What is Cooking?

Predict the type of cuisine based on the ingredients. Demo available at ~~https://ez-cuisine-classifier.herokuapp.com/~~ https://ez-cuisine-classifier.streamlit.app/

Data

The training data is from kaggle's Recipe Ingredients Dataset.

Modelling

My first take is to use some classifiers out of the box from scikit-learn even without any hyperparameter tuning. The result seems acceptable. Checkout the scikit-learn script.

mean accuracy	learner
0.716	BernoulliNB
0.619	DecisionTreeClassifier
0.099	DummyClassifier
0.530	ExtraTreeClassifier
0.730	KNeighborsClassifier
0.790	LinearSVC
0.673	MultinomialNB
0.763	PassiveAggressiveClassifier
0.720	Perceptron
0.759	RidgeClassifier
0.777	SGDClassifier
0.775	LogisticRegression
0.197	SVC

I have noticed the SVM's rbf kernel does not work with default parameters. Hence I try out a few penalty values and the results are better, shown as follow.

mean accuracy	learner
0.197	SVC (C=1)
0.489	SVC (C=10)
0.706	SVC (C=100)
0.784	SVC (C=1000)

Another approach of modelling

Transfer learning is a trending topic nowadays in machine learning domain. It allows us to take advantage of pre-trained models and fine tune them for another domain/task. This dataset may not be the best case to apply transfer learning practice because the ingredients are not written in natural language. I did it anyway for the sake of demonstration. For simplicity, I choose fast.ai.

fast.ai's learning rate finding function is pretty handy. And I only use the 1cycle policy to fit the model. Checkout the language model script.

Fine tune the pre-trained model

find lr 1

Choosing learning rate 0.

epoch	train_loss	valid_loss	accuracy	time
0	4.911732	3.926325	0.341775	00:14

find lr 2

To train a bit further, choose learning rate (1e-2)/2.

epoch	train_loss	valid_loss	accuracy	time
0	3.331526	3.207970	0.397344	00:19

Apply the tuned model for classification

find lr 3

Choosing learning rate 5e-1.

epoch	train_loss	valid_loss	accuracy	time
0	1.524449	1.477003	0.578630	02:51

find lr 4

To train a bit further, choose learning rate 2e-4.

epoch	train_loss	valid_loss	accuracy	time
0	1.284334	1.164963	0.652546	03:09

Explanation

Accuracy being about 40% after fine tuning means that the tuned language model is able to predict the next word based on context 2 out of 5 times. For example, lm_learner.predict("green bell") would return "green bell pepper".
Accuracy being about 65% after fitting for classification means that the classifier is able to predict the correct cuisine based on input ingredients 3 out of 5 times. For example, clf.predict("warp avocado beef") would return "southern_us".

Demo

Demo with LinearSVC model is available at ~~https://ez-cuisine-classifier.herokuapp.com/~~ https://ez-cuisine-classifier.streamlit.app/.