Basic Usage

Brush is designed to be used similarly to any sklearn-style estimator. That means it should be compatible with sklearn pipelines, wrappers, and so forth.

In addition, Brush provides functionality that allows you to feed in more complicated data types than just matrices of floating point values.

Regression

# load data
import pandas as pd

df = pd.read_csv('docs/examples/datasets/d_enc.csv')
X = df.drop(columns='label')
y = df['label']

# import and make a regressor
from pybrush import BrushRegressor

# you can set verbosity=1 to see the progress bar
est = BrushRegressor(verbosity=1)

# use like you would a sklearn regressor
est.fit(X,y)
y_pred = est.predict(X)

print('score:', est.score(X,y))

Classification

# load data
import pandas as pd

df = pd.read_csv('docs/examples/datasets/d_analcatdata_aids.csv')
X = df.drop(columns='target')
y = df['target']

# import and make a classifier
from pybrush import BrushClassifier
est = BrushClassifier(verbosity=1)

# use like you would a sklearn classifier
est.fit(X,y)

y_pred = est.predict(X)
y_pred_proba = est.predict_proba(X)

print('score:', est.score(X,y))