try:
if (args.classifier.upper() == "KNN"):
classifier_list = [("KNN",knn)]
elif (args.classifier.upper() == "NB"):
classifier_list = [("Naive Bayes",nb)]
except:
classifier_list = [("KNN",knn), ("Naive Bayes",nb)] #using imported functions above
# Loop through each tuple of the classifier list
for (classifier_string, classifier_function) in classifier_list:
print "\n-------- %s --------" % classifier_string
best_kfolds = 0
best_cv = 0
# Define specific set of folds that split the dataset in an even way
for kfolds in [2,3,5,10,15,30,50,75,150]:
cv = cross_validate(features, species, classifier_function, kfolds)
if cv > best_cv:
best_cv = cv
best_kfolds = kfolds
# For each fold, print out it's accuracy in the cross validation function
print "Fold <<%s>> :: Accuracy <<%s>>" % (kfolds, cv)
# At the end, print the highest accuracy fold
print "Highest Accuracy: <<%s>> :: Fold <<%s>>\n" % (best_cv, best_kfolds)
from hw1 import load_iris_data, cross_validate, knn, nb
# Imports functions and opject definitions from hw1.py
(iris_petal_measurements, iris_classes, iris_classnames) = load_iris_data()
classfiers_to_cv = [("kNN", knn), ("Naive Bayes", nb)]
for (c_label, classifer) in classfiers_to_cv:
print
print "---> %s <---" % c_label
best_k = 0
best_cv_a = 0
foldsizes = [2, 3, 5, 10, 15, 30, 50, 75, 150]
#These fold sizes are evenly divisible into the dataset intentionally
for k_f in foldsizes:
cv_a = cross_validate(iris_petal_measurements,
iris_classes,
classifer,
k_fold=k_f)
if cv_a > best_cv_a:
best_cv_a = cv_a
best_k = k_f
print "foldsize <<%s>> :: test accuracy <<%s>>" % (k_f, cv_a)
print "Highest Accuracy: fold <<%s>> :: <<%s>>" % (best_k, best_cv_a)
if (args.classifier.upper() == "KNN"):
classifier_list = [("KNN", knn)]
elif (args.classifier.upper() == "NB"):
classifier_list = [("Naive Bayes", nb)]
except:
classifier_list = [("KNN", knn),
("Naive Bayes", nb)] #using imported functions above
# Loop through each tuple of the classifier list
for (classifier_string, classifier_function) in classifier_list:
print "\n-------- %s --------" % classifier_string
best_kfolds = 0
best_cv = 0
# Define specific set of folds that split the dataset in an even way
for kfolds in [2, 3, 5, 10, 15, 30, 50, 75, 150]:
cv = cross_validate(features, species, classifier_function, kfolds)
if cv > best_cv:
best_cv = cv
best_kfolds = kfolds
# For each fold, print out it's accuracy in the cross validation function
print "Fold <<%s>> :: Accuracy <<%s>>" % (kfolds, cv)
# At the end, print the highest accuracy fold
print "Highest Accuracy: <<%s>> :: Fold <<%s>>\n" % (best_cv, best_kfolds)
from hw1 import load_iris_data, cross_validate, knn, nb
(XX,yy,y)=load_iris_data()
classfiers_to_cv=[("kNN",knn),("Naive Bayes",nb)]
for (c_label, classifer) in classfiers_to_cv :
print
print "---> %s <---" % c_label
best_k=0
best_cv_a=0
for k_f in [2,3,5,10,15,30,50,75,150] :
cv_a = cross_validate(XX, yy, classifer, k_fold=k_f)
if cv_a > best_cv_a :
best_cv_a=cv_a
best_k=k_f
print "fold <<%s>> :: acc <<%s>>" % (k_f, cv_a)
print "Highest Accuracy: fold <<%s>> :: <<%s>>" % (best_k, best_cv_a)
from hw1 import load_iris_data, cross_validate, knn, nb
# Imports functions and opject definitions from hw1.py
(iris_petal_measurements,iris_classes,iris_classnames)=load_iris_data()
classfiers_to_cv=[("kNN",knn),("Naive Bayes",nb)]
for (c_label, classifer) in classfiers_to_cv :
print
print "---> %s <---" % c_label
best_k=0
best_cv_a=0
foldsizes=[2,3,5,10,15,30,50,75,150]
#These fold sizes are evenly divisible into the dataset intentionally
for k_f in foldsizes :
cv_a = cross_validate(iris_petal_measurements, iris_classes, classifer, k_fold=k_f)
if cv_a > best_cv_a :
best_cv_a=cv_a
best_k=k_f
print "foldsize <<%s>> :: test accuracy <<%s>>" % (k_f, cv_a)
print "Highest Accuracy: fold <<%s>> :: <<%s>>" % (best_k, best_cv_a)