Product categorization github

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Product categorization github

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GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. We have dockerized our environment so that you can easily reproduce the results reported in the paper. Follow these steps to predict the category path for a product using pur pretrained product categoristion model:. Our pre-trained model predicts the category path and displays an output image that shows which part of the image has been focussed by our model to predict the category level.

Text Classification with Python

Similarly you can predict for other images using this command, by changing the path in --img parameter to point to your image location.

Few more sample predictions can be found in this section. The following line starts a container, mounts the folder in a folder named data in the container and names the container atlasso you can easily refer to this later. This will predict the category path and store the attention image in your local directory which you mounted into this container. You can see the image in the local directory path. To predict for other images, change the path in --img parameter to point to your image location.

We use attention based neural network Encoder-Decoder model to generate the sequences in the taxonomy. Encoder - The Encoder is a layered Residual Network ResNet trained on the ImageNet classification that converts the input image into a fixed size vector.

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This is the part of the model that predicts sequences for the taxonomy. It combines the output from the encoder and attention weights to predict category paths as sequences for the taxonomy. Attention - Attention Network learns which part of the image has to be focused to predict the next level in the category path while performing the sequence classification task. Constrained Beam Search - Constrained Beam Search restricts the model from generating category paths that are not predefined in our taxonomy.

It limits the sequences chosen by the Decoder in order to generate category paths within the taxonomy. We gathered taxonomy for the clothing top level category from popular Indian e-commerce fashion sites.

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We analyzed popular products, niche, and premium clothing products across these stores and developed our taxonomy with 52 category paths. The list of 52 category paths and additional details can be found here. For all categories in taxonomy tree, we collected product data and its images from popular Indian E-commerce stores. Web scraping tools like Scrapy and Selenium were used to extract the product title, breadcrumb, image and price of each product.

Check out this section to know more about our data collection strategy for Atlas dataset. The dataset, Atlaswe used for training our model is a high-quality product taxonomy dataset focusing on clothing products.

product categorization github

It containsimages under 52 clothing categories. A sample record from the JSON is shown below. After collecting data, we found that many product listing also included a zoomed in images that display intrinsic details such as the texture of the fabric, brand labels, button, and pocket styles. These zoomed in images would drastically affect the quality of the dataset. We automated the process of filtering out the noisy images with the help of a simple 3 layer CNN based classification model.

More details about the architecture of CNN Model and how we used it to clean our dataset can be found here. Note: Our Atlas dataset generated in the above section is already cleaned. No need to apply this Zoomed Vs Normal model on the dataset. We approach the product categorization problem as a sequence prediction problem by leveraging the dependency between each level in the category path.

We use attention based neural network Encoder-Decoder architecture to generate sequences.Text classification a. It is used to automatically assign predefined categories labels to free-text documents. The purpose of text classification is to give conceptual organization to a large collection of documents. It has become more relevant with exponential growth of the data, with wide applicability in real world applications e.

An interesting application of text classification is to categorize research papers by most suitable conferences. Finding and selecting a suitable academic conference has always been a challenging task especially for young researchers. We can define a 'suitable academic conference' as a conference, which is aligned with the researcher's work and have a good academic ranking.

Usually, a researcher has to consult with their supervisors and search extensively to find a suitable conference. Among many conferences, few are considered to be relevant to send a research work. To fulfil editorial and content specific demands of conferences, a researcher needs to go through the previously published proceedings of a certain conference. Based on the previous proceeding of conferences, the research work is sometimes modified to increase the chances of article acceptances and publication.

This problem can be solved to some extent using machine learning techniques e. Thus, the objective of this tutorial is to provide hands-on experience on how to perform text classification using conference proceedings dataset.

We will learn how to apply various classification algorithms to categorize research papers by conferences along with feature selection and dimensionality reduction methods using a popular scikit-learn library in Python. The dataset consists of short research paper titles, largely technology related.

They have been pre-classified manually into 5 categories by conferences. Figure 1, summarizes the distribution of research papers by different conferences. The loadData method will be used to read the dataset from a text file and return titles and conference names.

Similarly, preProcessing method will take titles as a parameter and will perform text pre-processing steps. By iterating over titles it will convert the text into lowercase and remove the stop words that are provided in the corpus of NLTK library. You can uncomment one line of code in this method if you wish to remove punctuation and numbers from the titles. Few research paper titles and corresponding conferences are shown in Figure 2.The analysis is performed on 9 food categories.

In order to recover missing data, some nutritional values like energy or protein concentration for product were used as features for classification. We defined our own nutritional score based on the Energy value, on the Nutritional French score and on the Number of ingredients with palm oil of each food product to advise the consumer about the healthiness of the products for each category.

We focus on French brands and we analyse the nutritional score and the number of ingredients containing palm oil for their products. We deduce a final score for all the French brands based on how much they produce healthy products. Let's start looking at the dataset and how we decide to analyse it.

The data set we analysed comes from the Open Food Facts database that gathers information and data on food products from around the world and it gets constantly updated.

The data set includes almost 2 GB of data, giving insights on nutritional values, additives content, brands and many other information about grocery products. Unfortunately, being the dataset curated on a voluntary basis, many data is missing. In our analysis, we decide to consider the impact on health of the food products stored in the dataset, by first trying to assess which food category is more dangerous for people.

We could come up with obvious results, but it is always better to check it! In the table below there is an example of how we divided the different products according the above cited categories.

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The goal of our project is to give the consumer advices about the healthiness of the products for the different categories according to a specific score. First, we explored the dataset, to find the intrinsic relationships that connect each category. From our analysis, many confirmations of what the common sense says have been found. For example, we show the average distributions of the categories for the variables carbohydrates and fats. What we can observe is that categories such as salty snacks, sugary snacks and potatoes have low fat content generallywhile high carbohydrate contents.

Other categories, such as dairy products, have higher fat content than carbohydrates, and so on. We decide to create our own score that includes three features available in the data set: Nutritional score, Energy over g and number of palm oil ingredients.

In order to fill the missing data for the nutritional score and the product categories, we decided to use a Random Forest Classifier based on the following features: energy, fat, sugars, proteins, carbohydrates and salt for g of products. We perfored a t-SNE dimensionality reduction to check whether the space in which we are representing the data is sufficiently good to distinguish between categories.

It is possible to see that the categories are well separated one another: this indicates the possibilities to build a classifier that categorizes unlabeled data. With our Random Forest we managed to achieve a F1 score of 0. Moreover, we can observe that subclusters are present in the data: this could indicate that a finer representation of food categories is needed to give better health advices!A constrained beam search and attention based sequence model for predicting the category path of a product using PyTorch.

Create a virtual environment named atlas and install all the project's dependencies listed in requirements. We will be using our Atlas dataset to train our model. Make sure you have the dataset or refer this section to get the dataset.

Research paper categorization using machine learning and NLP

Note: Do not modify the model and training parameters if you try to replicate our results. Instead if you would like to explore or play around with the model you can modify these parameters. To resume training at a checkpoint, point to the corresponding file with the checkpoint parameter at the beginning of the code. The records in the output CSV file looks like this:.

Command to run: python caption. This script predicts the category path and displays an output image that shows which part of the image has been focussed by our model to predict the category level.

E-Commerce product categorization tutorial with Watson Natural Language Classifier

We approach the product categorization problem as a sequence prediction problem by leveraging the dependency between each level in the category path. We use attention based neural network Encoder-Decoder architecture to generate sequences.

The input images are represented by the 3 color channels of RGB values. As we use the encoder only to encode images and not for classifying them, we remove the last two layers linear and pooling layers from the ResNet model. The final encoding produced by the encoder will have the dimensions: batch size x 14 x 14 x The decoder receives the encoded image from the encoder using which it initializes the hidden and cell state of the LSTM model through two linear layers.

The decoder LSTM uses teacher forcing for training. Subsequently, all other levels are predicted using the sequence generated so far along with the attention weights. At each time step, the decoder computes the weights and attention weighted encoding from the attention network using its previous hidden state. A final softmax layer transforms the hidden state into scores is stored so that it can be used later in beam search for selecting k best levels. The encoder-decoder architecture of our model is shown below:.

The attention network learns which part of the image has to be focused to predict the next level in the category path while performing the sequence classification task. The attention network generates weights by considering the relevance between the encoded image and the previous hidden state or previous output of the decoder. It consists of linear layers which transform the encoded image and the previous decoder's output to the same size.

These vectors are summed together and passed to another linear layer which calculates the values to be softmaxed and then to a ReLU layer. A final softmax layer calculates the weights alphas of the pixels which add up to 1.

product categorization github

Architectiure of our attention network is shown below. It enforces constraints over resulting output sequences that can be expressed in a finite-state machine.If you are already familiar with what text classification is, you might want to jump to this partor get the code here. Document or text classification is used to classify information, that is, assign a category to a text; it can be a document, a tweet, a simple message, an email, and so on.

In this article, I will show how you can classify retail products into categories. Although in this example the categories are structured in a hierarchy, to keep it simple I will consider all subcategories as top-level. If you are looking for complex implementations of large scale hierarchical text classification, I will leave links to some really good papers and projects at the end of this post.

The main packages used in this projects are: sklearnnltk and dataset. Run the following commands to setup the project structure and download the required packages:. The dataset that will be used was created by scraping some products from Amazon.

Scraping might be fine for projects where only a small amount of data is required, but it can be a really slow process since it is very simple for a server to detect a robot, unless you are rotating over a list of proxies, which can slow the process even more.

Using this scriptI downloaded information of over 22, products, organized into 42 top-level categories, and a total of subcategories. See the whole category tree structure here.

product categorization github

Including the subcategories, there are 36 categories in total. I did not specify the depth of the subcategories, but I did specify 50 as the minimum amount of samples is this case, products per category. The script will create a new file called products. In order to run machine learning algorithms, we need to transform the text into numerical vectors. Bag-of-words is one of the most used models, it assigns a numerical value to a word, creating a list of numbers.

It can also assign a value to a set of words, known as N-gram. Counting terms frequencies might not be enough sometimes. And there is where NLTK comes into play. For this project I used it to perform Lemmatisation and Part-of-speech tagging. With Lemmatisation we can group together the inflected forms of a word. That is why we need to POS tag each word as a noun, verb, adverb, and so on.

It is also worth noting that some words despite the fact that they appear frequently, they do not really make any difference for classification, in fact they could even help misclassify a text. These words can be ignored during the tokenization process. Now that we have all the features and labels, it is time to train the classifiers. Inside the file classify. Run it yourself using the command:. It will print out the accuracy of each category, along with the confusion matrix.

As I was only interested in nouns, verbs, adverbs and adjectives, I created a lookup dict to quicken up the process. Next, the tokenizer function was created. This function will receive all documents from the dataset.

At last the pipeline is defined; the first step is to call TfidfVectorizer, with the tokenizer function preprocessing each document, and then pass through the SGDClassifier. And here are the accuracy results for each algorithm I tested all algorithms were tested with their default parameters :.

The precision is the percentage of the test samples that were classified to the category and actually belonged to the category.Questionmark provides information on sustainability and health of food products in the supermarket. Right now, we are able to analyze about 40k products fully or partially. In past years, a lot of manual work was needed to type over details from photos like name, brand, nutrients, ingredientsand to link them to concepts we have data for researched ingredients, specific nutrients, etc.

Internally we have one taxonomy 1. Assigning this taxonomy is something to automate, using machine learning. Thankfully, we are not the first attempting to derive a taxonomy from product info using machine learning:. Everyone Likes Shopping! Some keywords are: hierarchical product categorization, document classificationsubject indexing. As we may have some external taxonomy information, this also applies.

Conclusion: there are different approaches, with several options for pre-processing and classifiers. You may have noticed that there is a Category field in the source data.

In many cases, there is a mapping from these source categories to our destination category. Currently we have a manual mapping for this.

But sometimes the source category is too broad, or missing altogether. So the classifier needs to work whether the source category is present or not.

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On classification of new products, we look both at the classifier and the manual mapping. This is somewhat similar to how Sun, Rampalli et al. If different approaches, like multiple hierarchical classifiers, are explored, this can be reconsidered.

Orange is a visual programming tool for machine learning. Starting from a spreadsheet, the orange3-text add-on transforms this into a list of tokens for each row. Each word becomes a feature present or not. Testing four different classification algorithms, SVM comes out as winner and random forest a close second. You can open the orange workflow with a data sample to experiment yourself. See classify.

Basic pre-processing like lower-casing except for brandremoving punctuation, transforming accented characters, and removing stopwords is done. To keep distinction between different fields, each token is first prefixed with a field-identifier inspired by Chen and Warren Other things to consider are stemming Sun, Rampalli et al. And what about n-grams?

product categorization github

What features help in getting a better accuracy? What is the influence of SVM parameters? This section shows a non-exhaustive search for a suitable choice.

Tests are done using libsvm 3.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository is meant to automatically extract features from product titles and descriptions. Below we explain how to install and run the code, and the implemented algorithms. We also provide background information including the current state-of-the-art in both sequence classification and sequence tagging, and suggest possible improvements to the current implemention.

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These are the methods used in this demonstrative implementation. For state-of-the-art extensions, we refer the reader to the references listed below. For the sequence classification task, we extract product titles, descriptions, and categories from the Amazon product corpus.

We then fit our CNN model to predict product category based on a combination of product title and description. For the sequence tagging task, we extract product titles and brands from the Amazon product corpus.

We then fit our bidirection LSTM model to label each word token in the product title to be either a brand or not. For both models we use the GloVe embedding with dimensions, though we note that a larger dimensional embedding might achieve superior performance. Additionally, we could be more careful in the data preprocessing to trim bad tokens e.

HTML remnants. Also for both models we use a dropout layer after embedding to combat overfitting the data.

The problem of extracting features from unstructured textual data can be given different names depending on the circumstances and desired outcome. Generally, we can split tasks into two camps: sequence classification and sequence tagging. In sequence classification, we take a text fragment usually a sentence up to an entire documentand try to project it into a categorical space.

This is considered a many-to-one classification in that we are taking a set of many features and producing a single output. Sequence tagging, on the other hand, is often considered a many-to-many problem since you take in an entire sequence and attempt to apply a label to each element of the sequence. An example of sequence tagging is part of speech labeling, where one attempts to label the part of speech of each word in a sentence.

Other methods that fall into this camp include chunking breaking a sentence into relational components and named entity recognition extracting pre-specified features like geographic locations or proper names.

An often important step in any natural language processing task is projecting from the character-based space that composes words and sentences to a numeric space on which computer models can operate.


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