Jigsaw-like AggMap: A Robust and Explainable Omics Deep Learning Tool
Project description
Jigsaw-like AggMap
A Robust and Explainable Omics Deep Learning Tool
Installation
install aggmap by:
# create an aggmap env
conda create -n aggmap python=3.7
conda activate aggmap
pip install --upgrade pip
pip install aggmap==1.1.0
Usage
import pandas as pd
from sklearn.datasets import load_breast_cancer
from aggmap import AggMap, AggMapNet
# Data loading
data = load_breast_cancer()
dfx = pd.DataFrame(data.data, columns=data.feature_names)
dfy = pd.get_dummies(pd.Series(data.target))
# AggMap object definition, fitting, and saving
mp = AggMap(dfx, metric = 'correlation')
mp.fit(cluster_channels=5, emb_method = 'umap', verbose=0)
mp.save('agg.mp')
# AggMap visulizations: Hierarchical tree, embeddng scatter and grid
mp.plot_tree()
mp.plot_scatter(enabled_data_labels=True, radius=5)
mp.plot_grid(enabled_data_labels=True)
# Transoformation of 1d vectors to 3D Fmaps (-1, w, h, c) by AggMap
X = mp.batch_transform(dfx.values, n_jobs=4, scale_method = 'minmax')
y = dfy.values
# AggMapNet training, validation, early stopping, and saving
clf = AggMapNet.MultiClassEstimator(epochs=50, gpuid=0)
clf.fit(X, y, X_valid=None, y_valid=None)
clf.save_model('agg.model')
# Model explaination by simply-explainer: global, local
simp_explainer = AggMapNet.simply_explainer(clf, mp)
global_simp_importance = simp_explainer.global_explain(clf.X_, clf.y_)
local_simp_importance = simp_explainer.local_explain(clf.X_[[0]], clf.y_[[0]])
# Model explaination by shapley-explainer: global, local
shap_explainer = AggMapNet.shapley_explainer(clf, mp)
global_shap_importance = shap_explainer.global_explain(clf.X_)
local_shap_importance = shap_explainer.local_explain(clf.X_[[0]])
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