prfr
Probabilistic random forest regressor: random forest model that accounts for errors in predictors and labels, yields calibrated probabilistic predictions, and corrects for bias.
For a faster and more elaborate calibration routine (highly recommended), a JAX installation is required. You can install the package with the extra jax feature, which will install the necessary dependencies.
Installation
From PyPI, with jax feature:
pip install "prfr[jax]"
From PyPI, without jax feature:
pip install prfr
From Github (latest), with jax feature:
pip install "prfr[jax] @ git+https://github.com/al-jshen/prfr"
From Github (latest), without jax feature:
pip install "git+https://github.com/al-jshen/prfr"
Example usage
import numpy as np
import prfr
x_obs = np.random.uniform(0., 10., size=10000).reshape(-1, 1)
x_err = np.random.exponential(1., size=10000).reshape(-1, 1)
y_obs = np.random.normal(x_obs, x_err).reshape(-1, 1) * 2. + 1.
y_err = np.ones_like(y_obs)
train, test, valid = prfr.split_arrays(x_obs, y_obs, x_err, y_err, test_size=0.2, valid_size=0.2)
model = prfr.ProbabilisticRandomForestRegressor(n_estimators=250, n_jobs=-1)
model.fit(train[0], train[1], eX=train[2], eY=train[3])
model.fit_bias(valid[0], valid[1], eX=valid[2])
# check whether the calibration routine will run with JAX
print(prfr.has_jax)
model.calibrate(valid[0], valid[1], eX=valid[2])
pred = model.predict(test[0], eX=test[2])
pred_qtls = np.quantile(pred, [0.16, 0.5, 0.84], axis=-1)
print(pred.shape)
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Release files for prfr 0.2.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| prfr-0.2.4.tar.gz | 14.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| prfr-0.2.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 27.7 kB
Release files / prfr-0.2.4.tar.gz
| Download URL | prfr-0.2.4.tar.gz |
|---|---|
| Size | 14.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.2.2 CPython/3.10.4 Darwin/22.2.0
|
Release files / prfr-0.2.4-py3-none-any.whl
| Download URL | prfr-0.2.4-py3-none-any.whl |
|---|---|
| Size | 13.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
31ef629509d63d84fb8a763aab981c64e9e14d4ce49ca093ee7dbb0b064f6923
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BLAKE2b-256 checksum How to use checksums |
6859580b808ea2cb6db6c81b2bcc06109e31e401bacc1067d2719ea4991dc840
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.2.2 CPython/3.10.4 Darwin/22.2.0
|
