EmeraldML

A machine learning library for streamlining the process of (1) cleaning and splitting data, (2) training, optimizing, and testing various models based on the task, and (3) scoring and ranking them during the exploratory phase for an elementary analysis of which models perform better for a specific dataset.

Installation

Dependencies

Python (>= 3.7)
NumPy (>= 1.21.2)
pandas (>= 1.3.3)
scikit-learn (>= 0.24.2)
statsmodels (>= 0.12.2)

User installation

pip install emeraldml

Development

Source code

You can check the latest sources with the command:

git clone https://github.com/yu3ufff/emeraldml.git

Demo

Getting the data:import pandas as pdaudi = pd.read_csv('audi.csv') audi.head()

|    | model   |   year |   price | transmission   |   mileage | fuelType   |   tax |   mpg |   engineSize |
|---:|:--------|-------:|--------:|:---------------|----------:|:-----------|------:|------:|-------------:|
|  0 | A1      |   2017 |   12500 | Manual         |     15735 | Petrol     |   150 |  55.4 |          1.4 |
|  1 | A6      |   2016 |   16500 | Automatic      |     36203 | Diesel     |    20 |  64.2 |          2   |
|  2 | A1      |   2016 |   11000 | Manual         |     29946 | Petrol     |    30 |  55.4 |          1.4 |
|  3 | A4      |   2017 |   16800 | Automatic      |     25952 | Diesel     |   145 |  67.3 |          2   |
|  4 | A3      |   2019 |   17300 | Manual         |      1998 | Petrol     |   145 |  49.6 |          1   |

Using EmeraldML:import emeraldfrom emerald.boa import RegressionBoa rboa = RegressionBoa(random_state=3) rboa.hunt(data=audi, target='price') rboa.ladder

[(OptimalRFRegressor, 0.9624889664024406),
 (OptimalDTRegressor, 0.9514992411732952),
 (OptimalKNRegressor, 0.9511411883559433),
 (OptimalLinearRegression, 0.8876961846248467),
 (OptimalABRegressor, 0.8491539140007975)]

for i in range(len(rboa)): print(rboa.model(i))

RandomForestRegressor(min_samples_split=5, n_estimators=500, random_state=3)
DecisionTreeRegressor(max_depth=15, min_samples_split=10, random_state=3)
KNeighborsRegressor(n_neighbors=3, p=1)
LinearRegression()
AdaBoostRegressor(learning_rate=0.1, n_estimators=100, random_state=3)