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Jayman391 / lnlp

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ValueError: The number of observations cannot be determined on an empty distance matrix. #25

Open Jayman391 opened 7 months ago

Jayman391 commented 7 months ago

python main.py tests/test_data/data.csv

Welcome to the NLLP CLI! Loaded data from tests/test_data/data.csv

  1. Run a Topic Model
  2. Run an Optimization routine for a Topic Model (GPU reccomended)
  3. Run a Classification Model
  4. Load Global Configuration Files
  5. Exit Choose an option: 1 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}]}
  6. Select LLM to generate Embeddings
  7. Select Dimensionality Reduction Technique
  8. Select Clustering Technique
  9. Fine Tuning
  10. Plotting
  11. Save Session Configuration
  12. Run Topic Model
  13. Load Session Configuration
  14. Back
  15. Exit Choose an option: 1 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}]}
  16. all-MiniLM-L6-v2
  17. all-MiniLM-L12-v2
  18. multi-qa-MiniLM-L6-cos-v1
  19. all-mpnet-base-v2
  20. Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit
  21. Muennighoff/SGPT-125M-weightedmean-nli-bitfit
  22. Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit
  23. Add huggingface Model
  24. Back
  25. Exit Choose an option: 5 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}]}
  26. Select LLM to generate Embeddings
  27. Select Dimensionality Reduction Technique
  28. Select Clustering Technique
  29. Fine Tuning
  30. Plotting
  31. Save Session Configuration
  32. Run Topic Model
  33. Load Session Configuration
  34. Back
  35. Exit Choose an option: 2 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}]}
  36. UMAP
  37. PCA
  38. t-SNE
  39. Truncated SVD
  40. Factor Analysis
  41. Back
  42. Exit Choose an option: 2 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}]}
  43. Select LLM to generate Embeddings
  44. Select Dimensionality Reduction Technique
  45. Select Clustering Technique
  46. Fine Tuning
  47. Plotting
  48. Save Session Configuration
  49. Run Topic Model
  50. Load Session Configuration
  51. Back
  52. Exit Choose an option: 3 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}]}
  53. hdbscan
  54. kmeans
  55. spectral clustering
  56. dbscan
  57. agglomerative clustering
  58. birch
  59. affinity propagation
  60. mean shift
  61. Back
  62. Exit Choose an option: 3 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}]}
  63. Select LLM to generate Embeddings
  64. Select Dimensionality Reduction Technique
  65. Select Clustering Technique
  66. Fine Tuning
  67. Plotting
  68. Save Session Configuration
  69. Run Topic Model
  70. Load Session Configuration
  71. Back
  72. Exit Choose an option: 4 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}, {'Topic': 'Fine Tuning'}]}
  73. Enable 2-grams
  74. Enable 3-grams
  75. Ignore Words
  76. Enable BM25 weighting
  77. Reduce frequent words
  78. Enable KeyBERT algorithm
  79. Enable ZeroShotClassification
  80. Enable Maximal Marginal Relevance
  81. Enable Part of Speech filtering
  82. Back
  83. Exit Choose an option: 2 {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 3-grams'}]}
  84. Select LLM to generate Embeddings
  85. Select Dimensionality Reduction Technique
  86. Select Clustering Technique
  87. Fine Tuning
  88. Plotting
  89. Save Session Configuration
  90. Run Topic Model
  91. Load Session Configuration
  92. Back
  93. Exit Choose an option: 7 Building topic model from logs [{'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}] [{'Dimensionality Reduction': 'PCA'}] [{'Clustering': 'spectral clustering'}] {} {} {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 3-grams'}, {'Topic': 'BERTopic(calculate_probabilities=False, ctfidf_model=ClassTfidfTransformer(...), embedding_model=SentenceTransformer(...), hdbscan_model=SpectralClustering(...), language=None, low_memory=False, min_topic_size=10, n_gram_range=(1, 1), nr_topics=None, representation_model=None, seed_topic_list=None, top_n_words=10, umap_model=PCA(...), vectorizer_model=CountVectorizer(...), verbose=False, zeroshot_min_similarity=0.7, zeroshot_topic_list=None)'}]} Building topic model from logs [{'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}] [{'Dimensionality Reduction': 'PCA'}] [{'Clustering': 'spectral clustering'}] {} {} SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) PCA() SpectralClustering() /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/numpy/core/_methods.py:176: RuntimeWarning: overflow encountered in multiply x = um.multiply(x, x, out=x) /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/utils/extmath.py:208: RuntimeWarning: overflow encountered in matmul ret = a @ b /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/metrics/pairwise.py:383: RuntimeWarning: invalid value encountered in add distances += XX /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/cluster/_kmeans.py:704: RuntimeWarning: overflow encountered in square lloyd_iter( /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/numpy/core/_methods.py:49: RuntimeWarning: overflow encountered in reduce return umr_sum(a, axis, dtype, out, keepdims, initial, where) [[-3.37943465e-01 6.29494131e-01 -2.42340565e-01 -1.21323444e-01 6.43052876e-01 1.01818740e+00 1.51308015e-01 -5.05418062e-01 3.90750527e-01 -8.81013811e-01 6.41503096e-01 1.00391102e+00 -5.65844953e-01 5.61737001e-01 -8.23109388e-01 -1.97309121e-01 1.73583314e-01 -3.83237422e-01 4.92538244e-01 -7.57770061e-01 -1.37655810e-01 8.14577520e-01 5.41032970e-01 -6.14629328e-01 -1.14306025e-02 1.35825467e+00 -7.58565366e-01 -9.36706781e-01 3.01892400e-01 1.70184040e+00 1.37070215e+00 -8.28083456e-01 -1.17205095e+00 9.42910135e-01 -5.10190070e-01 1.00378203e+00 3.77682686e-01 -2.76817656e+00 5.09495795e-01 -2.88637459e-01 -1.28382492e+00 -2.95686543e-01 9.23165083e-02 -1.57267833e+00 7.27139652e-01 3.96969587e-01 2.08877230e+00 8.09231937e-01 -1.44847140e-01 1.07452214e+00 4.58445966e-01 7.32348502e-01 3.16043317e-01 -2.94261038e-01 7.70114601e-01 1.63893417e-01 -8.42540383e-01 -6.49182558e-01 1.44444421e-01 6.59512058e-02 -1.55640483e-01 4.84877795e-01 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-5.12399197e-01 5.31018198e-01]] No plotting options selected. Visualizing all topics, documents, and terms. An error occurred. Please try again. Would you like to see the error trace? (y/n): y Traceback (most recent call last): File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 47, in run self._process_responses(self.landing, self.driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses self._process_responses(response, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses self._process_responses(response, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses self._process_responses(menu.parent, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses self._process_responses(response, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses self._process_responses(menu.parent, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses self._process_responses(response, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses self._process_responses(menu.parent, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses self._process_responses(response, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses self._process_responses(menu.parent, driver) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 70, in _process_responses driver.run_topic_model() File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 128, in run_topic_model self._visualize_topics(model, dir) File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 135, in _visualize_topics hierarchical_topics = model.hierarchical_topics(docs=self.session.data) File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 980, in hierarchical_topics File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 972, in File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scipy/cluster/hierarchy.py", line 1033, in linkage n = int(distance.num_obs_y(y)) File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scipy/spatial/distance.py", line 2657, in num_obs_y raise ValueError("The number of observations cannot be determined on " ValueError: The number of observations cannot be determined on an empty distance matrix.