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https://github.com/advestis/adilsm
https://github.com/advestis/adilsm
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/advestis/adilsm
- Owner: Advestis
- License: mit
- Created: 2024-03-11T13:44:46.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-02T07:39:45.000Z (6 months ago)
- Last Synced: 2024-11-11T01:32:25.593Z (2 months ago)
- Language: Python
- Size: 57.8 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Install
```bash
pip install adilsm
```
# Description
ILSM is Integrated Latent Multi Source Model.
# Reference
https://doi.org/10.36922/aih.3427
# Usage
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import adilsm.adilsm as ilsm
max_noise_level = 0.1
# Generate a random non-negative matrix with 100 rows and 10 columns
A = np.random.rand(100, 10)
# Swap the columns of the A and add some noise to generate B
B = np.random.permutation(A.T).T + np.random.uniform(low=0, high=max_noise_level, size=A.shape)
# Add noise to A
A += np.random.uniform(low=0, high=max_noise_level, size=A.shape)
# ISM is expected to recognize that A and B convey the same information up to some noise,
# albeit with the columns of B swapped around. Heatmaps of the loadings of A and B columns
# on ISM components show the effective permutation.
Xs = [A, B]
n_embedding, n_themes = [10,10]
ilsm_result = ilsm.ism(Xs, n_embedding, n_themes, norm_columns=False, update_h_ism=True,
max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)
hv = ilsm_result['HV']
hv_sparse = ilsm_result['HV_SPARSE']
hhii = ilsm_result['HHII']
w_ism = ilsm_result['W']
h_ism = ilsm_result['H']
q_ism = ilsm_result['Q']
Xs_emb = ilsm_result['EMBEDDING']
Xs_norm = ilsm_result['NORMED_VIEWS']
fig, ax = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True)
ax[0].imshow(hv[0], cmap='viridis', aspect='auto')
# Add labels and title
ax[0].set_xlabel('Component')
ax[0].set_ylabel('Column')
ax[0].set_title('Loadings of A columns on ISM components')
ax[1].imshow(hv[1], cmap='viridis', aspect='auto')
# Add labels and title
ax[1].set_xlabel('Component')
ax[1].set_ylabel('Column')
ax[1].set_title('Loadings of B columns on ISM components')
# Show the plot
plt.show()
```