The RoPWR library implements several mathematical programming formulations to compute the optimal continuous/discontinuous piecewise polynomial regression given a list of split points. It supports several monotonic constraints, objective functions and regularizations. The library is written in Python and relies on cvxpy (ECOS, OSQP, SCS and HIGHS solvers) to solve the underlying optimization problems. Other formulations are solved using a direct approach.
Table of Contents
To install the current release of ropwr from PyPI:
pip install ropwr
To install from source, download or clone the git repository
git clone https://github.com/guillermo-navas-palencia/ropwr.git
cd ropwr
python setup.py install
RoPWR requires
- cvxpy (>=1.1.14)
- numpy (>=1.16)
- scikit-learn (>=0.22)
- scipy (>=1.6.1)
Please visit the RoPWR documentation (current release) http://gnpalencia.org/ropwr/. You can get started following the tutorials and checking the API reference.
To get us started, let’s load a well-known dataset from the UCI repository and transform the data into a pandas.DataFrame.
import pandas as pd
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
x = df["NOX"].values
y = data.targetTo devise split points, we use the implementation of the unsupervised technique equal-size or equal-frequency interval implemented in scikit-learn KBinsDiscretizer.
from sklearn.preprocessing import KBinsDiscretizer
from ropwr import RobustPWRegression
est = KBinsDiscretizer(n_bins=10, strategy="quantile")
est.fit(x.reshape(-1, 1), y)
splits = est.bin_edges_[0][1:-1]If the trend of the relationship with the target is unclear, use the default piecewise regression.
pw = RobustPWRegression()
pw.fit(x, y, splits)
Since version 0.4.0 two unsupervised binning techniques, equal-size ("uniform") and equal-frequency interval ("quantile") from scikit-learn KBinsDiscretizer are available using parameters splits="quantile" or splits="uniform" and providing the desired number of bins using n_bins parameter. The previous code is equivalent to
pw = RobustPWRegression()
pw.fit(x, y, splits="quantile", n_bins=10)The relationship with the target exhibits a sort of U-shaped trend. Let's try to force convexity.
pw = RobustPWRegression(objective="l1", degree=1, monotonic_trend="convex")
pw.fit(x, y, splits)
To reduce the mean squared error (MSE) and mean absolute error (MAE), we replace convex by valley.
pw = RobustPWRegression(objective="l1", degree=1, monotonic_trend="valley")
pw.fit(x, y, splits)
RoPWR supports four objectives functions ("l1", "l2", "huber", "quantile") and the addition of a regularization term (l1-Lasso or l2-Ridge). Additionally, it permits imposing a lower or upper limit to the prediction.
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing()
df = pd.DataFrame(data.data, columns=data.feature_names)
x = df["MedInc"].values
y = df["target"].values
est = KBinsDiscretizer(n_bins=15, strategy="quantile")
est.fit(x.reshape(-1, 1), y)
splits = est.bin_edges_[0][1:-1]
pw = RobustPWRegression(objective="huber", monotonic_trend="ascending",
degree=2, regularization="l1", verbose=True)
pw.fit(x, y, splits, lb=1, ub=5)ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS
It pcost dcost gap pres dres k/t mu step sigma IR | BT
0 +0.000e+00 -3.418e+04 +8e+05 7e-01 5e+00 1e+00 7e+00 --- --- 2 1 - | - -
1 -5.445e+03 -1.409e+04 +3e+05 3e-01 8e-02 1e+00 2e+00 0.8351 2e-01 2 1 1 | 0 0
2 -5.079e+03 -1.370e+04 +3e+05 3e-01 7e-02 1e+00 2e+00 0.1140 9e-01 2 1 2 | 0 0
3 +1.681e+03 -2.408e+03 +2e+05 2e-01 4e-02 7e-01 1e+00 0.6098 2e-01 2 1 2 | 0 0
4 +6.977e+03 +5.329e+03 +7e+04 7e-02 2e-02 3e-01 5e-01 0.6562 1e-01 2 1 2 | 0 0
5 +1.037e+04 +9.826e+03 +2e+04 2e-02 1e-02 9e-02 2e-01 0.7604 1e-01 2 2 2 | 0 0
6 +1.102e+04 +1.066e+04 +2e+04 1e-02 9e-03 6e-02 1e-01 0.4819 3e-01 2 2 1 | 0 0
7 +1.202e+04 +1.189e+04 +6e+03 5e-03 5e-03 2e-02 5e-02 0.7116 1e-01 1 2 2 | 0 0
8 +1.202e+04 +1.189e+04 +6e+03 5e-03 5e-03 2e-02 5e-02 0.0642 9e-01 2 2 1 | 0 0
9 +1.216e+04 +1.206e+04 +4e+03 4e-03 4e-03 1e-02 3e-02 0.3528 3e-01 2 2 2 | 0 0
10 +1.216e+04 +1.206e+04 +4e+03 4e-03 4e-03 1e-02 3e-02 0.0043 1e+00 1 2 2 | 0 0
11 +1.215e+04 +1.206e+04 +4e+03 4e-03 4e-03 1e-02 3e-02 0.1560 9e-01 3 2 2 | 0 0
12 +1.220e+04 +1.212e+04 +4e+03 3e-03 3e-03 1e-02 3e-02 0.2911 6e-01 2 2 2 | 0 0
13 +1.219e+04 +1.211e+04 +3e+03 3e-03 3e-03 9e-03 3e-02 0.7226 9e-01 1 1 2 | 0 0
14 +1.246e+04 +1.242e+04 +1e+03 1e-03 1e-03 4e-03 1e-02 0.5864 3e-02 2 2 1 | 0 0
15 +1.255e+04 +1.253e+04 +8e+02 7e-04 8e-04 2e-03 7e-03 0.5172 1e-01 2 2 1 | 0 0
16 +1.261e+04 +1.260e+04 +4e+02 3e-04 4e-04 1e-03 3e-03 0.5858 8e-02 1 1 1 | 0 0
17 +1.264e+04 +1.264e+04 +1e+02 1e-04 1e-04 3e-04 1e-03 0.9487 3e-01 1 2 2 | 0 0
18 +1.266e+04 +1.266e+04 +2e+01 1e-05 2e-05 4e-05 1e-04 0.8967 3e-02 1 2 2 | 0 0
19 +1.266e+04 +1.266e+04 +2e+00 2e-06 2e-06 5e-06 2e-05 0.8827 1e-02 2 1 1 | 0 0
20 +1.266e+04 +1.266e+04 +6e-01 5e-07 6e-07 1e-06 5e-06 0.9890 3e-01 1 1 1 | 0 0
21 +1.266e+04 +1.266e+04 +1e-01 9e-08 1e-07 2e-07 8e-07 0.8542 3e-02 2 1 1 | 0 0
22 +1.266e+04 +1.266e+04 +4e-02 3e-08 4e-08 9e-08 3e-07 0.8281 2e-01 2 1 1 | 0 0
23 +1.266e+04 +1.266e+04 +2e-02 1e-08 2e-08 4e-08 1e-07 0.7671 2e-01 2 1 1 | 0 0
24 +1.266e+04 +1.266e+04 +3e-03 2e-09 3e-09 6e-09 2e-08 0.9531 1e-01 2 1 1 | 0 0
25 +1.266e+04 +1.266e+04 +4e-05 3e-11 4e-11 8e-11 3e-10 0.9862 1e-04 2 1 1 | 0 0
OPTIMAL (within feastol=3.7e-11, reltol=2.8e-09, abstol=3.5e-05).
Runtime: 4.340140 seconds.
