added clean code for NMF

Author: lksenel

__pycache__/utils.cpython-38.pyc

@@ -0,0 +1,69 @@
+from utils import LOG, setup_dict_entry, calc_and_update_score, load_gold, get_verse_alignments
+import argparse
+import os
+
+def main(args):
+    pros, surs = load_gold(args.gold_file)
+
+    save_name = os.path.basename(args.predicted_alignments_file)
+    save_name = save_name[:-4] + "_results.txt"
+
+    result_names = [
+        "base_intersection",
+        "base_gdfa",
+        "NMF",
+        "NMF + intersection",
+        "NMF + gdfa"
+    ]
+
+    results_all = {}
+    for name in result_names:
+        setup_dict_entry(results_all, name, {"p_hit_count": 0, "s_hit_count": 0, "total_hit_count": 0, "gold_s_hit_count": 0, "prec": 0, "rec": 0, "f1": 0, "aer": 0})
+
+    with open(args.predicted_alignments_file, 'r') as f_pred, \
+        open(args.intersection_alignments_file, 'r') as f_inter, \
+        open(args.gdfa_alignments_file, 'r') as f_gdfa:
+        
+        lines_predicted = f_pred.read().splitlines()
+        lines_intersection = f_inter.read().splitlines()
+        lines_gdfa = f_gdfa.read().splitlines()
+
+        for no, (line_pred, line_inter, line_gdfa) in enumerate(zip(lines_predicted, lines_intersection, lines_gdfa)):
+            verse_id, aligns_predicted = line_pred.split('\t')
+            _, aligns_intersection = line_inter.split('\t')
+            _, aligns_gdfa = line_gdfa.split('\t')
+            
+            # Convert string alignments to set
+            aligns_predicted =  set(aligns_predicted.split(' '))
+            aligns_intersection = set(aligns_intersection.split(' '))
+            aligns_gdfa = set(aligns_gdfa.split(' '))
+
+            # combine base alignments with predictions
+            aligns_NMF_plus_intersection = aligns_predicted.union(aligns_intersection)
+            aligns_NMF_plus_gdfa = aligns_predicted.union(aligns_gdfa)
+
+            # update results for all alignments
+            calc_and_update_score(aligns_intersection, pros[verse_id], surs[verse_id], results_all["base_intersection"])
+            calc_and_update_score(aligns_gdfa, pros[verse_id], surs[verse_id], results_all["base_gdfa"])
+            calc_and_update_score(aligns_predicted, pros[verse_id], surs[verse_id], results_all["NMF"])
+            calc_and_update_score(aligns_NMF_plus_intersection, pros[verse_id], surs[verse_id], results_all["NMF + intersection"])
+            calc_and_update_score(aligns_NMF_plus_gdfa, pros[verse_id], surs[verse_id], results_all["NMF + gdfa"]) 
+
+    with open(os.path.join(args.save_path, save_name), 'w') as f_out:
+        for i in results_all:
+            f_out.write(f'----{i}----\nPrecision: {results_all[i]["prec"]}\nRecall: {results_all[i]["rec"]}\nF1: {results_all[i]["f1"]}\nAER: {results_all[i]["aer"]}\nHits: {results_all[i]["total_hit_count"]}\n\n')
+            print(f'----{i}----\nPrecision: {results_all[i]["prec"]}\nRecall: {results_all[i]["rec"]}\nF1: {results_all[i]["f1"]}\nAER: {results_all[i]["aer"]}\nHits: {results_all[i]["total_hit_count"]}\n\n')
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('--save_path', default="/mounts/Users/cisintern/lksenel/Projects/pbc/graph-align/results/", type=str)
+    parser.add_argument('--gold_file', default="/mounts/Users/cisintern/lksenel/Projects/pbc/pbc_utils/data/eng_fra_pbc/eng-fra.gold", type=str)    
+    parser.add_argument('--predicted_alignments_file', default="/mounts/Users/cisintern/lksenel/Projects/pbc/graph-align/predicted_alignments/predicted_alignments_from_eng-x-bible-mixed_to_fra-x-bible-louissegond_with_max_83_editions_for_250_verses_NMF.txt", type=str)    
+    parser.add_argument('--intersection_alignments_file', default="/mounts/Users/cisintern/lksenel/Projects/pbc/graph-align/predicted_alignments/intersection_alignments_from_eng-x-bible-mixed_to_fra-x-bible-louissegond_for_250_verses.txt", type=str)    
+    parser.add_argument('--gdfa_alignments_file', default="/mounts/Users/cisintern/lksenel/Projects/pbc/graph-align/predicted_alignments/gdfa_alignments_from_eng-x-bible-mixed_to_fra-x-bible-louissegond_for_250_verses.txt", type=str)    
+    parser.add_argument('--source_edition', default="eng-x-bible-mixed", type=str) 
+    parser.add_argument('--target_edition', default="fra-x-bible-louissegond", type=str) 
+
+    args = parser.parse_args()
+    main(args)

evaluate_induced_alignments.py

@@ -0,0 +1,295 @@
+from surprise import Dataset, Reader
+import pandas as pd
+from surprise import NMF
+from utils import LOG, setup_dict_entry, load_editions, load_gold, get_verse_alignments
+import os, argparse, random, math
+from nltk.translate.gdfa import grow_diag_final_and
+import numpy as np
+from multiprocessing import Pool, Value
+
+
+def get_row_col_editions(source_edition, target_edition, all_editions=None):
+    row_editions = []
+    col_editions = []
+    for edition in all_editions:
+        if edition != source_edition and edition != target_edition:
+            row_editions.append(edition)
+            col_editions.append(edition)
+
+    row_editions.append(source_edition)
+    col_editions.append(target_edition)
+
+    return row_editions, col_editions
+
+def get_aligns(rf, cf, alignments):
+    raw_align = ''
+
+    if rf in alignments and cf in alignments[rf]:
+        raw_align = alignments[rf][cf]
+        alignment_line = [x.split('-') for x in raw_align.split()]
+        res = []
+        for x in alignment_line:
+            res.append( ( int(x[0]), int(x[1]) ) )
+    elif cf in alignments and rf in alignments[cf]: # re: aak, ce: aai, 
+        raw_align = alignments[cf][rf]
+        alignment_line = [x.split('-') for x in raw_align.split()]
+        res = []
+        for x in alignment_line:
+            res.append( ( int(x[1]), int(x[0]) ) )
+    elif rf in alignments and rf == cf: # if source and target are the same
+        keys = list(alignments[rf].keys())
+        max_count = 0
+        for key in keys:
+            align = alignments[rf][key]
+            for x in align.split():
+                count = int(x.split('-')[0])
+                if count > max_count:
+                    max_count = count
+        raw_align = "0-0"
+        for i in range(1,max_count):
+            raw_align += f" {i}-{i}"
+
+        alignment_line = [x.split('-') for x in raw_align.split()]
+        res = []
+        for x in alignment_line:
+            res.append( ( int(x[0]), int(x[1]) ) )
+    else:
+        return None
+    
+    return res
+
+def add_aligns(aligns, aligns_dict, token_counts, re, ce, existing_items):
+    for align in aligns:
+
+        aligns_dict['userID'].append(re + str(align[0]))
+        aligns_dict['itemID'].append(ce + str(align[1]))
+        aligns_dict['rating'].append(3)
+
+        if align[0] > token_counts[re]:
+            token_counts[re] = align[0]
+        if align[1] > token_counts[ce]:
+            token_counts[ce] = align[1]
+        
+        existing_items[re][ce].append(f"{align[0]},{align[1]}")
+
+def add_negative_samples(aligns_dict, existing_items, token_counts, verse_id):
+    for re in existing_items:
+        if token_counts[re] < 2:
+            continue
+        for ce in existing_items[re]:
+            if token_counts[ce] < 2:
+                continue
+            for item in existing_items[re][ce]:
+                i,j = tuple(item.split(","))
+                i,j = (int(i), int(j))
+                jp = random.randint(math.ceil(j+1), math.ceil(j+token_counts[ce] ))
+                ip = random.randint(math.ceil(i+1), math.ceil(i+token_counts[re] ))
+
+                jp %= (token_counts[ce] + 1)
+                aligns_dict['userID'].append(re + str(i))
+                aligns_dict['itemID'].append(ce + str(jp))
+                aligns_dict['rating'].append(1)
+                
+                ip %= (token_counts[re] + 1) 
+                aligns_dict['userID'].append(re + str(ip))
+                aligns_dict['itemID'].append(ce + str(j))
+                aligns_dict['rating'].append(1)
+
+def get_alignments_df(row_editions, col_editions, verse_alignments,
+         source_edition, target_edition, verse_id): #TODO can be improved a lot
+    token_counts = {}
+    existing_items = {}
+    aligns_dict = {'itemID': [], 'userID': [], 'rating': []}
+    for no, re in enumerate(row_editions):
+        token_counts[re] = 0
+        existing_items[re] = {}
+
+        for ce in col_editions:
+
+            setup_dict_entry(token_counts, ce, 0)
+            existing_items[re][ce] = []
+            aligns = get_aligns(re, ce, verse_alignments)
+                
+            if not aligns is None:
+                add_aligns(aligns, aligns_dict, token_counts, re, ce, existing_items)
+        
+    add_negative_samples(aligns_dict, existing_items, token_counts, verse_id)
+
+    return pd.DataFrame(aligns_dict), token_counts[source_edition], token_counts[target_edition]
+    
+def iter_max(sim_matrix: np.ndarray, max_count: int=2, alpha_ratio = 0.7) -> np.ndarray:
+    m, n = sim_matrix.shape
+    forward = np.eye(n)[sim_matrix.argmax(axis=1)]  # m x n
+    backward = np.eye(m)[sim_matrix.argmax(axis=0)]  # n x m
+    inter = forward * backward.transpose()
+
+    if min(m, n) <= 2:
+        return inter
+
+    new_inter = np.zeros((m, n))
+    count = 1
+    while count < max_count:
+        mask_x = 1.0 - np.tile(inter.sum(1)[:, np.newaxis], (1, n)).clip(0.0, 1.0)
+        mask_y = 1.0 - np.tile(inter.sum(0)[np.newaxis, :], (m, 1)).clip(0.0, 1.0)
+        mask = ((alpha_ratio * mask_x) + (alpha_ratio * mask_y)).clip(0.0, 1.0)
+        mask_zeros = 1.0 - ((1.0 - mask_x) * (1.0 - mask_y))
+        if mask_x.sum() < 1.0 or mask_y.sum() < 1.0:
+            mask *= 0.0
+            mask_zeros *= 0.0
+
+        new_sim = sim_matrix * mask
+        fwd = np.eye(n)[new_sim.argmax(axis=1)] * mask_zeros
+        bac = np.eye(m)[new_sim.argmax(axis=0)].transpose() * mask_zeros
+        new_inter = fwd * bac
+
+        if np.array_equal(inter + new_inter, inter):
+            break
+        inter = inter + new_inter
+        count += 1
+    return inter
+    
+def get_itermax_predictions(raw_s_predictions, max_count=2, alpha_ratio=0.9):
+    rows = len(raw_s_predictions)
+    cols = len(raw_s_predictions[0])
+    matrix = np.ndarray(shape=(rows, cols), dtype=float)
+
+    for i in raw_s_predictions:
+        for j, s in raw_s_predictions[i]:
+            matrix[i,j] = s
+    
+    itermax_res = iter_max(matrix, max_count, alpha_ratio)
+    res = []
+    for i in range(rows):
+        for j in range(cols):
+            if itermax_res[i,j] != 0:
+                res.append((i,j))
+    
+    return res
+
+def predict_alignments(algo, source_edition, target_edition):
+    raw_s_predictions = {}
+    raw_t_predictions = {}
+
+    for i in range(algo.s_tok_count + 1):
+        for j in range(algo.t_tok_count + 1):
+            pred = algo.predict(source_edition + str(i), target_edition + str(j))
+
+            setup_dict_entry(raw_s_predictions, i, [])
+            setup_dict_entry(raw_t_predictions, j, [])
+
+            raw_s_predictions[i].append((j, pred.est))
+            raw_t_predictions[j].append((i, pred.est))
+
+    # get predicted alignments from argmax (max_count=1 means argmax)
+    res = get_itermax_predictions(raw_s_predictions, max_count=1)
+
+    return res
+
+def train_model(df,  s_tok_count, t_tok_count, row_editions, col_editions):
+    algo = NMF()
+    reader = Reader(rating_scale=(1, 3))
+    data = Dataset.load_from_df(df[['userID', 'itemID', 'rating']], reader)
+    trainset = data.build_full_trainset()
+    algo.fit(trainset)
+
+    algo.s_tok_count = s_tok_count
+    algo.t_tok_count = t_tok_count
+    algo.row_editions = row_editions
+    algo.col_editions = col_editions
+    algo.df = df
+    
+    return algo
+
+def get_induced_alignments(source_edition, target_edition, verse_alignments_path, verse_id, all_editions):
+
+    verse_alignments = get_verse_alignments(verse_alignments_path, verse_id, editions=all_editions)
+    
+    # this is only for saving the gdfa alignments from source to target for the evauation
+    verse_alignments_gdfa = get_verse_alignments(verse_alignments_path, verse_id, editions=[source_edition, target_edition], gdfa=True)
+
+    ###  source -> row, target-> col###
+    row_editions, col_editions = get_row_col_editions(source_edition, target_edition, all_editions)
+    #itemid -> col, user -> row
+    df, s_tok_count, t_tok_count = get_alignments_df(row_editions, col_editions, verse_alignments, source_edition, target_edition, verse_id)
+
+    algo = train_model(df, s_tok_count, t_tok_count, row_editions, col_editions)
+    
+    predicted_alignments = predict_alignments(algo, source_edition, target_edition)
+    base_inter_alignments = verse_alignments[source_edition][target_edition]
+    base_gdfa_alignments = verse_alignments_gdfa[source_edition][target_edition]
+    
+    with cnt.get_lock():
+        cnt.value += 1
+        if cnt.value % 20 == 0:
+            LOG.info(f"Done inferring alignments for {cnt.value} verses")
+
+    return predicted_alignments, base_inter_alignments, base_gdfa_alignments,  len(algo.col_editions)+1
+
+
+def init_globals(counter):
+    global cnt
+    cnt = counter
+
+def main(args):
+    random.seed(args.seed)
+
+    pros, surs = load_gold(args.gold_file)
+    all_verses =list(pros.keys())
+    all_verses = all_verses
+
+    # Get languages and editions
+    editions, langs = load_editions(args.editions_file)
+    all_editions = [editions[lang] for lang in langs]
+
+    # print some info
+    LOG.info(f"Inferring alignments from {args.source_edition} to {args.target_edition}")
+    LOG.info(f"Number of verses whose alignments will be inferred: {len(all_verses)}")
+    LOG.info(f"Number of editions to use for the graph algorithms: {len(all_editions)}")
+    LOG.info(f"Number of cores to be used for processing: {args.core_count}")
+
+    # Prepare arguments for parallel processing
+    starmap_args = []
+    for verse_id in all_verses:
+        # aligns_predicted, used_edition_count = get_induced_alignments(args.source_edition, args.target_edition, args.verse_alignments, verse_id, all_editions)
+        starmap_args.append((args.source_edition, args.target_edition, args.verse_alignments_path, verse_id, all_editions))
+
+    # get predicted alignments using parallel processing
+    cnt = Value('i', 0)
+    with Pool(processes=args.core_count, initializer=init_globals, initargs=(cnt,)) as p:  
+        all_alignments = p.starmap(get_induced_alignments, starmap_args)
+
+    out_NMF_f_name = f"predicted_alignments_from_{args.source_edition}_to_{args.target_edition}_with_max_{len(all_editions)}_editions_for_{len(all_verses)}_verses_NMF.txt"
+    out_NMF_file = open(os.path.join(args.save_path, out_NMF_f_name), 'w')
+    out_inter_f_name = f"intersection_alignments_from_{args.source_edition}_to_{args.target_edition}_for_{len(all_verses)}_verses.txt"
+    out_inter_file = open(os.path.join(args.save_path, out_inter_f_name), 'w')
+    out_gdfa_f_name = f"gdfa_alignments_from_{args.source_edition}_to_{args.target_edition}_for_{len(all_verses)}_verses.txt"
+    out_gdfa_file = open(os.path.join(args.save_path, out_gdfa_f_name), 'w')
+
+    for id, verse_id in enumerate(all_verses):
+        aligns_predicted, inter_aligns, gdfa_aligns, used_edition_count = all_alignments[id]
+
+        # convert predicted alignments to string and write to a file
+        aligns_predicted = ' '.join([f"{align[0]}-{align[1]}" for align in aligns_predicted])
+        out_NMF_file.write(f"{verse_id}\t{aligns_predicted}\n")
+        out_inter_file.write(f"{verse_id}\t{inter_aligns.strip()}\n")
+        out_gdfa_file.write(f"{verse_id}\t{gdfa_aligns.strip()}\n")
+
+    out_NMF_file.close()
+    out_inter_file.close()
+    out_gdfa_file.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('--save_path', default="/mounts/Users/cisintern/lksenel/Projects/pbc/graph-align/predicted_alignments", type=str)
+    parser.add_argument('--gold_file', default="/mounts/Users/cisintern/lksenel/Projects/pbc/pbc_utils/data/eng_fra_pbc/eng-fra.gold", type=str)    
+    parser.add_argument('--verse_alignments_path', default="/mounts/data/proj/ayyoob/align_induction/verse_alignments/", type=str)
+    parser.add_argument('--source_edition', default="eng-x-bible-mixed", type=str) 
+    parser.add_argument('--target_edition', default="fra-x-bible-louissegond", type=str) 
+    parser.add_argument('--editions_file', default="/mounts/Users/cisintern/lksenel/Projects/pbc/pbc_utils/data/eng_fra_pbc/lang_list.txt", type=str)
+    parser.add_argument('--core_count', default=80, type=int)
+    parser.add_argument('--seed', default=42, type=int)
+
+    args = parser.parse_args()
+    main(args)
+