Update the c++ sources with new version

- Add a join_fit feature that can update a fitted discretizer. Making it possible to discretize a variable by taking into account the label and a list of other features of the dataset. Used in local discretization with bayesian estimators.
- Add factorize method to be able to simulate the pandas factorize method.
- Remove the algorithm hyperparameter as it is no longer needed
- Add get_states_feature method to obtain a list of states of any feature based on the number of cut points computed while fitting the discretizer

.gitignore

@@ -33,8 +33,8 @@ MANIFEST
 *.manifest
 *.spec
 
-# Installer log2s
-pip-log2.txt
+# Installer logs
+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 +1,4 @@
+include src/cppmdlp/CPPFImdlp.h
+include src/cppmdlp/typesFImdlp.h
+include src/cppmdlp/Metrics.h
+include src/fimdlp/Factorize.h

Makefile

@@ -37,6 +37,11 @@ install:  ## Build extension
 audit: ## Audit pip
 	pip-audit
 
+version:
+	@echo "Current Python version .: $(shell python --version)"
+	@echo "Current FImdlp version .: $(shell python -c "from fimdlp import _version; print(_version.__version__)")"
+	@echo "Installed FImdlp version: $(shell pip show fimdlp | grep Version | cut -d' ' -f2)"
+
 help: ## Show help message
 	@IFS=$$'\n' ; \
 	help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \

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 --proposal
+python samples/sample.py iris  
+python samples/sample.py iris --alternative
 python samples/sample.py -h # for more options
 ```
 

k.py

@@ -0,0 +1,12 @@
+from sklearn.datasets import load_wine
+from fimdlp.mdlp import FImdlp
+
+X, y = load_wine(return_X_y=True)
+trans = FImdlp()
+Xt = trans.join_transform(X, y, 12)
+print("X shape = ", X.shape)
+print("Xt.shape=", Xt.shape)
+print("Xt ", Xt[:10])
+print("trans.X_ shape = ", trans.X_.shape)
+print("trans.y_ ", trans.y_[:10])
+print("y_join ", trans.y_join_[:10])

pyproject.toml

@@ -18,7 +18,7 @@ authors = [
     { name = "Ricardo Montañana", email = "ricardo.montanana@alu.uclm.es" },
 ]
 dynamic = ['version']
-dependencies = ["numpy", "joblib"]
+dependencies = ["numpy", "joblib", "scikit-learn"]
 requires-python = ">=3.9"
 classifiers = [
     "Development Status :: 3 - Alpha",

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("--original", dest="proposal", action="store_false")
+ap.add_argument(
+    "--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()

setup.py

@@ -14,10 +14,13 @@ setup(
                 "src/fimdlp/cfimdlp.pyx",
                 "src/cppmdlp/CPPFImdlp.cpp",
                 "src/cppmdlp/Metrics.cpp",
+                "src/fimdlp/Factorize.cpp",
             ],
             language="c++",
             include_dirs=["fimdlp"],
-            extra_compile_args=["-std=c++2a"],
+            extra_compile_args=[
+                "-std=c++11",
+            ],
         ),
     ]
 )

src/fimdlp/Factorize.cpp

@@ -0,0 +1,18 @@
+#include "Factorize.h"
+
+namespace utils {
+    vector<int> cppFactorize(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;
+    }
+}

src/fimdlp/Factorize.h

@@ -0,0 +1,10 @@
+#ifndef FACTORIZE_H
+#define FACTORIZE_H
+#include <vector>
+#include <map>
+#include <string>
+namespace utils {
+    using namespace std;
+    vector<int> cppFactorize(const vector<string>&);
+}
+#endif

src/fimdlp/__init__.py

@@ -1,8 +1,4 @@
 from ._version import __version__
 
 
-def version():
-    return __version__
-
-
 all = ["FImdlp", "__version__"]

src/fimdlp/_version.py

@@ -1 +1 @@
-__version__ = "0.9.1"
+__version__ = "0.9.3"

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() except + 
         CPPFImdlp& fit(vector[precision_t]&, vector[int]&)
         vector[precision_t] getCutPoints()
+        string version()
         
-
 cdef class CFImdlp:
     cdef CPPFImdlp *thisptr
-    def __cinit__(self, proposal):
-        self.thisptr = new CPPFImdlp(proposal)
+    def __cinit__(self):
+        self.thisptr = new CPPFImdlp()
     def __dealloc__(self):
         del self.thisptr
     def fit(self, X, y):
@@ -22,4 +22,12 @@ cdef class CFImdlp:
         return self
     def get_cut_points(self):
         return self.thisptr.getCutPoints()
- 
+    def get_version(self):
+        return self.thisptr.version()
+    def __reduce__(self):
+        return (CFImdlp, ())
+
+cdef extern from "Factorize.h" namespace "utils":
+    vector[int] cppFactorize(vector[string] &input_vector)
+def factorize(input_vector):
+    return cppFactorize(input_vector)

src/fimdlp/mdlp.py

@@ -1,15 +1,17 @@
 import numpy as np
-from .cppfimdlp import CFImdlp
+from .cppfimdlp import CFImdlp, factorize
 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
 from joblib import Parallel, delayed
+from ._version import __version__
+
+# from ._version import __version__
 
 
 class FImdlp(TransformerMixin, BaseEstimator):
-    def __init__(self, n_jobs=-1, proposal=False):
+    def __init__(self, n_jobs=-1):
         self.n_jobs = n_jobs
-        self.proposal = proposal
 
     """Fayyad - Irani MDLP discretization algorithm based implementation.
 
@@ -22,27 +24,26 @@ class FImdlp(TransformerMixin, BaseEstimator):
 
     Attributes
     ----------
-    n_features_ : int
+    n_features_in_ : int
         The number of features of the data passed to :meth:`fit`.
     discretizer_ : list
         The list of discretizers, one for each feature.
     cut_points_ : list
         The list of cut points for each feature.
-    X_ : array
-        the samples used to fit, shape (n_samples, n_features)
-    y_ : array
-        the labels used to fit, shape (n_samples,)
+    X_ : array, shape (n_samples, n_features)
+        the samples used to fit
+    y_ : array, shape(n_samples,)
+        the labels used to fit
     features_ : list
         the list of features to be discretized
     """
 
-    def _check_params_fit(self, X, y, expected_args, kwargs):
-        """Check the common parameters passed to fit"""
+    def _more_tags(self):
+        return {"preserves_dtype": [np.int32], "requires_y": True}
+
+    def _check_args(self, X, y, expected_args, kwargs):
         # 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.features_ = [i for i in range(X.shape[1])]
         for key, value in kwargs.items():
@@ -63,15 +64,24 @@ class FImdlp(TransformerMixin, BaseEstimator):
             raise ValueError("Feature index out of range")
         return X, y
 
+    def _update_params(self, X, y):
+        # Store the classes seen during fit
+        self.classes_ = unique_labels(y)
+        self.n_classes_ = self.classes_.shape[0]
+        self.n_features_in_ = X.shape[1]
+
+    @staticmethod
+    def get_version():
+        return f"{__version__}({CFImdlp().get_version().decode()})"
+
     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)
+        X : array, 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.
+        y : array, shape (n_samples,)
+            the labels used to fit
         features : list, default=[i for i in range(n_features)]
             The list of features to be discretized.
         Returns
@@ -79,24 +89,30 @@ class FImdlp(TransformerMixin, BaseEstimator):
         self : object
             Returns self.
         """
-        X, y = self._check_params_fit(
+        X, y = self._check_args(
             X, y, expected_args=["features"], kwargs=kwargs
         )
-        self.n_features_ = X.shape[1]
+        self._update_params(X, y)
         self.X_ = X
         self.y_ = y
-        self.discretizer_ = [None] * self.n_features_
-        self.cut_points_ = [None] * self.n_features_
+        self.discretizer_ = [None] * self.n_features_in_
+        self.cut_points_ = [None] * self.n_features_in_
         Parallel(n_jobs=self.n_jobs, prefer="threads")(
             delayed(self._fit_discretizer)(feature)
-            for feature in range(self.n_features_)
+            for feature in range(self.n_features_in_)
         )
         return self
 
     def _fit_discretizer(self, feature):
-        self.discretizer_[feature] = CFImdlp(proposal=self.proposal)
-        self.discretizer_[feature].fit(self.X_[:, feature], self.y_)
-        self.cut_points_[feature] = self.discretizer_[feature].get_cut_points()
+        if feature in self.features_:
+            self.discretizer_[feature] = CFImdlp()
+            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_:
@@ -108,7 +124,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
         """Discretize X values.
         Parameters
         ----------
-        X : {array-like}, shape (n_samples, n_features)
+        X : array, shape (n_samples, n_features)
             The input samples.
         Returns
         -------
@@ -116,22 +132,41 @@ class FImdlp(TransformerMixin, BaseEstimator):
             The array containing the discretized values of ``X``.
         """
         # Check is fit had been called
-        check_is_fitted(self, "n_features_")
+        check_is_fitted(self, "n_features_in_")
         # Input validation
         X = check_array(X)
         # Check that the input is of the same shape as the one passed
         # during fit.
-        if X.shape[1] != self.n_features_:
+        if X.shape[1] != self.n_features_in_:
             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_in_:
+            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_)
+            for feature in range(self.n_features_in_)
         )
         return result
 
+    @staticmethod
+    def factorize(yy):
+        """Factorize the input labels
+
+        Parameters
+        ----------
+        yy : array, shape (n_samples,)
+            Labels to be factorized, MUST be bytes, i.e. b"0", b"1", ...
+
+        Returns
+        -------
+        array, shape (n_samples,)
+            Factorized labels
+        """
+        return factorize(yy)
+
     def get_cut_points(self):
         """Get the cut points for each feature.
         Returns
@@ -140,6 +175,70 @@ class FImdlp(TransformerMixin, BaseEstimator):
             The list of cut points for each feature.
         """
         result = []
-        for feature in range(self.n_features_):
+        for feature in range(self.n_features_in_):
             result.append(self.cut_points_[feature])
         return result
+
+    def get_states_feature(self, feature):
+        """Return the states a feature can take
+
+        Parameters
+        ----------
+        feature : int
+            feature to get the states
+
+        Returns
+        -------
+        list
+            states of the feature
+        """
+        if feature in self.features_:
+            return list(range(len(self.cut_points_[feature]) + 1))
+        return None
+
+    def join_fit(self, features, target, data):
+        """Join the selected features with the labels and fit the discretizer
+        of the target variable
+        join - fit - transform
+
+        Parameters
+        ----------
+        features : [list]
+            index of the features to join with the labels
+        target : [int]
+            index of the target variable to discretize
+        data: [array] shape (n_samples, n_features)
+            dataset that contains the features to join
+
+        Returns
+        -------
+        result: np.array
+            The target variable newly discretized
+        """
+        check_is_fitted(self, "n_features_in_")
+        if len(features) < 1 or len(features) > self.n_features_in_:
+            raise ValueError(
+                "Number of features must be in range [1, "
+                f"{self.n_features_in_}]"
+            )
+        for feature in features:
+            if feature < 0 or feature >= self.n_features_in_:
+                raise ValueError(
+                    f"Feature {feature} not in range [0, "
+                    f"{self.n_features_in_})"
+                )
+        if target < 0 or target >= self.n_features_in_:
+            raise ValueError(
+                f"Target {target} not in range [0, {self.n_features_in_})"
+            )
+        if target in features:
+            raise ValueError("Target cannot in features to join")
+        y_join = [
+            f"{str(item_y)}{''.join([str(x) for x in items_x])}".encode()
+            for item_y, items_x in zip(self.y_, data[:, features])
+        ]
+        self.y_join_ = y_join
+        self.discretizer_[target].fit(self.X_[:, target], factorize(y_join))
+        self.cut_points_[target] = self.discretizer_[target].get_cut_points()
+        # return the discretized target variable with the new cut points
+        return np.searchsorted(self.cut_points_[target], self.X_[:, target])

src/fimdlp/tests/FImdlp_test.py

@@ -1,73 +1,46 @@
 import unittest
 import sklearn
-from sklearn.datasets import load_iris
 import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.utils.estimator_checks import check_estimator
+from ..cppfimdlp import CFImdlp, factorize
 from ..mdlp import FImdlp
-from .. import version
-from .._version import __version__
+from .. import __version__
+
+# from .._version import __version__
 
 
 class FImdlpTest(unittest.TestCase):
     def test_version(self):
-        self.assertEqual(version(), __version__)
+        clf = FImdlp()
+        self.assertEqual(
+            clf.get_version(),
+            f"{__version__}({CFImdlp().get_version().decode()})",
+        )
 
     def test_init(self):
         clf = FImdlp()
         self.assertEqual(-1, clf.n_jobs)
-        self.assertFalse(clf.proposal)
-        clf = FImdlp(proposal=True, n_jobs=7)
-        self.assertTrue(clf.proposal)
+        clf = FImdlp(n_jobs=7)
         self.assertEqual(7, clf.n_jobs)
 
-    def test_fit_proposal(self):
-        clf = FImdlp(proposal=True)
-        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())
+    def test_fit_definitive(self):
+        clf = FImdlp()
         X, y = load_iris(return_X_y=True)
         clf.fit(X, y)
-        self.assertEqual(clf.n_features_, 4)
+        self.assertEqual(clf.n_features_in_, 4)
         self.assertTrue(np.array_equal(X, clf.X_))
         self.assertTrue(np.array_equal(y, clf.y_))
         expected = [
-            [
-                4.900000095367432,
-                5.0,
-                5.099999904632568,
-                5.400000095367432,
-                5.699999809265137,
-            ],
-            [2.6999998092651367, 2.9000000953674316, 3.1999998092651367],
-            [2.3499999046325684, 4.5, 4.800000190734863],
-            [0.75, 1.399999976158142, 1.5, 1.7000000476837158],
+            [5.449999809265137, 5.75],
+            [2.75, 2.8499999046325684, 2.95, 3.05, 3.3499999046325684],
+            [2.45, 4.75, 5.050000190734863],
+            [0.8, 1.75],
         ]
-        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):
-        clf = FImdlp(proposal=False)
-        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())
-        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 = [
-            [5.5, 5.800000190734863],
-            [2.9000000953674316, 3.3499999046325684],
-            [2.450000047683716, 4.800000190734863],
-            [0.800000011920929, 1.7999999523162842],
-        ]
-        self.assertListEqual(expected, clf.get_cut_points())
+        computed = clf.get_cut_points()
+        for item_computed, item_expected in zip(computed, expected):
+            for x_, y_ in zip(item_computed, item_expected):
+                self.assertAlmostEqual(x_, y_)
         self.assertListEqual([0, 1, 2, 3], clf.features_)
         clf.fit(X, y, features=[0, 2, 3])
         self.assertListEqual([0, 2, 3], clf.features_)
@@ -88,67 +61,169 @@ class FImdlpTest(unittest.TestCase):
             clf.fit([[1, 2], [3, 4]], [1, 2], features=[0, 2])
 
     def test_fit_features(self):
+        clf = FImdlp(n_jobs=-1)
+        # Two samples doesn't have enough information to split
+        clf.fit([[1, -2], [3, 4]], [1, 2], features=[0])
+        self.assertListEqual(clf.get_cut_points(), [[], []])
+        clf.fit([[1, -2], [3, 4], [5, 6]], [1, 2, 2], features=[0])
+        self.assertListEqual(clf.get_cut_points(), [[2], []])
+        res = clf.transform([[1, -2], [3, 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(self):
         clf = FImdlp()
-        clf.fit([[1, 2], [3, 4]], [1, 2], features=[0])
-        res = clf.transform([[1, 2], [3, 4]])
-        self.assertListEqual(res.tolist(), [[0, 2], [0, 4]])
-
-    def test_transform_original(self):
-        clf = FImdlp(proposal=False)
-        clf.fit([[1, 2], [3, 4]], [1, 2])
+        clf.fit([[1, 2], [3, 4], [5, 6]], [1, 2, 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.assertEqual(clf.n_features_in_, 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],
-            [2, 1, 1, 1],
             [1, 0, 1, 1],
+            [2, 3, 1, 1],
+            [2, 0, 1, 1],
             [0, 0, 1, 1],
             [1, 0, 1, 1],
-            [1, 1, 1, 1],
-            [1, 0, 1, 1],
+            [1, 3, 1, 1],
+            [1, 2, 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()
             clf.transform([[1, 2], [3, 4]])
 
-    def test_transform_proposal(self):
-        clf = FImdlp(proposal=True)
-        clf.fit([[1, 2], [3, 4]], [1, 2])
-        self.assertEqual(
-            clf.transform([[1, 2], [3, 4]]).tolist(), [[0, 0], [0, 0]]
+    def test_cppfactorize(self):
+        source = [
+            b"f0",
+            b"f1",
+            b"f2",
+            b"f3",
+            b"f4",
+            b"f5",
+            b"f6",
+            b"f1",
+            b"f1",
+            b"f7",
+            b"f8",
+        ]
+        expected = [0, 1, 2, 3, 4, 5, 6, 1, 1, 7, 8]
+        computed = factorize(source)
+        self.assertListEqual(expected, computed)
+
+    def test_join_fit(self):
+        y = np.array([b"f0", b"f0", b"f2", b"f3", b"f4"])
+        x = np.array(
+            [
+                [0, 1, 2, 3, 4],
+                [0, 1, 2, 3, 4],
+                [1, 2, 3, 4, 5],
+                [2, 3, 4, 5, 6],
+                [3, 4, 5, 6, 7],
+            ]
         )
+        expected = [0, 0, 1, 2, 2]
+        clf = FImdlp()
+        clf.fit(x, factorize(y))
+        computed = clf.join_fit([0, 2], 1, x)
+        self.assertListEqual(computed.tolist(), expected)
+        expected_y = [b"002", b"002", b"113", b"224", b"335"]
+        self.assertListEqual(expected_y, clf.y_join_)
+
+    def test_join_fit_error(self):
+        y = np.array([b"f0", b"f0", b"f2", b"f3", b"f4"])
+        x = np.array(
+            [
+                [0, 1, 2, 3, 4],
+                [0, 1, 2, 3, 4],
+                [1, 2, 3, 4, 5],
+                [2, 3, 4, 5, 6],
+                [3, 4, 5, 6, 7],
+            ]
+        )
+        clf = FImdlp()
+        clf.fit(x, factorize(y))
+        with self.assertRaises(ValueError) as exception:
+            clf.join_fit([], 1, x)
+        self.assertEqual(
+            str(exception.exception),
+            "Number of features must be in range [1, 5]",
+        )
+        with self.assertRaises(ValueError) as exception:
+            FImdlp().join_fit([0, 4], 1, x)
+        self.assertTrue(
+            str(exception.exception).startswith(
+                "This FImdlp instance is not fitted yet."
+            )
+        )
+        with self.assertRaises(ValueError) as exception:
+            clf.join_fit([0, 5], 1, x)
+        self.assertEqual(
+            str(exception.exception),
+            "Feature 5 not in range [0, 5)",
+        )
+        with self.assertRaises(ValueError) as exception:
+            clf.join_fit([0, 2], 5, x)
+        self.assertEqual(
+            str(exception.exception),
+            "Target 5 not in range [0, 5)",
+        )
+        with self.assertRaises(ValueError) as exception:
+            clf.join_fit([0, 2], 2, x)
+        self.assertEqual(
+            str(exception.exception),
+            "Target cannot in features to join",
+        )
+
+    def test_factorize(self):
+        y = np.array([b"f0", b"f0", b"f2", b"f3", b"f4"])
+        clf = FImdlp()
+        computed = clf.factorize(y)
+        self.assertListEqual([0, 0, 1, 2, 3], computed)
+        y = [b"f4", b"f0", b"f0", b"f2", b"f3"]
+        clf = FImdlp()
+        computed = clf.factorize(y)
+        self.assertListEqual([0, 1, 1, 2, 3], computed)
+
+    @staticmethod
+    def test_sklearn_transformer():
+        for check, test in check_estimator(FImdlp(), generate_only=True):
+            test(check)
+
+    def test_states_feature(self):
+        clf = FImdlp()
         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_))
-        self.assertListEqual(
-            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],
-            [5, 2, 1, 1],
-            [5, 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.transform([[1, 2], [3, 4]])
+        expected = []
+        for i in [3, 6, 4, 3]:
+            expected.append(list(range(i)))
+        for feature in range(X.shape[1]):
+            self.assertListEqual(
+                expected[feature], clf.get_states_feature(feature)
+            )
+
+    def test_states_no_feature(self):
+        clf = FImdlp()
+        X, y = load_iris(return_X_y=True)
+        clf.fit(X, y)
+        self.assertIsNone(clf.get_states_feature(4))