Elad Warshawsky

Possible Speedup

1

This library is not as fast as preferred, so I have a suggestion. you could use numba or jit to speedup your functions, I'll make my own version and submit...

Benchmark / GPlearn

4

Can you do a basic comparison between this and gplearn with regards to speed and flexibility?

Add Ability to Convert Continuous Data to Categorical

2

Add Ability to Convert Continuous Data to Categorical : sklearn.preprocessing.KBinsDiscretizer

Support for Continuous Data

1

This is pretty cool, I was just wondering if it also works on continuous data. I think resnet can work on continuous data, so I'm curious.

Creating a new axis for rgb is fine, but you could automatically implement this with a 2D Convolution. This is the example in keras: input_tensor = Input(shape=(128,128,1) ) x =...

While the theory is interesting and has some application, using what you've made is difficult. I would like to see an example

Won't work on predict statement

2

--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in ----> 1 learner.predict(ze.values) /kaggle/input/quantumforest1/python-package/quantum_forest/QForest_Net.py in predict(self, X_, pred_leaf, pred_contrib, raw_score, num_iteration, flag) 426 The predicted values. 427 """ --> 428 self.profile.Snapshot("PRED_0") 429...

Problem with Pop

1

/opt/conda/lib/python3.7/site-packages/joblib/parallel.py in (.0) 254 with parallel_backend(self._backend, n_jobs=self._n_jobs): 255 return [func(*args, **kwargs) --> 256 for func, args, kwargs in self.items] 257 258 def __len__(self): /kaggle/working/mGBDT/lib/mgbdt/model/online_xgb.py in fit_increment(self, X, y, num_boost_round, params)...

I feel that code written in python 3.5 would likely be compatible with other python 3 versions, are you sure that a build is necessary in 3.5?

I've been working on trying to use a personal function, is there an easy way to do this. Can you give an example using something like cosine.