def make_data_percentage_graph(input_tweets_file, input_labels_file):
"""
Creates a graph to determine the relationship between test set size and accuracy.
We try testing on 25 percent - 85 percent of the dataset, where we increment by 5 percent in every run
The value of alpha is kept as default - 1.0
"""
plt.title(
"Accuracy of Naive Bayes with varied test data size and fixed alpha"
)
plt.xlabel("Percentage of data used for testing")
plt.ylabel("Accuracy Achieved")
values = [
20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0,
75.0, 80.0, 85.0
]
accuracies = []
for val in values:
nb = NaiveBayes(input_tweets_file, input_labels_file, 1.0,
(val / 100.0))
nb.update_model()
accuracies.append(nb.evaluate_classifier_accuracy())
plt.plot(values, accuracies)
# Save the figure in the Figures directory
file_name = "Figures/accuracy_varied_test_ratio_" + input_tweets_file[
8:10] + ".jpeg"
plt.savefig(file_name)
# To refresh the graph
plt.close()
def make_accuracy_graph(input_tweets_file, input_labels_file):
"""
Creates a graph to determine the relationship between alpha and accuracy.
The values of that are tried are from 1 - 50
The test set size is kept as default - 20%
"""
plt.title(
"Accuracy of Naive Bayes with varied alpha and fixed test set size"
)
plt.xlabel("Alpha Value")
plt.ylabel("Accuracy Achieved")
accuracies = []
for i in range(1, 51):
nb = NaiveBayes(input_tweets_file, input_labels_file, i)
nb.update_model()
accuracies.append(nb.evaluate_classifier_accuracy())
plt.plot(range(1, 51), accuracies)
# Save the figure in the Figures directory
file_name = "Figures/accuracy_varied_alpha_" + input_tweets_file[
8:10] + ".jpeg"
plt.savefig(file_name)
# To refresh the graph
plt.close()
def run_nb():
print("Importing Naive Bayes...")
from NaiveBayes import NaiveBayes
print("Successfully Imported Naive Bayes.")
print("Running...")
nb_obj = NaiveBayes(file_paths.us_tweets_path, file_paths.us_labels_path)
nb_obj.update_model()
print("Execution Complete. Accuracy:" +
str(nb_obj.evaluate_classifier_accuracy()) + " %")
