Neural network for bread recipe generation – Part II

Welcome back! I previously described how I scraped the baking forum The Fresh Loaf, where people post their bread recipes, to get data to train a neural network to generate new bread recipes. I also detailed how I explored the data. In this post, I explain how I used some unsupervised learning techniques in the Natural Language Processing toolkit to further understand the textual data. Note: all the code I used for this project is in this repo.

A sourdough loaf I made with buckwheat groats to start us off!

Topic modeling

I knew the recipe data is unstructured and so I was curious if I could somehow create labels using statistical techniques. This is how I came across topic modeling, an unsupervised machine learning model to identify clusters of related words, and I thought maybe it could help make assign different topics to collections of recipes. Upon reading more, I found out about Latent Dirichlet allocation (LDA), a statistical model that uses a collection of documents, words and topics to learn which document belongs to which topic and what words describe that topic. This seemed like a great way to analyze the recipes I had to see if they fall under broad clusters that are distinct from each other. Using the count vectorizer from the scikit-learn toolkit to transform the words into numerical features, I applied LDA to fit the data to a defined number of topics and words describing them.

number_topics = 5 number_words = 15 count_vectorizer = CountVectorizer(max_df=1.0, min_df=2, stop_words='english') count_data = count_vectorizer.fit_transform(df_recipe['Cleaned Recipe']) lda = LDA(n_components=number_topics, n_jobs=-1, learning_method='batch', max_iter=50, random_state=42) lda.fit(count_data) print('Topics found via LDA:') print_topic_top_words(lda, count_vectorizer, number_words)
Code language: Python (python)

I played around with these parameters to see how the resulting topics changed, but generally too many words or topics led to overlap between topics, so I ended up choosing 5 topics and 15 words to examine the clusters. Looking at these below, Topics 0 and 1 are related to the fermentation process, with words like ‘hydration’, ‘hours’, ‘rest’ and ‘mix’, amongst others. Topic 2 is just general bread words. Topic 3 includes words related to baking process and Topic 4 seems to be about different ingredients and shapes (‘pizza’, ‘cheese’, ‘sugar’). I also used the pyLDAvis package to visualize these topics by doing dimensionality reduction using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. In these two reduced dimensions, the topics seem to be well-separated, at least past the top words for the first two topics.

Topics found via LDA: Topic #:0 flour dough water levain bread hydration crumb loaf time hours salt rye bake loaves starter Topic #:1 dough minutes let flour hours water oven mix temperature bowl bake place add rest bread Topic #:2 bread loaf baking recipe sourdough flour time im good make like oven yeast dough ive Topic #:3 water thermal heat baking energy process temperature density things like know specific moisture cake bakers Topic #:4 bread like time dough make pizza butter cheese breads flavor used baked sugar crust sourdough
Code language: CSS (css)
This image has an empty alt attribute; its file name is t-sne-analysis-1024x562.png
Visualizing the topic modeling results using the pyLDAvis package. The five topics are projected onto a 2D space on the left to show the difference between topics. The top 30 words for the first topic are shown on the right.

However, according to my domain knowledge as a baker, it’s a stretch to call these topics different, as there’s overlap between them and most recipes contain some or all of these topics in their texts. So I wanted to see if another topic modeling approach, Non-negative Matrix Factorization (NMF) would be better. This approach uses tf-idf vectorized representations to create matrices relating the number of words, topics and documents.

tfidf_vectorizer = TfidfVectorizer(max_df=1.0, min_df=2, stop_words='english') doc_term_matrix = tfidf_vectorizer.fit_transform(df_recipe['Cleaned Recipe']) nmf = NMF(n_components=number_topics, random_state=42) nmf.fit(doc_term_matrix) print('Topics found via NMF:') print_topic_top_words(nmf, tfidf_vectorizer, number_words)
Code language: Python (python)

Keeping the same number of words and topics (15 and 5, respectively), I obtained the topics shown below with NMF. These topics are definitely different from LDA! The first topic seems to be general bread words again, but Topic 1 is fermentation process-related. Topic 2 describes ingredients used in recipes and Topic 3 and 4 are also time and process-related. Again, based on my domain expertise, I would not say these are well-differentiated topics but it is interesting to see new ways to cluster the data using the NMF algorithm.

Topics found via NMF: Topic #:0 bread sourdough recipe like time im baking dough loaf think ive good make crumb breads Topic #:1 let dough hours minutes temperature pan bowl degrees sit refrigerator remove place oven steam covered Topic #:2 berries flour levain rye 50 day recipe seeds water extraction loaves kamut unbleached wheat spelt Topic #:3 flour pm water minutes min hours dough starter salt loaf hydration bulk folds 30 10 Topic #:4 dough minutes place counter let flour seam rounds speed mix cover add pull water middle
Code language: CSS (css)

Alright, that’s it with the unsupervised learning. My goal was to see if I could use it to assign labels to the recipes, but overlap between different topics prevented this. In the next post, I’ll write about how I built a couple language models to predict new bread recipes 🙂

A raisin-fennel sourdough loaf I made which worked great for sweet as well as savory purposes! Wonder which topic it should fall under…