308 lines
11 KiB
Python
308 lines
11 KiB
Python
# %%
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import pandas as pd
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import os
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import glob
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
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import numpy as np
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from utils import BertEmbedder, cosine_similarity_chunked
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from fuzzywuzzy import fuzz
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##################
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# global parameters
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DIAGNOSTIC = False
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THRESHOLD = 0.85
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FUZZY_SIM_THRESHOLD=95
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checkpoint_directory = "../../train/classification_bert_desc"
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###################
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# %%
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# helper functions
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class Embedder():
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input_df: pd.DataFrame
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fold: int
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def __init__(self, input_df):
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self.input_df = input_df
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def make_embedding(self, checkpoint_path):
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def generate_input_list(df):
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input_list = []
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for _, row in df.iterrows():
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desc = f"{row['tag_description']}"
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element = f"{desc}"
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input_list.append(element)
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return input_list
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# prepare reference embed
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train_data = list(generate_input_list(self.input_df))
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# Define the directory and the pattern
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embedder = BertEmbedder(train_data, checkpoint_path)
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embedder.make_embedding(batch_size=64)
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return embedder.embeddings.to('cpu')
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# the purpose of this function is to group the strings that are similar to each other
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# we need to form related groups of inputs
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def group_similar_strings(obj_list, threshold=80):
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groups = []
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processed_strings = set() # To keep track of already grouped strings
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for obj in obj_list:
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# tuple is (idx, string)
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if obj in processed_strings:
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continue
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# Find all strings similar to the current string above the threshold
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similar_strings = [s for s in obj_list if s[1] != obj[1] and fuzz.ratio(obj[1], s[1]) >= threshold]
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# Add the original string to the similar group
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similar_group = [obj] + similar_strings
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# Mark all similar strings as processed
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processed_strings.update(similar_group)
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# Add the group to the list of groups
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groups.append(similar_group)
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return groups
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# this function takes in groups of related terms and create candidate entries
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def make_candidates(groups):
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candidates = []
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for group in groups:
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first_tuple = group[0]
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# string_of_tuple = first_tuple[1]
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id_of_tuple = first_tuple[0]
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candidates.append(id_of_tuple)
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return candidates
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# the selection function takes in the full cos_sim_matrix then subsets the
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# matrix according to the test_candidates_mask and train_candidates_mask that we
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# give it
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# it returns the most likely source candidate index and score among the source
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# candidate list - aka it returns a local idx
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def selection(cos_sim_matrix, source_mask, target_mask):
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# subset_matrix = cos_sim_matrix[condition_source]
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# except we are subsetting 2D matrix (row, column)
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subset_matrix = cos_sim_matrix[np.ix_(source_mask, target_mask)]
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# we select top-k here
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# Get the indices of the top-k maximum values along axis 1
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top_k = 1
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top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
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# Get the values of the top 5 maximum scores
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top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
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# Calculate the average of the top-k scores along axis 1
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y_scores = np.mean(top_k_values, axis=1)
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max_idx = np.argmax(y_scores)
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max_score = y_scores[max_idx]
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return max_idx, max_score
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####################
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# global level
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# %%
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# obtain the full mdm_list
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data_path = '../../data_import/exports/data_mapping_mdm.csv'
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full_df = pd.read_csv(data_path, skipinitialspace=True)
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full_mdm_pattern_list = sorted(list((set(full_df['pattern']))))
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#####################
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# fold level
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def run_selection(fold):
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# set the fold
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# import test data
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data_path = f"../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv"
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df = pd.read_csv(data_path, skipinitialspace=True)
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df['p_pattern'] = df['p_thing'] + " " + df['p_property']
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# get target data
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data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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# generate your embeddings
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# checkpoint_directory defined at global level
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directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
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# Use glob to find matching paths
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# path is usually checkpoint_fold_1/checkpoint-<step number>
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# we are guaranteed to save only 1 checkpoint from training
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pattern = 'checkpoint-*'
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checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
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# we can generate the train embeddings once and re-use for every ship
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train_embedder = Embedder(input_df=train_df)
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train_embeds = train_embedder.make_embedding(checkpoint_path)
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# create global_answer array
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# the purpose of this array is to track the classification state at the global
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# level
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global_answer = np.zeros(len(df), dtype=bool)
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#############################
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# ship level
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# we have to split into per-ship analysis
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ships_list = sorted(list(set(df['ships_idx'])))
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for ship_idx in ships_list:
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# ship_idx = 1001 # choose an example ship
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ship_df = df[df['ships_idx'] == ship_idx]
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# required to map local ship_answer array to global_answer array
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map_local_index_to_global_index = ship_df.index.to_numpy()
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ship_df = df[df['ships_idx'] == ship_idx].reset_index(drop=True)
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# generate new embeddings for each ship
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test_embedder = Embedder(input_df=ship_df)
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test_embeds = test_embedder.make_embedding(checkpoint_path)
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# generate the cosine sim matrix
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cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=8).cpu().numpy()
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##############################
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# selection level
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# The general idea:
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# step 1: keep only pattern generations that belong to mdm list
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# -> this removes totally wrong datasets that mapped to totally wrong things
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# step 2: loop through the mdm list and isolate data in both train and test that
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# belong to the same pattern class
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# -> this is more tricky, because we have non-mdm mapping to correct classes
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# -> so we have to find which candidate is most similar to the training data
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# it is very tricky to keep track of classification across multiple stages so we
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# will use a boolean answer list
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# initialize the local answer list
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ship_answer_list = np.ones(len(ship_df), dtype=bool)
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###########
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# STEP 1
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# we want to loop through the generated class labels and find which ones match
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# our pattern list
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pattern_match_mask = ship_df['p_pattern'].apply(lambda x: x in full_mdm_pattern_list).to_numpy()
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# we assign only those that are False to our answer list
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# right now the 2 arrays are basically equal
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ship_answer_list[~pattern_match_mask] = False
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###########
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# STEP 2
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# we now go through each class found in our generated set
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# we want to identify per-ship mdm classes
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ship_predicted_classes = sorted(set(ship_df['p_pattern'][pattern_match_mask].to_list()))
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# this function performs the selection given a class
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# it takes in the cos_sim_matrix
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# it returns the selection by mutating the answer_list
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# it sets all relevant idxs to False initially, then sets the selected values to True
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def selection_for_class(select_class, cos_sim_matrix, answer_list):
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# separate the global variable from function variable
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answer_list = answer_list.copy()
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sample_df = ship_df[ship_df['p_pattern'] == select_class]
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# we need to set all idx of chosen entries as False in answer_list
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selected_idx_list = sample_df.index.to_list()
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answer_list[selected_idx_list] = False
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# basic assumption check
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# group related inputs by description similarity
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string_list = sample_df['tag_description'].to_list()
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index_list = sample_df.index.to_list()
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obj_list = list(zip(index_list, string_list))
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# groups is a list of list, where each list is composed of a
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# (idx, string) tuple
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groups = group_similar_strings(obj_list, threshold=FUZZY_SIM_THRESHOLD)
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# generate the masking arrays for both test and train embeddings
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# we select a tuple from each group, and use that as a candidate for selection
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test_candidates = make_candidates(groups)
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test_candidates_mask = np.zeros(len(ship_df), dtype=bool)
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test_candidates_mask[test_candidates] = True
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# we make candidates to compare against in the data sharing the same class
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train_candidates_mask = (train_df['pattern'] == select_class).to_numpy()
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# perform selection
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# it returns the group index that is most likely
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max_idx, max_score = selection(cos_sim_matrix, test_candidates_mask, train_candidates_mask)
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# consolidate all idx's in the same group
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chosen_group = groups[max_idx]
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chosen_idx_list = [tuple[0] for tuple in chosen_group]
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# before doing this, we have to use the max_score and evaluate if its close enough
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if max_score > THRESHOLD:
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answer_list[chosen_idx_list] = True
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return answer_list
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# we choose one mdm class
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for select_class in ship_predicted_classes:
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ship_answer_list = selection_for_class(select_class, cos_sim_matrix, ship_answer_list)
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# we want to write back to global_answer
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# first we convert local indices to global indices
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local_indices = np.where(ship_answer_list)[0]
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global_indices = map_local_index_to_global_index[local_indices]
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global_answer[global_indices] = True
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if DIAGNOSTIC:
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# evaluation at per-ship level
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y_true = ship_df['MDM'].to_list()
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y_pred = ship_answer_list
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# Compute metrics
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accuracy = accuracy_score(y_true, y_pred)
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f1 = f1_score(y_true, y_pred, average='macro')
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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# Print the results
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print(f'Accuracy: {accuracy:.5f}')
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print(f'F1 Score: {f1:.5f}')
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print(f'Precision: {precision:.5f}')
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print(f'Recall: {recall:.5f}')
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y_true = df['MDM'].to_list()
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y_pred = global_answer
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tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
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print(f"tp: {tp}")
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print(f"tn: {tn}")
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print(f"fp: {fp}")
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print(f"fn: {fn}")
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# Compute metrics
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accuracy = accuracy_score(y_true, y_pred)
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f1 = f1_score(y_true, y_pred, average='macro')
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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# Print the results
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print(f'Accuracy: {accuracy:.5f}')
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print(f'F1 Score: {f1:.5f}')
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print(f'Precision: {precision:.5f}')
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print(f'Recall: {recall:.5f}')
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# %%
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for fold in [1,2,3,4,5]:
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print(f'Perform selection for fold {fold}')
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run_selection(fold)
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# %%
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