marksweissma / reeses

scikit-learn plugin for piecewise models

reeses

reeses is a scikit-learn plugin for piecewise models with learned partitions.

learned partitions or groups can be assigned through tree based or clustering models, the only requirement is the estimator can be fit on the data and exposes a method that can assign the appropriate id of the learned partition for new data. prediction estimators can be optimized within node / leaf through grid search if neccesary

reeses tries to follow the utility patterns in scikit-learn i.e.

1. sample weight transformation for bootstrapping
2. joblib for parallelization

quick start

pip install reeses

regression

from sklearn.datasets import load_boston
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression

from reeses import PiecewiseRegressor

data = load_boston()

X = data['data']
y = data['target']

tree = DecisionTreeRegressor(min_samples_leaf=40)
ols = LinearRegression()

model = PiecewiseRegressor(assignment_estimator=tree, prediction_estimator=ols)
model.fit(X, y)

classification

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression

from reeses import PiecewiseClassifier

data = load_iris()

X = data['data']
y = data['target']

tree = DecisionTreeClassifier(min_samples_leaf=40)
logit = LogisticRegression()

model = PiecewiseClassifier(assignment_estimator=tree, prediction_estimator=logit)
model.fit(X, y)

ensembling & bootstrapping

reeses will introspect ensemble assignment estimators and maintain the bootstrapped sample fit in each assignment estimator for prediction estimators associated with that assignment estimator.

from sklearn.datasets import load_iris
from sklearn.tree import RandomForestClassifier
from sklearn.linear_model import LogisticRegression

from reeses import PiecewiseClassifier

data = load_iris()

X = data['data']
y = data['target']

forest = RandomForestClassifier(n_estimators=10, min_samples_leaf=40)
logit = LogisticRegression()

model = PiecewiseClassifier(assignment_estimator=forest, prediction_estimator=logit)
model.fit(X, y)

clustering assignment

reeses supports arbitary assignment estimators. scikit-learn clustering estimators use the predict method to make assignments. <0.0.3 reeses defaults to the apply method but con be configured of any assignment through the assignment_method attribute. >=0.0.3 defaults to apply if assignment estimator has apply else predict if has predict else raises.

Example

Consider y = sign(x) * (1 + abs(x)) - a linear function with a shock at the origin. This function is a poor candidate for linear methods (nonlinear function) and tree based methods (cannot extrapolate outside the observed bounds). Combining the two we can produce an effective estimator that can extrapolate.

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm

from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score, mean_absolute_error

from reeses import pieces

create data

observations = 10000
x_train = np.random.uniform(-1, 1, observations)
x_test = np.random.uniform(-2, 2, observations)
y_train = np.sign(x_train) * (1 + np.abs(x_train)) + norm.rvs(size=observations, loc=0, scale=.05)
y_test = np.sign(x_test) * (1 + np.abs(x_test)) + norm.rvs(size=observations, loc=0, scale=.05)

# reshape arrays to 2D
x_train = x_train[:, None]
x_test = x_test[:, None]

training data

create models

models = {
    'ols':
    LinearRegression(),
    'tree':
    DecisionTreeRegressor(max_depth=10),
    'piecewise_tree':
    pieces.PiecewiseRegressor(assignment_estimator=DecisionTreeRegressor(
        max_depth=1, criterion='mse'),
                              prediction_estimator=LinearRegression()),
}

grid_params = {
    'tree': {
        'max_depth': range(1, 20)
    },
    'piecewise_tree': {
        'assignment_estimator__max_depth': range(1, 5)
    }
}

fit models and make predictions on test data

predictions = {}
for name, estimator in models.items():
    if name in grid_params:
        predictions[name] = GridSearchCV(
            estimator, grid_params[name],
            n_jobs=-1).fit(x_train, y_train).best_estimator_.predict(x_test)
    else:
        predictions[name] = estimator.fit(x_train, y_train).predict(x_test)

Test Data

Results!

OLS

Decision Tree

Piecewise Estimator