gokart 0.3.14

A wrapper of luigi. This make it easy to define tasks.

gokart

A wrapper of the data pipeline library "luigi".

Getting Started

Run pip install gokart to install the latest version from PyPI. Documentation for the latest release is hosted on readthedocs.

How to Use

Please use gokart.TaskOnKart instead of luigi.Task to define your tasks.

Basic Task with gokart.TaskOnKart

import gokart

class BasicTask(gokart.TaskOnKart):
    def requires(self):
        return TaskA()

    def output(self):
        # please use TaskOnKart.make_target to make Target.
        return self.make_target('basic_task.csv')

    def run(self):
        # load data which TaskA output
        texts = self.load()
        
        # do something with texts, and make results.
        
        # save results with the file path {self.workspace_directory}/basic_task_{unique_id}.csv
        self.dump(results)

Details of base functions

Make Target with TaskOnKart

TaskOnKart.make_target judge Target type by the passed path extension. The following extensions are supported.

• pkl
• txt
• csv
• tsv
• gz
• json
• xml

Make Target for models which generate multiple files in saving.

TaskOnKart.make_model_target and TaskOnKart.dump are designed to save and load models like gensim.model.Word2vec.

class TrainWord2Vec(TaskOnKart):
    def output(self):
        # please use 'zip'.
        return self.make_model_target(
            'model.zip', 
            save_function=gensim.model.Word2Vec.save,
            load_function=gensim.model.Word2Vec.load)

    def run(self):
        # make word2vec
        self.dump(word2vec)

Load input data

Pattern 1: Load input data individually.

def requires(self):
    return dict(data=LoadItemData(), model=LoadModel())

def run(self):
    # pass a key in the dictionary `self.requires()`
    data = self.load('data')  
    model = self.load('model')

Pattern 2: Load input data at once

def run(self):
    input_data = self.load()
    """
    The above line is equivalent to the following:
    input_data = dict(data=self.load('data'), model=self.load('model'))
    """

Load input data as pd.DataFrame

def requires(self):
    return LoadDataFrame()

def run(self):
    data = self.load_data_frame(required_columns={'id', 'name'})