Simon

Character-level CNN+LSTM model for text classification.

The acronym Simon stands for Semantic Inference for the Modeling of ONtologies

This work is heavily influenced by the following academic article: http://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classifica

Getting Started

To get started, make sure you are using python v3.5+ and pip install via

pip3 install git+https://github.com/NewKnowledge/simon

Remember to first install all dependencies:

pip3 install -r requirements.txt

Then, study the scripts and pretrained models included in the Simon/scripts subdirectory.

The first included script is main_train_on_faker_data.py. This script will attempt to use CNN + LSTM to classify fake generated data into semantic types (ints, floats, strings, etc). The app will generate x columns (data_cols) with y rows (data_count) in training mode, using the Faker library as the data source. The code will automatically insert "null"s across the dataset, with a user-specified percentage (--nullpct flag). It will crop off each cell to have a max length of 20 chars, and will only look at the first 500 rows in a column. The training classes are specified by Categories.txt and types.json. The following command will achieve ~98% training/validation accuracy.

python3 main_train_on_faker_data.py --data_count 500 --data_cols 1000 --nb_epoch 20 --nullpct .1 --batch_size 32

To run the multigpu version of the same code, see main_train_on_faker_data.py script, and be sure to specify the number of GPUs available correctly. Also, you should be very judicious here about the cuda version you have installed, and whether it matches with a compatible version of installed tensorflow-gpu. A tensorflow-gpu version before 1.4.1 requires cuda 8.0, while later versions require version 9.0. The corresponding command is as follows.

python3 main_train_on_faker_data_multi_gpu.py --data_count 500 --data_cols 1000 --nb_epoch 20 --nullpct .1 --batch_size 32

A pretrained model is also included if one wants to proceed to performing semantic classification in predict mode (--no_train flag). Use the following command to generate some fake data and classify it using the included pretrained model (be sure to set checkpoint_dir to pretrained_models/ in script first, if not already set to it). This will classify into one of the base categories.

python3 main_train_on_faker_data.py --data_count 1000 --data_cols 10 --nullpct .1 --no_train --config Base.pkl

The following command will rigorously evaluate the same model on a prespecified dataset and print a comparison of hand-labeled to predicted classes. Please note that for this to work, the classes specified in the hand-labeled header cannot be outside those the specified model was trained to recognize, otherwise the software will error out.

python3 main_evaluate_model_on_dataset.py --config Base.pkl

To simply classify the columns of a dataset, execute the following command. The corresponding script also demonstrates how to use included rule-based classification functions for categorical/ordinal variables - however note that some of the included pretrained models can classify these using the neural net itself, but at a slightly lower accuracy.

python3 main_classify_dataset.py --config Base.pkl

Note that the code should work directly on a GPU-enabled machine. Be sure to set batch_size to something relatively large, like 64 (default is 5) to take full advantage of the GPU power with the flag addition --batch_size 64

Upcoming developments include transfer learning and both GPU and heterogeneous parallelization capabilities.

To run tensorflow's tensorboard, open up another terminal and run:

python -m tensorflow.tensorboard --logdir=logs

Then visit localhost:6006

Noteworthy files

• main_faker_data.py - Main entry point for data type classification
• types.json - maps Faker method generators to fairly specific types.
• DataGenerator - creates the fake data
• Encoder - encodes/reverse-encodes input characters and data labels

Other files

• FakeDataDescriptor - helper class to get a view of what Faker generates and how our types.json classifies it as