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| Original file line number | Diff line number | Diff line change |
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| from sentence_transformers import SentenceTransformer | ||
| from sklearn.metrics.pairwise import cosine_similarity | ||
| from sklearn.cluster import AgglomerativeClustering, DBSCAN | ||
| import matplotlib.pyplot as plt | ||
| from sklearn.manifold import TSNE | ||
| from matplotlib.patches import Ellipse | ||
| import numpy as np | ||
| import json | ||
| from collections import defaultdict | ||
| from sklearn.metrics import davies_bouldin_score | ||
| import h5py | ||
| from sklearn.metrics import silhouette_score | ||
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| model = SentenceTransformer("all-mpnet-base-v2") # all-MiniLM-L6-v2, , all-distilroberta-v1, sentence-t5-base,all-MiniLM-L12-v2,all-mpnet-base-v2 | ||
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| READ = False | ||
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| if READ: | ||
| path = "/users/ajaafar/data/shared/lanmp/sim_dataset.hdf5" | ||
| commands=[] | ||
| # Open the HDF5 file | ||
| with h5py.File(path, 'r') as hdf_file: | ||
| # Iterate through each trajectory group | ||
| for trajectory_name, trajectory_group in hdf_file.items(): | ||
| # Iterate through each timestep group within the trajectory | ||
| for timestep_name, timestep_group in trajectory_group.items(): | ||
| # Read and decode the JSON metadata | ||
| metadata = json.loads(timestep_group.attrs['metadata']) | ||
| commands.append(metadata['nl_command']) | ||
| break | ||
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| np.save("sim_commands.npy", commands) | ||
| else: | ||
| commands = np.load('sim_commands.npy', allow_pickle=True).tolist() | ||
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| # Convert the commands to embeddings | ||
| embeddings = model.encode(commands) | ||
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| # Compute the cosine similarity matrix | ||
| # sim_mat = cosine_similarity(embeddings) | ||
| # print(sim_mat) | ||
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| # Apply Agglomerative Clustering | ||
| clustering = AgglomerativeClustering(n_clusters=None, metric='cosine', linkage='average', distance_threshold=0.3) | ||
| clusters = clustering.fit_predict(embeddings) | ||
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| # Calculate the silhouette score | ||
| silhouette_avg = silhouette_score(embeddings, clusters, metric='cosine') | ||
| print(f'Silhouette Score: {silhouette_avg}') | ||
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| # Calculate the Davies-Bouldin index | ||
| db_index = davies_bouldin_score(embeddings, clusters) | ||
| print(f'Davies-Bouldin Index: {db_index}') | ||
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| # Create a defaultdict with lists as default values | ||
| cluster_dict = defaultdict(list) | ||
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| # Populate the dictionary | ||
| for string, cluster_id in zip(commands, clusters): | ||
| cluster_dict[int(cluster_id)].append(string) | ||
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| cluster_dict = dict(cluster_dict) | ||
| print(f'num clusters: {len(cluster_dict.keys())}') | ||
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| #save dict | ||
| # with open('cluster_dict.json', 'w') as f: | ||
| # json.dump(cluster_dict, f) | ||
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| # Find the cluster with the longest list | ||
| sorted_clusters = sorted(cluster_dict, key=lambda k: len(cluster_dict[k]), reverse=True) | ||
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| tot = 0 | ||
| # for i in [0,1,2,3,4,5,6,7,8,9]: #low number of clusters #240 | ||
| # for i in range(14, 106): #high number of clusters #238 | ||
| for i in range(10,14): #test 46 | ||
| print(f"{len(cluster_dict[sorted_clusters[i]])} elements.") | ||
| tot += len(cluster_dict[sorted_clusters[i]]) | ||
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| print(f"{tot} total") | ||
| breakpoint() | ||
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| # Apply t-SNE for dimensionality reduction | ||
| reduced_embeddings = TSNE(n_components=2, metric='cosine', perplexity=175).fit_transform(embeddings) | ||
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| # Plot the t-SNE results | ||
| plt.figure(figsize=(10, 8)) | ||
| for cluster in np.unique(clusters): | ||
| cluster_points = reduced_embeddings[clusters == cluster] | ||
| plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster}') | ||
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| # Calculate the ellipse for each cluster | ||
| if len(cluster_points) > 1: # Ellipse requires at least 2 points | ||
| cov = np.cov(cluster_points, rowvar=False) | ||
| mean = np.mean(cluster_points, axis=0) | ||
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| # Eigen decomposition of covariance matrix | ||
| eigenvalues, eigenvectors = np.linalg.eigh(cov) | ||
| order = eigenvalues.argsort()[::-1] | ||
| eigenvalues, eigenvectors = eigenvalues[order], eigenvectors[:, order] | ||
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| # Compute width, height and angle of ellipse | ||
| angle = np.degrees(np.arctan2(*eigenvectors[:, 0][::-1])) | ||
| width, height = 2 * np.sqrt(eigenvalues) | ||
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| # Draw the ellipse | ||
| ellipse = Ellipse(xy=mean, width=width, height=height, angle=angle, edgecolor='black', facecolor='none', lw=2) | ||
| plt.gca().add_patch(ellipse) | ||
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| plt.title('t-SNE with Cluster Ellipses') | ||
| plt.xlabel('t-SNE Dimension 1') | ||
| plt.ylabel('t-SNE Dimension 2') | ||
| plt.legend() | ||
| plt.grid(True) | ||
| plt.show() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,4 @@ | ||
| { | ||
| "train_envs": ["FloorPlan_Train8_1", "FloorPlan_Train1_3", "FloorPlan_Train5_1", "FloorPlan_Train12_3"] , | ||
| "test_env": "FloorPlan_Train7_5" | ||
| "train_envs": ["FloorPlan_Train8_1", "FloorPlan_Train5_1,", "FloorPlan_Train12_3"], | ||
| "test_env": "FloorPlan_Train1_3" | ||
| } |
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