Research notes on various machine learning projects

In view of the fact that the things I have seen recently are too complicated, I use a single document to record some machine learning projects I usually see.

NNI
https://nni.readthedocs.io/en/latest/FeatureEngineering/Overview.html

TreeBasedClassifier refers to ExtraTrees

SISSO, I don’t know anything, physical material machine learning Fortran?

https://arxiv.org/pdf/1710.03319.pdf

https://github.com/rouyang2017/SISSO

borutaPy can calculate the n_estimators parameter through the depth of the tree

    def _get_tree_num(self, n_feat):
        depth = None
        try:
            depth = self.estimator.get_params()['max_depth']
        except KeyError:
            warnings.warn(
                "The estimator does not have a max_depth property, as a result "
                " the number of trees to use cannot be estimated automatically."
            )
        if depth == None:
            depth = 10
        # how many times a feature should be considered on average
        f_repr = 100
        # n_feat * 2 because the training matrix is extended with n shadow features
        multi = ((n_feat * 2) / (np.sqrt(n_feat * 2) * depth))
        n_estimators = int(multi * f_repr)
        return n_estimators

List of sklearn evaluation indicators

Achieve KL divergence

import numpy as np

def KL(a, b):
    a = np.asarray(a, dtype=np.float)
    b = np.asarray(b, dtype=np.float)

    return np.sum(np.where(a != 0, a * np.log(a / b), 0))


values1 = [1.346112,1.337432,1.246655]
values2 = [1.033836,1.082015,1.117323]

print KL(values1, values2)