Fix fit/build/train mistake

balance-scale.csv

@@ -0,0 +1,215 @@
+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

@@ -7,11 +7,12 @@ 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 (
     TreeSearch,
     BayesianEstimator,
-    MaximumLikelihoodEstimator,
+    # MaximumLikelihoodEstimator,
 )
 from pgmpy.models import BayesianNetwork
 import matplotlib.pyplot as plt
@@ -21,10 +22,12 @@ class TAN(ClassifierMixin, BaseEstimator):
     """An example classifier which implements a 1-NN algorithm.
     For more information regarding how to build your own classifier, read more
     in the :ref:`User Guide <user_guide>`.
+
     Parameters
     ----------
     demo_param : str, default='demo'
         A parameter used for demonstation of how to pass and store paramters.
+
     Attributes
     ----------
     X_ : ndarray, shape (n_samples, n_features)
@@ -44,6 +47,7 @@ class TAN(ClassifierMixin, BaseEstimator):
 
     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)
@@ -55,6 +59,7 @@ class TAN(ClassifierMixin, BaseEstimator):
             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
@@ -86,8 +91,17 @@ class TAN(ClassifierMixin, BaseEstimator):
             raise ValueError("Head index out of range")
 
         self.X_ = X
-        self.y_ = y
+        self.y_ = y.astype(int)
+        self.dataset_ = pd.DataFrame(
+            self.X_, columns=self.features_, dtype="int16"
+        )
+        self.dataset_[self.class_name_] = self.y_
+        try:
+            check_is_fitted(self, ["X_", "y_", "fitted_"])
+        except NotFittedError:
+            self.__build()
         self.__train()
+        self.fitted_ = True
         # Return the classifier
         return self
 
@@ -101,6 +115,7 @@ class TAN(ClassifierMixin, BaseEstimator):
         Marco Zaffalon,
         Learning extended tree augmented naive structures,
         International Journal of Approximate Reasoning,
+
         Returns
         -------
         List
@@ -121,14 +136,12 @@ class TAN(ClassifierMixin, BaseEstimator):
         ]
         return list(combinations(reordered, 2))
 
-    def __train(self):
+    def __build(self):
         # Initialize a Naive Bayes model
         net = [(self.class_name_, feature) for feature in self.features_]
         self.model_ = BayesianNetwork(net)
         # initialize a complete network with all edges
         self.model_.add_edges_from(self.__initial_edges())
-        self.dataset_ = pd.DataFrame(self.X_, columns=self.features_)
-        self.dataset_[self.class_name_] = self.y_
         # learn graph structure
         root_node = None if self.head_ is None else self.features_[self.head_]
         est = TreeSearch(self.dataset_, root_node=root_node)
@@ -139,12 +152,17 @@ class TAN(ClassifierMixin, BaseEstimator):
         )
         if self.head_ is None:
             self.head_ = est.root_node
-        self.model_ = BayesianNetwork(dag.edges())
+        self.model_ = BayesianNetwork(
+            dag.edges(), show_progress=self.show_progress
+        )
+
+    def __train(self):
         self.model_.fit(
             self.dataset_,
             # estimator=MaximumLikelihoodEstimator,
             estimator=BayesianEstimator,
             prior_type="K2",
+            n_jobs=1,
         )
 
     def plot(self, title=""):
@@ -161,20 +179,54 @@ class TAN(ClassifierMixin, BaseEstimator):
 
     def predict(self, X):
         """A reference implementation of a prediction for a classifier.
+
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
             The input samples.
+
         Returns
         -------
         y : ndarray, shape (n_samples,)
             The label for each sample is the label of the closest sample
             seen during fit.
+
+        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.drop('E', axis=1, inplace=True)
+        >>> model = TAN(random_state=17)
+        >>> features.remove('E')
+        >>> model.fit(train_data, train_y, features=features, class_name='E')
+        TAN(random_state=17)
+        >>> predict_data = predict_data.copy()
+        >>> predict_data.drop('E', axis=1, inplace=True)
+        >>> y_pred = model.predict(predict_data)
+        >>> y_pred[:10]
+        array([[0],
+               [0],
+               [1],
+               [1],
+               [0],
+               [1],
+               [1],
+               [1],
+               [0],
+               [1]])
         """
         # Check is fit had been called
-        check_is_fitted(self, ["X_", "y_"])
+        check_is_fitted(self, ["X_", "y_", "fitted_"])
 
         # Input validation
         X = check_array(X)
-        dataset = pd.DataFrame(X, columns=self.features_)
+        dataset = pd.DataFrame(X, columns=self.features_, dtype="int16")
-        return self.model_.predict(dataset).to_numpy()
+        return self.model_.predict(dataset, n_jobs=1).to_numpy()

bayesclass/test.r

@@ -0,0 +1 @@
+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)

glass.csv

@@ -0,0 +1,215 @@
+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
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test.py

@@ -0,0 +1,111 @@
+#!/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)}"
+)