MLWave

Regardless of consolidating projecting and mapping, it would be cool to be able to pipeline lenses. ``` projected_X = mapper.fit_transform(X, projection=[PCA(2), custom_projection_function, "knn_distance_5"]) ```

I like how so much can now fit inside a single tweet: ```Python import kmapper as km from sklearn.datasets import make_circles as c from sklearn import manifold inverse_X, y =...

Wanting something like: ``` import keplermapper as km from sklearn import linear_model as lm mapper = km.KeplerMapper() projected_X = mapper.fit_transform( inverse_X=inverse_X, y=y, projection=lm.Ridge()) ``` Where the projected_X is created by...

Also, separating out fit_transform would make it easier to pickle? ``` from kmapper import Projecter, Mapper, Visualizer, Pipeline projecter = Projecter(projection="dist_mean") projected_X_train1 = projecter.fit_transform(X_train) projected_X_test1 = projecter.transform(X_test) joblib.dump(projecter, "projecter1.p") projecter...

I like being able to skip importing commonly used sklearn libraries like `decomposition` and `manifold`. I think we can use: ``` import kmapper as km mapper = km.KeplerMapper() projected_X =...

:thought_balloon: ``` import kmapper as km mapper = km.Mapper(cover=km.covers.Cubical(n_cubes=[20, 30], overlap_perc=[0.1, 0.2]), nerve=km.nerves.AdaptivePairwise(min_samples=3), clusterer=km.clusterers.HDBSCAN()) graph_train = mapper.fit_transform(projected_X, inverse_X, y=y) graph_test = mapper.transform(projected_X_unseen, inverse_X_unseen) ```

These are not in the current project set, but contributions to the lens library are very welcome! I think you can manually do Gaussian Kernel Density Estimation to build a...

No where public. Sorry!

Thats fairly weird indeed. Let me check if I have set all the seeds correctly. I could have sworn it was reproduceable.

Yeah, if you encode it sanely. Onehot-encoding or target-encoding should work. But care must be taking with target-encoding not to overfit.