commented unused part in plots.py

plots.py

@@ -8,87 +8,87 @@ from word2vec import *
 from sklearn.preprocessing import StandardScaler
 
 
-def make_3d_plot(new_3d_vectors):
-    """
-    Turn DataFrame of 3D vectors into a 3D plot
-    DataFrame structure: ['word', 'cosine_sim', '3d_vector']
-    """
-    fig = plt.figure()
-    ax = fig.add_subplot(projection='3d')
+# def make_3d_plot(new_3d_vectors):
+#     """
+#     Turn DataFrame of 3D vectors into a 3D plot
+#     DataFrame structure: ['word', 'cosine_sim', '3d_vector']
+#     """
+#     fig = plt.figure()
+#     ax = fig.add_subplot(projection='3d')
     
-    plt.ion()
+#     plt.ion()
 
-    # Unpack vectors and labels from DataFrame
-    labels = new_3d_vectors['word']
-    x = new_3d_vectors['3d_vector'].apply(lambda v: v[0])
-    y = new_3d_vectors['3d_vector'].apply(lambda v: v[1])
-    z = new_3d_vectors['3d_vector'].apply(lambda v: v[2])
+#     # Unpack vectors and labels from DataFrame
+#     labels = new_3d_vectors['word']
+#     x = new_3d_vectors['3d_vector'].apply(lambda v: v[0])
+#     y = new_3d_vectors['3d_vector'].apply(lambda v: v[1])
+#     z = new_3d_vectors['3d_vector'].apply(lambda v: v[2])
 
-    # Plot points
-    ax.scatter(x, y, z)
+#     # Plot points
+#     ax.scatter(x, y, z)
 
-    # Add labels
-    for i, label in enumerate(labels):
-        ax.text(x[i], y[i], z[i], label)
+#     # Add labels
+#     for i, label in enumerate(labels):
+#         ax.text(x[i], y[i], z[i], label)
 
-    # Set labels and title
-    ax.set_xlabel('X')
-    ax.set_ylabel('Y')
-    ax.set_zlabel('Z')
-    ax.set_title('3D plot of word vectors')
+#     # Set labels and title
+#     ax.set_xlabel('X')
+#     ax.set_ylabel('Y')
+#     ax.set_zlabel('Z')
+#     ax.set_title('3D plot of word vectors')
 
-    return fig
+#     return fig
 
 
-import plotly.express as px
+# import plotly.express as px
 
 
-def make_3d_plot2(df):
-    """
-        Turn DataFrame of 3D vectors into a 3D plot using plotly
-        DataFrame structure: ['word', 'cosine_sim', '3d_vector']
-    """
-    vectors = df['3d_vector'].tolist()
-    fig = px.scatter_3d(df, x=[v[0] for v in vectors], y=[v[1] for v in vectors], z=[v[2] for v in vectors], text=df['word'])
-    return fig
+# def make_3d_plot2(df):
+#     """
+#         Turn DataFrame of 3D vectors into a 3D plot using plotly
+#         DataFrame structure: ['word', 'cosine_sim', '3d_vector']
+#     """
+#     vectors = df['3d_vector'].tolist()
+#     fig = px.scatter_3d(df, x=[v[0] for v in vectors], y=[v[1] for v in vectors], z=[v[2] for v in vectors], text=df['word'])
+#     return fig
 
 
-def make_3d_plot3(vectors_list, word, time_slice_model):
-    """
-    Turn list of 100D vectors into a 3D plot using UMAP and Plotly.
-    List structure: [(word, model_name, vector, cosine_sim)]
-    """
-    # Load model
-    model = load_word2vec_model(f'models/{time_slice_model}.model')
+# def make_3d_plot3(vectors_list, word, time_slice_model):
+#     """
+#     Turn list of 100D vectors into a 3D plot using UMAP and Plotly.
+#     List structure: [(word, model_name, vector, cosine_sim)]
+#     """
+#     # Load model
+#     model = load_word2vec_model(f'models/{time_slice_model}.model')
     
-    # Make UMAP model and fit it to the vectors
-    umap_model = umap.UMAP(n_components=3)
-    umap_model.fit(model.wv.vectors)
+#     # Make UMAP model and fit it to the vectors
+#     umap_model = umap.UMAP(n_components=3)
+#     umap_model.fit(model.wv.vectors)
     
-    # Transform the vectors to 3D
-    transformed_vectors = umap_model.transform(model.wv.vectors)
+#     # Transform the vectors to 3D
+#     transformed_vectors = umap_model.transform(model.wv.vectors)
     
     
-    # Create DataFrame from the transformed vectors
-    df = pd.DataFrame(transformed_vectors, columns=['x', 'y', 'z'])
+#     # Create DataFrame from the transformed vectors
+#     df = pd.DataFrame(transformed_vectors, columns=['x', 'y', 'z'])
     
-    # Add word and cosine similarity to DataFrame
-    df['word'] = model.wv.index_to_key
+#     # Add word and cosine similarity to DataFrame
+#     df['word'] = model.wv.index_to_key
     
-    # Filter the DataFrame for words in vectors_list and add cosine similarity
-    word_list = [v[0] for v in vectors_list]
-    cosine_sim_list = [v[3] for v in vectors_list]
+#     # Filter the DataFrame for words in vectors_list and add cosine similarity
+#     word_list = [v[0] for v in vectors_list]
+#     cosine_sim_list = [v[3] for v in vectors_list]
     
-    # Ensure that the word list and cosine similarity list are aligned properly
-    df = df[df['word'].isin(word_list)]
-    df['cosine_sim'] = cosine_sim_list
+#     # Ensure that the word list and cosine similarity list are aligned properly
+#     df = df[df['word'].isin(word_list)]
+#     df['cosine_sim'] = cosine_sim_list
     
-    # Create plot
-    fig = px.scatter_3d(df, x='x', y='y', z='z', text='word', color='cosine_sim', color_continuous_scale='Reds')
-    fig.update_traces(marker=dict(size=5))
-    fig.update_layout(title=f'3D plot of nearest neighbours to {word}')
+#     # Create plot
+#     fig = px.scatter_3d(df, x='x', y='y', z='z', text='word', color='cosine_sim', color_continuous_scale='Reds')
+#     fig.update_traces(marker=dict(size=5))
+#     fig.update_layout(title=f'3D plot of nearest neighbours to {word}')
     
-    return fig, df
+#     return fig, df