In [1]:

    

import keras
import pandas as pd
import numpy as np
import matchzoo as mz
import json
print('matchzoo version', mz.__version__)
print()


    

    




Using TensorFlow backend.






    




matchzoo version 2.1.0

In [2]:

    

print('data loading ...')
train_pack_raw = mz.datasets.wiki_qa.load_data('train', task='ranking')
dev_pack_raw = mz.datasets.wiki_qa.load_data('dev', task='ranking', filtered=True)
test_pack_raw = mz.datasets.wiki_qa.load_data('test', task='ranking', filtered=True)
print('data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`')


    

    




data loading ...
data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`

In [3]:

    

ranking_task = mz.tasks.Ranking(loss=mz.losses.RankHingeLoss())
ranking_task.metrics = [
    mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
    mz.metrics.NormalizedDiscountedCumulativeGain(k=5),
    mz.metrics.MeanAveragePrecision()
]
print("`ranking_task` initialized with metrics", ranking_task.metrics)


    

    




`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]

In [4]:

    

print("loading embedding ...")
glove_embedding = mz.datasets.embeddings.load_glove_embedding(dimension=300)
print("embedding loaded as `glove_embedding`")


    

    




loading embedding ...
embedding loaded as `glove_embedding`

In [5]:

    

def append_params_to_readme(model):
    import tabulate
    
    with open('README.rst', 'a+') as f:
        subtitle = model.params['model_class'].__name__
        line = '#' * len(subtitle)
        subtitle = subtitle + '\n' + line + '\n\n'
        f.write(subtitle)
        
        df = model.params.to_frame()[['Name', 'Value']]
        table = tabulate.tabulate(df, tablefmt='rst', headers='keys') + '\n\n'
        f.write(table)