biosphere 0.3.0 (#96)

* Integrate biosphere=0.3.0. * Python. * R * Changelog. * Readme. * Version numbers.
mlondschien · Mar 15, 2022 · 157394c · 157394c
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,6 +1,21 @@
 
 # Changelog
 
+## 0.5.0 - (2022-03-15)
+
+**Breaking changes:**
+
+- The parameters `random_forest_mtry` and `random_forest_n_trees` of `Control` have been renamed to `random_forest_max_features` and `random_forest_n_estimators`.
+- The default value for `random_forest_max_features` now is `floor(sqrt(d))`.
+
+**New features:**
+
+- The parameter `random_forest_max_features` now can be supplied with a fraction `0 < f < 1`, an integer `i>=1`, `None` (Python, Rust) / `NULL` (R) and `"sqrt"`. Then, for each split, repsectively `floor(f d)`, `i`, `d` or `floor(sqrt(d))` features are considered.
+
+**Other changes:**
+
+- Bump `biosphere` dependency to 0.3.0
+
 ## 0.4.4 - (2022-02-22)
 
 **Other changes:**

diff --git a/Cargo.toml b/Cargo.toml
@@ -3,7 +3,7 @@ name = "changeforest"
 description = "Classifier based non-parametric change point detection."
 authors = ["Malte Londschien <malte@londschien.ch>"]
 repository = "https://github.com/mlondschien/changeforest/"
-version = "0.4.4"
+version = "0.5.0"
 edition = "2021"
 readme = "README.md"
 license = "BSD-3-Clause"
@@ -15,7 +15,7 @@ name = "changeforest"
 [dependencies]
 ndarray = "0.15"
 rand = "0.8"
-biosphere = "0.2.2"
+biosphere = "0.3.0"
 
 [dev-dependencies]
 rstest = "0.12"

diff --git a/README.md b/README.md
@@ -96,7 +96,7 @@ very few trees:
 
 ```python
 In [6]: from changeforest import Control
-   ...: changeforest(iris, "random_forest", "bs", Control(random_forest_n_trees=10))
+   ...: changeforest(iris, "random_forest", "bs", Control(random_forest_n_estimators=10))
 Out[6]:
                     best_split max_gain p_value
 (0, 150]                    50   96.071    0.01
@@ -185,7 +185,7 @@ which can be passed to `changeforest`. The following
 will build random forests with very few trees:
 
 ```R
-> changeforest(X, "random_forest", "bs", Control(random_forest_n_trees=10))
+> changeforest(X, "random_forest", "bs", Control(random_forest_n_estimators=10))
 ... TODO
 ```
 

diff --git a/changeforest-py/Cargo.toml b/changeforest-py/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "changeforest_py"
-version = "0.4.4"
+version = "0.5.0"
 edition = "2021"
 
 [lib]
@@ -14,4 +14,5 @@ name = "changeforest"
 numpy = "0.15"
 changeforest = { path = "../" }
 ndarray = "0.15.3"
-pyo3 = {version = "0.15", features = ["extension-module"]}
+pyo3 = {version = "0.15", features = ["extension-module"]}
+biosphere = "0.3.0"
diff --git a/changeforest-py/README.md b/changeforest-py/README.md
@@ -90,7 +90,7 @@ very few trees:
 
 ```python
 In [6]: from changeforest import Control
-   ...: changeforest(iris, "random_forest", "bs", Control(random_forest_n_trees=10))
+   ...: changeforest(iris, "random_forest", "bs", Control(random_forest_n_estimators=10))
 Out[6]:
                     best_split max_gain p_value
 (0, 150]                    50   96.071    0.01

diff --git a/changeforest-py/changeforest/control.py b/changeforest-py/changeforest/control.py
@@ -14,9 +14,9 @@ def __init__(
         number_of_wild_segments="default",
         seeded_segments_alpha="default",
         seed="default",
-        random_forest_n_trees="default",
+        random_forest_n_estimators="default",
         random_forest_max_depth="default",
-        random_forest_mtry="default",
+        random_forest_max_features="default",
         random_forest_n_jobs="default",
     ):
         self.minimal_relative_segment_length = _to_float(
@@ -28,9 +28,9 @@ def __init__(
         self.number_of_wild_segments = _to_int(number_of_wild_segments)
         self.seeded_segments_alpha = _to_float(seeded_segments_alpha)
         self.seed = _to_int(seed)
-        self.random_forest_n_trees = _to_int(random_forest_n_trees)
+        self.random_forest_n_estimators = _to_int(random_forest_n_estimators)
         self.random_forest_max_depth = _to_int(random_forest_max_depth)
-        self.random_forest_mtry = _to_int(random_forest_mtry)
+        self.random_forest_max_features = _to_int(random_forest_max_features)
         self.random_forest_n_jobs = _to_int(random_forest_n_jobs)
 
 

diff --git a/changeforest-py/pyproject.toml b/changeforest-py/pyproject.toml
@@ -2,7 +2,7 @@
 name = "changeforest"
 description = "Classifier based non-parametric change point detection"
 readme = "README.md"
-version = "0.4.4"
+version = "0.5.0"
 requires-python = ">=3.7"
 author = "Malte Londschien <malte@londschien.ch>"
 urls = {homepage = "https://github.com/mlondschien/changeforest/"}

diff --git a/changeforest-py/src/control.rs b/changeforest-py/src/control.rs
@@ -1,5 +1,8 @@
+use biosphere::MaxFeatures;
 use changeforest::Control;
-use pyo3::prelude::{PyObject, PyResult, Python};
+use pyo3::exceptions;
+use pyo3::prelude::{pyclass, FromPyObject, PyAny, PyErr, PyResult};
+use pyo3::prelude::{PyObject, Python};
 
 pub fn control_from_pyobj(py: Python, obj: Option<PyObject>) -> PyResult<Control> {
     let mut control = Control::default();
@@ -44,39 +47,92 @@ pub fn control_from_pyobj(py: Python, obj: Option<PyObject>) -> PyResult<Control
         if let Ok(pyvalue) = obj.getattr(py, "seed") {
             if let Ok(value) = pyvalue.extract::<u64>(py) {
                 control = control.with_seed(value);
+                control.random_forest_parameters =
+                    control.random_forest_parameters.with_seed(value);
             }
         };
 
-        if let Ok(pyvalue) = obj.getattr(py, "random_forest_n_trees") {
-            if let Ok(value) = pyvalue.extract::<usize>(py) {
-                control = control.with_random_forest_n_trees(value);
+        if let Ok(pyvalue) = obj.getattr(py, "seeded_segments_alpha") {
+            if let Ok(value) = pyvalue.extract::<f64>(py) {
+                control = control.with_seeded_segments_alpha(value);
             }
         };
 
-        if let Ok(pyvalue) = obj.getattr(py, "random_forest_max_depth") {
-            if let Ok(value) = pyvalue.extract::<Option<usize>>(py) {
-                control = control.with_random_forest_max_depth(value);
+        if let Ok(pyvalue) = obj.getattr(py, "random_forest_n_estimators") {
+            if let Ok(value) = pyvalue.extract::<usize>(py) {
+                control.random_forest_parameters =
+                    control.random_forest_parameters.with_n_estimators(value);
             }
         };
 
-        if let Ok(pyvalue) = obj.getattr(py, "random_forest_mtry") {
+        if let Ok(pyvalue) = obj.getattr(py, "random_forest_max_depth") {
             if let Ok(value) = pyvalue.extract::<Option<usize>>(py) {
-                control = control.with_random_forest_mtry(value);
+                control.random_forest_parameters =
+                    control.random_forest_parameters.with_max_depth(value);
             }
         };
 
-        if let Ok(pyvalue) = obj.getattr(py, "random_forest_n_jobs") {
-            if let Ok(value) = pyvalue.extract::<Option<usize>>(py) {
-                control = control.with_random_forest_n_jobs(value);
+        if let Ok(pyvalue) = obj.getattr(py, "random_forest_max_features") {
+            if let Ok(value) = pyvalue.extract::<PyMaxFeatures>(py) {
+                control.random_forest_parameters = control
+                    .random_forest_parameters
+                    .with_max_features(value.value);
             }
         };
 
-        if let Ok(pyvalue) = obj.getattr(py, "seeded_segments_alpha") {
-            if let Ok(value) = pyvalue.extract::<f64>(py) {
-                control = control.with_seeded_segments_alpha(value);
+        if let Ok(pyvalue) = obj.getattr(py, "random_forest_n_jobs") {
+            if let Ok(value) = pyvalue.extract::<Option<i32>>(py) {
+                control.random_forest_parameters =
+                    control.random_forest_parameters.with_n_jobs(value);
             }
         };
     }
 
     Ok(control)
 }
+
+#[pyclass(name = "MaxFeatures")]
+pub struct PyMaxFeatures {
+    pub value: MaxFeatures,
+}
+
+impl PyMaxFeatures {
+    fn new(value: MaxFeatures) -> Self {
+        PyMaxFeatures { value }
+    }
+}
+
+impl FromPyObject<'_> for PyMaxFeatures {
+    fn extract(ob: &'_ PyAny) -> PyResult<Self> {
+        if let Ok(value) = ob.extract::<usize>() {
+            Ok(PyMaxFeatures::new(MaxFeatures::Value(value)))
+        } else if let Ok(value) = ob.extract::<f64>() {
+            if value > 1. || value <= 0. {
+                Err(PyErr::new::<exceptions::PyTypeError, _>(format!(
+                    "Got max_features {}",
+                    value
+                )))
+            } else {
+                Ok(PyMaxFeatures::new(MaxFeatures::Fraction(value)))
+            }
+        } else if let Ok(value) = ob.extract::<Option<String>>() {
+            if value.is_none() {
+                Ok(PyMaxFeatures::new(MaxFeatures::None))
+            } else {
+                if value.as_ref().unwrap() == "sqrt" {
+                    Ok(PyMaxFeatures::new(MaxFeatures::Sqrt))
+                } else {
+                    Err(PyErr::new::<exceptions::PyTypeError, _>(format!(
+                        "Unknown value for max_features: {}",
+                        value.unwrap()
+                    )))
+                }
+            }
+        } else {
+            Err(PyErr::new::<exceptions::PyTypeError, _>(format!(
+                "Unknown value for max_features: {}",
+                ob
+            )))
+        }
+    }
+}
diff --git a/changeforest-py/src/lib.rs b/changeforest-py/src/lib.rs
@@ -16,11 +16,13 @@ fn changeforest(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
     fn changeforest(
         py: Python<'_>,
         X: PyReadonlyArray2<f64>,
-        method: String,
-        segmentation_type: String,
+        method: Option<String>,
+        segmentation_type: Option<String>,
         control: Option<PyObject>,
     ) -> PyResult<MyBinarySegmentationResult> {
         let control = control_from_pyobj(py, control).unwrap();
+        let method = method.unwrap_or("random_forest".to_string());
+        let segmentation_type = segmentation_type.unwrap_or("bs".to_string());
         Ok(MyBinarySegmentationResult {
             result: wrapper::changeforest(&X.as_array(), &method, &segmentation_type, &control),
         })

diff --git a/changeforest-py/tests/test_changeforest.py b/changeforest-py/tests/test_changeforest.py
@@ -1,13 +1,18 @@
 import numpy as np
 import pytest
 
-from changeforest import changeforest
+from changeforest import Control, changeforest
 
 
 @pytest.mark.parametrize("method", ["knn", "change_in_mean", "random_forest"])
 @pytest.mark.parametrize("segmentation_type", ["sbs", "wbs", "bs"])
 def test_changeforest(iris_dataset, method, segmentation_type):
-    result = changeforest(iris_dataset, method, segmentation_type)
+    result = changeforest(
+        iris_dataset,
+        method,
+        segmentation_type,
+        control=Control(minimal_relative_segment_length=0.1),
+    )
     np.testing.assert_array_equal(result.split_points(), [50, 100])
 
 
@@ -17,10 +22,10 @@ def test_changeforest_repr(iris_dataset):
         result.__repr__()
         == """\
                     best_split max_gain p_value
-(0, 150]                    50   96.322    0.01
- ¦--(0, 50]                 23  -10.204       1
- °--(50, 150]              100   53.822    0.01
-     ¦--(50, 100]           79   -18.63       1
-     °--(100, 150]         134  -10.839       1\
+(0, 150]                    50   96.233    0.01
+ ¦--(0, 50]                  2  -14.191       1
+ °--(50, 150]              100   52.799    0.01
+     ¦--(50, 100]           53     5.44    0.29
+     °--(100, 150]         136   -2.398    0.96\
 """
     )
diff --git a/changeforest-py/tests/test_control.py b/changeforest-py/tests/test_control.py
@@ -25,14 +25,14 @@
         # model_selection_alpha
         ("iris", "bs", "knn", {"model_selection_alpha": 0.001}, []),
         ("iris", "bs", "knn", {"model_selection_alpha": 0.05}, [50, 100]),
-        # random_forest_ntree
+        # random_forest_n_estimators
         # This is impressive and unexpected.
-        ("iris", "bs", "random_forest", {"random_forest_n_trees": 1}, [37, 52, 99]),
-        ("iris", "bs", "random_forest", {"random_forest_n_trees": 100}, [50, 100]),
+        ("iris", "bs", "random_forest", {"random_forest_n_estimators": 1}, [47, 99]),
+        ("iris", "bs", "random_forest", {"random_forest_n_estimators": 100}, [50, 100]),
         # Use X_test instead
-        ("X_test", "bs", "random_forest", {"random_forest_n_trees": 1}, []),
-        ("X_test", "bs", "random_forest", {"random_forest_n_trees": 1.0}, []),
-        ("X_test", "bs", "random_forest", {"random_forest_n_trees": 100}, [5]),
+        ("X_test", "bs", "random_forest", {"random_forest_n_estimators": 1}, []),
+        ("X_test", "bs", "random_forest", {"random_forest_n_estimators": 1.0}, []),
+        ("X_test", "bs", "random_forest", {"random_forest_n_estimators": 100}, [5]),
         ("X_correlated", "bs", "random_forest", {"random_forest_max_depth": 1}, []),
         ("X_correlated", "bs", "random_forest", {"random_forest_max_depth": 2}, [49]),
         ("iris", "bs", "random_forest", {"model_selection_n_permutations": 10}, []),
@@ -98,10 +98,10 @@ def test_control_segmentation_parameters(
 @pytest.mark.parametrize(
     "key, default_value, another_value",
     [
-        ("random_forest_n_trees", 100, 11),
-        ("minimal_relative_segment_length", 0.1, 0.05),
+        ("random_forest_n_estimators", 100, 11),
+        ("minimal_relative_segment_length", 0.01, 0.05),
         ("seed", 0, 1),
-        ("random_forest_mtry", None, 1),
+        ("random_forest_max_features", "default", 1),
         ("random_forest_max_depth", 8, None),
     ],
 )

diff --git a/changeforest-r/DESCRIPTION b/changeforest-r/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: changeforest
 Type: Package
 Title: Classifier Based Non-Parametric Change Point Detection
-Version: 0.4.4
+Version: 0.5.0
 Author: Malte Londschien
 Maintainer: Malte Londschien <malte@londschien.ch>
 Description: Perform classifier based multivariate, non-parametric change point detection.

diff --git a/changeforest-r/R/control.R b/changeforest-r/R/control.R
@@ -5,30 +5,30 @@
 Control = R6::R6Class(
     "control",
     list(
-        minimal_relative_segment_length = NULL,
-        minimal_gain_to_split = NULL,
-        model_selection_alpha = NULL,
-        model_selection_n_permutations = NULL,
-        number_of_wild_segments = NULL,
-        seeded_segments_alpha = NULL,
-        seed = NULL,
-        random_forest_n_trees = NULL,
-        random_forest_mtry = NULL,     
-        random_forest_max_depth = NULL,
-        random_forest_n_jobs = NULL,
+        minimal_relative_segment_length = "default",
+        minimal_gain_to_split = "default",
+        model_selection_alpha = "default",
+        model_selection_n_permutations = "default",
+        number_of_wild_segments = "default",
+        seeded_segments_alpha = "default",
+        seed = "default",
+        random_forest_n_estimators = "default",
+        random_forest_max_features = "default",     
+        random_forest_max_depth = "default",
+        random_forest_n_jobs = "default",
 
         initialize = function(
-            minimal_relative_segment_length = NULL,
-            minimal_gain_to_split = NULL,
-            model_selection_alpha = NULL,
-            model_selection_n_permutations = NULL,
-            number_of_wild_segments = NULL,
-            seeded_segments_alpha = NULL,
-            seed = NULL,
-            random_forest_n_trees = NULL,
-            random_forest_mtry = NULL,
-            random_forest_max_depth = NULL,
-            random_forest_n_jobs = NULL
+            minimal_relative_segment_length = "default",
+            minimal_gain_to_split = "default",
+            model_selection_alpha = "default",
+            model_selection_n_permutations = "default",
+            number_of_wild_segments = "default",
+            seeded_segments_alpha = "default",
+            seed = "default",
+            random_forest_n_estimators = "default",
+            random_forest_max_features = "default",
+            random_forest_max_depth = "default",
+            random_forest_n_jobs = "default"
         ) {
             self$minimal_relative_segment_length = minimal_relative_segment_length
             self$minimal_gain_to_split = minimal_gain_to_split
@@ -37,8 +37,8 @@ Control = R6::R6Class(
             self$number_of_wild_segments = number_of_wild_segments
             self$seeded_segments_alpha = seeded_segments_alpha
             self$seed = seed
-            self$random_forest_n_trees = random_forest_n_trees 
-            self$random_forest_mtry = random_forest_mtry
+            self$random_forest_n_estimators = random_forest_n_estimators 
+            self$random_forest_max_features = random_forest_max_features
             self$random_forest_max_depth = random_forest_max_depth
             self$random_forest_n_jobs = random_forest_n_jobs
         }