feat: add ml.metrics.pairwise.cosine_similarity function#374
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gcf-merge-on-green[bot] merged 5 commits intomainfrom Feb 13, 2024
Merged
feat: add ml.metrics.pairwise.cosine_similarity function#374gcf-merge-on-green[bot] merged 5 commits intomainfrom
gcf-merge-on-green[bot] merged 5 commits intomainfrom
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ashleyxuu
reviewed
Feb 12, 2024
| Input data. X and Y are mapped by indexes, must have the same index. | ||
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| Returns: | ||
| DataFrame with columns of X, Y and cosine_similarity |
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Add the type hint as well: bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and cosine_similarity
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| return df | ||
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| def distance( |
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Can we add docstring here too?
| import bigframes.pandas as bpd | ||
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| def test_cosine_similarity(): |
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I assume this ML.DISTANCE COSINE in BQML is different from sklearn package sklearn.metrics.pairwise import cosine_similarity, are these two comparable?
from sklearn.metrics.pairwise import cosine_similarity
X = [[4.1, 0.5, 1.0]]
Y = [[3.0, 0, 2.5]]
cosine_similarity(X, Y)
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Totally different, sklearn takes matrixes and returns matrixes, which we will be hard to support.
ashleyxuu
approved these changes
Feb 13, 2024
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Fixes #<issue_number_goes_here> 🦕