emeraldml 0.0.1

Accelerating machine learning during the exploratory phase

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.

Demo

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)