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Dialogflow-RQ1.py
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Dialogflow-RQ1.py
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import datetime
import inquirer
import os
import glob
import pytz
from classes.data_tools import DataTools
from classes.engineer import Engineer
from classes.labeling_functions import LabelingFunctions
import json
from inquirer.themes import GreenPassion
from google.cloud import dialogflow
import asyncio
import yaml
import re
import time
import random
from google.api_core.exceptions import InvalidArgument
import pandas as pd
from csv import reader
def calculate_f1(path):
with open(path, 'r') as csv_file:
csv_reader = reader(csv_file)
first_list_of_rows = list(csv_reader)
dict_of_intents = {}
for row in first_list_of_rows:
if row[1] not in dict_of_intents.keys():
dict_of_intents[row[1]] = {'TP': 0, 'TF': 0, 'FP': 0, 'FN': 0, 'support': 0, 'f1': 0.0}
if row[2] not in dict_of_intents.keys():
dict_of_intents[row[2]] = {'TP': 0, 'TF': 0, 'FP': 0, 'FN': 0, 'support': 0, 'f1': 0.0}
dict_of_intents[row[1]]['support'] += 1
if row[1] == row[2]:
dict_of_intents[row[1]]['TP'] += 1
else:
dict_of_intents[row[1]]['FN'] += 1
dict_of_intents[row[2]]['FP'] += 1
total = 0
for key, values in dict_of_intents.items():
dict_of_intents[key]['f1'] = values['TP']/(values['TP'] + ((values['FP'] + values['FN'])/2))
total += values['support']
sum_all = 0.0
for key, value in dict_of_intents.items():
sum_all += value['f1'] * value['support']
return (sum_all/total) * 100
async def train_agent():
await dialogflow.TrainAgentRequest()
def get_intent_ids(project_id, display_name):
intents_client = dialogflow.IntentsClient()
parent = dialogflow.AgentsClient.agent_path(project_id)
intents = intents_client.list_intents(request={"parent": parent})
intent_names = [
intent.name for intent in intents if intent.display_name == display_name
]
intent_ids = [intent_name.split("/")[-1] for intent_name in intent_names]
return intent_ids
def delete_intent(project_id, intent_id):
"""Delete intent with the given intent type and intent value."""
intents_client = dialogflow.IntentsClient()
intent_path = intents_client.intent_path(project_id, intent_id)
intents_client.delete_intent(request={"name": intent_path})
def create_intent(project_id, display_name, training_phrases_parts):
"""Create an intent of the given intent type."""
intents_client = dialogflow.IntentsClient()
parent = dialogflow.AgentsClient.agent_path(project_id)
training_phrases = []
for training_phrases_part in training_phrases_parts:
part = dialogflow.Intent.TrainingPhrase.Part(text=training_phrases_part)
# Here we create a new training phrase for each provided part.
training_phrase = dialogflow.Intent.TrainingPhrase(parts=[part])
training_phrases.append(training_phrase)
intent = dialogflow.Intent(display_name=display_name, training_phrases=training_phrases)
response = intents_client.create_intent(request={"parent": parent, "intent": intent})
print("Intent created: {}".format(response))
def prepare_data(training_data_path):
with open(training_data_path, 'r') as f:
doc = yaml.safe_load(f)
# Extract the training examples from nlu.yml and store them in the training_data dictionary
# The key is the intent name, and the value is a list of examples for that intent
training_data = {}
for record in doc["nlu"]:
if 'intent' in record.keys():
# remove the tagged entity that is used by Rasa (e.g., [filename])
examples = re.sub(r'\([^)]*\)', '', record['examples'])
# Text cleaning, remove [, ], and new line. Then split based on -
examples = examples.replace('\n', '').replace('[', '').replace(']', '').split('- ')
examples = list(filter(None, examples))
training_data[record['intent']] = examples
return training_data
def prepare_data_test(testing_data_path):
with open(testing_data_path, 'r') as f:
doc = yaml.safe_load(f)
# Extract the training examples from nlu.yml and store them in the training_data dictionary
# The key is the intent name, and the value is a list of examples for that intent
testing_data = {}
for record in doc["nlu"]:
if 'intent' in record.keys():
# remove the tagged entity that is used by Rasa (e.g., [filename])
examples = re.sub(r'\([^)]*\)', '', record['examples'])
# Text cleaning, remove [, ], and new line. Then split based on -
examples = examples.replace('\n', '').replace('[', '').replace(']', '').split('- ')
examples = list(filter(None, examples))
for example in examples:
testing_data[example] = [record['intent']]
return testing_data
def continue_to_run_question(question):
qu = [
inquirer.Confirm('continue',
message=question, default=True),
]
ans = inquirer.prompt(qu, theme=GreenPassion())
if not ans['continue']:
print('bye!')
exit(0)
else:
return True
if __name__ == '__main__':
display_name = "commit_creator"
parent = dialogflow.AgentsClient.agent_path("chatbot-ws-arqe")
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = 'keys/private_arqe.json'
DIALOGFLOW_PROJECT_ID = 'chatbot-ws-arqe'
SESSION_ID = 'me'
DIALOGFLOW_LANGUAGE_CODE = 'en'
intents_client = dialogflow.IntentsClient()
current_datetime = datetime.datetime.now(pytz.timezone('America/Montreal'))
timestamp = current_datetime.strftime("%Y%m%d-%H%M%S")
# Getting the fraction for train-test and the output directory: ##############################
total = 0
while abs(total - 1.00) > 1e-5:
questions = [
inquirer.Text('training-fraction', message='Please enter the training-fraction for Google Dialogflow',
default=0.4),
inquirer.Text('testing-fraction', message='Please enter the testing-fraction', default=0.3),
inquirer.Text('validation-fraction', message='Please enter the validation-fraction for Weak Supervision',
default=0.3),
inquirer.Text('default-directory', message='Please enter the default directory for the outputs',
default=timestamp),
]
answers = inquirer.prompt(questions, theme=GreenPassion())
training_fraction = float(answers['training-fraction'])
testing_fraction = float(answers['testing-fraction'])
validation_fraction = float(answers['validation-fraction'])
total = training_fraction + testing_fraction + validation_fraction
if abs(total - 1.00) > 1e-5:
print('training_fraction + testing_fraction + validation_fraction is not equal to 1 (or 100 percent)!')
print('Please try again:')
# Creating the project directory: ##############################
project_directory = "/vagrant/Output/" + str(answers['default-directory']) + '/'
command = "mkdir " + project_directory
os.system(command)
# Choosing the proper dataset to use: ##############################
questions = [
inquirer.List('dataset',
message="Which dataset do you want to use?",
choices=[
('Dataset WITH general intents', 'w'),
('Dataset WITHOUT general intents', 'wo')
],
carousel=True,
default='wo'
),
]
# answers = inquirer.prompt(questions, theme=GreenPassion())
command = "cp -r /vagrant/Dataset/Paper/ " + project_directory + 'data'
# if answers['dataset'] == 'wo':
# command = "cp -r /vagrant/Dataset/Paper/ " + project_directory + 'data'
# else:
# command = "cp -r /vagrant/Dataset/Original/ " + project_directory + 'data'
os.system(command)
questions = [
inquirer.List('ask-question-in-each-step',
message="Should I ask the question for continue each single step?",
choices=[
('No just finish the evaluation!', 'N'),
('Yes please. I want to track every step you are doing!', 'Y')
],
carousel=True,
default='N'
),
]
answers = inquirer.prompt(questions, theme=GreenPassion())
ask_question = False
if answers['ask-question-in-each-step'] == 'Y':
ask_question = True
testing_fraction = testing_fraction / (1 - training_fraction)
validation_fraction = 1 - testing_fraction
command = "bash Bash/Rasa/rasa_data_split_with_validation.sh " + \
" -f " + str(training_fraction) + \
" -t " + str(testing_fraction) + \
" -p " + project_directory[:len(project_directory)-1]
print('Splitting the Google NLU data to training, testing, and validation sets:')
os.system(command)
command = "bash Bash/Rasa/make_data_for_train.sh " + \
"-t " + timestamp
print('Making data ready for training the Google NLU:')
os.system(command)
if ask_question:
continue_to_run_question("Test-Training-Validation split is finished. Should I continue?")
# Training the first Google Model: ##############################
print('Training the Google NLU model:')
trainingData = prepare_data(project_directory + 'train/nlu.yml')
print(trainingData)
# Delete the intent
i = 0
for key in trainingData.keys():
i += 1
intent_id = get_intent_ids(DIALOGFLOW_PROJECT_ID, key)
print(intent_id)
if intent_id:
print('\n' + 'deleting ' + str(i) + ' : ' + str(intent_id) + '\n')
delete_intent(DIALOGFLOW_PROJECT_ID, intent_id[0])
time.sleep(random.randint(1, 3))
# Create the intent
for key in trainingData.keys():
create_intent(DIALOGFLOW_PROJECT_ID,
key,
trainingData[key])
time.sleep(random.randint(1, 3))
# Train the Dialogflow
asyncio.wait(train_agent)
print('\n\nTraining Dialogflow...Please Wait...\n\n')
time.sleep(600)
if ask_question:
continue_to_run_question("Training Google model is finished. Should I continue?")
# Testing the first Google model ##############################
print('Testing the Google model:')
print('Testing the test file on the trained model:')
testing_path = project_directory + 'testing_validation/test_data.yml'
testingData = prepare_data_test(testing_path)
command = "mkdir " + project_directory + 'first_test_results'
os.system(command)
session_client = dialogflow.SessionsClient()
session = session_client.session_path(DIALOGFLOW_PROJECT_ID, SESSION_ID)
for example, intent in testingData.items():
text_input = dialogflow.TextInput(text=example, language_code=DIALOGFLOW_LANGUAGE_CODE)
query_input = dialogflow.QueryInput(text=text_input)
try:
response = session_client.detect_intent(session=session, query_input=query_input)
except InvalidArgument:
raise
print("Query text:", response.query_result.query_text)
print("Detected intent:", response.query_result.intent.display_name)
print("Detected intent confidence:", response.query_result.intent_detection_confidence)
intent.append(response.query_result.intent.display_name)
testingData[example] = intent
time.sleep(random.randint(1, 2))
pd.DataFrame.from_dict(data=testingData, orient='index').to_csv(project_directory + 'first_test_results/' +
'results.csv', header=False)
if ask_question:
continue_to_run_question("Testing the first Google model is finished. Should I continue?")
labeling_function = LabelingFunctions()
functions = {
labeling_function.most_recent_prs_1: True,
labeling_function.top_contributors_1: True,
labeling_function.number_of_commits_in_branch_1: True,
labeling_function.pr_closing_date_1: True,
labeling_function.pr_closing_date_2: True,
labeling_function.pr_creation_date_1: True,
labeling_function.pr_creation_date_2: True,
labeling_function.pr_assignees_1: True,
labeling_function.number_of_prs_1: True,
labeling_function.number_of_prs_2: True,
labeling_function.activity_report_1: True,
labeling_function.activity_report_2: True,
labeling_function.contributions_by_developer_1: True,
labeling_function.latest_commit_1: True,
labeling_function.default_branch_1: True,
labeling_function.developers_with_most_open_issues_1: True,
labeling_function.developers_with_most_open_issues_2: True,
labeling_function.files_changed_by_pr_1: True,
labeling_function.intial_commit_in_branch_1: True,
labeling_function.issue_closer_1: True,
labeling_function.issue_closing_date_1: True,
labeling_function.issue_closing_date_2: True,
labeling_function.issue_contributors_1: True,
labeling_function.issue_creation_date_1: True,
labeling_function.issue_creation_date_2: True,
labeling_function.issue_creator_1: True,
labeling_function.last_developer_to_touch_a_file_1: True,
labeling_function.latest_commit_in_branch_1: True,
labeling_function.latest_release_1: True,
labeling_function.list_languages_1: True,
labeling_function.number_of_collaborators_2: True,
labeling_function.number_of_collaborators_1: True,
labeling_function.number_of_downloads_1: True,
labeling_function.number_of_stars_1: True,
labeling_function.number_of_subscribers_1: True,
labeling_function.number_of_watchers_1: True,
labeling_function.pr_contributors_1: True,
labeling_function.pr_creator_1: True,
labeling_function.repository_creation_date_1: True,
labeling_function.repository_license_1: True,
labeling_function.repository_owner_1: True,
labeling_function.repository_owner_2: True,
labeling_function.repository_topics_1: True,
labeling_function.commits_in_pr_1: True,
labeling_function.issue_assignees_1: True,
labeling_function.longest_open_pr_1: True,
labeling_function.largest_files_1: True,
labeling_function.file_creator_1: True,
labeling_function.issue_related_commits_1: True,
labeling_function.list_branches_1: True,
labeling_function.list_collaborators_2: True,
labeling_function.list_collaborators_1: True,
labeling_function.list_releases_1: True,
labeling_function.number_of_branches_1: True,
labeling_function.number_of_branches_2: True,
labeling_function.number_of_commits_1: True,
labeling_function.number_of_commits_2: True,
labeling_function.number_of_forks_1: True,
labeling_function.number_of_forks_2: True,
labeling_function.number_of_issues_1: True,
labeling_function.number_of_issues_2: True,
labeling_function.pr_closer_1: True,
labeling_function.most_recent_issues_1: True,
labeling_function.longest_open_issue_1: True,
labeling_function.most_recent_prs_2: True,
labeling_function.intial_commit_1: True,
labeling_function.top_contributors_2: True,
labeling_function.main_programming_language_1: True,
labeling_function.file_creator_2: True,
labeling_function.number_of_commits_in_branch_2: True
}
command = "mkdir " + project_directory + 'engineer'
os.system(command)
dataTools = DataTools(project_directory + 'engineer/', fixed_directory=True)
list_of_commands = dataTools.yml_to_input_commands(project_directory + 'testing_validation/training_data.yml')
pandas_dataframe = dataTools.list_of_commands_to_pandas_dataframe(list_of_commands)
dataTools.pandas_dataframe_to_csv(pandas_dataframe)
engineer = Engineer(pandas_dataframe, functions, labeling_function.CARDINALITY, dataTools)
engineer.produce_labeling_matrix()
engineer.print_labeling_matrix()
engineer.print_labeling_functions_summary()
engineer.predict_by_majority_vote_model()
dict_of_intents = {0: 'MOST_RECENT_PRS', 1: 'NUMBER_OF_COMMITS_IN_BRANCH', 2: 'TOP_CONTRIBUTORS',
3: 'INTIAL_COMMIT', 4: 'MOST_RECENT_ISSUES', 5: 'LONGEST_OPEN_ISSUE',
6: 'LATEST_COMMIT', 7: 'CONTRIBUTIONS_BY_DEVELOPER', 8: 'ACTIVITY_REPORT',
9: 'NUMBER_OF_PRS', 10: 'PR_ASSIGNEES', 11: 'PR_CREATION_DATE',
12: 'PR_CLOSING_DATE', 13: 'PR_CLOSER', 14: 'NUMBER_OF_ISSUES', 15: 'NUMBER_OF_FORKS',
16: 'NUMBER_OF_COMMITS', 17: 'NUMBER_OF_BRANCHES', 18: 'LIST_RELEASES', 19: 'LIST_COLLABORATORS',
20: 'LIST_BRANCHES', 21: 'ISSUE_RELATED_COMMITS', 22: 'FILE_CREATOR', 23: 'LARGEST_FILES',
24: 'LONGEST_OPEN_PR', 25: 'ISSUE_ASSIGNEES', 26: 'COMMITS_IN_PR',
27: 'REPOSITORY_TOPICS', 28: 'REPOSITORY_OWNER', 29: 'REPOSITORY_LICENSE',
30: 'REPOSITORY_CREATION_DATE', 31: 'PR_CREATOR', 32: 'PR_CONTRIBUTORS',
33: 'NUMBER_OF_WATCHERS', 34: 'NUMBER_OF_SUBSCRIBERS', 35: 'NUMBER_OF_STARS',
36: 'NUMBER_OF_DOWNLOADS', 37: 'NUMBER_OF_COLLABORATORS', 38: 'LIST_LANGUAGES',
39: 'LATEST_RELEASE', 40: 'LATEST_COMMIT_IN_BRANCH', 41: 'LAST_DEVELOPER_TO_TOUCH_A_FILE',
42: 'ISSUE_CREATOR', 43: 'ISSUE_CREATION_DATE', 44: 'ISSUE_CONTRIBUTORS',
45: 'ISSUE_CLOSING_DATE', 46: 'ISSUE_CLOSER', 47: 'INTIAL_COMMIT_IN_BRANCH',
48: 'FILES_CHANGED_BY_PR', 49: 'DEVELOPERS_WITH_MOST_OPEN_ISSUES', 50: 'DEFAULT_BRANCH',
51: 'MAIN_PROGRAMMING_LANGUAGE'}
dataTools.save_predicted_commands_with_intents(dict_of_intents, model='majority')
nlu_file_path = project_directory + 'train/nlu.yml'
dataTools.add_predictions_to_nlu(nlu_file_path, dict_of_intents, model='majority')
if ask_question:
continue_to_run_question("Engineer finished his job. Should I continue?")
# Making data for the second Google model from the predictions of the Label model: ##############################
command = "bash Bash/Rasa/make_data_for_train_from_new_nlu.sh " + \
" -t " + timestamp + \
" -f " + 'new_nlu.yml'
print('Creating the new data directory for re-training:')
os.system(command)
if ask_question:
continue_to_run_question("The re-train directory (with its files) is created. Should I continue?")
# Training the second Google model: ##############################
print('deleting previous trained model:')
trainingData = prepare_data(project_directory + 're-train/nlu.yml')
print(trainingData)
# Delete the intent
i = 0
for key in trainingData.keys():
i += 1
intent_id = get_intent_ids(DIALOGFLOW_PROJECT_ID, key)
print(intent_id)
if intent_id:
print('\n' + 'deleting ' + str(i) + ' : ' + str(intent_id) + '\n')
delete_intent(DIALOGFLOW_PROJECT_ID, intent_id[0])
time.sleep(random.randint(1, 3))
print('re-training Google NLU again from scratch:')
# Create the intent
for key in trainingData.keys():
create_intent(DIALOGFLOW_PROJECT_ID,
key,
trainingData[key])
time.sleep(random.randint(1, 3))
# Train the Dialogflow
asyncio.wait(train_agent)
print('\n\nTraining Dialogflow...Please Wait...\n\n')
time.sleep(600)
if ask_question:
continue_to_run_question("Re-training Google model is finished. Should I continue?")
# Testing the second Google model: ##############################
print('Testing the re-trained Google model:')
command = "mkdir " + project_directory + 'second_test_results'
os.system(command)
print('Testing the test file on the re-trained model:')
testing_path = project_directory + 'testing_validation/test_data.yml'
testingData = prepare_data_test(testing_path)
for example, intent in testingData.items():
text_input = dialogflow.TextInput(text=example, language_code=DIALOGFLOW_LANGUAGE_CODE)
query_input = dialogflow.QueryInput(text=text_input)
try:
response = session_client.detect_intent(session=session, query_input=query_input)
except InvalidArgument:
raise
print("Query text:", response.query_result.query_text)
print("Detected intent:", response.query_result.intent.display_name)
print("Detected intent confidence:", response.query_result.intent_detection_confidence)
intent.append(response.query_result.intent.display_name)
testingData[example] = intent
time.sleep(random.randint(1, 2))
pd.DataFrame.from_dict(data=testingData, orient='index').to_csv(project_directory + 'second_test_results/' +
'results.csv', header=False)
if ask_question:
continue_to_run_question("Testing the second Google model is finished. Should I continue?")
print('\nPrinting the results:')
first_f1 = calculate_f1(project_directory + 'first_test_results/' + 'results.csv')
second_f1 = calculate_f1(project_directory + 'second_test_results/' + 'results.csv')
print('\nBaseline F1-score: ', first_f1)
print('\nAlphaBot F1-score: ', second_f1)
print('\nSee you soon!')