Add example of usage

.gitignore

@@ -65,3 +65,5 @@ doc/generated/
 
 # PyBuilder
 target/
+
+.ipynb_checkpoints

Makefile

@@ -20,6 +20,7 @@ push:  ## Push code with tags
 	git push && git push --tags
 
 test:  ## Run tests
+	python -m doctest bayesclass/bayesclass.py
 	pytest
 
 doc:  ## Update documentation

balance-scale.csv

@@ -1,215 +0,0 @@
-RI,Na,Mg,Al,Si,'K',Ca,Ba,Fe,Type
-35,11,11,16,41,29,18,0,0,0
-22,3,11,23,40,25,13,0,0,1
-35,21,11,26,23,26,6,0,0,0
-3,41,5,28,48,0,3,0,0,2
-68,4,0,13,0,10,31,0,6,3
-22,9,6,27,42,25,19,1,5,3
-34,26,11,5,41,2,20,0,0,1
-46,20,6,23,38,24,20,0,0,0
-8,34,0,43,45,5,20,2,2,4
-35,20,12,23,20,24,8,0,6,3
-22,24,11,27,28,16,3,0,0,3
-32,4,7,17,46,27,20,0,6,3
-63,21,11,9,10,10,28,0,0,0
-57,32,11,5,7,9,24,0,0,1
-23,20,11,32,22,26,4,0,5,1
-27,26,11,30,22,27,3,0,0,3
-28,41,0,38,41,0,13,3,4,4
-22,9,7,27,43,30,3,0,0,3
-50,23,0,32,41,17,30,0,0,5
-40,16,7,27,40,26,19,1,0,3
-58,19,11,12,18,13,27,0,5,0
-66,0,0,37,17,33,31,0,6,3
-48,16,11,15,20,29,20,0,5,3
-33,25,11,19,34,25,3,0,5,0
-52,12,4,42,15,32,25,1,8,5
-10,22,11,27,42,15,3,0,0,3
-14,11,11,37,39,30,3,0,0,3
-24,41,0,38,43,0,10,3,3,4
-25,22,11,27,36,25,3,0,0,3
-16,24,11,23,25,25,4,0,0,1
-23,16,11,30,42,30,3,0,0,3
-45,24,8,28,14,25,20,0,0,1
-0,41,0,1,48,0,1,0,0,2
-22,26,11,28,22,30,3,0,0,3
-33,17,11,23,41,25,5,0,5,0
-11,9,11,29,42,30,3,0,6,0
-14,11,11,30,40,29,3,0,6,0
-30,4,6,30,40,30,20,0,0,3
-23,12,11,27,41,30,3,1,6,3
-5,37,7,39,20,33,3,0,0,3
-37,9,11,23,40,29,18,0,0,0
-38,17,11,13,42,25,4,0,5,3
-22,17,11,28,35,26,3,0,0,3
-14,22,8,27,42,16,3,0,0,3
-49,26,11,8,24,11,20,1,6,1
-33,9,11,20,42,26,17,0,0,0
-6,31,5,44,0,34,0,4,0,5
-33,21,11,23,22,25,3,0,0,0
-35,19,11,23,39,26,10,0,0,0
-60,20,11,17,31,23,10,0,0,3
-33,7,11,23,45,26,14,0,3,0
-48,20,11,13,35,25,6,1,5,3
-32,23,11,16,42,26,3,0,0,0
-14,20,11,28,42,30,3,0,0,3
-22,41,0,43,38,0,23,2,0,4
-31,17,11,30,30,23,8,0,3,0
-64,41,5,38,1,32,26,0,0,4
-55,27,11,16,10,6,22,0,0,0
-34,26,11,15,33,9,16,0,0,1
-48,25,12,23,22,21,4,0,0,3
-48,26,12,23,10,23,4,0,6,3
-49,26,11,15,23,10,18,0,0,0
-9,23,11,20,31,25,15,0,0,0
-63,35,11,2,7,9,24,0,0,0
-64,27,11,2,6,6,28,0,5,0
-8,28,0,43,42,10,23,3,2,4
-49,20,7,23,21,26,20,0,5,0
-62,35,11,13,5,13,20,0,7,1
-68,0,0,41,0,26,31,4,6,3
-58,19,11,12,20,13,27,0,5,0
-42,41,5,30,22,0,21,0,0,2
-48,26,11,23,22,25,3,0,5,3
-48,41,2,30,22,0,27,0,0,2
-42,22,12,26,20,24,4,0,5,3
-64,23,11,9,10,10,28,0,2,0
-22,26,11,27,22,29,3,0,0,3
-33,7,11,27,42,25,14,0,0,0
-2,17,11,16,41,27,4,0,6,0
-22,18,10,23,41,21,15,0,0,1
-29,16,11,23,41,25,6,0,0,0
-33,11,11,23,41,26,15,0,0,0
-31,23,11,21,42,24,3,0,0,0
-57,39,12,11,5,0,24,0,0,1
-34,21,9,23,31,26,15,0,0,0
-58,1,5,29,39,17,30,0,0,5
-38,27,12,28,8,23,3,0,5,3
-68,8,0,6,10,2,31,0,0,3
-33,11,11,27,31,23,10,0,5,0
-33,17,11,20,41,30,13,0,0,0
-60,27,3,23,17,15,29,0,0,3
-22,41,0,36,42,0,17,3,0,4
-35,9,11,19,40,27,18,0,6,0
-58,20,11,12,18,13,27,0,5,0
-49,27,5,19,31,0,27,0,0,2
-6,41,0,43,46,0,5,2,0,4
-59,26,11,12,10,11,24,0,3,0
-30,41,0,33,41,0,17,3,0,4
-51,29,3,30,6,16,29,0,5,3
-15,16,11,27,42,16,3,0,0,3
-48,20,12,19,22,26,6,0,0,3
-33,29,11,23,30,18,3,0,0,0
-23,23,11,28,22,30,3,0,6,3
-64,41,5,23,1,14,17,3,0,4
-24,41,0,38,42,0,5,3,0,4
-22,41,0,38,42,0,4,3,0,4
-4,17,0,44,2,35,2,0,0,5
-27,17,11,33,28,30,3,0,0,3
-30,41,0,43,43,0,20,2,0,4
-49,26,8,20,13,26,20,0,0,0
-49,8,0,30,47,15,30,0,0,5
-40,8,6,11,47,15,23,0,5,3
-18,41,0,43,43,0,18,2,0,4
-49,29,11,19,13,2,20,0,0,0
-22,41,0,38,46,0,9,3,0,4
-26,15,11,23,22,26,19,0,0,1
-64,26,8,20,22,26,20,0,0,4
-54,26,5,30,15,22,24,1,5,3
-47,39,7,43,4,34,0,3,0,4
-40,27,0,3,47,0,29,0,0,3
-34,5,6,23,46,25,20,0,6,0
-23,17,7,20,40,26,19,0,6,3
-13,16,11,24,43,30,3,0,6,0
-66,27,7,18,3,5,29,0,0,3
-68,27,7,6,2,5,30,0,0,3
-56,17,1,27,45,10,30,0,6,5
-33,15,11,23,42,23,4,0,5,0
-22,2,0,19,48,34,20,0,0,4
-21,34,0,43,22,5,20,3,0,4
-55,26,13,14,7,2,18,0,0,0
-33,7,11,23,43,26,10,0,0,0
-14,17,11,28,42,30,3,0,0,3
-23,11,11,28,44,30,3,0,0,3
-53,41,0,38,45,0,8,3,0,4
-33,9,11,23,42,26,18,0,5,0
-65,26,0,29,18,15,30,0,0,5
-33,20,11,13,42,25,3,0,0,0
-33,26,11,18,41,26,3,0,0,0
-28,16,11,29,40,26,3,0,0,3
-61,27,11,7,6,11,25,0,0,0
-14,20,11,28,40,30,3,0,4,3
-35,9,11,17,42,26,18,0,0,0
-49,29,11,23,8,21,18,1,0,0
-49,27,11,23,6,10,17,2,0,0
-23,15,0,35,47,33,28,0,0,5
-22,24,11,29,40,26,3,0,0,3
-48,16,11,29,22,26,14,0,5,3
-27,27,11,34,12,29,3,0,0,3
-54,27,5,27,10,19,27,0,5,3
-12,41,11,30,8,11,3,0,5,3
-40,25,12,19,22,26,3,0,0,3
-1,27,7,34,35,34,0,3,0,4
-63,35,11,9,5,0,25,0,0,0
-66,27,0,23,3,13,31,0,5,3
-40,24,11,22,34,21,3,0,0,3
-22,25,9,23,23,21,17,0,0,1
-33,11,11,23,41,27,15,0,0,0
-6,41,0,43,46,0,4,2,0,4
-49,9,5,35,26,26,28,0,0,5
-49,41,11,0,10,1,20,0,0,0
-48,22,11,23,22,26,6,0,0,3
-66,0,0,8,42,0,31,0,0,3
-59,26,11,12,7,13,24,0,5,0
-15,41,0,43,43,0,18,2,4,4
-4,17,0,44,2,35,2,0,0,5
-68,0,0,8,42,0,31,0,0,3
-63,35,11,2,7,9,24,0,0,0
-33,11,11,16,42,25,14,0,0,0
-48,12,11,21,22,27,18,0,6,3
-22,25,11,22,35,30,3,0,0,3
-15,41,0,43,42,1,20,2,0,4
-23,16,11,23,31,25,16,0,0,3
-12,20,11,29,42,3,5,0,5,3
-67,29,11,7,6,1,27,0,5,0
-44,41,0,34,39,34,0,4,0,4
-49,33,11,23,13,25,4,0,0,0
-18,28,5,33,42,0,17,3,0,4
-61,41,11,12,5,11,20,0,0,0
-41,16,11,23,40,26,6,0,0,0
-58,0,4,29,45,26,30,0,0,5
-49,41,0,3,46,0,29,0,0,2
-19,17,11,27,40,26,3,0,0,3
-22,25,11,28,24,30,3,0,5,3
-36,26,8,30,9,25,19,0,4,1
-63,41,0,13,25,7,30,0,4,3
-35,9,11,23,40,25,18,0,0,0
-28,36,0,39,44,0,17,3,0,4
-31,9,11,23,34,26,18,0,0,0
-39,25,6,19,36,24,20,0,0,0
-23,22,11,23,27,25,8,0,5,1
-52,25,0,24,19,15,30,0,0,5
-49,26,11,23,10,24,20,0,0,0
-34,21,6,23,41,21,20,0,4,0
-49,30,5,29,22,0,24,0,0,2
-8,41,0,43,42,1,20,2,0,4
-49,34,0,40,31,0,29,0,0,2
-48,17,11,13,20,29,20,0,5,3
-14,17,11,27,42,30,3,0,0,3
-14,22,11,27,42,26,3,0,0,3
-22,6,11,36,42,28,3,0,4,3
-23,16,11,30,40,29,3,0,5,3
-25,24,11,30,22,30,3,0,0,3
-48,20,9,19,28,25,20,0,5,0
-34,26,11,28,11,26,19,0,0,1
-34,12,11,20,40,26,15,1,5,0
-48,24,11,27,20,21,16,0,0,3
-29,25,11,17,38,20,6,0,0,0
-21,35,0,43,46,1,20,2,4,4
-19,26,11,28,41,16,3,0,0,0
-33,11,11,20,42,26,5,0,0,0
-16,25,10,20,26,26,4,0,0,1
-14,15,11,41,24,30,3,0,0,3
-18,29,11,22,40,15,3,0,5,3
-25,9,7,30,42,30,14,0,0,3
-48,34,5,30,25,0,21,0,0,2

bayesclass/bayesclass.py

@@ -7,13 +7,8 @@ import pandas as pd
 from sklearn.base import ClassifierMixin, BaseEstimator
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
 from sklearn.utils.multiclass import unique_labels
-from sklearn.exceptions import NotFittedError
 import networkx as nx
-from pgmpy.estimators import (
+from pgmpy.estimators import TreeSearch, BayesianEstimator
-    TreeSearch,
-    BayesianEstimator,
-    # MaximumLikelihoodEstimator,
-)
 from pgmpy.models import BayesianNetwork
 import matplotlib.pyplot as plt
 
@@ -39,32 +34,13 @@ class TAN(ClassifierMixin, BaseEstimator):
     """
 
     def __init__(
-        self, simple_init=False, show_progress=False, random_state=None
+        self, simple_init=True, show_progress=False, random_state=None
     ):
         self.simple_init = simple_init
         self.show_progress = show_progress
         self.random_state = random_state
 
-    def fit(self, X, y, **kwargs):
+    def __check_params_fit(self, X, y, kwargs):
-        """A reference implementation of a fitting function for a classifier.
-
-        Parameters
-        ----------
-        X : array-like, shape (n_samples, n_features)
-            The training input samples.
-        y : array-like, shape (n_samples,)
-            The target values. An array of int.
-        **kwargs : dict
-            class_name : str (default='class') Name of the class column
-            features: list (default=None) List of features
-            head: int (default=None) Index of the head node. Default value
-            gets the node with the highest sum of weights (mutual_info)
-
-        Returns
-        -------
-        self : object
-            Returns self.
-        """
         # Check that X and y have correct shape
         X, y = check_X_y(X, y)
         # Store the classes seen during fit
@@ -90,16 +66,55 @@ class TAN(ClassifierMixin, BaseEstimator):
         if self.head_ is not None and self.head_ >= len(self.features_):
             raise ValueError("Head index out of range")
 
+    def fit(self, X, y, **kwargs):
+        """A reference implementation of a fitting function for a classifier.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            The training input samples.
+        y : array-like, shape (n_samples,)
+            The target values. An array of int.
+        **kwargs : dict
+            class_name : str (default='class') Name of the class column
+            features: list (default=None) List of features
+            head: int (default=None) Index of the head node. Default value
+            gets the node with the highest sum of weights (mutual_info)
+
+        Returns
+        -------
+        self : object
+            Returns self.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import pandas as pd
+        >>> from bayesclass import TAN
+        >>> features = ['A', 'B', 'C', 'D', 'E']
+        >>> np.random.seed(17)
+        >>> values = pd.DataFrame(np.random.randint(low=0, high=2,
+        ...                       size=(1000, 5)), columns=features)
+        >>> train_data = values[:800]
+        >>> train_y = train_data['E']
+        >>> predict_data = values[800:]
+        >>> train_data = train_data.drop('E', axis=1)
+        >>> model = TAN(random_state=17)
+        >>> features.remove('E')
+        >>> model.fit(train_data, train_y, features=features, class_name='E')
+        TAN(random_state=17)
+        """
+        self.__check_params_fit(X, y, kwargs)
+        # Store the information needed to build the model
         self.X_ = X
         self.y_ = y.astype(int)
         self.dataset_ = pd.DataFrame(
             self.X_, columns=self.features_, dtype="int16"
         )
         self.dataset_[self.class_name_] = self.y_
-        try:
+        # Build the DAG
-            check_is_fitted(self, ["X_", "y_", "fitted_"])
-        except NotFittedError:
         self.__build()
+        # Train the model
         self.__train()
         self.fitted_ = True
         # Return the classifier
@@ -145,24 +160,23 @@ class TAN(ClassifierMixin, BaseEstimator):
         # learn graph structure
         root_node = None if self.head_ is None else self.features_[self.head_]
         est = TreeSearch(self.dataset_, root_node=root_node)
-        dag = est.estimate(
+        self.dag_ = est.estimate(
             estimator_type="tan",
             class_node=self.class_name_,
             show_progress=self.show_progress,
         )
         if self.head_ is None:
             self.head_ = est.root_node
-        self.model_ = BayesianNetwork(
-            dag.edges(), show_progress=self.show_progress
-        )
 
     def __train(self):
+        self.model_ = BayesianNetwork(
+            self.dag_.edges(), show_progress=self.show_progress
+        )
         self.model_.fit(
             self.dataset_,
             # estimator=MaximumLikelihoodEstimator,
             estimator=BayesianEstimator,
             prior_type="K2",
-            n_jobs=1,
         )
 
     def plot(self, title=""):
@@ -203,7 +217,7 @@ class TAN(ClassifierMixin, BaseEstimator):
         >>> train_data = values[:800]
         >>> train_y = train_data['E']
         >>> predict_data = values[800:]
-        >>> train_data.drop('E', axis=1, inplace=True)
+        >>> train_data = train_data.drop('E', axis=1)
         >>> model = TAN(random_state=17)
         >>> features.remove('E')
         >>> model.fit(train_data, train_y, features=features, class_name='E')

bayesclass/test.r

@@ -1 +0,0 @@
-m0 <- ulam(alist(height ~ dnorm(mu, sigma), mu <- a, a ~ dnorm(186, 10), sigma ~ dexp(1)), data = d, chains  = 4, iter = 2000, cores = 4, log_lik=TRUE)

bayesclass/tests/test_bayesclass.py

@@ -19,7 +19,7 @@ def data():
 def test_TAN_constructor():
     clf = TAN()
     # Test default values of hyperparameters
-    assert not clf.simple_init
+    assert clf.simple_init
     assert not clf.show_progress
     assert clf.random_state is None
     clf = TAN(simple_init=True, show_progress=True, random_state=17)
@@ -34,6 +34,14 @@ def test_TAN_random_head(data):
     assert clf.head_ == 3
 
 
+def test_TAN_dag_initializer(data):
+    clf_not_simple = TAN(simple_init=False)
+    clf_simple = TAN(simple_init=True)
+    clf_not_simple.fit(*data, head=0)
+    clf_simple.fit(*data, head=0)
+    assert clf_simple.dag_.edges == clf_not_simple.dag_.edges
+
+
 def test_TAN_classifier(data):
     clf = TAN()
 

example.py

@@ -0,0 +1,24 @@
+from benchmark import Discretizer
+from bayesclass import TAN
+import sys
+from sklearn.model_selection import cross_val_score, StratifiedKFold
+
+
+if len(sys.argv) < 2:
+    print("Usage: python3 example.py <dataset> [n_folds]")
+    exit(1)
+random_state = 17
+name = sys.argv[1]
+n_folds = int(sys.argv[2]) if len(sys.argv) == 3 else 5
+dt = Discretizer()
+X, y = dt.load(name)
+clf = TAN(random_state=random_state)
+fit_params = dict(
+    features=dt.get_features(), class_name=dt.get_class_name(), head=0
+)
+kfold = StratifiedKFold(
+    n_splits=n_folds, shuffle=True, random_state=random_state
+)
+score = cross_val_score(clf, X, y, cv=kfold, fit_params=fit_params)
+print(f"Accuracy in {n_folds} folds stratified crossvalidation")
+print(f"{name}{'.' * 10}{score.mean():9.7f}")

glass.csv

@@ -1,215 +0,0 @@
-RI,Na,Mg,Al,Si,'K',Ca,Ba,Fe,Type
-35,11,11,16,41,29,18,0,0,0
-22,3,11,23,40,25,13,0,0,1
-35,21,11,26,23,26,6,0,0,0
-3,41,5,28,48,0,3,0,0,2
-68,4,0,13,0,10,31,0,6,3
-22,9,6,27,42,25,19,1,5,3
-34,26,11,5,41,2,20,0,0,1
-46,20,6,23,38,24,20,0,0,0
-8,34,0,43,45,5,20,2,2,4
-35,20,12,23,20,24,8,0,6,3
-22,24,11,27,28,16,3,0,0,3
-32,4,7,17,46,27,20,0,6,3
-63,21,11,9,10,10,28,0,0,0
-57,32,11,5,7,9,24,0,0,1
-23,20,11,32,22,26,4,0,5,1
-27,26,11,30,22,27,3,0,0,3
-28,41,0,38,41,0,13,3,4,4
-22,9,7,27,43,30,3,0,0,3
-50,23,0,32,41,17,30,0,0,5
-40,16,7,27,40,26,19,1,0,3
-58,19,11,12,18,13,27,0,5,0
-66,0,0,37,17,33,31,0,6,3
-48,16,11,15,20,29,20,0,5,3
-33,25,11,19,34,25,3,0,5,0
-52,12,4,42,15,32,25,1,8,5
-10,22,11,27,42,15,3,0,0,3
-14,11,11,37,39,30,3,0,0,3
-24,41,0,38,43,0,10,3,3,4
-25,22,11,27,36,25,3,0,0,3
-16,24,11,23,25,25,4,0,0,1
-23,16,11,30,42,30,3,0,0,3
-45,24,8,28,14,25,20,0,0,1
-0,41,0,1,48,0,1,0,0,2
-22,26,11,28,22,30,3,0,0,3
-33,17,11,23,41,25,5,0,5,0
-11,9,11,29,42,30,3,0,6,0
-14,11,11,30,40,29,3,0,6,0
-30,4,6,30,40,30,20,0,0,3
-23,12,11,27,41,30,3,1,6,3
-5,37,7,39,20,33,3,0,0,3
-37,9,11,23,40,29,18,0,0,0
-38,17,11,13,42,25,4,0,5,3
-22,17,11,28,35,26,3,0,0,3
-14,22,8,27,42,16,3,0,0,3
-49,26,11,8,24,11,20,1,6,1
-33,9,11,20,42,26,17,0,0,0
-6,31,5,44,0,34,0,4,0,5
-33,21,11,23,22,25,3,0,0,0
-35,19,11,23,39,26,10,0,0,0
-60,20,11,17,31,23,10,0,0,3
-33,7,11,23,45,26,14,0,3,0
-48,20,11,13,35,25,6,1,5,3
-32,23,11,16,42,26,3,0,0,0
-14,20,11,28,42,30,3,0,0,3
-22,41,0,43,38,0,23,2,0,4
-31,17,11,30,30,23,8,0,3,0
-64,41,5,38,1,32,26,0,0,4
-55,27,11,16,10,6,22,0,0,0
-34,26,11,15,33,9,16,0,0,1
-48,25,12,23,22,21,4,0,0,3
-48,26,12,23,10,23,4,0,6,3
-49,26,11,15,23,10,18,0,0,0
-9,23,11,20,31,25,15,0,0,0
-63,35,11,2,7,9,24,0,0,0
-64,27,11,2,6,6,28,0,5,0
-8,28,0,43,42,10,23,3,2,4
-49,20,7,23,21,26,20,0,5,0
-62,35,11,13,5,13,20,0,7,1
-68,0,0,41,0,26,31,4,6,3
-58,19,11,12,20,13,27,0,5,0
-42,41,5,30,22,0,21,0,0,2
-48,26,11,23,22,25,3,0,5,3
-48,41,2,30,22,0,27,0,0,2
-42,22,12,26,20,24,4,0,5,3
-64,23,11,9,10,10,28,0,2,0
-22,26,11,27,22,29,3,0,0,3
-33,7,11,27,42,25,14,0,0,0
-2,17,11,16,41,27,4,0,6,0
-22,18,10,23,41,21,15,0,0,1
-29,16,11,23,41,25,6,0,0,0
-33,11,11,23,41,26,15,0,0,0
-31,23,11,21,42,24,3,0,0,0
-57,39,12,11,5,0,24,0,0,1
-34,21,9,23,31,26,15,0,0,0
-58,1,5,29,39,17,30,0,0,5
-38,27,12,28,8,23,3,0,5,3
-68,8,0,6,10,2,31,0,0,3
-33,11,11,27,31,23,10,0,5,0
-33,17,11,20,41,30,13,0,0,0
-60,27,3,23,17,15,29,0,0,3
-22,41,0,36,42,0,17,3,0,4
-35,9,11,19,40,27,18,0,6,0
-58,20,11,12,18,13,27,0,5,0
-49,27,5,19,31,0,27,0,0,2
-6,41,0,43,46,0,5,2,0,4
-59,26,11,12,10,11,24,0,3,0
-30,41,0,33,41,0,17,3,0,4
-51,29,3,30,6,16,29,0,5,3
-15,16,11,27,42,16,3,0,0,3
-48,20,12,19,22,26,6,0,0,3
-33,29,11,23,30,18,3,0,0,0
-23,23,11,28,22,30,3,0,6,3
-64,41,5,23,1,14,17,3,0,4
-24,41,0,38,42,0,5,3,0,4
-22,41,0,38,42,0,4,3,0,4
-4,17,0,44,2,35,2,0,0,5
-27,17,11,33,28,30,3,0,0,3
-30,41,0,43,43,0,20,2,0,4
-49,26,8,20,13,26,20,0,0,0
-49,8,0,30,47,15,30,0,0,5
-40,8,6,11,47,15,23,0,5,3
-18,41,0,43,43,0,18,2,0,4
-49,29,11,19,13,2,20,0,0,0
-22,41,0,38,46,0,9,3,0,4
-26,15,11,23,22,26,19,0,0,1
-64,26,8,20,22,26,20,0,0,4
-54,26,5,30,15,22,24,1,5,3
-47,39,7,43,4,34,0,3,0,4
-40,27,0,3,47,0,29,0,0,3
-34,5,6,23,46,25,20,0,6,0
-23,17,7,20,40,26,19,0,6,3
-13,16,11,24,43,30,3,0,6,0
-66,27,7,18,3,5,29,0,0,3
-68,27,7,6,2,5,30,0,0,3
-56,17,1,27,45,10,30,0,6,5
-33,15,11,23,42,23,4,0,5,0
-22,2,0,19,48,34,20,0,0,4
-21,34,0,43,22,5,20,3,0,4
-55,26,13,14,7,2,18,0,0,0
-33,7,11,23,43,26,10,0,0,0
-14,17,11,28,42,30,3,0,0,3
-23,11,11,28,44,30,3,0,0,3
-53,41,0,38,45,0,8,3,0,4
-33,9,11,23,42,26,18,0,5,0
-65,26,0,29,18,15,30,0,0,5
-33,20,11,13,42,25,3,0,0,0
-33,26,11,18,41,26,3,0,0,0
-28,16,11,29,40,26,3,0,0,3
-61,27,11,7,6,11,25,0,0,0
-14,20,11,28,40,30,3,0,4,3
-35,9,11,17,42,26,18,0,0,0
-49,29,11,23,8,21,18,1,0,0
-49,27,11,23,6,10,17,2,0,0
-23,15,0,35,47,33,28,0,0,5
-22,24,11,29,40,26,3,0,0,3
-48,16,11,29,22,26,14,0,5,3
-27,27,11,34,12,29,3,0,0,3
-54,27,5,27,10,19,27,0,5,3
-12,41,11,30,8,11,3,0,5,3
-40,25,12,19,22,26,3,0,0,3
-1,27,7,34,35,34,0,3,0,4
-63,35,11,9,5,0,25,0,0,0
-66,27,0,23,3,13,31,0,5,3
-40,24,11,22,34,21,3,0,0,3
-22,25,9,23,23,21,17,0,0,1
-33,11,11,23,41,27,15,0,0,0
-6,41,0,43,46,0,4,2,0,4
-49,9,5,35,26,26,28,0,0,5
-49,41,11,0,10,1,20,0,0,0
-48,22,11,23,22,26,6,0,0,3
-66,0,0,8,42,0,31,0,0,3
-59,26,11,12,7,13,24,0,5,0
-15,41,0,43,43,0,18,2,4,4
-4,17,0,44,2,35,2,0,0,5
-68,0,0,8,42,0,31,0,0,3
-63,35,11,2,7,9,24,0,0,0
-33,11,11,16,42,25,14,0,0,0
-48,12,11,21,22,27,18,0,6,3
-22,25,11,22,35,30,3,0,0,3
-15,41,0,43,42,1,20,2,0,4
-23,16,11,23,31,25,16,0,0,3
-12,20,11,29,42,3,5,0,5,3
-67,29,11,7,6,1,27,0,5,0
-44,41,0,34,39,34,0,4,0,4
-49,33,11,23,13,25,4,0,0,0
-18,28,5,33,42,0,17,3,0,4
-61,41,11,12,5,11,20,0,0,0
-41,16,11,23,40,26,6,0,0,0
-58,0,4,29,45,26,30,0,0,5
-49,41,0,3,46,0,29,0,0,2
-19,17,11,27,40,26,3,0,0,3
-22,25,11,28,24,30,3,0,5,3
-36,26,8,30,9,25,19,0,4,1
-63,41,0,13,25,7,30,0,4,3
-35,9,11,23,40,25,18,0,0,0
-28,36,0,39,44,0,17,3,0,4
-31,9,11,23,34,26,18,0,0,0
-39,25,6,19,36,24,20,0,0,0
-23,22,11,23,27,25,8,0,5,1
-52,25,0,24,19,15,30,0,0,5
-49,26,11,23,10,24,20,0,0,0
-34,21,6,23,41,21,20,0,4,0
-49,30,5,29,22,0,24,0,0,2
-8,41,0,43,42,1,20,2,0,4
-49,34,0,40,31,0,29,0,0,2
-48,17,11,13,20,29,20,0,5,3
-14,17,11,27,42,30,3,0,0,3
-14,22,11,27,42,26,3,0,0,3
-22,6,11,36,42,28,3,0,4,3
-23,16,11,30,40,29,3,0,5,3
-25,24,11,30,22,30,3,0,0,3
-48,20,9,19,28,25,20,0,5,0
-34,26,11,28,11,26,19,0,0,1
-34,12,11,20,40,26,15,1,5,0
-48,24,11,27,20,21,16,0,0,3
-29,25,11,17,38,20,6,0,0,0
-21,35,0,43,46,1,20,2,4,4
-19,26,11,28,41,16,3,0,0,0
-33,11,11,20,42,26,5,0,0,0
-16,25,10,20,26,26,4,0,0,1
-14,15,11,41,24,30,3,0,0,3
-18,29,11,22,40,15,3,0,5,3
-25,9,7,30,42,30,14,0,0,3
-48,34,5,30,25,0,21,0,0,2

test.py

@@ -1,111 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-from mdlp import MDLP
-import pandas as pd
-from benchmark import Datasets
-from bayesclass import TAN
-from sklearn.model_selection import (
-    cross_validate,
-    StratifiedKFold,
-    KFold,
-    cross_val_score,
-    train_test_split,
-)
-import numpy as np
-import warnings
-from stree import Stree
-
-# In[2]:
-
-
-# Get data as a dataset
-dt = Datasets()
-data = dt.load("glass", dataframe=True)
-features = dt.dataset.features
-class_name = dt.dataset.class_name
-factorization, class_factors = pd.factorize(data[class_name])
-data[class_name] = factorization
-data.head()
-
-
-# In[3]:
-
-
-# Fayyad Irani
-discretiz = MDLP()
-Xdisc = discretiz.fit_transform(
-    data[features].to_numpy(), data[class_name].to_numpy()
-)
-features_discretized = pd.DataFrame(Xdisc, columns=features)
-dataset_discretized = features_discretized.copy()
-dataset_discretized[class_name] = data[class_name]
-X = dataset_discretized[features]
-y = dataset_discretized[class_name]
-dataset_discretized
-
-
-# In[4]:
-
-
-n_folds = 5
-score_name = "accuracy"
-random_state = 17
-test_size = 0.3
-
-
-def validate_classifier(model, X, y, stratified, fit_params):
-    stratified_class = StratifiedKFold if stratified else KFold
-    kfold = stratified_class(
-        shuffle=True, random_state=random_state, n_splits=n_folds
-    )
-    # return cross_validate(model, X, y, cv=kfold, return_estimator=True,
-    # scoring=score_name)
-    return cross_val_score(model, X, y, fit_params=fit_params)
-
-
-def split_data(X, y, stratified):
-    if stratified:
-        return train_test_split(
-            X,
-            y,
-            test_size=test_size,
-            random_state=random_state,
-            stratify=y,
-            shuffle=True,
-        )
-    else:
-        return train_test_split(
-            X, y, test_size=test_size, random_state=random_state, shuffle=True
-        )
-
-
-# In[5]:
-
-
-warnings.filterwarnings("ignore")
-for simple_init in [False, True]:
-    model = TAN(simple_init=simple_init)
-    for head in range(4):
-        X_train, X_test, y_train, y_test = split_data(X, y, stratified=False)
-        model.fit(
-            X_train,
-            y_train,
-            head=head,
-            features=features,
-            class_name=class_name,
-        )
-        y = model.predict(X_test)
-        model.plot()
-
-# In[ ]:
-
-
-model = TAN(simple_init=simple_init)
-model.fit(X, y, features=features, class_name=class_name)
-model.plot(
-    f"**simple_init={simple_init} head={head}  score={model.score(X, y)}"
-)