From 180365d727836a80134f4623997e7734600a2766 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana?= Date: Thu, 10 Nov 2022 09:37:12 +0100 Subject: [PATCH] Fix fit/build/train mistake --- balance-scale.csv | 215 +++++++ bayesclass/bayesclass.py | 70 ++- bayesclass/test.r | 1 + glass.csv | 215 +++++++ test.ipynb | 1193 +++++++++++++++++++------------------- test.py | 111 ++++ 6 files changed, 1198 insertions(+), 607 deletions(-) create mode 100644 balance-scale.csv create mode 100644 bayesclass/test.r create mode 100644 glass.csv create mode 100644 test.py diff --git a/balance-scale.csv b/balance-scale.csv new file mode 100644 index 0000000..5e4c093 --- /dev/null +++ b/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 diff --git a/bayesclass/bayesclass.py b/bayesclass/bayesclass.py index 63cade9..1db5d08 100644 --- a/bayesclass/bayesclass.py +++ b/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 `. + 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_) - return self.model_.predict(dataset).to_numpy() + dataset = pd.DataFrame(X, columns=self.features_, dtype="int16") + return self.model_.predict(dataset, n_jobs=1).to_numpy() diff --git a/bayesclass/test.r b/bayesclass/test.r new file mode 100644 index 0000000..14eab63 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/glass.csv b/glass.csv new file mode 100644 index 0000000..5e4c093 --- /dev/null +++ b/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 +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 diff --git a/test.ipynb b/test.ipynb index 6a42616..b716f7b 100644 --- a/test.ipynb +++ b/test.ipynb @@ -9,402 +9,100 @@ "source": [ "from mdlp import MDLP\n", "import pandas as pd\n", - "from benchmark import Datasets\n", - "from bayesclass import TAN" + "from benchmark import Discretizer, Datasets\n", + "from bayesclass import TAN\n", + "from sklearn.model_selection import cross_validate, StratifiedKFold, KFold, cross_val_score, train_test_split\n", + "import numpy as np\n", + "n_folds = 5\n", + "score_name = \"accuracy\"\n", + "random_state=17\n", + "test_size=.3" ] }, { "cell_type": "code", "execution_count": 2, - "id": "8ff3f4d6-e681-4252-ac4d-dc5bd14dcede", + "id": "2840a103-99fb-466f-ae75-45e11c1b9c5a", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RINaMgAlSi'K'CaBaFeType
01.5179312.793.501.1273.030.648.770.00.000
11.5164312.163.521.3572.890.578.530.00.001
21.5179313.213.481.4172.640.598.430.00.000
31.5129914.401.741.5474.550.007.590.00.002
41.5339312.300.001.0070.160.1216.190.00.243
\n", - "
" - ], - "text/plain": [ - " RI Na Mg Al Si 'K' Ca Ba Fe Type\n", - "0 1.51793 12.79 3.50 1.12 73.03 0.64 8.77 0.0 0.00 0\n", - "1 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0.0 0.00 1\n", - "2 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0.0 0.00 0\n", - "3 1.51299 14.40 1.74 1.54 74.55 0.00 7.59 0.0 0.00 2\n", - "4 1.53393 12.30 0.00 1.00 70.16 0.12 16.19 0.0 0.24 3" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Get data as a dataset\n", - "dt = Datasets()\n", - "data = dt.load(\"glass\", dataframe=True)\n", - "features = dt.dataset.features\n", - "class_name = dt.dataset.class_name\n", - "factorization, class_factors = pd.factorize(data[class_name])\n", - "data[class_name] = factorization\n", - "data.head()" + "def validate_classifier(model, X, y, stratified, fit_params):\n", + " stratified_class = StratifiedKFold if stratified else KFold\n", + " kfold = stratified_class(shuffle=True, random_state=random_state, n_splits=n_folds)\n", + " return cross_validate(model, X, y, cv=kfold, return_estimator=True, scoring=score_name, fit_params=fit_params)\n", + "\n", + "def split_data(X, y, stratified):\n", + " if stratified:\n", + " return train_test_split(X, y, test_size=test_size, random_state=random_state, stratify=y, shuffle=True)\n", + " else:\n", + " return train_test_split(X, y, test_size=test_size, random_state=random_state, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 3, - "id": "7c9e1eae-6a66-4930-a125-f9f3def45574", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RINaMgAlSi'K'CaBaFeType
030.014.016.018.038.032.014.00.00.00
117.03.018.021.034.024.010.00.00.01
230.024.015.022.022.027.06.00.00.00
33.051.06.023.047.00.03.00.00.02
462.04.00.013.00.08.030.00.05.03
.................................
20913.033.011.019.023.027.04.00.00.01
21011.019.018.029.023.033.03.00.00.03
21114.041.018.020.034.014.03.00.05.03
21220.08.08.023.042.033.011.00.00.03
21343.046.06.023.023.00.015.00.00.02
\n", - "

214 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " RI Na Mg Al Si 'K' Ca Ba Fe Type\n", - "0 30.0 14.0 16.0 18.0 38.0 32.0 14.0 0.0 0.0 0\n", - "1 17.0 3.0 18.0 21.0 34.0 24.0 10.0 0.0 0.0 1\n", - "2 30.0 24.0 15.0 22.0 22.0 27.0 6.0 0.0 0.0 0\n", - "3 3.0 51.0 6.0 23.0 47.0 0.0 3.0 0.0 0.0 2\n", - "4 62.0 4.0 0.0 13.0 0.0 8.0 30.0 0.0 5.0 3\n", - ".. ... ... ... ... ... ... ... ... ... ...\n", - "209 13.0 33.0 11.0 19.0 23.0 27.0 4.0 0.0 0.0 1\n", - "210 11.0 19.0 18.0 29.0 23.0 33.0 3.0 0.0 0.0 3\n", - "211 14.0 41.0 18.0 20.0 34.0 14.0 3.0 0.0 5.0 3\n", - "212 20.0 8.0 8.0 23.0 42.0 33.0 11.0 0.0 0.0 3\n", - "213 43.0 46.0 6.0 23.0 23.0 0.0 15.0 0.0 0.0 2\n", - "\n", - "[214 rows x 10 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fayyad Irani\n", - "discretiz = MDLP()\n", - "Xdisc = discretiz.fit_transform(\n", - " data[features].to_numpy(), data[class_name].to_numpy()\n", - ")\n", - "features_discretized = pd.DataFrame(Xdisc, columns=features)\n", - "dataset_discretized = features_discretized.copy()\n", - "dataset_discretized[class_name] = data[class_name]\n", - "X = dataset_discretized[features]\n", - "y = dataset_discretized[class_name]\n", - "dataset_discretized" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2840a103-99fb-466f-ae75-45e11c1b9c5a", + "id": "08673f9e", "metadata": {}, "outputs": [], "source": [ - "from sklearn.model_selection import cross_validate, StratifiedKFold, KFold, cross_val_score\n", - "import numpy as np\n", - "n_folds = 5\n", - "score_name = \"accuracy\"\n", - "random_state=17\n", - "def validate_classifier(model, X, y, stratified, fit_params):\n", - " stratified_class = StratifiedKFold if stratified else KFold\n", - " kfold = stratified_class(shuffle=True, random_state=random_state, n_splits=n_folds)\n", - " #return cross_validate(model, X, y, cv=kfold, return_estimator=True, scoring=score_name)\n", - " return cross_val_score(model, X, y, fit_params=fit_params)" + "def splitter(dt, name):\n", + " X, y = dt.load(name)\n", + " features, class_name = dt.get_features(), dt.get_class_name()\n", + " clf = TAN()\n", + " X_train, X_test, y_train, y_test = split_data(X, y, stratified=False)\n", + " clf.fit(X, y, features=features, class_name=class_name, head=0)\n", + " score = clf.score(X_test, y_test)\n", + " clf.plot(f\"{name} score={score}\")\n", + " \n", + "def crossval(dt, name):\n", + " X, y = dt.load(name)\n", + " features, class_name = dt.get_features(), dt.get_class_name()\n", + " fit_params=dict(features=features, class_name=class_name, head=0)\n", + " clf.fit(X, y, **fit_params)\n", + " score = validate_classifier(clf, X, y, fit_params=fit_params, stratified=False)\n", + " clf = score[\"estimator\"][0]\n", + " clf.plot(f\"{name} score={score['test_score'].mean()}\")" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "6a1aad95-370f-4854-ae9a-32205aff5d39", + "execution_count": 4, + "id": "34c3dd9c", "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7bdb666c5e5140e688141356958b362f", - "version_major": 2, - "version_minor": 0 - }, + "image/png": "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", "text/plain": [ - " 0%| | 0/43 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -416,13 +114,475 @@ "text": [ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 18 is out of bounds for axis 0 with size 14\n", + "\n", " warnings.warn(\n", "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 33 is out of bounds for axis 0 with size 27\n", + "\n", " warnings.warn(\n", "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 31 is out of bounds for axis 0 with size 30\n", + "\n", + " warnings.warn(\n", + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 42 is out of bounds for axis 0 with size 37\n", + "\n", + " warnings.warn(\n", + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 56 is out of bounds for axis 1 with size 54\n", + "\n", + " warnings.warn(\n", + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 35 is out of bounds for axis 0 with size 35\n", + "\n", + " warnings.warn(\n", + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 35 is out of bounds for axis 0 with size 34\n", + "\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 27 is out of bounds for axis 0 with size 27\n", + "\n", + " warnings.warn(\n", + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 25 is out of bounds for axis 0 with size 24\n", + "\n", + " warnings.warn(\n", + "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", + " warnings.warn(\n", + "/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", + "Traceback (most recent call last):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n", + " scores = scorer(estimator, X_test, y_test)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n", + " return self._score(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 261, in _score\n", + " y_pred = method_caller(estimator, \"predict\", X)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 71, in _cached_call\n", + " return getattr(estimator, method)(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/bayesclass/bayesclass/bayesclass.py\", line 223, in predict\n", + " return self.model_.predict(dataset, n_jobs=1).to_numpy()\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/models/BayesianNetwork.py\", line 735, in predict\n", + " pred_values = Parallel(n_jobs=n_jobs)(\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 1088, in __call__\n", + " while self.dispatch_one_batch(iterator):\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n", + " self._dispatch(tasks)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 819, in _dispatch\n", + " job = self._backend.apply_async(batch, callback=cb)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n", + " result = ImmediateResult(func)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n", + " self.results = batch()\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in __call__\n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py\", line 288, in \n", + " return [func(*args, **kwargs)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 559, in map_query\n", + " final_distribution = reduced_ve._variable_elimination(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 183, in _variable_elimination\n", + " working_factors = self._get_working_factors(evidence)\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/inference/ExactInference.py\", line 53, in _get_working_factors\n", + " factor_reduced = factor.reduce(\n", + " File \"/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py\", line 561, in reduce\n", + " phi.values = phi.values[tuple(slice_)]\n", + "IndexError: index 10 is out of bounds for axis 0 with size 9\n", + "\n", " warnings.warn(\n", "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", " warnings.warn(\n", @@ -432,263 +592,100 @@ }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0110029f56a4451cb877eeaae42e8e3f", - "version_major": 2, - "version_minor": 0 - }, + "image/png": "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", "text/plain": [ - " 0%| | 0/43 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n" - ] - }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1e8130ed7d3a4f4da499dc481df369ab", - "version_major": 2, - "version_minor": 0 - }, + "image/png": "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", "text/plain": [ - " 0%| | 0/43 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n", - "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "52adf5efe6874dcfa1e776c6a3c47f2c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/43 [00:00 10\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msimple_init=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msimple_init\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m head=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mhead\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m score=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscore\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Code/bayesclass/bayesclass/bayesclass.py:148\u001b[0m, in \u001b[0;36mTAN.plot\u001b[0;34m(self, title)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 147\u001b[0m nx\u001b[38;5;241m.\u001b[39mdraw_circular(\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_\u001b[49m,\n\u001b[1;32m 149\u001b[0m with_labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 150\u001b[0m arrowsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m,\n\u001b[1;32m 151\u001b[0m node_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m800\u001b[39m,\n\u001b[1;32m 152\u001b[0m alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m,\n\u001b[1;32m 153\u001b[0m font_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbold\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 154\u001b[0m )\n\u001b[1;32m 155\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(title)\n\u001b[1;32m 156\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", - "\u001b[0;31mAttributeError\u001b[0m: 'TAN' object has no attribute 'model_'" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [4], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m dt \u001b[39m=\u001b[39m Discretizer()\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m name \u001b[39min\u001b[39;00m dt:\n\u001b[0;32m----> 4\u001b[0m splitter(dt, name) \u001b[39mif\u001b[39;00m splitter \u001b[39melse\u001b[39;00m crossval(dt, name)\n", + "Cell \u001b[0;32mIn [3], line 14\u001b[0m, in \u001b[0;36mcrossval\u001b[0;34m(dt, name)\u001b[0m\n\u001b[1;32m 12\u001b[0m features, class_name \u001b[39m=\u001b[39m dt\u001b[39m.\u001b[39mget_features(), dt\u001b[39m.\u001b[39mget_class_name()\n\u001b[1;32m 13\u001b[0m fit_params\u001b[39m=\u001b[39m\u001b[39mdict\u001b[39m(features\u001b[39m=\u001b[39mfeatures, class_name\u001b[39m=\u001b[39mclass_name, head\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m score \u001b[39m=\u001b[39m validate_classifier(TAN(), X, y, fit_params\u001b[39m=\u001b[39;49mfit_params, stratified\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m 15\u001b[0m clf \u001b[39m=\u001b[39m score[\u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m0\u001b[39m]\n\u001b[1;32m 16\u001b[0m clf\u001b[39m.\u001b[39mplot(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m}\u001b[39;00m\u001b[39m score=\u001b[39m\u001b[39m{\u001b[39;00mscore[\u001b[39m'\u001b[39m\u001b[39mtest_score\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mmean()\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn [2], line 4\u001b[0m, in \u001b[0;36mvalidate_classifier\u001b[0;34m(model, X, y, stratified, fit_params)\u001b[0m\n\u001b[1;32m 2\u001b[0m stratified_class \u001b[39m=\u001b[39m StratifiedKFold \u001b[39mif\u001b[39;00m stratified \u001b[39melse\u001b[39;00m KFold\n\u001b[1;32m 3\u001b[0m kfold \u001b[39m=\u001b[39m stratified_class(shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, random_state\u001b[39m=\u001b[39mrandom_state, n_splits\u001b[39m=\u001b[39mn_folds)\n\u001b[0;32m----> 4\u001b[0m \u001b[39mreturn\u001b[39;00m cross_validate(model, X, y, cv\u001b[39m=\u001b[39;49mkfold, return_estimator\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, scoring\u001b[39m=\u001b[39;49mscore_name, fit_params\u001b[39m=\u001b[39;49mfit_params)\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:266\u001b[0m, in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[39m# We clone the estimator to make sure that all the folds are\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[39m# independent, and that it is pickle-able.\u001b[39;00m\n\u001b[1;32m 265\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, verbose\u001b[39m=\u001b[39mverbose, pre_dispatch\u001b[39m=\u001b[39mpre_dispatch)\n\u001b[0;32m--> 266\u001b[0m results \u001b[39m=\u001b[39m parallel(\n\u001b[1;32m 267\u001b[0m delayed(_fit_and_score)(\n\u001b[1;32m 268\u001b[0m clone(estimator),\n\u001b[1;32m 269\u001b[0m X,\n\u001b[1;32m 270\u001b[0m y,\n\u001b[1;32m 271\u001b[0m scorers,\n\u001b[1;32m 272\u001b[0m train,\n\u001b[1;32m 273\u001b[0m test,\n\u001b[1;32m 274\u001b[0m verbose,\n\u001b[1;32m 275\u001b[0m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 276\u001b[0m fit_params,\n\u001b[1;32m 277\u001b[0m return_train_score\u001b[39m=\u001b[39;49mreturn_train_score,\n\u001b[1;32m 278\u001b[0m return_times\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 279\u001b[0m return_estimator\u001b[39m=\u001b[39;49mreturn_estimator,\n\u001b[1;32m 280\u001b[0m error_score\u001b[39m=\u001b[39;49merror_score,\n\u001b[1;32m 281\u001b[0m )\n\u001b[1;32m 282\u001b[0m \u001b[39mfor\u001b[39;49;00m train, test \u001b[39min\u001b[39;49;00m cv\u001b[39m.\u001b[39;49msplit(X, y, groups)\n\u001b[1;32m 283\u001b[0m )\n\u001b[1;32m 285\u001b[0m _warn_or_raise_about_fit_failures(results, error_score)\n\u001b[1;32m 287\u001b[0m \u001b[39m# For callabe scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[1;32m 288\u001b[0m \u001b[39m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[1;32m 289\u001b[0m \u001b[39m# the correct key.\u001b[39;00m\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:1085\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1076\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1077\u001b[0m \u001b[39m# Only set self._iterating to True if at least a batch\u001b[39;00m\n\u001b[1;32m 1078\u001b[0m \u001b[39m# was dispatched. In particular this covers the edge\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[39m# was very quick and its callback already dispatched all the\u001b[39;00m\n\u001b[1;32m 1083\u001b[0m \u001b[39m# remaining jobs.\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m-> 1085\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdispatch_one_batch(iterator):\n\u001b[1;32m 1086\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_original_iterator \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 1088\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdispatch_one_batch(iterator):\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:901\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m 899\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 900\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 901\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dispatch(tasks)\n\u001b[1;32m 902\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:819\u001b[0m, in \u001b[0;36mParallel._dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m 818\u001b[0m job_idx \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jobs)\n\u001b[0;32m--> 819\u001b[0m job \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend\u001b[39m.\u001b[39;49mapply_async(batch, callback\u001b[39m=\u001b[39;49mcb)\n\u001b[1;32m 820\u001b[0m \u001b[39m# A job can complete so quickly than its callback is\u001b[39;00m\n\u001b[1;32m 821\u001b[0m \u001b[39m# called before we get here, causing self._jobs to\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[39m# grow. To ensure correct results ordering, .insert is\u001b[39;00m\n\u001b[1;32m 823\u001b[0m \u001b[39m# used (rather than .append) in the following line\u001b[39;00m\n\u001b[1;32m 824\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jobs\u001b[39m.\u001b[39minsert(job_idx, job)\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py:208\u001b[0m, in \u001b[0;36mSequentialBackend.apply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply_async\u001b[39m(\u001b[39mself\u001b[39m, func, callback\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m 207\u001b[0m \u001b[39m\"\"\"Schedule a func to be run\"\"\"\u001b[39;00m\n\u001b[0;32m--> 208\u001b[0m result \u001b[39m=\u001b[39m ImmediateResult(func)\n\u001b[1;32m 209\u001b[0m \u001b[39mif\u001b[39;00m callback:\n\u001b[1;32m 210\u001b[0m callback(result)\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py:597\u001b[0m, in \u001b[0;36mImmediateResult.__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 594\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, batch):\n\u001b[1;32m 595\u001b[0m \u001b[39m# Don't delay the application, to avoid keeping the input\u001b[39;00m\n\u001b[1;32m 596\u001b[0m \u001b[39m# arguments in memory\u001b[39;00m\n\u001b[0;32m--> 597\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults \u001b[39m=\u001b[39m batch()\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:288\u001b[0m, in \u001b[0;36mBatchedCalls.__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 285\u001b[0m \u001b[39m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[1;32m 286\u001b[0m \u001b[39m# change the default number of processes to -1\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \u001b[39mwith\u001b[39;00m parallel_backend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, n_jobs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_n_jobs):\n\u001b[0;32m--> 288\u001b[0m \u001b[39mreturn\u001b[39;00m [func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 289\u001b[0m \u001b[39mfor\u001b[39;00m func, args, kwargs \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mitems]\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:288\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 285\u001b[0m \u001b[39m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[1;32m 286\u001b[0m \u001b[39m# change the default number of processes to -1\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \u001b[39mwith\u001b[39;00m parallel_backend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, n_jobs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_n_jobs):\n\u001b[0;32m--> 288\u001b[0m \u001b[39mreturn\u001b[39;00m [func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 289\u001b[0m \u001b[39mfor\u001b[39;00m func, args, kwargs \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mitems]\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/sklearn/utils/fixes.py:117\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 116\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig):\n\u001b[0;32m--> 117\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunction(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:686\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m 684\u001b[0m estimator\u001b[39m.\u001b[39mfit(X_train, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[1;32m 685\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 686\u001b[0m estimator\u001b[39m.\u001b[39;49mfit(X_train, y_train, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params)\n\u001b[1;32m 688\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m 689\u001b[0m \u001b[39m# Note fit time as time until error\u001b[39;00m\n\u001b[1;32m 690\u001b[0m fit_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m start_time\n", + "File \u001b[0;32m~/Code/bayesclass/bayesclass/bayesclass.py:94\u001b[0m, in \u001b[0;36mTAN.fit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mX_ \u001b[39m=\u001b[39m X\n\u001b[1;32m 93\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39my_ \u001b[39m=\u001b[39m y\u001b[39m.\u001b[39mastype(\u001b[39mint\u001b[39m)\n\u001b[0;32m---> 94\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__train()\n\u001b[1;32m 95\u001b[0m \u001b[39m# Return the classifier\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n", + "File \u001b[0;32m~/Code/bayesclass/bayesclass/bayesclass.py:152\u001b[0m, in \u001b[0;36mTAN.__train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhead_ \u001b[39m=\u001b[39m est\u001b[39m.\u001b[39mroot_node\n\u001b[1;32m 149\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_ \u001b[39m=\u001b[39m BayesianNetwork(\n\u001b[1;32m 150\u001b[0m dag\u001b[39m.\u001b[39medges(), show_progress\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshow_progress\n\u001b[1;32m 151\u001b[0m )\n\u001b[0;32m--> 152\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_\u001b[39m.\u001b[39mfit(\n\u001b[1;32m 153\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset_,\n\u001b[1;32m 154\u001b[0m \u001b[39m# estimator=MaximumLikelihoodEstimator,\u001b[39;00m\n\u001b[1;32m 155\u001b[0m estimator\u001b[39m=\u001b[39mBayesianEstimator,\n\u001b[1;32m 156\u001b[0m prior_type\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mK2\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 157\u001b[0m n_jobs\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m 158\u001b[0m )\n", + "File \u001b[0;32m~/Code/pgmpy/pgmpy/models/BayesianNetwork.py:587\u001b[0m, in \u001b[0;36mBayesianNetwork.fit\u001b[0;34m(self, data, estimator, state_names, complete_samples_only, n_jobs, **kwargs)\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mEstimator object should be a valid pgmpy estimator.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 581\u001b[0m _estimator \u001b[39m=\u001b[39m estimator(\n\u001b[1;32m 582\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 583\u001b[0m data,\n\u001b[1;32m 584\u001b[0m state_names\u001b[39m=\u001b[39mstate_names,\n\u001b[1;32m 585\u001b[0m complete_samples_only\u001b[39m=\u001b[39mcomplete_samples_only,\n\u001b[1;32m 586\u001b[0m )\n\u001b[0;32m--> 587\u001b[0m cpds_list \u001b[39m=\u001b[39m _estimator\u001b[39m.\u001b[39;49mget_parameters(n_jobs\u001b[39m=\u001b[39;49mn_jobs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 588\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_cpds(\u001b[39m*\u001b[39mcpds_list)\n", + "File \u001b[0;32m~/Code/pgmpy/pgmpy/estimators/BayesianEstimator.py:97\u001b[0m, in \u001b[0;36mBayesianEstimator.get_parameters\u001b[0;34m(self, prior_type, equivalent_sample_size, pseudo_counts, n_jobs)\u001b[0m\n\u001b[1;32m 89\u001b[0m cpd \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimate_cpd(\n\u001b[1;32m 90\u001b[0m node,\n\u001b[1;32m 91\u001b[0m prior_type\u001b[39m=\u001b[39mprior_type,\n\u001b[1;32m 92\u001b[0m equivalent_sample_size\u001b[39m=\u001b[39m_equivalent_sample_size,\n\u001b[1;32m 93\u001b[0m pseudo_counts\u001b[39m=\u001b[39m_pseudo_counts,\n\u001b[1;32m 94\u001b[0m )\n\u001b[1;32m 95\u001b[0m \u001b[39mreturn\u001b[39;00m cpd\n\u001b[0;32m---> 97\u001b[0m parameters \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39;49mn_jobs, prefer\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mthreads\u001b[39;49m\u001b[39m\"\u001b[39;49m)(\n\u001b[1;32m 98\u001b[0m delayed(_get_node_param)(node) \u001b[39mfor\u001b[39;49;00m node \u001b[39min\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mnodes()\n\u001b[1;32m 99\u001b[0m )\n\u001b[1;32m 101\u001b[0m \u001b[39mreturn\u001b[39;00m parameters\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:1098\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1095\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 1097\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1098\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mretrieve()\n\u001b[1;32m 1099\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/parallel.py:975\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 974\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> 975\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39;49mget(timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout))\n\u001b[1;32m 976\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 977\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n", + "File \u001b[0;32m~/.virtualenvs/310/lib/python3.10/site-packages/joblib/_parallel_backends.py:567\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m 565\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m 566\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 567\u001b[0m \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39;49mresult(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m 568\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 569\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n", + "File \u001b[0;32m/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/python3.10/concurrent/futures/_base.py:453\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m 451\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 453\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m 455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m 456\u001b[0m \u001b[39mraise\u001b[39;00m CancelledError()\n", + "File \u001b[0;32m/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/python3.10/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[39mtry\u001b[39;00m: \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m 319\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m 321\u001b[0m gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 322\u001b[0m \u001b[39melse\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "import warnings\n", - "from stree import Stree\n", - "warnings.filterwarnings('ignore')\n", - "for simple_init in [False, True]:\n", - " model = TAN(simple_init=simple_init)\n", - " for head in range(4):\n", - " #model.fit(X, y, head=head, features=features, class_name=class_name)\n", - " score = validate_classifier(model, X, y, stratified=False, fit_params=dict(head=head, features=features, class_name=class_name))\n", - " #model.plot(f\"simple_init={simple_init} head={head} score={np.mean(score['test_score'])}\")\n", - " model.plot(f\"simple_init={simple_init} head={head} score={np.mean(score)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c389ff1e-76d9-4c5b-9860-ea6d4752fac7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([nan, nan, nan, nan, nan])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "score" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9c58629f-000b-4d8c-8896-efd032f1090c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "b 10\n", - "c 9\n", - "d 8\n", - "e 7\n", - "a 6\n" - ] - } - ], - "source": [ - "from queue import PriorityQueue\n", - "q = PriorityQueue()\n", - "lista = ['b', 'c', 'd', 'e', 'a']\n", - "for i, c in zip(lista, range(len(lista))):\n", - " print(i,10-c)\n", - " q.put(i,10-c)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e2a768c0-3e21-48f3-b118-25408122d01c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a\n", - "b\n", - "c\n", - "d\n", - "e\n" - ] - } - ], - "source": [ - "while not q.empty():\n", - " print(q.get())" + "splitter = False\n", + "dt = Discretizer()\n", + "for name in dt:\n", + " splitter(dt, name) if splitter else crossval(dt, name)" ] }, { "cell_type": "code", "execution_count": null, - "id": "96bb1acd-f450-4b9c-8f54-f020e23dfc14", + "id": "727ba1a8", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "name = \"balance-scale\"\n", + "X, y = dt.load(name)\n", + "features, class_name = dt.get_features(), dt.get_class_name()\n", + "fit_params=dict(features=features, class_name=class_name, head=0)\n", + "score = validate_classifier(TAN(), X, y, fit_params=fit_params, stratified=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "366a49a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'fit_time': array([0.06072092, 0.04777884, 0.08816218, 0.15080619, 1.62921786]), 'score_time': array([0.20613718, 0.20061731, 0.21159077, 0.20808077, 0.20848513]), 'estimator': [TAN(), TAN(), TAN(), TAN(), TAN()], 'test_score': array([0.856, 0.888, 0.848, 0.848, 0.824])}\n" + ] + } + ], + "source": [ + "print(score)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10.8 ('310')", "language": "python", "name": "python3" }, diff --git a/test.py b/test.py new file mode 100644 index 0000000..eab3854 --- /dev/null +++ b/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)}" +)