semvec/pypi-packages huggingface dataset:
rikki 0.0.49
A small runner script which allows to run behaviour tests backed by proxy
Requires Python: >=3.6

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# Rikki - complicated testing environment 

This project it's set of tools and utilities which allow to run behavioral tests backed up by a proxy. The configuration allows to tests things that couldn't be automated before:

- Ads implementations 
- In-app Tracking
- GDPR complaints
- And more

## Dependencies 
Currently the project use Behave and Mitmproxy.
emeraldml 0.0.1
Accelerating machine learning during the exploratory phase
Requires Python: >=3.7

![Emerald](emerald.jpg)
# 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:
```python
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:
```python
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)]
```
```python
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)
```