def test_BayesianRuleList2():
dataset = load_breast_cancer()
x, y = dataset['data'], dataset['target']
feature_names = dataset['feature_names']
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.33, random_state=42)
discretizer = MDLP(random_state=42).fit(x_train, y_train)
x_train_cat = discretizer.transform(x_train)
category_names = compute_intervals(discretizer)
rule_list = BayesianRuleList(seed=1, feature_names=feature_names, category_names=category_names, verbose=2)
rule_list.fit(x_train_cat, y_train)
print(rule_list)
x_test_cat = discretizer.transform(x_test)
print('acc: %.4f' % rule_list.score(x_test_cat, y_test))
def bayesian_rule_list(X_train, y_train, X_test, y_test):
from mdlp.discretization import MDLP
from sklearn import preprocessing
# First one hot encode
X_train, X_test = apply_one_hot_encoding(X_train, X_test)
# Then need to convert classes to integers
encoder = preprocessing.LabelEncoder()
y_train = encoder.fit_transform(y_train)
y_test = encoder.transform(y_test)
# Then discretize features
transformer = MDLP()
X_train = transformer.fit_transform(X_train, y_train)
X_test = transformer.transform(X_test)
brl = pysbrl.BayesianRuleList()
brl.fit(X_train, y_train)
print(brl)
# The complexity is the number of split points + the number of extra conditions
# (i.e. if x1 > 0 and x2 = 1 then .. counts as 2 not 1), for this reason we do not use brl.n_rules
brl_str = str(brl)
brl_complexity = brl_str.count("IF") + brl_str.count("AND")
training_recreations = brl.predict(X_train)
brl_training_recreating_pct = scorer(training_recreations, y_train) * 100
testing_recreations = brl.predict(X_test)
brl_testing_recreating_pct = scorer(testing_recreations, y_test) * 100
return brl_training_recreating_pct, brl_testing_recreating_pct, brl_complexity
class SupervisedDiscretizationStrategy(object):
"""
A class used for supervised data discretization.
"""
def __init__(self):
self.transformer = MDLP()
def discretize(self, data_set, validation_size, nb_bins=None):
""" Discretize continuous attribute using MDLP method.
Args:
data_set: The data set containing continuous data.
validation_size: The validation size of the newly created discretized data set.
Returns:
discretized_dataset: A DataSet object containing discretized data.
"""
# Create strategy object to further create the discretized data set.
galaxy_dataset_feature_strategy = GalaxyDataSetFeatureStrategy()
# Get data from training set.
X_train = data_set.train.get_features
y_train = data_set.train.get_labels
# Supervised discretization of the training data set using MDLP.
X_train_discretized = self.transformer.fit_transform(X=X_train,
y=y_train)
# Get data from validation set.
X_valid = data_set.valid.get_features
y_valid = data_set.valid.get_labels
# Unsupervised discretization using MDLP.
X_valid_discretized = self.transformer.transform(X=X_valid)
# Merge both training and validation data.
X = np.append(X_train_discretized, X_valid_discretized, axis=0)
y = np.append(y_train, y_valid, axis=0)
# Create a new data set.
discretized_dataset = galaxy_dataset_feature_strategy.create_datasets(
X, y, validation_size)
return discretized_dataset
plt.figure()
plot_confusion_matrix(cm_galaxy_test_k3u,
classes=['Smooth', 'Spiral'],
normalize=True,
title='Confusion matrix, with normalization with k:3')
plt.show()
# In[33]:
# In[33]:
print(" Bayes Naif models with hold-out set ")
# Scale data for train, validation (hold out), and test
# first method of discretization using MDLP
from mdlp.discretization import MDLP
mdlp = MDLP()
Xtrain_galaxy_MDLP = mdlp.fit_transform(X_train_galaxy, Y_train_galaxy)
Xtest_galaxy_MDLP = mdlp.transform(X_test_galaxy, Y_test_galaxy)
Xvalid_galaxy_MDLP = mdlp.transform(X_valid_galaxy, Y_valid_galaxy)
# In[33]:
# Second method of discretization using MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
Xtrain_galaxy_unsupervised = scaler.fit_transform(X_train_galaxy)
Xtest_galaxy_unsupervised = scaler.transform(X_test_galaxy)
Xvalid_galaxy_unsupervised = scaler.transform(X_valid_galaxy)
# In[33]:
# Bayes naïf gaussien with 2 different parameters i.e.
# 1. priors = probaility of each class
# In[33]:
