Removed vector_graph.py, functions weren't used anymore

app.py

@@ -3,7 +3,6 @@ from streamlit_option_menu import option_menu
 from word2vec import *
 import pandas as pd
 from autocomplete import *
-from vector_graph import *
 from plots import *
 from lsj_dict import *
 import json

vector_graph.py

@@ -1,72 +0,0 @@
-from word2vec import *
-import numpy as np
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-import pandas as pd
-
-
-
-def create_3d_vectors(word, time_slice, nearest_neighbours_vectors):
-    """
-    Turn word vectors into 3D vectors
-    """
-    model = load_word2vec_model(f'models/{time_slice}.model')
-    
-    # Compress all vectors to 3D
-    model_df = pd.DataFrame(model.wv.vectors)
-    pca_vectors = PCA(n_components=3)
-    pca_model = pca_vectors.fit_transform(model_df)
-    pca_model_df = pd.DataFrame(
-        data = pca_model,
-        columns = ['x', 'y', 'z']
-    )
-    pca_model_df.insert(0, 'word', model.wv.index_to_key)
-    
-    return pca_model_df
-
-
-
-
-def create_3d_models(time_slice):
-    """
-    Create 3D models for each time slice
-    """
-    time_slice_model = convert_time_name_to_model(time_slice)
-    model = load_word2vec_model(f'models/{time_slice_model}.model')
-    
-    # Compress all vectors to 3D
-    model_df = pd.DataFrame(model.wv.vectors)
-    pca_vectors = PCA(n_components=3)
-    pca_model = pca_vectors.fit_transform(model_df)
-    pca_model_df = pd.DataFrame(
-        data = pca_model,
-        columns = ['x', 'y', 'z']
-    )
-    
-    pca_model_df.insert(0, 'word', model.wv.index_to_key)
-    
-    pca_model_df.to_csv(f'3d_models/{time_slice}_3d.csv', index=False)
-    return pca_model_df, pca_vectors
-
-
-def nearest_neighbours_to_pca_vectors(word, time_slice, nearest_neighbours_vectors):
-    """
-    Turn nearest neighbours into 3D vectors
-    """
-    model_df = pd.read_csv(f'3d_models/{time_slice}_3d.csv')
-    
-    new_data = []
-
-    # Get the word vector for the nearest neighbours
-    for neighbour in nearest_neighbours_vectors:
-        word = neighbour[0]
-        cosine_sim = neighbour[3]
-        vector_3d = model_df[model_df['word'] == word][['x', 'y', 'z']].values[0]
-        
-        # Add word, cosine_sim and 3D vector to new data list
-        new_data.append({'word': word, 'cosine_sim': cosine_sim, '3d_vector': vector_3d})
-
-    # Convert the list of dictionaries to a DataFrame
-    new_df = pd.DataFrame(new_data)
-
-    return new_df