# Artificial Intelligence Management
> This is a toolbox to help AI & ML teams to have a better management of their metrics and processes.
Our desire is to enable the company with data related to AI solution, in a easy way to read and use. Some new goals are going to be included later
[Confluence Documentation Link]()
[Tangram Link](https://tangram.adeo.com/products/1d6f6abb-63ba-4663-bd1e-18007bffde36/overview)
## Table of Contents
- [Project Structure](#project-structure)
- [Features](#features)
- [Installation/Usage](#Installation/Usage)
- [Contact](#Contact)
## Project Structure
Describe the structure of the `project` folder, including the organization of modules, directories, and any important files.
```
ai_management/
├── __init__.py
├── main.py
├── constants.py
├── examples/
│ ├── usage_examples.ipynb
```
Explain the purpose of each module or significant files.
## ModelEvaluation
Historize the technical model evaluation results.
## Installation
## Usage
### Binary classification
```python
# binary classification
y_true = [1, 0, 0, 1, 1]
y_pred = [1, 0, 0, 0, 1]
y_test_a_lst = y_true
y_pred_a_lst = y_pred
y_test_a_arr = np.array(y_true)
y_pred_a_arr = np.array(y_pred)
```
### Multi class classification
```python
y_true = [0, 1, 2, 1, 2]
y_pred = [[0.9, 0.1, 0.0], [0.3, 0.2, 0.5], [0.2, 0.3, 0.5], [0.1, 0.8, 0.1], [0.1, 0.2, 0.7]]
y_test_b_lst = y_true
y_pred_b_lst = y_pred
y_test_b_arr = np.array(y_true)
y_pred_b_arr = np.array(y_pred)
```
### Multi label classification
```python
y_true = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
y_pred = [[0, 1, 2], [3, 4, 5], [6, 7, 9]]
y_test_c_lst = y_test
y_pred_c_lst = y_pred
y_test_c_arr = np.array(y_true)
y_pred_c_arr = np.array(y_pred)
```
### Solution Evaluation
```python
pip install ai_management
import ai_management as aim
me = aim.ModelEvaluation()
me.historize_model_evaluation(
soltn_nm = 'Solution X',
lst_mdls = [
{
'mdl_nm' : 'Model A',
'algrthm_typ' : 'binary_classification',
'data' : [y_test_a_lst, y_pred_a_lst]},
{
'mdl_nm' : 'Model B',
'algrthm_typ' : 'multi_class_classification',
'data' : [y_test_b_lst, y_pred_b_lst]},
{
'mdl_nm' : 'Model C',
'algrthm_typ' : 'multi_label_classification',
'data' : [y_test_c_lst, y_pred_c_lst]},
],
destination='bi-dev-brlm.ods.FT_ARTFCL_INTLGNC_MDL_MTRC'
)
```
## Contact
* Leroy Merlin Brazil AI developers: chapter_inteligencia_artificia@leroymerlin.com.br
* Rafael Fernandes de Proença Cordeiro (Tech Lead) : rfcordeiro@leroymerlin.com.br
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"description": "# Artificial Intelligence Management\n\n> This is a toolbox to help AI & ML teams to have a better management of their metrics and processes.\n\nOur desire is to enable the company with data related to AI solution, in a easy way to read and use. Some new goals are going to be included later\n\n[Confluence Documentation Link]()\n\n[Tangram Link](https://tangram.adeo.com/products/1d6f6abb-63ba-4663-bd1e-18007bffde36/overview)\n\n## Table of Contents\n\n- [Project Structure](#project-structure)\n- [Features](#features)\n- [Installation/Usage](#Installation/Usage)\n- [Contact](#Contact)\n\n## Project Structure\n\nDescribe the structure of the `project` folder, including the organization of modules, directories, and any important files.\n\n```\nai_management/\n\u251c\u2500\u2500 __init__.py\n\u251c\u2500\u2500 main.py\n\u251c\u2500\u2500 constants.py\n\u251c\u2500\u2500 examples/\n\u2502 \u251c\u2500\u2500 usage_examples.ipynb\n```\n\nExplain the purpose of each module or significant files.\n\n## ModelEvaluation\n\nHistorize the technical model evaluation results.\n\n## Installation\n\n## Usage\n\n### Binary classification\n```python\n# binary classification\ny_true = [1, 0, 0, 1, 1]\ny_pred = [1, 0, 0, 0, 1]\n\ny_test_a_lst = y_true\ny_pred_a_lst = y_pred\n\ny_test_a_arr = np.array(y_true)\ny_pred_a_arr = np.array(y_pred)\n```\n\n### Multi class classification\n```python\ny_true = [0, 1, 2, 1, 2]\ny_pred = [[0.9, 0.1, 0.0], [0.3, 0.2, 0.5], [0.2, 0.3, 0.5], [0.1, 0.8, 0.1], [0.1, 0.2, 0.7]]\n\ny_test_b_lst = y_true\ny_pred_b_lst = y_pred\n\ny_test_b_arr = np.array(y_true)\ny_pred_b_arr = np.array(y_pred)\n```\n\n### Multi label classification\n```python\ny_true = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]\ny_pred = [[0, 1, 2], [3, 4, 5], [6, 7, 9]]\n\ny_test_c_lst = y_test\ny_pred_c_lst = y_pred\n\ny_test_c_arr = np.array(y_true)\ny_pred_c_arr = np.array(y_pred)\n```\n\n\n\n### Solution Evaluation\n```python\npip install ai_management\nimport ai_management as aim \nme = aim.ModelEvaluation()\nme.historize_model_evaluation(\n soltn_nm = 'Solution X', \n lst_mdls = [\n {\n 'mdl_nm' : 'Model A',\n 'algrthm_typ' : 'binary_classification',\n 'data' : [y_test_a_lst, y_pred_a_lst]}, \n {\n 'mdl_nm' : 'Model B',\n 'algrthm_typ' : 'multi_class_classification',\n 'data' : [y_test_b_lst, y_pred_b_lst]},\n {\n 'mdl_nm' : 'Model C',\n 'algrthm_typ' : 'multi_label_classification',\n 'data' : [y_test_c_lst, y_pred_c_lst]},\n ], \n destination='bi-dev-brlm.ods.FT_ARTFCL_INTLGNC_MDL_MTRC'\n)\n```\n\n\n## Contact\n\n* Leroy Merlin Brazil AI developers: chapter_inteligencia_artificia@leroymerlin.com.br\n* Rafael Fernandes de Proen\u00e7a Cordeiro (Tech Lead) : rfcordeiro@leroymerlin.com.br\n\n\n\n",
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