mlregression 0.1.4

Machine learning regression off-the-shelf

*** ATTENTION ***

Don't immidiately run pip install mlregression. See Section Installation.

Machine learning regression (mlregression)

Machine Learning Regression (mlregrresion) is an off-the-shelf implementation of the most popular ML methods that automatically takes care of fitting and parameter tuning.

Currently, the fully implemented models include:

• Ensemble trees (Random forests, XGBoost, LightGBM, GradientBoostingRegressor, ExtraTreesRegressor)
• Penalized regression (Ridge, Lasso, ElasticNet, Lars, LassoLars)
• Neural nets (Simple neural nets with 1-5 hidden layers, rely activation, and early stopping)

NB! When using penalized regressions, consider using the native CV-implementation from scikit-learn for speed. See Example 6 below.

In addition, all scikit-learn regressors can be supplied (e.g., LinearRegression, HuberRegressor, or BayesianRidge), but then one has to provide a parameter grid as well!

Please contact the authors below if you find any bugs or have any suggestions for improvement. Thank you!

Author: Nicolaj Søndergaard Mühlbach (n.muhlbach at gmail dot com, muhlbach at mit dot edu)

Code dependencies

This code has the following dependencies:

• Python >=3.6
• numpy >=1.19
• pandas >=1.3
• scikit-learn >=1
• scikit-learn-intelex >= 2021.3
• daal >= 2021.3
• daal4py >= 2021.3
• tbb >= 2021.4
• xgboost >=1.3
• lightgbm >=3.2

Installation

Before calling pip install mlregression, we recommend using conda to install the dependencies. In our experience, calling the following command works like a charm:

conda install -c conda-forge numpy">=1.19" pandas">=1.3" scikit-learn">=1" scikit-learn-intelex">=2021.3" daal">=2021.3" daal4py">=2021.3" tbb">=2021.4" xgboost">=1.3" lightgbm">=3.2" --force-reinstall

After this, install mlregression by calling pip install mlregression. Note that without installing the dependensies, the package will not work. As of now, it does not work when installing the dependensies via pip install. The reason is that we are using the Intel® Extension for Scikit-learn to massively speed up computations, but the dependensies are not properly installed via pip install.

Usage

We demonstrate the use of mlregression below, using random forests, xgboost, and lightGBM as underlying regressors.

#------------------------------------------------------------------------------
# Libraries
#------------------------------------------------------------------------------
# Standard
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

# This library
from mlregression.mlreg import MLRegressor

#------------------------------------------------------------------------------
# Data
#------------------------------------------------------------------------------
# Generate data
X, y = make_regression(n_samples=500,
                       n_features=10, 
                       n_informative=5,
                       n_targets=1,
                       bias=0.0,
                       coef=False,
                       random_state=1991)

X_train, X_test, y_train, y_test = train_test_split(X, y)

#------------------------------------------------------------------------------
# Example 1: Prediction
#------------------------------------------------------------------------------
# Specify any of the following estimators:
"""
"LinearRegression",
"RidgeCV", "LassoCV", "ElasticNetCV",
"RandomForestRegressor","ExtraTreesRegressor", "GradientBoostingRegressor",
"XGBRegressor", "LGBMegressor",
"MLPRegressor",
"""

# For instance, pick "RandomForestRegressor"
estimator = "RandomForestRegressor"
# Note that the 'estimator' may also be an instance of a class, e.g., RandomForestRegressor(), conditional on being imported first, e.g. from sklearn.ensemble import RandomForestRegressor

# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models') and the number of cross-validation folds ('n_cv_folds')
mlreg = MLRegressor(estimator=estimator,
                    n_cv_folds=5,
                    max_n_models=2)

# Fit
mlreg.fit(X=X_train, y=y_train)

# Predict
y_hat = mlreg.predict(X=X_test)

# Access all the usual attributes
mlreg.best_score_
mlreg.best_estimator_

# Compute the score
mlreg.score(X=X_test,y=y_test)

#------------------------------------------------------------------------------
# Example 2: Cross-fitting
#------------------------------------------------------------------------------
# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models'), the number of cross-validation folds ('n_cv_folds'), AND the number of cross-fitting folds ('n_cf_folds')
mlreg = MLRegressor(estimator=estimator,
                    n_cv_folds=5,
                    max_n_models=2,
                    n_cf_folds=2)

# Cross fit
mlreg.cross_fit(X=X_train, y=y_train)

# Extract in-sample that are estimated in an out-of-sample way (e.g., via cross-fitting)
y_hat = mlreg.y_pred_cf_

# Likewise, extract the residualized outcomes used in e.g., double machine learning. This is \tilde{Y} = Y - E[Y|X=x]
y_res = mlreg.y_res_cf_