Build sklearn transformer

.gitignore

@@ -4,7 +4,7 @@ __pycache__/
 *$py.class
 
 # C extensions
-*.so
+build/**/*.so
 
 # Distribution / packaging
 .Python

MANIFEST.in

@@ -1 +1 @@
-include fimdlp/FImdlp.h
+include fimdlp/CPPFImdlp.h

Makefile

@@ -16,9 +16,13 @@ push:  ## Push code with tags
 build:  ## Build package
 	rm -fr dist/*
 	rm -fr build/*
-	#python setup.py build_ext
 	python -m build
 
+buildext:  ## Build extension
+	rm -fr dist/*
+	rm -fr build/*
+	python setup.py build_ext
+
 audit: ## Audit pip
 	pip-audit
 

fimdlp/CPPFImdlp.cpp

@@ -1,13 +1,13 @@
-#include "FImdlp.h"
+#include "CPPFImdlp.h"
-namespace FImdlp
+namespace CPPFImdlp
 {
-    FImdlp::FImdlp()
+    CPPFImdlp::CPPFImdlp()
     {
     }
-    FImdlp::~FImdlp()
+    CPPFImdlp::~CPPFImdlp()
     {
     }
-    std::vector<float> FImdlp::cutPoints(std::vector<int> &X, std::vector<int> &y)
+    std::vector<float> CPPFImdlp::cutPoints(std::vector<int> &X, std::vector<int> &y)
     {
         std::vector<float> cutPts;
         int i, ant = X.at(0);

fimdlp/CPPFImdlp.h

@@ -1,14 +1,14 @@
-#ifndef FIMDLP_H
+#ifndef CPPFIMDLP_H
-#define FIMDLP_H
+#define CPPFIMDLP_H
 #include <vector>
 #include <Python.h>
-namespace FImdlp
+namespace CPPFImdlp
 {
-    class FImdlp
+    class CPPFImdlp
     {
     public:
-        FImdlp();
+        CPPFImdlp();
-        ~FImdlp();
+        ~CPPFImdlp();
         std::vector<float> cutPoints(std::vector<int> &, std::vector<int> &);
     };
 }

fimdlp/__init__.py

@@ -1 +1,3 @@
 from ._version import __version__
+
+all = ["FImdlp", "__version__"]

fimdlp/cfimdlp.pyx

@@ -2,15 +2,15 @@
 # cython: language_level = 3
 from libcpp.vector cimport vector
 
-cdef extern from "FImdlp.h" namespace "FImdlp":
+cdef extern from "CPPFImdlp.h" namespace "CPPFImdlp":
-    cdef cppclass FImdlp:
+    cdef cppclass CPPFImdlp:
-        FImdlp() except + 
+        CPPFImdlp() except + 
         vector[float] cutPoints(vector[int]&, vector[int]&)
 
 cdef class CFImdlp:
-    cdef FImdlp *thisptr
+    cdef CPPFImdlp *thisptr
     def __cinit__(self):
-        self.thisptr = new FImdlp()
+        self.thisptr = new CPPFImdlp()
     def __dealloc__(self):
         del self.thisptr
     def cut_points(self, X, y):

fimdlp/mdlp.py

@@ -0,0 +1,103 @@
+import numpy as np
+from .cppfimdlp import CFImdlp
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.utils.multiclass import unique_labels
+from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
+
+
+class FImdlp(TransformerMixin, BaseEstimator):
+    """Fayyad - Irani MDLP discretization algorithm.
+
+    Parameters
+    ----------
+    demo_param : str, default='demo'
+        A parameter used for demonstation of how to pass and store paramters.
+
+    Attributes
+    ----------
+    n_features_ : int
+        The number of features of the data passed to :meth:`fit`.
+    """
+
+    def __init__(self):
+        pass
+
+    def _check_params_fit(self, X, y, expected_args, kwargs):
+        """Check the common parameters passed to fit"""
+        # Check that X and y have correct shape
+        X, y = check_X_y(X, y)
+        # Store the classes seen during fit
+        self.classes_ = unique_labels(y)
+        self.n_classes_ = self.classes_.shape[0]
+        # Default values
+        self.class_name_ = "class"
+        self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
+        for key, value in kwargs.items():
+            if key in expected_args:
+                setattr(self, f"{key}_", value)
+            else:
+                raise ValueError(f"Unexpected argument: {key}")
+        if len(self.features_) != X.shape[1]:
+            raise ValueError(
+                "Number of features does not match the number of columns in X"
+            )
+        return X, y
+
+    def fit(self, X, y, **kwargs):
+        """A reference implementation of a fitting function for a transformer.
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            The training input samples.
+        y : None
+            There is no need of a target in a transformer, yet the pipeline API
+            requires this parameter.
+        Returns
+        -------
+        self : object
+            Returns self.
+        """
+        X, y = self._check_params_fit(
+            X, y, expected_args=["class_name", "features"], kwargs=kwargs
+        )
+
+        self.n_features_ = X.shape[1]
+        self.X_ = X
+        self.y_ = y
+        self.discretizer_ = CFImdlp()
+
+        return self
+
+    def transform(self, X):
+        """Discretize X values.
+        Parameters
+        ----------
+        X : {array-like}, shape (n_samples, n_features)
+            The input samples.
+        Returns
+        -------
+        X_transformed : array, shape (n_samples, n_features)
+            The array containing the discretized values of ``X``.
+        """
+        # Check is fit had been called
+        check_is_fitted(self, "n_features_")
+
+        # Input validation
+        X = check_array(X)
+        if (X != self.X_).any():
+            raise ValueError(
+                "X values are not the same as the ones used to fit the model."
+            )
+
+        # Check that the input is of the same shape as the one passed
+        # during fit.
+        if X.shape[1] != self.n_features_:
+            raise ValueError(
+                "Shape of input is different from what was seen" "in `fit`"
+            )
+        print("Cut points for each feature in Iris dataset:")
+        for i in range(0, self.n_features_):
+            data = np.sort(X[:, i])
+            Xcutpoints = self.discretizer_.cut_points(data, self.y_)
+            print(f"{self.features_[i]:20s}: {Xcutpoints}")
+        return X

pyproject.toml

@@ -37,3 +37,21 @@ classifiers = [
 
 [project.urls]
 Home = "https://github.com/doctorado-ml/FImdlp"
+
+[tool.black]
+line-length = 79
+target_version = ['py38', 'py39', 'py310']
+include = '\.pyi?$'
+exclude = '''
+/(
+    \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | _build
+  | buck-out
+  | build
+  | dist
+)/
+'''

sample.py

@@ -1,14 +1,9 @@
-import numpy as np
 from sklearn.datasets import load_iris
-from fimdlp import CFImdlp
+from fimdlp.mdlp import FImdlp
 
 data = load_iris()
 X = data.data
 y = data.target
 features = data.feature_names
-test = CFImdlp()
+test = FImdlp()
-print("Cut points for each feature in Iris dataset:")
+Xcutpoints = test.fit(X, y, features=features).transform(X)
-for i in range(0, X.shape[1]):
-    data = np.sort(X[:, i])
-    Xcutpoints = test.cut_points(data, y)
-    print(f"{features[i]:20s}: {Xcutpoints}")

setup.py

@@ -9,24 +9,10 @@ from setuptools import Extension, setup
 setup(
     ext_modules=[
         Extension(
-            name="fimdlp",
+            name="cppfimdlp",
-            sources=["fimdlp/cfimdlp.pyx", "fimdlp/FImdlp.cpp"],  
+            sources=["fimdlp/cfimdlp.pyx", "fimdlp/CPPFImdlp.cpp"],
             language="c++",
             include_dirs=["fimdlp"],
         ),
     ]
 )
-
-# from Cython.Build import cythonize
-# setup(
-#     ext_modules=cythonize(
-#         Extension(
-#             "fimdlp", 
-#             sources=["fimdlp/cfimdlp.pyx", "fimdlp/FImdlp.cpp"],  
-#             language="c++", 
-#             include_dirs=["fimdlp"], 
-#         ),
-#         include_path=["./fimdlp"],
-#     )
-# )
-