Intent classification with sklearn

An array X containing vectors describing each of the sentences in the ATIS dataset has been created for you, along with a 1D array y containing the labels. The labels are integers corresponding to the intents in the dataset. For example, label 0 corresponds to the intent atis_flight.

Now, you'll use the scikit-learn library to train a classifier on this same dataset. Specifically, you will fit and evaluate a support vector classifier.

This exercise is part of the course

Building Chatbots in Python

Exercise instructions

Import the SVC class from sklearn.svm.
Instantiate a classifier clf by calling SVC with a single keyword argument C with value 1.
Fit the classifier to the training data X_train and y_train.
Predict the labels of the test set, X_test.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import SVC
____

# Create a support vector classifier
clf = ____

# Fit the classifier using the training data
____

# Predict the labels of the test set
y_pred = ____

# Count the number of correct predictions
n_correct = 0
for i in range(len(y_test)):
    if y_pred[i] == y_test[i]:
        n_correct += 1

print("Predicted {0} correctly out of {1} test examples".format(n_correct, len(y_test)))

Edit and Run Code

This exercise is part of the course

Building Chatbots in Python

IntermediateSkill Level

4.7+

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In this chapter, you'll learn how to build your first chatbot. After gaining a bit of historical context, you'll set up a basic structure for receiving text and responding to users, and then learn how to add the basic elements of personality. You'll then build rule-based systems for parsing text.

Exercise 1: Introduction to conversational software Exercise 2: EchoBot I Exercise 3: EchoBot II Exercise 4: Creating a personality Exercise 5: Chitchat Exercise 6: Adding variety Exercise 7: ELIZA I: asking questions Exercise 8: Text processing with regular expressions Exercise 9: ELIZA II: Extracting key phrases Exercise 10: ELIZA III: Pronouns Exercise 11: ELIZA IV: Putting it all together

Here, you'll use machine learning to turn natural language into structured data using spaCy, scikit-learn, and rasa NLU. You'll start with a refresher on the theoretical foundations and then move onto building models using the ATIS dataset, which contains thousands of sentences from real people interacting with a flight booking system.

Exercise 1: Understanding intents and entities Exercise 2: Intent classification with regex I Exercise 3: Intent classification with regex II Exercise 4: Entity extraction with regex Exercise 5: Word vectors Exercise 6: word vectors with spaCy Exercise 7: Intents and classification Exercise 8: Intent classification with sklearn

Current Exercise

Exercise 9: Entity extraction Exercise 10: Using spaCy's entity recognizer Exercise 11: Assigning roles using spaCy's parser Exercise 12: Robust language understanding with rasa NLU Exercise 13: Rasa NLU Exercise 14: Data-efficient entity recognition

In this chapter, you'll build a personal assistant to help you plan a trip. It will be able to respond to questions like "are there any cheap hotels in the north of town?" by looking inside a hotel’s database for matching results.

Exercise 1: Virtual assistants and accessing data Exercise 2: SQL basics Exercise 3: SQL statements in Python Exercise 4: Exploring a DB with natural language Exercise 5: Creating queries from parameters Exercise 6: Using your custom function to find hotels Exercise 7: Creating SQL from natural language Exercise 8: Incremental slot filling and negation Exercise 9: Refining your search Exercise 10: Basic negation Exercise 11: Filtering with excluded slots

Everything you've built so far has statelessly mapped intents to actions and responses. It's amazing how far you can get with that! But to build more sophisticated bots you will always want to add some statefulness. That's what you'll do here, as you build a chatbot that helps users order coffee.

Exercise 1: Why statefulness is key Exercise 2: Form filling Exercise 3: Asking contextual questions Exercise 4: Dealing with rejection Exercise 5: Asking questions & queuing answers Exercise 6: Pending actions I Exercise 7: Pending actions II Exercise 8: Pending state transitions Exercise 9: Putting it all together I Exercise 10: Putting it all together II Exercise 11: Frontiers of dialogue research Exercise 12: Generating text with neural networks Exercise 13: Congratulations!