https://github.com/cadcad-org/cadcad_machine_search

Using cadCAD for performing machine search on optimal hyper-surfaces
https://github.com/cadcad-org/cadcad_machine_search

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Using cadCAD for performing machine search on optimal hyper-surfaces

• Host: GitHub
• URL: https://github.com/cadcad-org/cadcad_machine_search
• Owner: cadCAD-org
• License: mit
• Created: 2021-02-02T22:29:42.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2023-12-04T18:29:42.000Z (about 1 year ago)
• Last Synced: 2025-01-08T16:20:10.545Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 1.4 MB
• Stars: 5
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# cadCAD_machine_search

cadCAD tools for preparing & analyzing experiments where

 large-scale machine search for selecting parameters are involved.

**How the choice of parameters that optimizes a given KPI affects the combined KPIs**

![Goal Ternary](https://i.imgur.com/15EZJrO.png)

## Installation

The recommended way is through pip:

```sh

pip install cadCAD_machine_search

```

Alternatively, you can install a branch directly from the repository through:

```sh

pip install git+https://github.com/danlessa/cadCAD_machine_search@master

```

## Usage

### Tools

#### Cartesian product for parameter sweeping

```python

# The sweep_cartesian_product makes it easy to create large-scale simulations

from cadcad_machine_search.tools import sweep_cartesian_product

PARAMS_TO_SWEEP = {    

    "predator_death_const": np.linspace(0.9, 1.1, 5),

    "prey_death_const": np.linspace(0.02, 0.04, 5),

    "dt": np.linspace(0.04, 0.06, 4)

    }

sweep_params = sweep_cartesian_product(PARAMS_TO_SWEEP)

params = {

    "prey_birth_rate": [1.0],

    "predator_birth_rate": [0.01],

    **sweep_params

```

### Visualizations

#### Sensitivity of a KPI towards a set of control parameters

```python

from cadcad_machine_search.visualizations import kpi_sensitivity_plot

# KPI: prey population is 5x above the predator populaton at least 90% of the time

control_params = set(sweep_params.keys())

WEIGHT = 5.0

kpi = lambda df: (df.prey_population > WEIGHT * df.predator_population).mean() > 0.9

kpi_sensitivity_plot(df, kpi, control_params)

```

![Sensitivity analysis on the Prey & Predator model](https://i.imgur.com/QG617Q2.png)

#### Impact of parameter selection in terms of specific vs general goals

```python

from cadcad_machine_search.visualizations import plot_goal_ternary

PREY_ABUNDANCE_THRESHOLD = (df.prey_population / df.predator_population).mean()

PREDATOR_ABUNDANCE_THRESHOLD = (df.predator_population / df.prey_population).mean()

PREY_VARIANCE_THRESHOLD = df.prey_population.std()

PREDATOR_VARIANCE_THRESHOLD = df.predator_population.std()

kpis = {'prey_abundance': lambda df: df.prey_population.mean(),

        'predator_abundance': lambda df: df.predator_population.mean(),

        'prey_variance': lambda df: 1 / df.prey_population.std(),

        'predator_variance': lambda df: 1 / df.predator_population.std(),

        }

# Define goals as sides of the triangle

goals = {}

goals['Prey Desirability'] = lambda metrics: (metrics['prey_abundance'] + metrics['prey_variance']) / 2

goals['Predator Desirability'] = lambda metrics: (metrics['predator_abundance'] + metrics['predator_variance']) / 2

goals['System Stability'] = lambda metrics: (metrics['prey_variance'] + metrics['predator_variance']) / 2

goals['combined'] = lambda goals: goals[0] + goals[1] + goals[2]

plot_goal_ternary(df, kpis, goals, control_params)

```

![Goal Ternary](https://i.imgur.com/15EZJrO.png)

## Examples

We recommend having a look on the notebooks/ folder, as we have a P&P model

that makes usage of the parameter sweep helpers together with the KPI sensitivity

visualization.