Merge pull request #7 from promised-ai/feature/pickle

Pickle Support

changepoint/CHANGELOG.md

@@ -0,0 +1,12 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.3.1] - 
+
+### Added
+
+- (#7) Added `PartialEq` derive macro to `Bocpd` and `Argpcpd` structs.

changepoint/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "changepoint"
-version = "0.14.0"
+version = "0.14.1"
 authors = [
  "Mike Schmidt <[email protected]>",
  "Baxter Eaves <[email protected]>",

changepoint/src/bocpd.rs

@@ -14,7 +14,7 @@ use serde::{Deserialize, Serialize};
 
 /// Online Bayesian Change Point Detection state container.
 #[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-#[derive(Clone, Debug)]
+#[derive(Clone, Debug, PartialEq)]
 pub struct Bocpd<X, Fx, Pr>
 where
  Fx: Rv<X> + HasSuffStat<X>,

pychangepoint/CHANGELOG.md

@@ -0,0 +1,11 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.3.1] - 
+- Added Pickle support for `ArgpCpd` and `Bocpd`
+- Fix type error that occurred converting numpy `bool_` and `int` types
+- Added default arguments `NormalInvChiSquared` constructor (`m=0, k=1, v=1, s2=1`)

pychangepoint/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "pychangepoint"
 description = "A Python module for changepoint"
-version = "0.3.0"
+version = "0.3.1"
 repository = "https://github.com/promised-ai/changepoint"
 authors = ["Redpoll <[email protected]>"]
 edition = "2021"
@@ -16,12 +16,14 @@ name = "changepoint"
 crate-type = ["cdylib"]
 
 [dependencies]
-changepoint = { path = "../changepoint" }
+bincode = "1.3.3"
+changepoint = { path = "../changepoint", features = ["serde1"] }
 nalgebra = { version = "0.32" }
 numpy = "0.19"
 pyo3 = { version ="0.19", features = ["extension-module"] }
 rand = { version = "0.8", features = ["small_rng"] }
 rv = { version = "0.16", features = ["process", "arraydist"] }
+serde = "1.0.175"
 
 [features]
 extension-module = ["pyo3/extension-module"]

pychangepoint/ChangePointExample.ipynb

@@ -103,12 +103,19 @@
  "\n",
  " data_lower, data_upper = 0.975 * np.min(data), 1.05 * np.max(data)\n",
  "\n",
- " regime_bot, regime_top = data_lower - 0.1 * (data_upper - data_lower), data_lower\n",
+ " regime_bot, regime_top = (\n",
+ " data_lower - 0.1 * (data_upper - data_lower),\n",
+ " data_lower,\n",
+ " )\n",
  " data_ax.set_ylim(regime_bot, data_upper)\n",
  "\n",
- " map_change_points = np.hstack([chp.map_changepoints(change_point_history), n])\n",
+ " map_change_points = np.hstack(\n",
+ " [chp.map_changepoints(change_point_history), n]\n",
+ " )\n",
  "\n",
- " for i, (a, b) in enumerate(zip(map_change_points[:-1], map_change_points[1:])):\n",
+ " for i, (a, b) in enumerate(\n",
+ " zip(map_change_points[:-1], map_change_points[1:])\n",
+ " ):\n",
  " data_ax.fill_between(\n",
  " [a, b],\n",
  " [regime_bot, regime_bot],\n",
@@ -496,7 +503,9 @@
  }
  ],
  "source": [
- "_, (data_ax, _data_ax), change_points = change_point_plot(cpi, change_point_history)\n",
+ "_, (data_ax, _data_ax), change_points = change_point_plot(\n",
+ " cpi, change_point_history\n",
+ ")\n",
  "\n",
  "data_ax.set_xticks([5 * 12 * i for i in range(cpi.shape[0] // (5 * 12))])\n",
  "data_ax.set_xticklabels([cpi.index[i][:4] for i in data_ax.get_xticks()])\n",
@@ -826,7 +835,9 @@
  }
  ],
  "source": [
- "_, (data_ax, _data_ax), change_points = change_point_plot(cpi, change_point_history)\n",
+ "_, (data_ax, _data_ax), change_points = change_point_plot(\n",
+ " cpi, change_point_history\n",
+ ")\n",
  "\n",
  "data_ax.set_xticks([5 * 12 * i for i in range(cpi.shape[0] // (5 * 12))])\n",
  "data_ax.set_xticklabels([cpi.index[i][:4] for i in data_ax.get_xticks()])\n",

pychangepoint/README.md

@@ -20,26 +20,26 @@ See [rustup.rs](https://rustup.rs/) for instructions on installing Rust.
 ## Quick Docs
 By convention in these docs and examples, 
 ```python
-import changepoint as chp
+import changepoint as cpt
 ```
 
 ### Models
 #### Bocpd
 
 The Bayesian change point detector, `Bocpd`, takes a prior distribution, aka one of
 ```python,ignore
-chp.BetaBernoulli
-chp.NormalGamma
-chp.NormalInvChiSquared
-chp.NormalInvGamma
-chp.NormalInvWishart
-chp.PoissonGamma
+cpt.BetaBernoulli
+cpt.NormalGamma
+cpt.NormalInvChiSquared
+cpt.NormalInvGamma
+cpt.NormalInvWishart
+cpt.PoissonGamma
 ```
 
 Then, a `Bocpd` may be created:
 ```python
-cpd = chp.Bocpd(
- prior=chp.NormalGamma(),
+cpd = cpt.Bocpd(
+ prior=cpt.NormalGamma(),
  lam=12,
 )
 ```
@@ -58,7 +58,7 @@ change_point_history = np.zeros((n, n))
 for i, x in enumerate(data):
  change_point_history[i, : i + 1] = cpd.step(x)
 
-print(chp.map_changepoints(change_point_history))
+print(cpt.map_changepoints(change_point_history))
 ```
 
 
@@ -69,7 +69,7 @@ where `c` is the scale, `X_{i-l-1, ..., i-1}` is the previous vales in the seque
 It behaves similarity to the `Bocpd` class; for example,
 
 ```python
-argp = chp.ArgpCpd(logistic_hazard_h=-2, scale=3, noise_level=0.01)
+argp = cpt.ArgpCpd(logistic_hazard_h=-2, scale=3, noise_level=0.01)
 n = len(data)
 change_point_history = np.zeros((n + 1, n + 1))
 xs = []
@@ -78,7 +78,7 @@ for i, x in enumerate(data):
  cps = argp.step(x)
  change_point_history[i, : len(cps)] = cps
 
-print(chp.map_changepoints(change_point_history))
+print(cpt.map_changepoints(change_point_history))
 ```
 
 ## Example

pychangepoint/pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "maturin"
 
 [project]
 name = "changepoint"
-version = "0.3.0"
+version = "0.3.1"
 authors = [
  {name = "Redpoll", email = "[email protected]" },
 ]
@@ -28,3 +28,6 @@ Repository = "https://github.com/promised-ai/changepoint"
 
 [tool.maturin]
 bindings = "pyo3"
+
+[tool.black]
+line-length = 80

pychangepoint/src/argpcpd.rs

@@ -1,7 +1,10 @@
+use bincode::{deserialize, serialize};
 use changepoint::gp::Argpcp;
 use changepoint::BocpdLike;
 use pyo3::exceptions::PyValueError;
 use pyo3::prelude::*;
+use pyo3::pyclass::CompareOp;
+use pyo3::types::PyBytes;
 use rv::process::gaussian::kernel::{
  AddKernel, ConstantKernel, ProductKernel, RBFKernel, WhiteKernel,
 };
@@ -30,7 +33,7 @@ use rv::process::gaussian::kernel::{
 /// Roughly the slope of the logistic hazard function
 /// logistic_hazard_b: float
 /// The offset of the logistic hazard function.
-#[pyclass]
+#[pyclass(module = "changepoint")]
 pub struct ArgpCpd {
  argpcpd: Argpcp<
  AddKernel<ProductKernel<ConstantKernel, RBFKernel>, WhiteKernel>,
@@ -105,4 +108,33 @@ impl ArgpCpd {
  pub fn step(&mut self, datum: f64) -> Vec<f64> {
  self.argpcpd.step(&datum).to_vec()
  }
+
+ pub fn __setstate__(
+ &mut self,
+ py: Python,
+ state: PyObject,
+ ) -> PyResult<()> {
+ match state.extract::<&PyBytes>(py) {
+ Ok(s) => {
+ self.argpcpd = deserialize(s.as_bytes()).unwrap();
+ Ok(())
+ }
+ Err(e) => Err(e),
+ }
+ }
+
+ pub fn __getstate__(&self, py: Python) -> PyResult<PyObject> {
+ Ok(PyBytes::new(py, &serialize(&self.argpcpd).unwrap()).to_object(py))
+ }
+
+ fn __richcmp__(&self, other: &Self, op: CompareOp) -> PyResult<bool> {
+ match op {
+ CompareOp::Lt => Ok(false),
+ CompareOp::Le => Ok(false),
+ CompareOp::Eq => Ok(self.argpcpd == other.argpcpd),
+ CompareOp::Ne => Ok(self.argpcpd != other.argpcpd),
+ CompareOp::Gt => Ok(false),
+ CompareOp::Ge => Ok(false),
+ }
+ }
 }