Attentive Chrome Kipoi

Dependency Requirements

• python>=3.5
• numpy
• pytorch-cpu
• torchvision-cpu

Quick Start

Creating new conda environtment using kipoi

kipoi env create AttentiveChrome

Activating environment

conda activate kipoi-AttentiveChrome

Command Line

Getting example input file

Replace {model_name} with the actual name of model (e.g. E003, E005, etc.)

kipoi get-example AttentiveChrome/{model_name} -o example_file

example: kipoi get-example AttentiveChrome/E003 -o example_file

Predicting using example file

kipoi predict AttentiveChrome/{model_name} --dataloader_args='{"input_file": "example_file/input_file", "bin_size": 100}' -o example_predict.tsv

This should produce a tsv file containing the results.

Python

Fetching the model

First, import kipoi: import kipoi

Next, get the model. Replace {model_name} with the actual name of model (e.g. E003, E005, etc.)

model = kipoi.get_model("AttentiveChrome/{model_name}")

Predicting using pipeline

prediction = model.pipeline.predict({"input_file": "path to input file", "bin_size": {some integer}})

This returns a numpy array containing the output from the final softmax function.

e.g. model.pipeline.predict({"input_file": "data/input_file", "bin_size": 100})

Predicting for a single batch

First, we need to set up our dataloader dl.

dl = model.default_dataloader(input_file="path to input file", bin_size={some integer})

Next, we can use the iterator functionality of the dataloader.

it = dl.batch_iter(batch_size=32)

single_batch = next(it)

First line gets us an iterator named it with each batch containing 32 items. We can use next(it) to get a batch.

Then, we can perform prediction on this single batch.

prediction = model.predict_on_batch(single_batch['inputs'])

This also returns a numpy array containing the output from the final softmax function.