Metadata-Version: 2.1
Name: pysloth
Version: 0.0.2
Summary: Probabilistic Predictions
Home-page: https://github.com/PySloth/pysloth
Author: Valery Manokhin
Author-email: valery.manokhin.2015@live.rhul.ac.uk
Maintainer: leonarduschen
Maintainer-email: leonardus.chen@gmail.com
License: MIT
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: statsmodels (>=0.12.2)
Requires-Dist: scikit-learn (>=0.24.1)
Requires-Dist: numpy (>=1.19.2)

# pysloth
Python package for probabilistic prediction

v0.0.2

# Installation
This package supports Python 3.6, 3.7, 3.8, and 3.9

### Install via PyPI
- Run `pip install pysloth`

### Install from repository
- Clone repo with SSH `git clone git@github.com:PySloth/pysloth.git`
- Change directory to where `README.md` (this file) is located and run `pip install .`

# Quickstart
The following is a code sample showing `scpd` and `ccpd` in action

```python
from pysloth import scpd_function, ccpd_function

import numpy as np
import statsmodels.api as sm

np.random.seed(142)
n = 1000  # training set
m = int(0.8 * n)  # proper training set
n_cal = n - m  # Calibration = training - proper training

n_test = 100
sd_noise = 1

n_delta = 1000  # discretization for y values in interval y_hat +/- 3 * delta
w = 2  # the weights

x_train = w * np.random.random(m) - 1
x_cal = w * np.random.random(n_cal) - 1
x_test = w * np.random.random(n_test) - 1

y_train = w * x_train + np.random.randn(m) * sd_noise
y_cal = w * x_cal + np.random.randn(n_cal) * sd_noise
y_test = w * x_test + np.random.randn(n_test) * sd_noise

x_train_cal = np.reshape(np.hstack((x_train, x_cal)), (n, 1))
y_train_cal = np.reshape(np.hstack((y_train, y_cal)), (n, 1))
xy_train_cal = np.hstack((x_train_cal, y_train_cal))

model = sm.OLS(y_train, x_train).fit()
predictions = model.predict(x_train)
model.summary()
y_hat = model.predict(x_test)

delta = 3 * np.std(y_hat)
y_grid = np.linspace(y_hat.min() - delta, y_hat.max() + delta, n_delta)

print(ccpd_function(x_train_cal, y_train_cal, x_test, y_grid, 5, n_delta))
print(scpd_function(x_train, x_cal, y_train, y_cal, x_test, y_test, y_grid, 5, n_delta))
```

