fmcquillan99 edited a comment on issue #432: MADLIB-1351 : Added stopping criteria on perplexity to LDA URL: https://github.com/apache/madlib/pull/432#issuecomment-548912324 (6) NULLs not being handled properly ``` DROP TABLE IF EXISTS lda_model_perp, lda_output_data_perp; SELECT madlib.lda_train( 'documents_tf', -- documents table in the form of term frequency 'lda_model_perp', -- model table created by LDA training (not human readable) 'lda_output_data_perp', -- readable output data table 384, -- vocabulary size 5, -- number of topics 20, -- number of iterations 5, -- Dirichlet prior for the per-doc topic multinomial (alpha) 0.01, -- Dirichlet prior for the per-topic word multinomial (beta) NULL, -- Evaluate perplexity every n iterations NULL -- Stopping perplexity tolerance ); InternalError: (psycopg2.InternalError) plpy.Error: invalid argument: perplexity_tol should not be less than 0 (plpython.c:5038) CONTEXT: Traceback (most recent call last): PL/Python function "lda_train", line 22, in <module> voc_size, topic_num, iter_num, alpha, beta,evaluate_every , perplexity_tol) PL/Python function "lda_train", line 525, in lda_train PL/Python function "lda_train", line 96, in _assert PL/Python function "lda_train" [SQL: "SELECT madlib.lda_train( 'documents_tf', -- documents table in the form of term frequency\n 'lda_model_perp', -- model table created by LDA training (not human readable)\n 'lda_output_data_perp', -- readable output data table \n 384, -- vocabulary size\n 5, -- number of topics\n 20, -- number of iterations\n 5, -- Dirichlet prior for the per-doc topic multinomial (alpha)\n 0.01, -- Dirichlet prior for the per-topic word multinomial (beta)\n NULL, -- Evaluate perplexity every n iterations\n NULL -- Stopping perplexity tolerance\n );"] ``` Please implement as per ``` evaluate_every (optional) INTEGER, default: 0. How often to evaluate perplexity. Set it to 0 or a negative number to not evaluate perplexity in training at all. Evaluating perplexity can help you check convergence during the training process, but it will also increase total training time. For example, evaluating perplexity in every iteration might increase training time up to two-fold. perplexity_tol (optional) DOUBLE PRECISION, default: 0.1. Perplexity tolerance to stop iteration. Only used when the parameter 'evaluate_every' is greater than 0. ```
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