Delete src/synonyms_final_vf.py

src/synonyms_final_vf.py

@@ -1,125 +0,0 @@
-
-import pandas as pd
-import spacy
-import numpy as np
-from sklearn.cluster import DBSCAN
-from sklearn.metrics.pairwise import cosine_distances
-import matplotlib.pyplot as plt
-import nltk
-from nltk.corpus import wordnet
-
-def load_data(file_path):
-    """
-    This function loads the data from a given file_path
-
-    parameter: str the file path
-
-    Returns: the unique words in gloss column
-    """
-    data = pd.read_csv(file_path, delimiter=";")
-    return data["gloss"].unique()
-
-def initialize_spacy_model(model_name="en_core_web_md"):
-    return spacy.load(model_name)
-
-def download_wordnet():
-    """
-    This function downloads a dictionary that will be used to find antonyms
-    """
-    nltk.download('wordnet')
-
-def generate_word_vectors(words, model):
-    return np.array([model(word).vector for word in words])
-
-def plot_k_distance_graph(distances, k):
-    k_distances = np.sort(distances, axis=1)[:, k]
-    k_distances = np.sort(k_distances)
-    plt.figure(figsize=(10, 5))
-    plt.plot(k_distances)
-    plt.xlabel('Points sorted by distance')
-    plt.ylabel(f'{k}-th Nearest Neighbor Distance')
-    plt.title(f'k-distance Graph for k={k}')
-    plt.grid(True)
-    plt.show()
-
-def perform_dbscan_clustering(word_vectors, eps, min_samples=5):
-    dbscan = DBSCAN(metric='cosine', eps=eps, min_samples=min_samples)
-    dbscan.fit(word_vectors)
-    return dbscan
-
-def create_cluster_mapping(words, dbscan_labels):
-    cluster_to_words = {}
-    for word, cluster in zip(words, dbscan_labels):
-        if cluster not in cluster_to_words:
-            cluster_to_words[cluster] = []
-        cluster_to_words[cluster].append(word)
-    return cluster_to_words
-
-def find_antonyms(word):
-    antonyms = set()
-    for syn in wordnet.synsets(word):
-        for lemma in syn.lemmas():
-            if lemma.antonyms():
-                antonyms.add(lemma.antonyms()[0].name())
-    return antonyms
-
-def find_synonyms_in_cluster(word, model, cluster_to_words, dbscan_model):
-    """
-    This function finds the most similar word in the same cluster, and excludes antonyms
-    """
-    word_vector = model(word).vector
-    cluster_label = dbscan_model.fit_predict([word_vector])[0]
-    cluster_words = cluster_to_words.get(cluster_label, [])
-
-    if not cluster_words:
-        return None
-
-    antonyms = find_antonyms(word)
-    similarities = [(dict_word, model(dict_word).similarity(model(word))) for dict_word in cluster_words if dict_word != word and dict_word not in antonyms]
-
-    if not similarities:
-        return None
-
-    most_similar_word = sorted(similarities, key=lambda item: -item[1])[0][0]
-    return most_similar_word
-
-def display_clusters(cluster_to_words):
-    for cluster_label, words in cluster_to_words.items():
-        if cluster_label != -1:  # Exclude noise points
-            print(f"Cluster {cluster_label}: {words}")
-        else:
-            print(f"Noise: {words}")
-
-def main(file_path, model_name="en_core_web_md", eps=0.23, min_samples=5, k=5):
-    global nlp, cluster_to_words, dbscan
-
-    dict_2000 = load_data(file_path)
-    nlp = initialize_spacy_model(model_name)
-    download_wordnet()
-
-    word_vectors = generate_word_vectors(dict_2000, nlp)
-
-    # distances = cosine_distances(word_vectors)
-    # plot_k_distance_graph(distances, k)
-
-    dbscan = perform_dbscan_clustering(word_vectors, eps, min_samples)
-    cluster_to_words = create_cluster_mapping(dict_2000, dbscan.labels_)
-
-if __name__ == "__main__":
-    main("filtered_WLASL.csv")
-
-##TEST##
-#target_word = "unhappy"
-#synonym = find_synonyms_in_cluster(target_word, nlp, cluster_to_words, dbscan)
-#print(f"The most similar word to '{target_word}' is '{synonym}'")
-
-##If you want to see clusters##
-#num_clusters = len(set(dbscan.labels_)) - (1 if -1 in dbscan.labels_ else 0)
-#print(f"Number of clusters: {num_clusters}")
-
-#cluster_label = dbscan.fit_predict([nlp("unhappy").vector])[0]
-#same_cluster_words = cluster_to_words.get(cluster_label, [])
-#print(f"Words in the same cluster as 'unhappy': {same_cluster_words}")
-
-#display_clusters(cluster_to_words)
-