Feature selection for hard voting classifier and NN sparse weight initialization.
Project description
maxjoshua
Feature selection for hard voting classifier and NN sparse weight initialization.
Preface
I am naming this software package in memory of my late nephew Max Joshua Hamster (* 2005 to † June 18, 2022).
Usage
Forward Selection for Hard Voting Classifier
Load toy data set and convert features to binary.
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import scale
X = scale(load_breast_cancer().data, axis=0) > 0 # convert to binary features
y = load_breast_cancer().target
Select binary features. Each row in the results list contains the n_select column indices of X, the notice if the binary features were negated, and the sum of absolute MCC correlation coeffcients between the selected features.
import maxjoshua as mh
idx, neg, rho, results = mh.binsel(
X, y, preselect=0.8, oob_score=True, subsample=0.5,
n_select=5, unique=True, n_draws=100, random_state=42)
Algorithm. The task is to select e.g. n_select features from a pool of many features. These features might be the prediction of binary classifiers. The selected features are then combined into one hard-voting classifier.
A voting classifier should have the following properties
each voter (a binary feature) should be highly correlated to the target variable
the selected features should be uncorrelated.
The algorithm works as follows
Generate multiple correlation matrices by bootstrapping. This includes corr(X_i, X_j) as well as corr(Y, X_i) computation. Also store the oob samples for evaluation.
For each correlation matrix do …
Preselect the i* with the highest abs(corr(Y, X_i)) estimates (e.g. pick the n_pre=? highest absolute correlations)
Slice a correlation matrix corr(X_i*, X_j*) and find the least correlated combination of n_select features. (see `korr.mincorr <https://github.com/kmedian/korr/blob/master/korr/mincorr.py>`__)
Compute the out-of-bag (OOB) performance (see step 1) of the hard-voter with the selected n_select=? features
Select the feature combination with the best OOB performance as final model.
Forward Selection for Linear Regression
Load toy dataset.
from sklearn.preprocessing import scale
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
X = scale(housing["data"], axis=0)
y = scale(housing["target"])
Select real-numbered features. Each row in the results list contains the n_select column indices of X, the ridge regression coefficents beta and the RMSE loss. Warning! Please note that the features X and the target y must be scaled because mh.fltsel uses an L2-penalty on beta coefficients, and doesn’t used an intercept term to shift y.
import maxjoshua as mh
from sklearn.preprocessing import scale
idx, beta, loss, results = mh.fltsel(
scale(X), scale(y), preselect=0.8, oob_score=True, subsample=0.5,
n_select=5, unique=True, n_draws=100, random_state=42, l2=0.01)
Initialize Sparse NN Layer
The idea is to run mh.fltsel to generate an ensemble of linear models, and combine them in a sparse linear neural network layer, i.e., the number of output neurons is the ensemble size. In case of small datasets, the sparse NN layer is non-trainable because because each submodel was already estimated and selected with two-way data splits in mh.fltsel (see oob_scores and subsample). The sparse NN layers basically produces submodel predictions for meta model in the next layer, i.e., a simple dense linear layer. The inputs of the sparse NN layer must be normalized for which a layer normalization layers is trained.
import maxjoshua as mh
import tensorflow as tf
import sklearn.preprocessing
# create toy dataset
import sklearn.datasets
X, y = sklearn.datasets.make_regression(
n_samples=1000, n_features=100, n_informative=20, n_targets=3)
# feature selection
# - always scale the inputs and targets -
indices, values, num_in, num_out = mh.pretrain_submodels(
sklearn.preprocessing.scale(X),
sklearn.preprocessing.scale(y),
num_out=64, n_select=3)
# specify the model
model = tf.keras.models.Sequential([
# sub-models
mh.SparseLayerAsEnsemble(
num_in=num_in,
num_out=num_out,
sp_indices=indices,
sp_values=values,
sp_trainable=False,
norm_trainable=True,
),
# meta model
tf.keras.layers.Dense(
units=3, use_bias=False,
# kernel_constraint=tf.keras.constraints.NonNeg()
),
# scale up
mh.InverseTransformer(
units=3,
init_bias=y.mean(),
init_scale=y.std()
)
])
model.compile(
optimizer=tf.keras.optimizers.Adam(
learning_rate=3e-4, beta_1=.9, beta_2=.999, epsilon=1e-7, amsgrad=True),
loss='mean_squared_error'
)
# train
history = model.fit(X, y, epochs=3)
Appendix
Installation
The maxjoshua git repo is available as PyPi package
pip install maxjoshua
Install a virtual environment
python3.7 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install -r requirements-demo.txt
(If your git repo is stored in a folder with whitespaces, then don’t use the subfolder .venv. Use an absolute path without whitespaces.)
Python commands
Jupyter for the examples: jupyter lab
Check syntax: flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
Run Unit Tests: pytest
Publish
pandoc README.md --from markdown --to rst -s -o README.rst
python setup.py sdist
twine upload -r pypi dist/*
Clean up
find . -type f -name "*.pyc" | xargs rm find . -type d -name "__pycache__" | xargs rm -r rm -r .venv
Support
Please open an issue for support.
Contributing
Please contribute using Github Flow. Create a branch, add commits, and open a pull request.
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