How to fit a basic model

These examples show how to fit a model using MonoBoost. There are two model

types: MonoBoost, and MonoBoostEnsemble. MonoBoost sequentially fits num_estimators partially monotone cone rules to the dataset using gradient boosting. MonoBoostEnsemble fits a sequence of MonoBoost classifiers each of size learner_num_estimators (up to a total of num_estimators) using gradient boosting. The advantage of MonoBoostEnsemble is to allow the added feature of stochastic subsampling of fraction sample_fract after every learner_num_estimators cones.

import numpy as np
import monoboost as mb
from sklearn.datasets import load_boston

Load the data

First we load the standard data source on Boston Housing, and convert the output from real valued (regression) to binary classification with roughly 50-50 class distribution:

data = load_boston()
y = data['target']
X = data['data']
features = data['feature_names']

Specify the monotone features

There are 13 predictors for house price in the Boston dataset:

1. CRIM - per capita crime rate by town
2. ZN - proportion of residential land zoned for lots over 25,000 sq.ft.
3. INDUS - proportion of non-retail business acres per town.
4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)
5. NOX - nitric oxides concentration (parts per 10 million)
6. RM - average number of rooms per dwelling
7. AGE - proportion of owner-occupied units built prior to 1940
8. DIS - weighted distances to five Boston employment centres
9. RAD - index of accessibility to radial highways
10. TAX - full-value property-tax rate per :math:`10,000
11. PTRATIO - pupil-teacher ratio by town
12. B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
13. LSTAT - % lower status of the population

The output is MEDV - Median value of owner-occupied homes in `1000’s, but we convert it to a binary y in +/-1 indicating whether MEDV is less than $21(,000):

y[y< 21]=-1 # convert real output to 50-50 binary classification
y[y>=21]=+1

We suspect that the number of rooms (6. RM) and the highway accessibility (9. RAD) would, if anything, increase the price of a house (all other things being equal). Likewise we suspect that crime rate (1. CRIM), distance from employment (8. DIS) and percentage of lower status residents (13. LSTAT) would be likely to, if anything, decrease house prices. So we have:

incr_feats=[6,9]
decr_feats=[1,8,13]

Fit a MonoBoost model

We now fit our classifier. To understand the hyperparameters, please refer to the original paper available here:

# Specify hyperparams for model solution
vs = [0.01, 0.1, 0.2, 0.5, 1]
eta = 0.25
learner_type = 'two-sided'
num_estimators = 10
# Solve model
mb_clf = mb.MonoBoost(n_feats=X.shape[1], incr_feats=incr_feats,
                          decr_feats=decr_feats, num_estimators=num_estimators,
                          fit_algo='L2-one-class', eta=eta, vs=vs,
                          verbose=False, learner_type=learner_type)
mb_clf.fit(X, y)
# Assess the model
y_pred = mb_clf.predict(X)
acc = np.sum(y == y_pred) / len(y)

Fit a MonoBoostEnsemble model

We now fit our classifier. To understand the hyperparameters, please refer to the original paper available here:

# Specify hyperparams for model solution
vs = [0.01, 0.1, 0.2, 0.5, 1]
eta = 0.25
learner_type = 'one-sided'
num_estimators = 10
learner_num_estimators = 2
learner_eta = 0.25
learner_v_mode = 'random'
sample_fract = 0.5
random_state = 1
standardise = True
# Solve model
mb_clf = mb.MonoBoostEnsemble(
    n_feats=X.shape[1],
    incr_feats=incr_feats,
    decr_feats=decr_feats,
    num_estimators=num_estimators,
    fit_algo='L2-one-class',
    eta=eta,
    vs=vs,
    verbose=False,
    learner_type=learner_type,
    learner_num_estimators=learner_num_estimators,
    learner_eta=learner_eta,
    learner_v_mode=learner_v_mode,
    sample_fract=sample_fract,
    random_state=random_state,
    standardise=standardise)
mb_clf.fit(X, y)
# Assess the model (MonoBoostEnsemble)
y_pred = mb_clf.predict(X)
acc = np.sum(y == y_pred) / len(y)

Final notes

In a real scenario we would use a hold out technique such as cross-validation to tune the hyperparameters v, eta and num_estimators but this is standard practice and not covered in these basic examples. Note that for tuning num_estimators we can use predict(X,cum=True) because as standard for boosting, the stagewise predictions are stored. Enjoy!

Total running time of the script: ( 0 minutes 0.000 seconds)