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aggmap-1.1.7
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مشاهده بیشترتوضیحات
Jigsaw-like AggMap: A Robust and Explainable Omics Deep Learning Tool
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aggmap-1.1.7 |
| نام | aggmap |
| نسخه کتابخانه | 1.1.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | WanXiang Shen |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/shenwanxiang/bidd-aggmap/tree/master |
| آدرس اینترنتی | https://pypi.org/project/aggmap/ |
| مجوز | - |
[](https://doi.org/10.5281/zenodo.6474351)
[](https://github.com/shenwanxiang/bidd-aggmap/tree/master/paper/example)
[](https://badge.fury.io/py/aggmap)
[](https://bidd-aggmap.readthedocs.io/en/latest/?badge=latest)
<a href="url"><img src="https://raw.githubusercontent.com/shenwanxiang/bidd-aggmap/master/docs/images/logo.png" align="left" height="170" width="130" ></a>
# Jigsaw-like AggMap
## A Robust and Explainable Omics Deep Learning Tool
----
### Installation
install aggmap by:
```bash
# create an aggmap env
conda create -n aggmap python=3.7
conda activate aggmap
pip install --upgrade pip
pip install aggmap
```
----
### Usage
```python
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]])
```
### How It Works?
- AggMap flowchart of feature mapping and agglomeration into ordered (spatially correlated) multi-channel feature maps (Fmaps)
**a**, AggMap flowchart of feature mapping and aggregation into ordered (spatially-correlated) channel-split feature maps (Fmaps).**b**, CNN-based AggMapNet architecture for Fmaps learning. **c**, proof-of-concept illustration of AggMap restructuring of unordered data (randomized MNIST) into clustered channel-split Fmaps (reconstructed MNIST) for CNN-based learning and important feature analysis. **d**, typical biomedical applications of AggMap in restructuring omics data into channel-split Fmaps for multi-channel CNN-based diagnosis and biomarker discovery (explanation `saliency-map` of important features).
----
### Proof-of-Concepts of reconstruction ability on MNIST Dataset
- It can reconstruct to the original image from completely randomly permuted (disrupted) MNIST data:
`Org1`: the original grayscale images (channel = 1), `OrgRP1`: the randomized images of Org1 (channel = 1), `RPAgg1, 5`: the reconstructed images of `OrgPR1` by AggMap feature restructuring (channel = 1, 5 respectively, each color represents features of one channel). `RPAgg5-tkb`: the original images with the pixels divided into 5 groups according to the 5-channels of `RPAgg5` and colored in the same way as `RPAgg5`.
----
### The effect of the number of channels on model performance
- Multi-channel Fmaps can boost the model performance notably:
The performance of AggMapNet using different number of channels on the `TCGA-T (a)` and `COV-D (b)`. For `TCGA-T`, ten-fold cross validation average performance, for `COV-D`, a fivefold cross validation was performed and repeat 5 rounds using different random seeds (total 25 training times), their average performances of the validation set were reported.
----
### Example for Restructured Fmaps
- The example on WDBC dataset: click [here](https://github.com/shenwanxiang/bidd-aggmap/blob/master/paper/example/00_breast_cancer/00_WDBC_example_flow.ipynb) to find out more!
----
نیازمندی
| مقدار | نام |
|---|---|
| ==0.3.9 | umap-learn |
| ==0.39.0 | shap |
| ==1.1.5 | pandas |
| ==0.9.1 | seaborn |
| ==0.22.2.post1 | scikit-learn |
| - | scipy |
| ==0.13.2 | joblib |
| ==0.4.2 | python-highcharts |
| - | tqdm |
| - | colored |
| ==4.0.2 | colorlog |
| - | lapjv |
| - | numpy |
| - | numba |
| ==2.9.1 | tensorflow-gpu |
| - | pytest |
نحوه نصب
نصب پکیج whl aggmap-1.1.7:
pip install aggmap-1.1.7.whl
نصب پکیج tar.gz aggmap-1.1.7:
pip install aggmap-1.1.7.tar.gz