ma2gedev / gokart

A wrapper of the data pipeline library "luigi"

gokart

A wrapper of the data pipeline library "luigi".

How to Use

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

Basic Task with M3Task

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

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'})