def inside():
global ls
ls = []
c = request.form
choice = c['val']
if choice == 'r':
return redirect("http://localhost:5000/recommend")
else:
user_choice = int(choice) - 1
selected_doc = user_docs[user_choice]
all_docs = lst[0]
classifier_list = lst[2]
# classifiers are sorted according to their f_measure in decreasing order. It helps when all
# three classifiers differ in their predictions.
classifier_list = sorted(classifier_list,
key=lambda cl: cl.stats['f_measure'],
reverse=True)
prediction_list = list()
for classifier in classifier_list:
prediction_list.append(classifier.classify([selected_doc])[0])
prediction_count = Counter(prediction_list)
top_prediction = prediction_count.most_common(1)
if top_prediction[0][1] > 1:
prediction = top_prediction[0][0]
else:
prediction = prediction_list[0]
# create knn instance using documents of predicted topic. and find k closest documents.
knn = KNN(all_docs[prediction])
k_neighbours = knn.find_k_neighbours(selected_doc, k_n)
ls.append(selected_doc)
ls.append(k_neighbours)
return render_template('scnd.html', ls=ls)
def recommendation(all_docs, test_docs, classifier_list):
print("Recommendation System")
print("---------------------")
# ask user for the desired option count and recommendation count. set default value in case invalid inputs.
try:
option_count = int(raw_input("\nEnter number of articles to choose from. [number from 5 to 10 suggested]: "))
if option_count < 1 or option_count > 20:
print("Invalid Choice.. By default selected 5.")
option_count = 5
except:
print("Invalid Choice.. By default selected 5.")
option_count = 5
try:
k_n = int(raw_input("\nEnter number of recommendation per article. [number from 5 to 10 suggested]: "))
if k_n < 1 or k_n > 20:
print("Invalid Choice.. By default selected 5.")
k_n = 5
except:
print("Invalid Choice.. By default selected 5.")
k_n = 5
end = False
# run the loop until user quits.
while not end:
# pick random documents from test docs and provide titles to the user.
user_docs = random.sample(test_docs, option_count)
while True:
print("\n---Available Choices For Articles(Titles)---\n")
for i in range(len(user_docs)):
print(str(i+1) + ": " + user_docs[i].title)
print("r: Refresh List")
print("q: Quit()\n")
choice = raw_input("Enter Choice: ")
if choice == 'q':
end = True
break
elif choice == 'r':
break
else:
try:
user_choice = int(choice) - 1
if user_choice < 0 or user_choice >= len(user_docs):
print("Invalid Choice.. Try Again..")
continue
except:
print("Invalid Choice.. Try Again..")
continue
selected_doc = user_docs[user_choice]
# classifiers are sorted according to their f_measure in decreasing order. It helps when all
# three classifiers differ in their predictions.
classifier_list = sorted(classifier_list, key=lambda cl: cl.stats['f_measure'], reverse=True)
prediction_list = list()
for classifier in classifier_list:
prediction_list.append(classifier.classify([selected_doc])[0])
prediction_count = Counter(prediction_list)
top_prediction = prediction_count.most_common(1)
if top_prediction[0][1] > 1:
prediction = top_prediction[0][0]
else:
prediction = prediction_list[0]
# create knn instance using documents of predicted topic. and find k closest documents.
knn = KNN(all_docs[prediction])
k_neighbours = knn.find_k_neighbours(selected_doc, k_n)
while True:
print("\nRecommended Articles for : " + selected_doc.title)
for i in range(len(k_neighbours)):
print(str(i+1) + ": " + k_neighbours[i].title)
next_choice = raw_input("\nEnter Next Choice: [Article num to read the article. "
"'o' to read the original article. "
"'b' to go back to article choice list.] ")
if next_choice == 'b':
break
elif next_choice == 'o':
text = selected_doc.text
print("\nArticle Text for original title : " + selected_doc.title)
print(text)
else:
try:
n_choice = int(next_choice) - 1
if n_choice < 0 or n_choice >= k_n:
print("Invalid Choice.. Try Again..")
continue
except:
print("Invalid Choice.. Try Again..")
continue
text = k_neighbours[n_choice].text
print("\nArticle Text for recommended title : " + k_neighbours[n_choice].title)
print(text)
def recommendation(all_docs, test_docs, classifier_list):
print("Recommendation System")
print("---------------------")
# ask user for the desired option count and recommendation count. set default value in case invalid inputs.
try:
option_count = int(
raw_input(
"\nEnter number of articles to choose from. [number from 5 to 10 suggested]: "
))
if option_count < 1 or option_count > 20:
print("Invalid Choice.. By default selected 5.")
option_count = 5
except:
print("Invalid Choice.. By default selected 5.")
option_count = 5
try:
k_n = int(
raw_input(
"\nEnter number of recommendation per article. [number from 5 to 10 suggested]: "
))
if k_n < 1 or k_n > 20:
print("Invalid Choice.. By default selected 5.")
k_n = 5
except:
print("Invalid Choice.. By default selected 5.")
k_n = 5
end = False
# run the loop until user quits.
while not end:
# pick random documents from test docs and provide titles to the user.
user_docs = random.sample(test_docs, option_count)
while True:
print("\n---Available Choices For Articles(Titles)---\n")
for i in range(len(user_docs)):
print(str(i + 1) + ": " + user_docs[i].title)
print("r: Refresh List")
print("q: Quit()\n")
choice = raw_input("Enter Choice: ")
if choice == 'q':
end = True
break
elif choice == 'r':
break
else:
try:
user_choice = int(choice) - 1
if user_choice < 0 or user_choice >= len(user_docs):
print("Invalid Choice.. Try Again..")
continue
except:
print("Invalid Choice.. Try Again..")
continue
selected_doc = user_docs[user_choice]
# classifiers are sorted according to their f_measure in decreasing order. It helps when all
# three classifiers differ in their predictions.
classifier_list = sorted(classifier_list,
key=lambda cl: cl.stats['f_measure'],
reverse=True)
prediction_list = list()
for classifier in classifier_list:
prediction_list.append(
classifier.classify([selected_doc])[0])
prediction_count = Counter(prediction_list)
top_prediction = prediction_count.most_common(1)
if top_prediction[0][1] > 1:
prediction = top_prediction[0][0]
else:
prediction = prediction_list[0]
# create knn instance using documents of predicted topic. and find k closest documents.
knn = KNN(all_docs[prediction])
k_neighbours = knn.find_k_neighbours(selected_doc, k_n)
while True:
print("\nRecommended Articles for : " + selected_doc.title)
for i in range(len(k_neighbours)):
print(str(i + 1) + ": " + k_neighbours[i].title)
next_choice = raw_input(
"\nEnter Next Choice: [Article num to read the article. "
"'o' to read the original article. "
"'b' to go back to article choice list.] ")
if next_choice == 'b':
break
elif next_choice == 'o':
text = selected_doc.text
print("\nArticle Text for original title : " +
selected_doc.title)
print(text)
else:
try:
n_choice = int(next_choice) - 1
if n_choice < 0 or n_choice >= k_n:
print("Invalid Choice.. Try Again..")
continue
except:
print("Invalid Choice.. Try Again..")
continue
text = k_neighbours[n_choice].text
print("\nArticle Text for recommended title : " +
k_neighbours[n_choice].title)
print(text)
def recommendation(all_docs, test_docs, classifier_list):
print("Recommendation System")
print("---------------------")
try:
option_count = int(
input(
"\nEnter number of articles to choose from. [number from 5 to 10 suggested]: "
))
if option_count < 1 or option_count > 20:
print("Invalid Choice.. By default selected 5.")
option_count = 5
except:
print("Invalid Choice.. By default selected 5.")
option_count = 5
try:
k_n = int(
input(
"\nEnter number of recommendation per article. [number from 5 to 10 suggested]: "
))
if k_n < 1 or k_n > 20:
print("Invalid Choice.. By default selected 5.")
k_n = 5
except:
print("Invalid Choice.. By default selected 5.")
k_n = 5
end = False
while not end:
user_docs = random.sample(test_docs, option_count)
while True:
print("\n---Available Choices For Articles(Titles)---\n")
for i in range(len(user_docs)):
print(str(i + 1) + ": " + user_docs[i].title)
print("r: Refresh List")
print("q: Quit()\n")
choice = input("Enter Choice: ")
if choice == 'q':
end = True
break
elif choice == 'r':
break
else:
try:
user_choice = int(choice) - 1
if user_choice < 0 or user_choice >= len(user_docs):
print("Invalid Choice.. Try Again..")
continue
except:
print("Invalid Choice.. Try Again..")
continue
selected_doc = user_docs[user_choice]
classifier_list = sorted(classifier_list,
key=lambda cl: cl.stats['f_measure'],
reverse=True)
prediction_list = list()
for classifier in classifier_list:
prediction_list.append(
classifier.classify([selected_doc])[0])
prediction_count = Counter(prediction_list)
top_prediction = prediction_count.most_common(1)
if top_prediction[0][1] > 1:
prediction = top_prediction[0][0]
else:
prediction = prediction_list[0]
knn = KNN(all_docs[prediction])
k_neighbours = knn.find_k_neighbours(selected_doc, k_n)
while True:
print("\nRecommended Articles for : " + selected_doc.title)
for i in range(len(k_neighbours)):
print(str(i + 1) + ": " + k_neighbours[i].title)
next_choice = input(
"\nEnter Next Choice: [Article num to read the article. "
"'o' to read the original article. "
"'b' to go back to article choice list.] ")
if next_choice == 'b':
break
elif next_choice == 'o':
text = selected_doc.text
print("\nArticle Text for original title : " +
selected_doc.title)
print(text)
else:
try:
n_choice = int(next_choice) - 1
if n_choice < 0 or n_choice >= k_n:
print("Invalid Choice.. Try Again..")
continue
except:
print("Invalid Choice.. Try Again..")
continue
text = k_neighbours[n_choice].text
print("\nArticle Text for recommended title : " +
k_neighbours[n_choice].title)
print(text)
