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| import umap.umap_ as umap | |
| import plotly.express as px | |
| import pandas as pd | |
| import random | |
| import numpy | |
| from sklearn.manifold import TSNE | |
| from sklearn.decomposition import PCA | |
| import os | |
| os.chdir(os.path.dirname(os.path.abspath(__file__))) | |
| movies_df = pd.read_csv("./sampled_movie_dataset/movies_metadata.csv") | |
| ##all_genres = movies_df['genres'].unique().tolist() # Adjust the column name if needed | |
| genres = movies_df['genres'].tolist()[671:] # Offset to start at movies | |
| ##can't get to work for coloring by genre | |
| def get_genre_for_movie(movie_index): | |
| genres_str = movies_df.iloc[movie_index]['genres'] | |
| # You might need to parse genres_str if it's not a simple list | |
| return genres_str # Or a list of genres | |
| print(get_genre_for_movie(20)) | |
| def visualize_embeddings_umap(embedding_df, n_neighbors=15, min_dist=0.1, n_components=3): | |
| # Convert Series to DataFrame | |
| #embedding_df = pd.DataFrame(embedding_series.tolist(), columns=[f'dim_{i+1}' for i in range(len(embedding_series[0]))]) | |
| # Perform UMAP dimensionality reduction | |
| umap_embedded = umap.UMAP( | |
| n_neighbors=n_neighbors, | |
| min_dist=min_dist, | |
| n_components=n_components, | |
| random_state=42, | |
| ).fit_transform(embedding_df.values) | |
| # Plot the UMAP embedding | |
| umap_df = pd.DataFrame(umap_embedded, columns=['UMAP Dimension 1', 'UMAP Dimension 2', 'UMAP Dimension 3']) | |
| umap_df['Label'] = embedding_df.index | |
| color = [0]*671 + [1]*9025 | |
| umap_df['color'] = color | |
| # Plot the UMAP embedding using Plotly Express | |
| fig = px.scatter_3d(umap_df, x='UMAP Dimension 1', y='UMAP Dimension 2',z='UMAP Dimension 3',color='color',hover_data=['Label'], title='UMAP Visualization of Embeddings') | |
| return fig | |
| def visualize_embeddings_tsne(embedding_df, n_components=3, perplexity=30.0, early_exaggeration=12.0, learning_rate=200.0): | |
| # Perform t-SNE dimensionality reduction | |
| tsne_embedded = TSNE( | |
| n_components=n_components, | |
| perplexity=perplexity, | |
| early_exaggeration=early_exaggeration, | |
| learning_rate=learning_rate, | |
| random_state=42, | |
| ).fit_transform(embedding_df.values) | |
| # Plot the t-SNE embedding | |
| tsne_df = pd.DataFrame(tsne_embedded, columns=[f't-SNE Dimension {i+1}' for i in range(n_components)]) | |
| tsne_df['Label'] = embedding_df.index | |
| # Add color column (adjust how colors are applied based on your data) | |
| tsne_df['color'] = [0]*671 + [1]*9025 | |
| fig = px.scatter_3d(tsne_df, x='t-SNE Dimension 1', y='t-SNE Dimension 2', z='t-SNE Dimension 3', color='color', hover_data=['Label'], title='t-SNE Visualization of Embeddings') | |
| return fig | |
| def visualize_embeddings_pca(embedding_df, n_components=3): | |
| # Perform PCA | |
| pca = PCA(n_components=n_components, random_state=42) | |
| pca_embedded = pca.fit_transform(embedding_df.values) | |
| # Plot the PCA embedding | |
| pca_df = pd.DataFrame(pca_embedded, columns=[f'PCA Dimension {i+1}' for i in range(n_components)]) | |
| pca_df['Label'] = embedding_df.index | |
| # Add color column (adjust how colors are applied based on your data) | |
| pca_df['color'] = [0]*671 + [1]*9025 | |
| fig = px.scatter_3d(pca_df, x='PCA Dimension 1', y='PCA Dimension 2', z='PCA Dimension 3', color='color', hover_data=['Label'], title='PCA Visualization of Embeddings') | |
| return fig | |
| def save_visualization(fig, filename): | |
| fig.write_html(f"{filename}.html") | |