d2e86cb3cd
## What changes were proposed in this pull request? This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model. * The document frequency is returned as an `Array[Long]` * If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms * numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value * Pyspark changes ## How was this patch tested? The existing test case was edited to also check for the document frequency values. I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything Reviewer request: mengxr zjffdu yinxusen Closes #23549 from purijatin/master. Authored-by: Jatin Puri <purijatin@gmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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| .. | ||
| linalg | ||
| stat | ||
| tests | ||
| __init__.py | ||
| classification.py | ||
| clustering.py | ||
| common.py | ||
| evaluation.py | ||
| feature.py | ||
| fpm.py | ||
| random.py | ||
| recommendation.py | ||
| regression.py | ||
| tree.py | ||
| util.py | ||