Dialog State Agent

Dialog State Agent created for the course 1-GS Methods in AI research (INFOMAIR) 2020-2021 at Utrecht University. Current functionality is train and test classifier, classify user utterance.

Run Dialogue_Agent.py for the text based chatbot for restaurant domain. classification.py contains functions to train and test classifiers.

Packages

Pandas
python-Levenshtein
NLTK
re
random
Numpy
SkLearn

baseline.py

Implementation of 2 baselines:

1. classify every utterance as majority class
2. classify every utterance based on self-defined rules Score for both baselines is based on accuracy. To get the error, output 1-accuracy

Example code:

    from baseline import Baseline

    b = Baseline()
    b.open_dataset("dialog_acts.dat")
    b.split_dataset()
    b.get_highest_label()
    b.test_highest_label()
    print(b.score())

To test the keyword rules, simply run the function:

    b.test_keyword_rule()
    print(b.score())

To classify user utterance, simply run the following command:

    b.user_input()

classification.py

Class to split data and train and test classifier. Usage:

1. create instance of class Classification()

    clf=Classification()

2. initialize data using initialize_data(filename)

    clf.initialize_data("dialog_acts.dat")

3. train and test classifier using one of the classifiers (LR or NN)

    clf.train_lr()#or
    clf.train_nn()

4. Testing and getting performance measures:

Test and make confusion matrix

    clf.test_clf()

Cross Validation. For this function, create a classifier and call the cv function. Second parameter for cv function is a boolean indicating whether or not to oversample.

    lr=LogisticRegression(random_state=0, max_iter=200, penalty='l2')
    clf.cv(lr,False) 

Predict a single sentence

    sentence="Hi, I would like to get a suggestion"
    clf.predict(sentence):