autofeat

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Input contains NaN, infinity or a value too large for dtype('float32') on fit_transform

1

Facing the following issue when running `AutoFeatRegressor.fit_transform(featuresDf, targetFeature)`: Already checked if there are any infinity values or nan. Also, converted everything to float32. Any pointers? Thanks! Update: tried the same...

nickjtch

Minor point but it would be great if I could create my own dict of ureg objects and pass them instead of a s dict of strings that need parsed.

adzsroka

Speed up tranform()

6

Thank you for the great product! My question is - how can I speed up .tranform() function. I use for input dataframe with only ONE row, but the time needed...

Antitesla

Data validation error when using Buckingham's Pi Theorem on Classification task

1

Hi! While trying to use the AutoFeatClassifier using units, I stumbled upon a validation error caused by an infinite value. Presumably one of the generated features (I assume from the...

aclemente-bigml

Great package! Have you looked into using joblib on this loop, for i, (feat1, feat2) in enumerate(feature_tuples) in feateng.py? Seems like it could be easily parallelized. You have several global...

amirsani

Cannot reproduce results

1

Every time I call fit_transform I get different results. I noticed that np.random.permutation changes the random_state, so I used np.random.RandomState(seed=seed).permutation() to solve. I also noticed that np.random.seed(i) is used in...

RomuloPaiva01

Original feature names are changed to x000, x001, x002,etc., How to avoid this?

1

saleemsam

Cython implementation

2

I was thinking of using cython to run autofeat in C and changing some of the variables to static. Do you think this would make autofeat significantly faster?

fkhan00

Reproducibility issue

1

Hello, I noticed that results are not reproducible by using the library i.e. when using sklearn drop-down-replacement classes, they will each time produce slightly different results. For example, when using:...

janezlapajne