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Dialogflow-RQ2.py
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Dialogflow-RQ2.py
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import datetime
import os
import pytz
from classes.data_tools import DataTools
from classes.engineer import Engineer
from classes.labeling_functions import LabelingFunctions
import json
import glob
import inquirer
from inquirer.themes import GreenPassion
from snorkel.labeling import labeling_function
import ast
import random
from google.cloud import dialogflow
import asyncio
import yaml
import re
import time
from google.api_core.exceptions import InvalidArgument
import pandas as pd
import csv
import sys
from csv import reader
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.font_manager as fm # Collect all the font names available to matplotlib
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
def calculate_results(path):
with open(path, 'r') as csv_file:
csv_reader = reader(csv_file)
first_list_of_rows = list(csv_reader)
first_total = len(first_list_of_rows)
first_correct = 0
for row in first_list_of_rows:
if row[1] == row[2]:
first_correct += 1
first_accuracy = (first_correct / first_total) * 100
return [first_correct, first_total, first_accuracy]
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 shuffle_dictionary(input_dictionary):
l = list(input_dictionary.items())
random.shuffle(l)
return dict(l)
@labeling_function()
def dummy_method(x):
return -1
if __name__ == '__main__':
# Edit the font, font size, and axes width
mpl.rcParams['font.family'] = 'DejaVu Sans'
plt.rcParams['font.size'] = 20
plt.rcParams['axes.linewidth'] = 2
plt.rcParams["font.weight"] = "bold"
# Generate 2 colors from the 'tab10' colormap
colors = cm.get_cmap('tab10', 2)
# font
font_names = [f.name for f in fm.fontManager.ttflist]
current_datetime = datetime.datetime.now(pytz.timezone('America/Montreal'))
timestamp = current_datetime.strftime("%Y%m%d-%H%M%S")
if len(sys.argv) - 1:
argument_list = sys.argv
answer_model = int(argument_list[1])
answer_experiment = int(argument_list[2])
answer_iteration = int(argument_list[3])
answer_directory = timestamp
answer_home_directory = argument_list[4]
answer_pause = int(argument_list[5])
answer_train_pause = int(argument_list[6])
answer_bot_name = argument_list[7]
answer_bot_key = argument_list[8]
else:
questions = [
inquirer.List('dataset',
message="Which baseline model do you want to use for this experiment?",
choices=[
('10-45-45 splits.', 10),
('30-35-35 splits.', 30),
('50-25-25 splits.', 50),
('70-15-15 splits.', 70),
('90-5-5 splits.', 90)
],
carousel=True,
default=10
),
inquirer.Text('experiment', message='Please enter experiment (random shuffle of LFs)', default=1),
inquirer.Text('iteration', message='Please enter iteration (more iteration, more time needed)', default=1),
inquirer.Text('default-directory', message='Please enter the default directory for the outputs',
default=timestamp),
]
answers = inquirer.prompt(questions, theme=GreenPassion())
answer_model = int(answers['dataset'])
answer_experiment = int(answers['experiment'])
answer_iteration = int(answers['iteration'])
answer_directory = answers['default-directory']
answer_home_directory = '/vagrant/'
answer_pause = 3
answer_train_pause = 500
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()
chatbot_directory = answer_home_directory
project_directory = chatbot_directory + "Output/" + answer_directory + '/'
command = "mkdir " + project_directory
os.system(command)
labeling_function_class = LabelingFunctions()
functions_helper = {
labeling_function_class.most_recent_prs_1: 'most_recent_prs_1',
labeling_function_class.top_contributors_1: 'top_contributors_1',
labeling_function_class.number_of_commits_in_branch_1: 'number_of_commits_in_branch_1',
labeling_function_class.pr_closing_date_1: 'pr_closing_date_1',
labeling_function_class.pr_closing_date_2: 'pr_closing_date_2',
labeling_function_class.pr_creation_date_1: 'pr_creation_date_1',
labeling_function_class.pr_creation_date_2: 'pr_creation_date_2',
labeling_function_class.pr_assignees_1: 'pr_assignees_1',
labeling_function_class.number_of_prs_1: 'number_of_prs_1',
labeling_function_class.number_of_prs_2: 'number_of_prs_2',
labeling_function_class.activity_report_1: 'activity_report_1',
labeling_function_class.activity_report_2: 'activity_report_2',
labeling_function_class.contributions_by_developer_1: 'contributions_by_developer_1',
labeling_function_class.latest_commit_1: 'latest_commit_1',
labeling_function_class.default_branch_1: 'default_branch_1',
labeling_function_class.developers_with_most_open_issues_1: 'developers_with_most_open_issues_1',
labeling_function_class.developers_with_most_open_issues_2: 'developers_with_most_open_issues_2',
labeling_function_class.files_changed_by_pr_1: 'files_changed_by_pr_1',
labeling_function_class.intial_commit_in_branch_1: 'intial_commit_in_branch_1',
labeling_function_class.issue_closer_1: 'issue_closer_1',
labeling_function_class.issue_closing_date_1: 'issue_closing_date_1',
labeling_function_class.issue_closing_date_2: 'issue_closing_date_2',
labeling_function_class.issue_contributors_1: 'issue_contributors_1',
labeling_function_class.issue_creation_date_1: 'issue_creation_date_1',
labeling_function_class.issue_creation_date_2: 'issue_creation_date_2',
labeling_function_class.issue_creator_1: 'issue_creator_1',
labeling_function_class.last_developer_to_touch_a_file_1: 'last_developer_to_touch_a_file_1',
labeling_function_class.latest_commit_in_branch_1: 'latest_commit_in_branch_1',
labeling_function_class.latest_release_1: 'latest_release_1',
labeling_function_class.list_languages_1: 'list_languages_1',
labeling_function_class.number_of_collaborators_2: 'number_of_collaborators_2',
labeling_function_class.number_of_collaborators_1: 'number_of_collaborators_1',
labeling_function_class.number_of_downloads_1: 'number_of_downloads_1',
labeling_function_class.number_of_stars_1: 'number_of_stars_1',
labeling_function_class.number_of_subscribers_1: 'number_of_subscribers_1',
labeling_function_class.number_of_watchers_1: 'number_of_watchers_1',
labeling_function_class.pr_contributors_1: 'pr_contributors_1',
labeling_function_class.pr_creator_1: 'pr_creator_1',
labeling_function_class.repository_creation_date_1: 'repository_creation_date_1',
labeling_function_class.repository_license_1: 'repository_license_1',
labeling_function_class.repository_owner_1: 'repository_owner_1',
labeling_function_class.repository_owner_2: 'repository_owner_2',
labeling_function_class.repository_topics_1: 'repository_topics_1',
labeling_function_class.commits_in_pr_1: 'commits_in_pr_1',
labeling_function_class.issue_assignees_1: 'issue_assignees_1',
labeling_function_class.longest_open_pr_1: 'longest_open_pr_1',
labeling_function_class.largest_files_1: 'largest_files_1',
labeling_function_class.file_creator_1: 'file_creator_1',
labeling_function_class.issue_related_commits_1: 'issue_related_commits_1',
labeling_function_class.list_branches_1: 'list_branches_1',
labeling_function_class.list_collaborators_2: 'list_collaborators_2',
labeling_function_class.list_collaborators_1: 'list_collaborators_1',
labeling_function_class.list_releases_1: 'list_releases_1',
labeling_function_class.number_of_branches_1: 'number_of_branches_1',
labeling_function_class.number_of_branches_2: 'number_of_branches_2',
labeling_function_class.number_of_commits_1: 'number_of_commits_1',
labeling_function_class.number_of_commits_2: 'number_of_commits_2',
labeling_function_class.number_of_forks_1: 'number_of_forks_1',
labeling_function_class.number_of_forks_2: 'number_of_forks_2',
labeling_function_class.number_of_issues_1: 'number_of_issues_1',
labeling_function_class.number_of_issues_2: 'number_of_issues_2',
labeling_function_class.pr_closer_1: 'pr_closer_1',
labeling_function_class.most_recent_issues_1: 'most_recent_issues_1',
labeling_function_class.longest_open_issue_1: 'longest_open_issue_1',
labeling_function_class.most_recent_prs_2: 'most_recent_prs_2',
labeling_function_class.intial_commit_1: 'intial_commit_1',
labeling_function_class.top_contributors_2: 'top_contributors_2',
labeling_function_class.main_programming_language_1: 'main_programming_language_1',
labeling_function_class.file_creator_2: 'file_creator_2',
labeling_function_class.number_of_commits_in_branch_2: 'number_of_commits_in_branch_2'
}
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'
}
experiments = [answer_experiment]
iterations = answer_iteration
path = project_directory
for experiment in experiments:
functions = shuffle_dictionary(functions_helper)
temporary_dict = {}
i = 1
for key, value in functions.items():
temporary_dict[i] = value
i += 1
with open(project_directory + "shuffle-functions-" + str(experiment) + ".txt", 'w') as file:
file.write(json.dumps(temporary_dict)) # use `json.loads` to do the reverse
file = open(
project_directory + "shuffle-functions-" + str(experiment) + ".txt", "r")
contents = file.read()
dictionary = ast.literal_eval(contents)
file.close()
for number, func in dictionary.items():
lf = list(functions_helper.keys())[list(functions_helper.values()).index(func)]
functions[lf] = func
experiment_directory = path + 'e' + str(experiment) + '/'
command = "mkdir " + experiment_directory
os.system(command)
temporary_dict = {}
i = 1
for key, value in functions.items():
temporary_dict[i] = value
i += 1
with open(experiment_directory + 'shuffle-functions.txt', 'w') as file:
file.write(json.dumps(temporary_dict))
for iteration in range(1, iterations + 1):
iteration_directory = experiment_directory + 'i' + str(iteration) + '/'
command = "mkdir " + iteration_directory
os.system(command)
for model in [answer_model]:
model_directory = iteration_directory + str(model) + '/'
command = "mkdir " + model_directory
os.system(command)
dict_of_function = {dummy_method: True}
counter = 1
for function, function_name in functions.items():
dict_of_function[function] = True
step_directory = model_directory + str(counter) + '/'
counter += 1
command = "mkdir " + step_directory
os.system(command)
path_of_engineer = step_directory + 'engineer/'
command = "mkdir " + path_of_engineer
os.system(command)
dataTools = DataTools(path_of_engineer, fixed_directory=True)
list_of_commands = dataTools.yml_to_input_commands(
chatbot_directory + 'Dialogflow-Baseline-Results/' + str(model) +
'/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, dict_of_function, cardinality=52,
data_tools_instance=dataTools)
engineer.produce_labeling_matrix()
engineer.predict_by_majority_vote_model()
dataTools.save_predicted_commands_with_intents(dict_of_intents, model='majority')
nlu_file_path = chatbot_directory + 'Dialogflow-Baseline-Results/' + str(model) + '/train/nlu.yml'
dataTools.add_predictions_to_nlu(nlu_file_path, dict_of_intents, model='majority')
command = "bash Bash/Dialogflow/make_data_for_train_from_new_nlu.sh " + \
" -t " + step_directory + \
" -f " + 'new_nlu.yml'
print('Creating the new data directory for re-training:')
os.system(command)
# Train the google NLU
print('Training the Google NLU model:')
trainingData = prepare_data(step_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(answer_pause, answer_pause + 1))
# Create the intent
for key in trainingData.keys():
create_intent(DIALOGFLOW_PROJECT_ID,
key,
trainingData[key])
time.sleep(random.randint(answer_pause, answer_pause + 1))
# Train the Dialogflow
asyncio.wait(train_agent)
print('\n\nTraining Dialogflow...Please Wait...\n\n')
time.sleep(answer_train_pause)
# Testing the google NLU
print('Testing the Google model:')
print('Testing the test file on the trained model:')
testing_path = chatbot_directory + 'Dialogflow-Baseline-Results/' + str(model) + \
'/testing_validation/test_data.yml'
testingData = prepare_data_test(testing_path)
command = "mkdir " + step_directory + '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(answer_pause, answer_pause + 1))
pd.DataFrame.from_dict(data=testingData, orient='index').to_csv(
step_directory + 'test_results/' +
'results.csv', header=False)
exp_list = [name for name in os.listdir(project_directory) if os.path.isdir(os.path.join(project_directory, name))]
for exp in exp_list:
exp_directory = project_directory + exp + '/'
iteration_list = [name for name in os.listdir(exp_directory) if
os.path.isdir(os.path.join(exp_directory, name))]
for iteration in iteration_list:
iteration_directory = exp_directory + iteration + '/'
model_list = [name for name in os.listdir(iteration_directory) if
os.path.isdir(os.path.join(iteration_directory, name))]
for model in model_list:
model_directory = iteration_directory + model + '/'
before_path = 'Dialogflow-Baseline-Results/' + model + '/' + \
'test_results/results.csv'
f1_before = calculate_f1(before_path)
errors_before = 0
step_list = [name for name in os.listdir(model_directory) if
os.path.isdir(os.path.join(model_directory, name))]
results = []
for step in step_list:
step_directory = model_directory + step + '/'
after_path = step_directory + 'test_results/results.csv'
if os.path.isfile(after_path):
f1_after = calculate_f1(after_path)
errors_after = 0
number_of_predictions = len(open(step_directory + 'engineer/predicted.csv').readlines()) - 1
results.append([exp, iteration, model, step, "%0.4f" % f1_before,
"%0.4f" % f1_after, "%0.4f" % (f1_after - f1_before),
number_of_predictions, errors_before, errors_after,
errors_after - errors_before])
fields = ['experiment', 'iteration', 'model', 'step_lf', 'f1_before', 'f1_after', 'difference',
'number_of_predictions', 'intent_errors_before', 'intent_errors_after', 'errors_difference']
results = sorted(results, key=lambda x: int(x[3])) # step: 3
with open(project_directory + 'Dialogflow-rq2-' + exp + '-' + iteration + '-' + model + '.csv',
'w') as f:
write = csv.writer(f)
write.writerow(fields)
write.writerows(results)
data_1 = pd.read_csv(
project_directory + 'Dialogflow-rq2-' + exp + '-' + iteration + '-' + model + '.csv')
data_plot = data_1['difference']
# Create figure object and store it in a variable called 'fig'
fig = plt.figure(figsize=(5, 5))
# Add axes object to our figure that takes up entire figure
ax = fig.add_axes([0, 0, 1, 1])
# Edit the major and minor ticks of the x and y axes
ax.xaxis.set_tick_params(which='major', size=7, width=2, direction='out')
ax.xaxis.set_tick_params(which='minor', size=4, width=1, direction='out')
ax.yaxis.set_tick_params(which='major', size=7, width=2, direction='out')
ax.yaxis.set_tick_params(which='minor', size=4, width=1, direction='out')
# Edit the major and minor tick locations of x and y axes
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(10))
ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(10))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(1))
ax.plot(data_1['step_lf'].to_numpy(), data_plot.to_numpy(), 'g--', linewidth=2.5, color='orange',
label='Dialogflow-rq2-' + exp + '-' + iteration + '-' + model)
# Add the x and y-axis labels
ax.set_xlabel('Number of applied LFs', labelpad=10, fontweight='bold', fontsize=18.5)
ax.set_ylabel('Improvement in F1-score (%)', labelpad=10, fontweight='bold', fontsize=18.5)
# Set the axis limits
ax.set_xlim(0, 70)
ax.set_ylim(-10, 60)
# Add legend to plot
ax.legend(bbox_to_anchor=(1, 1), loc=1, frameon=False, fontsize=18.5)
# Save figure
plt.savefig(project_directory + 'Dialogflow-rq2-' + exp + '-' + iteration + '-' + model + '.pdf',
dpi=300,
transparent=False, bbox_inches='tight')
plt.savefig(project_directory + 'Dialogflow-rq2-' + exp + '-' + iteration + '-' + model + '.png',
dpi=300,
transparent=False, bbox_inches='tight')
# Show figure
# plt.show()