Update README to include link to pypi

fix: 🐛 Fix a bug when pip install tries to build the package of F…

.gitignore

@@ -33,8 +33,8 @@ MANIFEST
 *.manifest
 *.spec
 
-# Installer log2s
+# Installer logs
-pip-log2.txt
+pip-log.txt
 pip-delete-this-directory.txt
 
 # Unit test / coverage reports
@@ -56,7 +56,7 @@ coverage.xml
 *.pot
 
 # Django stuff:
-*.log2
+*.log
 local_settings.py
 db.sqlite3
 db.sqlite3-journal
@@ -134,4 +134,6 @@ cmake-build-debug
 cmake-build-debug/**
 **/lcoverage/**
 **/x/*
-**/*.so
+**/*.so
+**/CMakeFiles
+wheelhouse

MANIFEST.in

@@ -0,0 +1,3 @@
+include src/cppmdlp/CPPFImdlp.h
+include src/cppmdlp/typesFImdlp.h
+include src/cppmdlp/Metrics.h

README.md

@@ -3,14 +3,14 @@
 [![CodeQL](https://github.com/Doctorado-ML/FImdlp/actions/workflows/codeql.yml/badge.svg)](https://github.com/Doctorado-ML/FImdlp/actions/workflows/codeql.yml)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/8b4d784fee13401588aa8c06532a2f6d)](https://www.codacy.com/gh/Doctorado-ML/FImdlp/dashboard?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/FImdlp&utm_campaign=Badge_Grade)
 [![codecov](https://codecov.io/gh/Doctorado-ML/FImdlp/branch/main/graph/badge.svg?token=W8I45B5Z3J)](https://codecov.io/gh/Doctorado-ML/FImdlp)
-[![pypy](https://img.shields.io/pypi/v/FImdlp?color=g)](https://img.shields.io/pypi/v/FImdlp?color=g)
+[![pypy](https://img.shields.io/pypi/v/FImdlp?color=g)](https://pypi.org/project/FImdlp)
 ![https://img.shields.io/badge/python-3.9%2B-blue](https://img.shields.io/badge/python-3.9%2B-brightgreen)
 
 Discretization algorithm based on the paper by Usama M. Fayyad and Keki B. Irani 
 
-```
+
 Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI-95), pages 1022-1027, Montreal, Canada, August 1995.
-```
+
 
 ## Installation
 
@@ -24,8 +24,8 @@ git clone --recurse-submodules https://github.com/doctorado-ml/FImdlp.git
 
 ```bash
 pip install -e .
-python samples/sample.py iris --original 
+python samples/sample.py iris  
-python samples/sample.py iris --proposal
+python samples/sample.py iris --alternative
 python samples/sample.py -h # for more options
 ```
 

samples/ArffFiles.cpp

@@ -1,117 +0,0 @@
-#include "ArffFiles.h"
-
-#include <fstream>
-#include <sstream>
-#include <map>
-#include <iostream>
-
-using namespace std;
-
-ArffFiles::ArffFiles()
-{
-}
-vector<string> ArffFiles::getLines()
-{
-    return lines;
-}
-unsigned long int ArffFiles::getSize()
-{
-    return lines.size();
-}
-vector<tuple<string, string>> ArffFiles::getAttributes()
-{
-    return attributes;
-}
-string ArffFiles::getClassName()
-{
-    return className;
-}
-string ArffFiles::getClassType()
-{
-    return classType;
-}
-vector<vector<float>>& ArffFiles::getX()
-{
-    return X;
-}
-vector<int>& ArffFiles::getY()
-{
-    return y;
-}
-void ArffFiles::load(string fileName, bool classLast)
-{
-    ifstream file(fileName);
-    string keyword, attribute, type;
-    if (file.is_open()) {
-        string line;
-        while (getline(file, line)) {
-            if (line[0] == '%' || line.empty() || line == "\r" || line == " ") {
-                continue;
-            }
-            if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
-                stringstream ss(line);
-                ss >> keyword >> attribute >> type;
-                attributes.push_back(make_tuple(attribute, type));
-                continue;
-            }
-            if (line[0] == '@') {
-                continue;
-            }
-            lines.push_back(line);
-        }
-        file.close();
-        if (attributes.empty())
-            throw invalid_argument("No attributes found");
-        if (classLast) {
-            className = get<0>(attributes.back());
-            classType = get<1>(attributes.back());
-            attributes.pop_back();
-        } else {
-            className = get<0>(attributes.front());
-            classType = get<1>(attributes.front());
-            attributes.erase(attributes.begin());
-        }
-        generateDataset(classLast);
-    } else
-        throw invalid_argument("Unable to open file");
-}
-void ArffFiles::generateDataset(bool classLast)
-{
-    X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
-    vector<string> yy = vector<string>(lines.size(), "");
-    int labelIndex = classLast ? attributes.size() : 0;
-    for (int i = 0; i < lines.size(); i++) {
-        stringstream ss(lines[i]);
-        string value;
-        int pos = 0, xIndex = 0;
-        while (getline(ss, value, ',')) {
-            if (pos++ == labelIndex) {
-                yy[i] = value;
-            } else {
-                X[xIndex++][i] = stof(value);
-            }
-        }
-    }
-    y = factorize(yy);
-}
-string ArffFiles::trim(const string& source)
-{
-    string s(source);
-    s.erase(0, s.find_first_not_of(" \n\r\t"));
-    s.erase(s.find_last_not_of(" \n\r\t") + 1);
-    return s;
-}
-vector<int> ArffFiles::factorize(const vector<string>& labels_t)
-{
-    vector<int> yy;
-    yy.reserve(labels_t.size());
-    map<string, int> labelMap;
-    int i = 0;
-    for (string label : labels_t) {
-        if (labelMap.find(label) == labelMap.end()) {
-            labelMap[label] = i++;
-        }
-        yy.push_back(labelMap[label]);
-    }
-    return yy;
-}

samples/ArffFiles.h

@@ -1,28 +0,0 @@
-#ifndef ARFFFILES_H
-#define ARFFFILES_H
-#include <string>
-#include <vector>
-#include <tuple>
-using namespace std;
-class ArffFiles {
-private:
-    vector<string> lines;
-    vector<tuple<string, string>> attributes;
-    string className, classType;
-    vector<vector<float>> X;
-    vector<int> y;
-    void generateDataset(bool);
-public:
-    ArffFiles();
-    void load(string, bool = true);
-    vector<string> getLines();
-    unsigned long int getSize();
-    string getClassName();
-    string getClassType();
-    string trim(const string&);
-    vector<vector<float>>& getX();
-    vector<int>& getY();
-    vector<tuple<string, string>> getAttributes();
-    vector<int> factorize(const vector<string>& labels_t);
-};
-#endif

samples/CMakeLists.txt

@@ -3,4 +3,4 @@ project(main)
 
 set(CMAKE_CXX_STANDARD 14)
 
-add_executable(sample sample.cpp ArffFiles.cpp ../src/cppmdlp/Metrics.cpp ../src/cppmdlp/CPPFImdlp.cpp)
+add_executable(sample sample.cpp ../src/cppmdlp/tests/ArffFiles.cpp ../src/cppmdlp/Metrics.cpp ../src/cppmdlp/CPPFImdlp.cpp)

samples/sample.cpp

@@ -1,4 +1,4 @@
-#include "ArffFiles.h"
+#include "../src/cppmdlp/tests/ArffFiles.h"
 #include <iostream>
 #include <vector>
 #include <iomanip>
@@ -41,7 +41,7 @@ int main(int argc, char** argv)
         }
         cout << y[i] << endl;
     }
-    mdlp::CPPFImdlp test = mdlp::CPPFImdlp(false);
+    mdlp::CPPFImdlp test = mdlp::CPPFImdlp(0);
     for (auto i = 0; i < attributes.size(); i++) {
         cout << "Cut points for " << get<0>(attributes[i]) << endl;
         cout << "--------------------------" << setprecision(3) << endl;

samples/sample.py

@@ -14,8 +14,9 @@ datasets = {
 }
 
 ap = argparse.ArgumentParser()
-ap.add_argument("--proposal", action="store_true")
+ap.add_argument(
-ap.add_argument("--original", dest="proposal", action="store_false")
+    "--alternative", dest="proposal", action="store_const", const=1
+)
 ap.add_argument("dataset", type=str, choices=datasets.keys())
 args = ap.parse_args()
 relative = "" if os.path.isdir("src") else ".."
@@ -29,7 +30,7 @@ class_name = df.columns.to_list()[class_column]
 X = df.drop(class_name, axis=1)
 y, _ = pd.factorize(df[class_name])
 X = X.to_numpy()
-test = FImdlp(proposal=args.proposal)
+test = FImdlp(algorithm=args.proposal if args.proposal is not None else 0)
 now = time.time()
 test.fit(X, y)
 fit_time = time.time()

src/fimdlp/_version.py

@@ -1 +1 @@
-__version__ = "0.9.1"
+__version__ = "0.9.2"

src/fimdlp/cfimdlp.pyx

@@ -1,20 +1,20 @@
 # distutils: language = c++
 # cython: language_level = 3
 from libcpp.vector cimport vector
-from libcpp cimport bool
+from libcpp.string cimport string
 
 cdef extern from "../cppmdlp/CPPFImdlp.h" namespace "mdlp":
     ctypedef float precision_t
     cdef cppclass CPPFImdlp:
-        CPPFImdlp(bool) except + 
+        CPPFImdlp(int) except + 
         CPPFImdlp& fit(vector[precision_t]&, vector[int]&)
         vector[precision_t] getCutPoints()
+        string version()
         
-
 cdef class CFImdlp:
     cdef CPPFImdlp *thisptr
-    def __cinit__(self, proposal):
+    def __cinit__(self, algorithm):
-        self.thisptr = new CPPFImdlp(proposal)
+        self.thisptr = new CPPFImdlp(algorithm)
     def __dealloc__(self):
         del self.thisptr
     def fit(self, X, y):
@@ -22,4 +22,5 @@ cdef class CFImdlp:
         return self
     def get_cut_points(self):
         return self.thisptr.getCutPoints()
- 
+    def get_version(self):
+        return self.thisptr.version()

src/fimdlp/mdlp.py

@@ -7,14 +7,18 @@ from joblib import Parallel, delayed
 
 
 class FImdlp(TransformerMixin, BaseEstimator):
-    def __init__(self, n_jobs=-1, proposal=False):
+    def __init__(self, algorithm=0, n_jobs=-1):
+        self.algorithm = algorithm
         self.n_jobs = n_jobs
-        self.proposal = proposal
 
     """Fayyad - Irani MDLP discretization algorithm based implementation.
 
     Parameters
     ----------
+    algorithm : int, default=0
+        The type of algorithm to use computing the cut points.
+        0 - Definitive implementation
+        1 - Alternative proposal
     n_jobs : int, default=-1
         The number of jobs to run in parallel. :meth:`fit` and
         :meth:`transform`, are parallelized over the features. ``-1`` means
@@ -94,9 +98,15 @@ class FImdlp(TransformerMixin, BaseEstimator):
         return self
 
     def _fit_discretizer(self, feature):
-        self.discretizer_[feature] = CFImdlp(proposal=self.proposal)
+        if feature in self.features_:
-        self.discretizer_[feature].fit(self.X_[:, feature], self.y_)
+            self.discretizer_[feature] = CFImdlp(algorithm=self.algorithm)
-        self.cut_points_[feature] = self.discretizer_[feature].get_cut_points()
+            self.discretizer_[feature].fit(self.X_[:, feature], self.y_)
+            self.cut_points_[feature] = self.discretizer_[
+                feature
+            ].get_cut_points()
+        else:
+            self.discretizer_[feature] = None
+            self.cut_points_[feature] = []
 
     def _discretize_feature(self, feature, X, result):
         if feature in self.features_:
@@ -125,7 +135,10 @@ class FImdlp(TransformerMixin, BaseEstimator):
             raise ValueError(
                 "Shape of input is different from what was seen in `fit`"
             )
-        result = np.zeros_like(X, dtype=np.int32) - 1
+        if len(self.features_) == self.n_features_:
+            result = np.zeros_like(X, dtype=np.int32) - 1
+        else:
+            result = np.zeros_like(X) - 1
         Parallel(n_jobs=self.n_jobs, prefer="threads")(
             delayed(self._discretize_feature)(feature, X[:, feature], result)
             for feature in range(self.n_features_)

src/fimdlp/tests/FImdlp_test.py

@@ -14,47 +14,41 @@ class FImdlpTest(unittest.TestCase):
     def test_init(self):
         clf = FImdlp()
         self.assertEqual(-1, clf.n_jobs)
-        self.assertFalse(clf.proposal)
+        self.assertEqual(0, clf.algorithm)
-        clf = FImdlp(proposal=True, n_jobs=7)
+        clf = FImdlp(algorithm=1, n_jobs=7)
-        self.assertTrue(clf.proposal)
+        self.assertEqual(1, clf.algorithm)
         self.assertEqual(7, clf.n_jobs)
 
-    def test_fit_proposal(self):
+    def test_fit_definitive(self):
-        clf = FImdlp(proposal=True)
+        clf = FImdlp(algorithm=0)
         clf.fit([[1, 2], [3, 4]], [1, 2])
         self.assertEqual(clf.n_features_, 2)
         self.assertListEqual(clf.X_.tolist(), [[1, 2], [3, 4]])
         self.assertListEqual(clf.y_.tolist(), [1, 2])
-        self.assertListEqual([[], []], clf.get_cut_points())
+        self.assertListEqual([[2.0], [3.0]], clf.get_cut_points())
         X, y = load_iris(return_X_y=True)
         clf.fit(X, y)
         self.assertEqual(clf.n_features_, 4)
         self.assertTrue(np.array_equal(X, clf.X_))
         self.assertTrue(np.array_equal(y, clf.y_))
         expected = [
-            [
+            [5.449999809265137, 6.25],
-                4.900000095367432,
+            [2.8499999046325684, 3.0, 3.049999952316284, 3.3499999046325684],
-                5.0,
+            [2.450000047683716, 4.75, 5.050000190734863],
-                5.099999904632568,
+            [0.800000011920929, 1.4500000476837158, 1.75],
-                5.400000095367432,
-                5.699999809265137,
-            ],
-            [2.6999998092651367, 2.9000000953674316, 3.1999998092651367],
-            [2.3499999046325684, 4.5, 4.800000190734863],
-            [0.75, 1.399999976158142, 1.5, 1.7000000476837158],
         ]
         self.assertListEqual(expected, clf.get_cut_points())
         self.assertListEqual([0, 1, 2, 3], clf.features_)
         clf.fit(X, y, features=[0, 2, 3])
         self.assertListEqual([0, 2, 3], clf.features_)
 
-    def test_fit_original(self):
+    def test_fit_alternative(self):
-        clf = FImdlp(proposal=False)
+        clf = FImdlp(algorithm=1)
         clf.fit([[1, 2], [3, 4]], [1, 2])
         self.assertEqual(clf.n_features_, 2)
         self.assertListEqual(clf.X_.tolist(), [[1, 2], [3, 4]])
         self.assertListEqual(clf.y_.tolist(), [1, 2])
-        self.assertListEqual([[], []], clf.get_cut_points())
+        self.assertListEqual([[2], [3]], clf.get_cut_points())
         X, y = load_iris(return_X_y=True)
         clf.fit(X, y)
         self.assertEqual(clf.n_features_, 4)
@@ -62,10 +56,10 @@ class FImdlpTest(unittest.TestCase):
         self.assertTrue(np.array_equal(y, clf.y_))
 
         expected = [
-            [5.5, 5.800000190734863],
+            [5.449999809265137, 5.75],
-            [2.9000000953674316, 3.3499999046325684],
+            [2.8499999046325684, 3.3499999046325684],
-            [2.450000047683716, 4.800000190734863],
+            [2.450000047683716, 4.75],
-            [0.800000011920929, 1.7999999523162842],
+            [0.800000011920929, 1.75],
         ]
         self.assertListEqual(expected, clf.get_cut_points())
         self.assertListEqual([0, 1, 2, 3], clf.features_)
@@ -89,45 +83,58 @@ class FImdlpTest(unittest.TestCase):
 
     def test_fit_features(self):
         clf = FImdlp()
-        clf.fit([[1, 2], [3, 4]], [1, 2], features=[0])
+        clf.fit([[1, -2], [3, 4]], [1, 2], features=[0])
-        res = clf.transform([[1, 2], [3, 4]])
+        res = clf.transform([[1, -2], [3, 4]])
-        self.assertListEqual(res.tolist(), [[0, 2], [0, 4]])
+        self.assertListEqual(res.tolist(), [[0, -2], [1, 4]])
+        X, y = load_iris(return_X_y=True)
+        X_expected = X[:, [0, 2]].copy()
+        clf.fit(X, y, features=[1, 3])
+        X_computed = clf.transform(X)
+        self.assertListEqual(
+            X_expected[:, 0].tolist(), X_computed[:, 0].tolist()
+        )
+        self.assertListEqual(
+            X_expected[:, 1].tolist(), X_computed[:, 2].tolist()
+        )
+        self.assertEqual(X_computed.dtype, np.float64)
 
-    def test_transform_original(self):
+    def test_transform_definitive(self):
-        clf = FImdlp(proposal=False)
+        clf = FImdlp(algorithm=0)
         clf.fit([[1, 2], [3, 4]], [1, 2])
         self.assertEqual(
-            clf.transform([[1, 2], [3, 4]]).tolist(), [[0, 0], [0, 0]]
+            clf.transform([[1, 2], [3, 4]]).tolist(), [[0, 0], [1, 1]]
         )
         X, y = load_iris(return_X_y=True)
         clf.fit(X, y)
         self.assertEqual(clf.n_features_, 4)
         self.assertTrue(np.array_equal(X, clf.X_))
         self.assertTrue(np.array_equal(y, clf.y_))
+        X_transformed = clf.transform(X)
         self.assertListEqual(
-            clf.transform(X).tolist(), clf.fit(X, y).transform(X).tolist()
+            X_transformed.tolist(), clf.fit(X, y).transform(X).tolist()
         )
+        self.assertEqual(X_transformed.dtype, np.int32)
         expected = [
-            [0, 0, 1, 1],
+            [1, 0, 1, 1],
-            [2, 1, 1, 1],
+            [1, 1, 1, 1],
             [1, 0, 1, 1],
             [0, 0, 1, 1],
             [1, 0, 1, 1],
             [1, 1, 1, 1],
-            [1, 0, 1, 1],
+            [1, 1, 1, 1],
         ]
         self.assertTrue(np.array_equal(clf.transform(X[90:97]), expected))
         with self.assertRaises(ValueError):
             clf.transform([[1, 2, 3], [4, 5, 6]])
         with self.assertRaises(sklearn.exceptions.NotFittedError):
-            clf = FImdlp(proposal=False)
+            clf = FImdlp(algorithm=0)
             clf.transform([[1, 2], [3, 4]])
 
-    def test_transform_proposal(self):
+    def test_transform_alternative(self):
-        clf = FImdlp(proposal=True)
+        clf = FImdlp(algorithm=1)
         clf.fit([[1, 2], [3, 4]], [1, 2])
         self.assertEqual(
-            clf.transform([[1, 2], [3, 4]]).tolist(), [[0, 0], [0, 0]]
+            clf.transform([[1, 2], [3, 4]]).tolist(), [[0, 0], [1, 1]]
         )
         X, y = load_iris(return_X_y=True)
         clf.fit(X, y)
@@ -138,17 +145,17 @@ class FImdlpTest(unittest.TestCase):
             clf.transform(X).tolist(), clf.fit(X, y).transform(X).tolist()
         )
         expected = [
-            [4, 0, 1, 1],
-            [5, 2, 2, 2],
-            [5, 0, 1, 1],
             [1, 0, 1, 1],
-            [4, 1, 1, 1],
+            [2, 1, 1, 1],
-            [5, 2, 1, 1],
+            [2, 0, 1, 1],
-            [5, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
         ]
         self.assertTrue(np.array_equal(clf.transform(X[90:97]), expected))
         with self.assertRaises(ValueError):
             clf.transform([[1, 2, 3], [4, 5, 6]])
         with self.assertRaises(sklearn.exceptions.NotFittedError):
-            clf = FImdlp(proposal=True)
+            clf = FImdlp(algorithm=1)
             clf.transform([[1, 2], [3, 4]])