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ml

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

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JonTriebenbach
JonTriebenbach commented Sep 2, 2020

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    
PhilipMay
PhilipMay commented Jun 12, 2020

MLflow seems to have a length limit of 5000 when setting tags (see below).

[...]
  File "/home/smay/miniconda3/envs/py38/lib/python3.8/site-packages/mlflow/utils/validation.py", line 136, in _validate_length_limit
    raise MlflowException(
mlflow.exceptions.MlflowException: Tag value '[0.8562690322984875, 0.8544098885636596, 0.8544098885636596, 0.8544098885636596, 0.85440988856365
justinormont
justinormont commented Dec 5, 2020

Internal user reported a stall during the .Fit() of the word embedding transform.

On first use of the word embedding transform, it downloads the word embedding model from the CDN.

To test:

  1. Clear any copies of the fastText300D word embedding file from local machine
    Check local folder, and ~/.local/share/mlnet-resources/WordVectors/ for a file named wiki.en.vec
  2. Create example c
mmlspark
brunocous
brunocous commented Sep 2, 2020

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

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