def test_categorical_data_digits_all_negative():
digits = load_digits()
X = digits['data']
y = digits['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
mixed_nb = MixedNB(categorical_features='all')
with pytest.raises(ValueError):
mixed_nb.fit(X, y)
def test_categorical_data_digits():
digits = load_digits()
X = digits['data']
y = digits['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
gaussian_nb.score(X, y)
mixed_nb = MixedNB(categorical_features='all',
max_categories=np.repeat(17, 64))
mixed_nb.fit(X[:1440], y[:1440])
mixed_nb.score(X[:1440], y[:1440])
def test_continuous_data_digits():
digits = load_digits()
X = digits['data']
y = digits['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
gaussian_nb_score = gaussian_nb.score(X, y)
mixed_nb = MixedNB()
mixed_nb.fit(X, y)
mixed_nb_score = mixed_nb.score(X, y)
assert np.isclose(gaussian_nb_score, mixed_nb_score)
def test_continuous_data_wine():
wine = load_wine()
X = wine['data']
y = wine['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
gaussian_nb_score = gaussian_nb.score(X, y)
mixed_nb = MixedNB()
mixed_nb.fit(X, y)
mixed_nb_score = mixed_nb.score(X, y)
assert np.isclose(gaussian_nb_score, mixed_nb_score)
def test_continuous_data_iris():
iris = load_iris()
X = iris['data']
y = iris['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
gaussian_nb_pred = gaussian_nb.predict(X)
mixed_nb = MixedNB()
mixed_nb.fit(X, y)
mixed_nb_pred = mixed_nb.predict(X)
assert (mixed_nb_pred == gaussian_nb_pred).all()
def test_continuous_data_breast_cancer():
breast_cancer = load_breast_cancer()
X = breast_cancer['data']
y = breast_cancer['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
gaussian_nb_score = gaussian_nb.score(X, y)
mixed_nb = MixedNB()
mixed_nb.fit(X, y)
mixed_nb_score = mixed_nb.score(X, y)
assert np.isclose(gaussian_nb_score, mixed_nb_score)
def test_input_y_not_encoded():
clf = MixedNB()
with pytest.raises(ValueError):
clf.fit([[1, 2], [2, 2], [3, 3]], [0, 8, 0])
def test_input_wrong_dims_2():
clf = MixedNB()
with pytest.raises(ValueError):
clf.fit([[0, 1, 2]], [[0, 1]])
def test_input_string_y():
clf = MixedNB()
with pytest.raises(TypeError):
clf.fit([[2], [1]], [0, '1'])
def test_input_string_x():
clf = MixedNB()
with pytest.raises(TypeError):
clf.fit([['X'], ['y']], [0, 1])
def test_input_param():
clf = MixedNB(alpha='l')
with pytest.raises(TypeError):
clf.fit([0, 1, 2], [0, 1, 0])
def test_categorical_data_simple():
X, y = load_example()
mixed_nb = MixedNB([0, 1])
mixed_nb.fit(X, y)
mixed_nb.score(X, y)
#Divide the dataset into y and X
y = pro2['Severity']
X = pro2.iloc[:,1:]
# Change categorical variables into numerical variables
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
X.iloc[:,[0,1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,26,28,30,31,32,33,34,35]] = X.iloc[:,[0,1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,26,28,30,31,32,33,34,35]].apply(LabelEncoder().fit_transform)
# Split the dataset into training dataset and test dataset
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size =.2,random_state=1234, stratify=y)
# Build a Bayesian Classification Model and predict the type using the test data.
gnb = MixedNB(categorical_features=[0,1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,26,28,30,31,32,33,34,35])
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)
# Calculate the accuracy
accuracy = gnb.score(X_test, y_test)
print('Accuracy: {0:.2f}'.format(accuracy))
# Build a confusion matrix
cm = metrics.confusion_matrix(y_test,y_pred)
print(metrics.classification_report(y_test,y_pred))
#Bayesian for PCA dataset
# Load dataset
import numpy as np
import pandas as pd
filename2 = Output_name+'Naive_Bayes_fitted_model.mod'
with open( filename2, "wb") as file:
pickle.dump(best_clf, file)
print(out)
# Fit model
#best_clf.fit(SX_train.astype(float),y_train.astype(int))
# Train the model using the training sets
#scores = cross_val_score(best_clf, SX_train.astype(float), y_train.astype(int), n_jobs=16, cv=StratifiedKFold(5))
#accuracy = scores.mean()
#sd= (scores.std())
# 0.8330165782220578 +/- 0.029993214054194695
# Fit optimised model
best_clf.fit(SX_train.astype(float),y_train.astype(int))
SX_test.to_csv(Output_name+'SX_test.csv')
filename = Output_name+'Naive_Bayes_finalized_model.mod'
with open( filename, "wb") as file:
pickle.dump(best_clf, file)
### Evalaute performance on the training set ###
y_train_pred = best_clf.predict(SX_train)
# Print confusion matrix
cm_train = confusion_matrix(y_train, y_train_pred)
print(cm_train)
metrics.accuracy_score(ytest, multinomial_pred)
# Multinomial NB model accuracy is 0.774966
metrics.confusion_matrix(ytest, multinomial_pred)
# [[10891, 469],
# [ 2920, 780]]
############# Model 3 using Mixed NB
# here we will use Gaussian NB for continuous predictors and MUltinomial NB
# for categorical predictors
# first install it using - pip install MixedNB
from mixed_naive_bayes import MixedNB
salary_train_raw.dtypes
mixed_model = MixedNB(categorical_features=[1, 2, 4, 5, 6, 7, 8, 12])
mixed_model_pred = mixed_model.fit(Xtrain, ytrain).predict(Xtest)
metrics.accuracy_score(ytest, mixed_model_pred)
# 0.8242
# we can see that the mixed model has highest accuracy score
#################### Model 4 using Logistic regression
from sklearn.linear_model import LogisticRegression
sal_logreg = LogisticRegression()
sal_logreg.fit(Xtrain, ytrain)
logreg_pred = sal_logreg.predict(Xtest)
Counter(ytest) # {0: 11360, 1: 3700}
Run benchmarks on toy datasets provided by sklearn.
This is to ensure our implementation of Gaussian Naive
Bayes is the same as sklearn's.
"""
from sklearn.datasets import load_iris, load_digits, \
load_wine, load_breast_cancer
from sklearn.naive_bayes import GaussianNB
from mixed_naive_bayes import MixedNB
for load_data in [load_iris, load_digits, load_wine,
load_breast_cancer]:
print(f"--- {''.join(load_data.__name__.split('_')[1:])} ---")
dataset = load_data()
X = dataset['data']
y = dataset['target']
gaussian_nb = GaussianNB()
gaussian_nb.fit(X, y)
gaussian_nb_pred = gaussian_nb.predict(X)
mixed_nb = MixedNB()
mixed_nb.fit(X, y)
mixed_nb_pred = mixed_nb.predict(X)
print(f"GaussianNB: {gaussian_nb.score(X,y)}")
print(f"MixedNB : {mixed_nb.score(X,y)}")
