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

@@ -136,3 +136,4 @@ cmake-build-debug/**
 **/x/*
 **/*.so
 **/CMakeFiles
+wheelhouse

MANIFEST.in

@@ -1 +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

@@ -3,7 +3,7 @@
 [![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 

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",

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__ = "0.9.2"
+__version__ = "0.9.3"

src/fimdlp/cfimdlp.pyx

@@ -6,16 +6,15 @@ from libcpp.string cimport string
 cdef extern from "../cppmdlp/CPPFImdlp.h" namespace "mdlp":
     ctypedef float precision_t
     cdef cppclass CPPFImdlp:
-        CPPFImdlp(int) 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, algorithm):
+    def __cinit__(self):
-        self.thisptr = new CPPFImdlp(algorithm)
+        self.thisptr = new CPPFImdlp()
     def __dealloc__(self):
         del self.thisptr
     def fit(self, X, y):
@@ -25,4 +24,10 @@ cdef class CFImdlp:
         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

@@ -1,24 +1,22 @@
 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, algorithm=0, n_jobs=-1):
+    def __init__(self, n_jobs=-1):
-        self.algorithm = algorithm
         self.n_jobs = n_jobs
 
     """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
@@ -26,27 +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
+    X_ : array, shape (n_samples, n_features)
-        the samples used to fit, shape (n_samples, n_features)
+        the samples used to fit
-    y_ : array
+    y_ : array, shape(n_samples,)
-        the labels used to fit, 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):
+    def _more_tags(self):
-        """Check the common parameters passed to fit"""
+        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():
@@ -67,15 +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
+        y : array, shape (n_samples,)
-            There is no need of a target in a transformer, yet the pipeline API
+            the labels used to fit
-            requires this parameter.
         features : list, default=[i for i in range(n_features)]
             The list of features to be discretized.
         Returns
@@ -83,23 +89,23 @@ 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.discretizer_ = [None] * self.n_features_in_
-        self.cut_points_ = [None] * self.n_features_
+        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):
         if feature in self.features_:
-            self.discretizer_[feature] = CFImdlp(algorithm=self.algorithm)
+            self.discretizer_[feature] = CFImdlp()
             self.discretizer_[feature].fit(self.X_[:, feature], self.y_)
             self.cut_points_[feature] = self.discretizer_[
                 feature
@@ -118,7 +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
         -------
@@ -126,25 +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`"
             )
-        if len(self.features_) == self.n_features_:
+        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
@@ -153,6 +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

@@ -1,67 +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 .. import __version__
-from .._version 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.assertEqual(0, clf.algorithm)
+        clf = FImdlp(n_jobs=7)
-        clf = FImdlp(algorithm=1, n_jobs=7)
-        self.assertEqual(1, clf.algorithm)
         self.assertEqual(7, clf.n_jobs)
 
     def test_fit_definitive(self):
-        clf = FImdlp(algorithm=0)
+        clf = FImdlp()
-        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([[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.assertEqual(clf.n_features_in_, 4)
         self.assertTrue(np.array_equal(X, clf.X_))
         self.assertTrue(np.array_equal(y, clf.y_))
-        expected = [
-            [5.449999809265137, 6.25],
-            [2.8499999046325684, 3.0, 3.049999952316284, 3.3499999046325684],
-            [2.450000047683716, 4.75, 5.050000190734863],
-            [0.800000011920929, 1.4500000476837158, 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_alternative(self):
-        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([[2], [3]], 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, 5.75],
-            [2.8499999046325684, 3.3499999046325684],
+            [2.75, 2.8499999046325684, 2.95, 3.05, 3.3499999046325684],
-            [2.450000047683716, 4.75],
+            [2.45, 4.75, 5.050000190734863],
-            [0.800000011920929, 1.75],
+            [0.8, 1.75],
         ]
-        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_)
@@ -82,8 +61,12 @@ class FImdlpTest(unittest.TestCase):
             clf.fit([[1, 2], [3, 4]], [1, 2], features=[0, 2])
 
     def test_fit_features(self):
-        clf = FImdlp()
+        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)
@@ -98,15 +81,15 @@ class FImdlpTest(unittest.TestCase):
         )
         self.assertEqual(X_computed.dtype, np.float64)
 
-    def test_transform_definitive(self):
+    def test_transform(self):
-        clf = FImdlp(algorithm=0)
+        clf = FImdlp()
-        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], [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)
@@ -116,46 +99,131 @@ class FImdlpTest(unittest.TestCase):
         self.assertEqual(X_transformed.dtype, np.int32)
         expected = [
             [1, 0, 1, 1],
-            [1, 1, 1, 1],
+            [2, 3, 1, 1],
-            [1, 0, 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(algorithm=0)
-            clf.transform([[1, 2], [3, 4]])
-
-    def test_transform_alternative(self):
-        clf = FImdlp(algorithm=1)
-        clf.fit([[1, 2], [3, 4]], [1, 2])
-        self.assertEqual(
-            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_))
-        self.assertListEqual(
-            clf.transform(X).tolist(), clf.fit(X, y).transform(X).tolist()
-        )
-        expected = [
-            [1, 0, 1, 1],
-            [2, 1, 1, 1],
             [2, 0, 1, 1],
             [0, 0, 1, 1],
             [1, 0, 1, 1],
-            [1, 1, 1, 1],
+            [1, 3, 1, 1],
-            [1, 1, 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(algorithm=1)
+            clf = FImdlp()
             clf.transform([[1, 2], [3, 4]])
+
+    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)
+        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))