Monotonic composite quantile gradient boost regressor
Project description
MQBoost
A multiple quantiles estimation model that maintains the non-crossing condition (or monotone quantile condition) based on:
with the hyperparameter optimization framework Optuna.
Installation
Install using pip:
pip install mqboost
Usage
Features
- MQRegressor: quantile regressor
Parameters
x # Explanatory data (e.g. pd.DataFrame)
# Column name '_tau' must be not included
y # Response data (e.g. np.ndarray)
alphas # Target quantiles
# It must be in ascending order and not contain duplicates
objective # [Optional] objective to minimize, "check"(default) or "huber"
model # [Optional] boost algorithm to use, "lightgbm"(default) or "xgboost"
delta # [Optional] parameter in "huber" objective, only used when objective == "huber"
# It must be smaller than 0.1
Methods
train # train quantile model
# Any params related to model can be used except "objective"
predict # predict with input data
optimize_params # Optimize hyperparameter with using optuna
Example
import numpy as np
from mqboost import MQRegressor
## Generate sample
sample_size = 500
x = np.linspace(-10, 10, sample_size)
y = np.sin(x) + np.random.uniform(-0.4, 0.4, sample_size)
x_test = np.linspace(-10, 10, sample_size)
y_test = np.sin(x_test) + np.random.uniform(-0.4, 0.4, sample_size)
## target quantiles
alphas = [0.3, 0.4, 0.5, 0.6, 0.7]
## model name
model = "lightgbm" # "xgboost"
## objective funtion
objective = "huber" # "check"
delta = 0.01 # set when objective is huber default 0.05
## LightGBM based quantile regressor
mq_lgb = MQRegressor(
x=x,
y=y_test,
alphas=alphas,
objective=objective,
model=model,
delta=delta,
)
## train with fixed params
lgb_params = {
"max_depth": 4,
"num_leaves": 15,
"learning_rate": 0.1,
"boosting_type": "gbdt",
}
mq_lgb.train(params=lgb_params)
## train with optuna
mq_lgb.train(n_trials = 10) # the number of trials
## Same process
# best_params = mq_lgb.optimize_params(n_trials = 10)
# mq_lgb.train(params=best_params)
## predict
preds_lgb = mq_lgb.predict(x=x_test, alphas=alphas)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
mqboost-0.1.0.tar.gz
(7.5 kB
view hashes)