more memory-efficient word count calculation #4968
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travis is unhappy |
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@aupiff Is this still being worked on? |
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@jmschrei Yes! I forgot about this for a little while. I'm fixing a few failing tests now. |
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@jmschrei all tests passing now! sorry this took so long to fix. |
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Excellent! Would it be possible for you to provide some simple benchmarks, and the code which runs those benchmarks as well? It would make it easier for us to ensure the quality of your code. |
| for feature in analyze(doc): | ||
| try: | ||
| j_indices.append(vocabulary[feature]) | ||
| vocab_idx = vocabulary[feature] | ||
| word_occurences = j_idx_to_count_dict.get(vocab_idx, 0) |
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👍 for some benchmarks :) |
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I've added this vectorizer to my benchmark script for #5122 and added a line to the results table. |
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Superseded by #7272 |
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(SPLITTING UP PR #4941)
previously, line 760 in
text.pywould cause memory issues as
len(j_indices)is equal to the entire corpus' word count. I had issues with a dataset of 200,000 documents with ~4000 words each when many gigabytes would be allocated temporarily. I've eliminated the need for this line and theX.sum_duplicatescalculation without a perceptible performance hit.