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.
- Python (>= 3.7)
- NumPy (>= 1.21.2)
- pandas (>= 1.3.3)
- scikit-learn (>= 0.24.2)
- statsmodels (>= 0.12.2)
pip install emeraldml
You can check the latest sources with the command:
git clone https://github.com/yu3ufff/emeraldml.git
Getting the data:
import pandas as pd
audi = 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 emerald
from 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)