############################
## Rohini Pandhi ##
## Homework Assignment 2 ##
## Run File ##
## 01/23/2014 ##
############################
import argparse
from hw2 import load_iris_data, cross_validate, knn, nb, lr
# Load Iris dataset
(features, species, species_names) = load_iris_data()
# Argument parse
parser = argparse.ArgumentParser(description='Select KNN, Naive Bayes, or Logistic Regression')
parser.add_argument('-c', '--classifier', help='a classifier type: KNN, NB, or LR (if none selected, default will run all', required=False)
args = parser.parse_args()
try:
if (args.classifier.upper() == "KNN"):
classifier_list = [("KNN",knn)]
elif (args.classifier.upper() == "NB"):
classifier_list = [("Naive Bayes",nb)]
elif (args.classifier.upper() == "LR"):
classifier_list = [("Logistic Regression",lr)]
except:
classifier_list = [("KNN",knn), ("Naive Bayes",nb), ("Logistic Regression",lr)] #using imported functions above
# Loop through each tuple of the classifier list
for (classifier_string, classifier_function) in classifier_list:
from hw2 import load_iris_data, cross_validate, knn, nb, lr, logistic
(XX,yy,y)=load_iris_data()
classfiers_to_cv=[("kNN",knn),("Naive Bayes",nb), ("Linear Regression",lr), ("Logistic REgression",logistic)]
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] :
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 "\n %s Highest Accuracy: fold <<%s>> :: <<%s>>\n" % (c_label, best_k, best_cv_a)
############################
## Rohini Pandhi ##
## Homework Assignment 2 ##
## Run File ##
## 01/23/2014 ##
############################
import argparse
from hw2 import load_iris_data, cross_validate, knn, nb, lr
# Load Iris dataset
(features, species, species_names) = load_iris_data()
# Argument parse
parser = argparse.ArgumentParser(
description='Select KNN, Naive Bayes, or Logistic Regression')
parser.add_argument(
'-c',
'--classifier',
help=
'a classifier type: KNN, NB, or LR (if none selected, default will run all',
required=False)
args = parser.parse_args()
try:
if (args.classifier.upper() == "KNN"):
classifier_list = [("KNN", knn)]
elif (args.classifier.upper() == "NB"):
classifier_list = [("Naive Bayes", nb)]
elif (args.classifier.upper() == "LR"):
classifier_list = [("Logistic Regression", lr)]
