9924999999999999
-Accuracy: 0.92968
-F1 Score: 0.77680
-Precision: 0.83395
-Recall: 0.72698
+Best threshold: 0.9
+Accuracy: 0.84147
+F1 Score: 0.66416
+Precision: 0.51612
+Recall: 0.93129

post_process/similarity_classifier/run.py

@@ -110,27 +110,41 @@ def run_similarity_classifier(fold):
         sim_list.append(top_sim_value)
 
     # analysis 1: using threshold to perform find-back prediction success
-    threshold_values = np.linspace(0.85, 1.00, 21) # test 20 values, 21 to get nice round numbers
-    best_threshold = 0
-    best_f1 = 0
-    for threshold in threshold_values:
-        predict_list = [ elem > threshold for elem in sim_list ]
+    # threshold_values = np.linspace(0.85, 1.00, 21) # test 20 values, 21 to get nice round numbers
+    # best_threshold = 0
+    # best_f1 = 0
+    # for threshold in threshold_values:
+    #     predict_list = [ elem > threshold for elem in sim_list ]
 
-        y_true = test_df['MDM'].to_list()
-        y_pred = predict_list
+    #     y_true = test_df['MDM'].to_list()
+    #     y_pred = predict_list
 
-        # Compute metrics
-        accuracy = accuracy_score(y_true, y_pred)
-        f1 = f1_score(y_true, y_pred)
-        precision = precision_score(y_true, y_pred)
-        recall = recall_score(y_true, y_pred)
+    #     # Compute metrics
+    #     accuracy = accuracy_score(y_true, y_pred)
+    #     f1 = f1_score(y_true, y_pred)
+    #     precision = precision_score(y_true, y_pred)
+    #     recall = recall_score(y_true, y_pred)
 
-        if f1 > best_f1:
-            best_threshold = threshold
-            best_f1 = f1
+    #     if f1 > best_f1:
+    #         best_threshold = threshold
+    #         best_f1 = f1
+    
+    # just manually set best_threshold
+    best_threshold = 0.90
 
     # compute metrics again with best threshold
     predict_list = [ elem > best_threshold for elem in sim_list ]
+
+    # save
+    pred_labels = np.array(predict_list, dtype=bool)
+
+    # append the mdm prediction to the test_df for analysis later
+    df_out = pd.DataFrame({
+        'p_mdm': pred_labels, 
+    })
+    df_out.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+
     y_true = test_df['MDM'].to_list()
     y_pred = predict_list
     # Compute metrics

train/classification_bert_complete_desc_unit_name/classification_prediction/output.txt

@@ -1,31 +1,31 @@
 
 ********************************************************************************
 Fold: 1
-Accuracy: 0.68859
-F1 Score: 0.62592
-Precision: 0.60775
-Recall: 0.68859
+Accuracy: 0.77142
+F1 Score: 0.70728
+Precision: 0.67509
+Recall: 0.77142
 ********************************************************************************
 Fold: 2
-Accuracy: 0.72150
-F1 Score: 0.65739
-Precision: 0.63652
-Recall: 0.72150
+Accuracy: 0.74065
+F1 Score: 0.68315
+Precision: 0.66680
+Recall: 0.74065
 ********************************************************************************
 Fold: 3
-Accuracy: 0.72038
-F1 Score: 0.65781
-Precision: 0.63249
-Recall: 0.72038
+Accuracy: 0.74849
+F1 Score: 0.68717
+Precision: 0.65975
+Recall: 0.74849
 ********************************************************************************
 Fold: 4
-Accuracy: 0.74167
-F1 Score: 0.68167
-Precision: 0.65489
-Recall: 0.74167
+Accuracy: 0.71836
+F1 Score: 0.65179
+Precision: 0.63155
+Recall: 0.71836
 ********************************************************************************
 Fold: 5
-Accuracy: 0.67705
-F1 Score: 0.61273
-Precision: 0.59472
-Recall: 0.67705
+Accuracy: 0.71461
+F1 Score: 0.65512
+Precision: 0.63375
+Recall: 0.71461

train/train.bash

@@ -1,12 +1,12 @@
 #!/bin/bash
 
-# cd classification_bert_complete_desc
-# micromamba run -n hug accelerate launch train.py
-# cd ..
-# 
-# cd classification_bert_complete_desc_unit
-# micromamba run -n hug accelerate launch train.py
-# cd ..
+cd classification_bert_complete_desc
+micromamba run -n hug accelerate launch train.py
+cd ..
+
+cd classification_bert_complete_desc_unit
+micromamba run -n hug accelerate launch train.py
+cd ..
 
 cd classification_bert_complete_desc_unit_name
 micromamba run -n hug accelerate launch train.py
@@ -22,4 +22,4 @@ cd ..
 # 
 # cd mapping_t5_complete_name_desc_unit
 # micromamba run -n hug accelerate launch train.py
-# cd ..
+# cd ..