Fix fit/build/train mistake

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)}"
+)