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https://github.com/advestis/adnmtf

Non-Negative Matrix and Tensor Factorizations
https://github.com/advestis/adnmtf

data-completion data-science dimensionality-reduction machine-learning matrix nmtf

Last synced: about 2 months ago
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Non-Negative Matrix and Tensor Factorizations

Host: GitHub
URL: https://github.com/advestis/adnmtf
Owner: Advestis
License: mit
Created: 2021-03-26T09:21:30.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2024-04-29T16:34:40.000Z (9 months ago)
Last Synced: 2024-11-16T23:12:19.319Z (2 months ago)
Topics: data-completion, data-science, dimensionality-reduction, machine-learning, matrix, nmtf
Language: Python
Homepage: https://advestis.github.io/adnmtf/
Size: 8.31 MB
Stars: 3
Watchers: 1
Forks: 1
Open Issues: 1
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

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#### Status

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##### Contact

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# NMTF

Non-Negative Matrix and Tensor Factorizations

Developped in collaboration with [Advestis](https://advestis.com/) ([Github](https://github.com/Advestis))

## NMF Example

```python

from adnmtf import NMF

import numpy as np

w = np.array([[1, 2],

              [3, 4],

              [5, 6],

              [7, 8],

              [9, 10],

              [11, 12]])

h = np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6],

              [0.9, 0.8, 0.7, 0.6, 0.5, 0.4]])

m0 = w.dot(h)

my_nmfmodel = NMF(n_components=2)

estimator_ = my_nmfmodel.fit_transform(m0)

estimator_ = my_nmfmodel.predict(estimator_)

```

In this example, the matrix to be factorized is generated by the dot product of:

| W  |    |

|----|----|

| 1  | 2  |

| 3  | 4  |

| 5  | 6  |

| 7  | 8  |

| 9  | 10 |

| 11 | 12 |

and

| H   |     |     |     |     |     |

|-----|-----|-----|-----|-----|-----|

| 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 |

| 0.9 | 0.8 | 0.7 | 0.6 | 0.4 | 0.4 |

- *NMF* instantiates a NMF class with 2 components.

- *fit_transform* calls the functions below in the given order:

  - *nmtf_base* module: *non_negative_factorization*, *nmf_init*, *r_ntf_solve*

  - *nmtf_core* module: *ntf_solve*, *ntf_solve_simple*, *ntf_update*

- *predict* derives from *fit_transform* outputs ordered sample and

feature indexes for future use in ordered heatmaps. 

*predict* calls *nmf_predict* and *build_clusters* in the *nmtf_base* module

## NTF Example

```python

from adnmtf import NTF

import pandas as pd

DATA_PATH = ...

df = pd.read_csv(DATA_PATH)

m0 = df.values

n_blocks = 5

my_ntfmodel = NTF(n_components=5)

estimator_ = my_ntfmodel.fit_transform(m0, n_blocks)

estimator_ = my_ntfmodel.predict(estimator_)

```

In this example, the tensor to be factorized is read in file *data_ntf.csv*. 

The tensor has 5 layers in the 3rd dimension and is formatted as a table

with 5 blocks concatenated horizontally.

- *NTF* instantiates a NTF class with 5 components.

- *fit_transform* calls the functions below in the given order:

  - *nmtf_base* module: *non_negative_tensor_factorization*, *ntf_init*, *r_ntf_solve*

  - *nmtf_core* module: *ntf_solve*, *ntf_solve_simple*, *ntf_update*

- *predict* derives from *fit_transform* outputs ordered sample and

feature indexes for future use in ordered heatmaps. 

*predict* calls *nmf_predict* and *build_clusters* in the *nmtf_base* module

## Articles

### Peer-reviewed articles

* [(researchgate) A Tale of Two Matrix Factorizations](https://www.researchgate.net/publication/263216872_A_Tale_of_Two_Matrix_Factorizations)

* [(researchgate) Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations](https://www.researchgate.net/publication/220241471_Fast_Local_Algorithms_for_Large_Scale_Nonnegative_Matrix_and_Tensor_Factorizations)

* [(nature) Learning the parts of objects by non-negative matrix factorization](https://www.nature.com/articles/44565)

### Blog articles

* [(Medium) Using Non-negative matrix factorization to classify companies](https://medium.com/@chtill.g/using-nmf-to-classify-companies-a77e176f276f)

* [(offconvex) Tensor Methods in Machine Learning](https://www.offconvex.org/2015/12/17/tensor-decompositions/?source=post_page---------------------------)