flamby

It seems one has to retrieve npartitions from original dask dataframe, and pass it to append. So I fixed it this way: ```python collection.append(symbol, df_diff, npartitions=item.data.npartitions) ``` Will it work...

Hi @ulupo Sure. Here it is : ```python import numpy as np import giotto.time_series as ts s=np.random.rand(39000, 1) embedder = ts.TakensEmbedding(parameters_type='search', dimension=4, time_delay=5, n_jobs=-1) embedder.fit(s) ``` On a 12 cores...

silly me. i did not see the `while True` but anyway, no other model than LinearModel are stored...

Hi @maxim5 Thanks for the clarification. I like the idea of having an ensemble of different ML algorithm, instead of an ensemble of 5 LinearModel. So I came to this...

This great article on de Prado's handling of bars came last week: [Financial Machine Learning Part 0: Bars](https://towardsdatascience.com/financial-machine-learning-part-0-bars-745897d4e4ba?gi=5815592bd146) It appears the author will add more articles on the book

This pull request is also required for the VMware Cloud Director OVA deployment, and almost all VMware products deployable from OVA.

Hi @Y-oHr-N Is it that you want to make your `mllib.ensemble.RandomSeedAveragingRegressor` and `mllib.ensemble.RandomSeedAveragingClassifier` compatible somehow w/ OptGBM? I'd thought that since OptGBM follows the sklearn API, it would be compatible...

Hi @Y-oHr-N, Thanks for the clarification. I ran all your examples with my dataset (OptGBM 0.5.0 and mllib of current git master branch) and had small improvements indeed. I must...

> I noticed the bug yesterday, immediately fixed it and released 0.5.0. If you are really using 0.5.0, please tell me your environment in detail. Example 5 works fine in...

Thank you very much @Y-oHr-N I'll test it in the coming days.