Add wrapper functions for tabular data (type: `pandas.DataFrame`)

[noelchen90]

Closes #56

This PR adds two functions wrap_tabular_dataset() and wrap_tabular_output(). This makes it easier for Dataframe datasets/system outputs in memory easier to use.

The reason for the separation of "dataset" and "output" is because:

1. users may be uploading to a datalab dataset (which we don't need to upload "dataset")
2. "dataset" and "output" used different columns (e.g., "true_label" vs "predicted_label")

Example Usage

import explainaboard_client
import pandas as pd

client = explainaboard_client.ExplainaboardClient()

data = pd.DataFrame(
       [['snli-dataset', 'This church choir sings to the masses as they sing joyous songs from the book at a church.', 'The church has cracks in the ceiling.', 'neutral'], 
       ['snli-dataset', 'This church choir sings to the masses as they sing joyous songs from the book at a church.', 'The church is filled with song.', 'entailment'], 
       ['snli-dataset', 'This church choir sings to the masses as they sing joyous songs from the book at a church.', 'A choir singing at a baseball game.', 'contradiction'], 
       ['snli-dataset', 'A woman with a green headscarf, blue shirt and a very big grin.', 'The woman is young.', 'neutral'],
       ['snli-dataset', 'A woman with a green headscarf, blue shirt and a very big grin.', 'The woman is very happy.', 'entailment'], 
       ['snli-dataset', 'A woman with a green headscarf, blue shirt and a very big grin.', 'The woman has been shot.', 'contradiction']],
       columns=['dataset', 'text1', 'text2', 'true_label'])

predict_data = pd.DataFrame(
       [['neutral'], 
       ['neutral'], 
       ['neutral'], 
       ['neutral'], 
       ['neutral'], 
       ['neutral']],
       columns=['predicted_label'])

wrapped_dataset = client.wrap_tabular_dataset(
    data, 
    task="text-pair-classification", 
    columns_to_analyze=['text1', 'text2', 'true_label']
)
wrapped_output = client.wrap_tabular_output(
    predict_data, 
    task="text-pair-classification"
)

evaluation_result = client.evaluate_system(
    task='text-pair-classification',
    system_name='text-pair-classification-test',
    system_output=wrapped_output,
    custom_dataset=wrapped_dataset,
    split='test',
    source_language='en',
    system_details={}
)

Next Steps

• add documentation with example on how to use these functions