#!/usr/bin/env python

"""Demonstrates medical term feature generation from spaCy parsed natural

langauge.

"""

__author__ = 'Paul Landes'

from dataclasses import dataclass, field

import itertools as it

import pandas as pd

from zensols.cli import CliHarness ; CliHarness.add_sys_path('src/python')

from zensols.cli import ProgramNameConfigurator

from zensols.nlp import FeatureDocumentParser, FeatureDocument

from zensols.nlp.dataframe import FeatureDataFrameFactory

DEFAULT_SENT = 'He was diagnosed with kidney failure in the United States.'

# the definition of the application class executed from the CLI glue code

@dataclass

class Application(object):

    """Demonstrates access to UTS.

    """

    # tell the application not mistake the `doc_parser` as an option when

    # generating the online help with the -h option

    CLI_META = {'option_excludes': {'doc_parser'}}

    doc_parser: FeatureDocumentParser = field()

    """Parses and NER tags medical terms."""

    def _boundary(self, s: str):

        print(''.join(['-' * 5, s, '-' * 5]))

    def dump(self, sent: str):

        """Dump all features available to a CSV file."""

        doc: FeatureDocument = self.doc_parser(sent)

        df = pd.DataFrame(map(lambda t: t.asdict(), doc.tokens))

        df.to_csv('features.csv')

    def show(self, sent: str = None):

        """Parse a sentence and print all features for each token.

        :param sent: the sentence to parse and generate features

        """

        if sent is None:

            sent = DEFAULT_SENT

        self._boundary(f'sentence: <{sent}>')

        # parse the text in to a hierarchical langauge data structure

        doc: FeatureDocument = self.doc_parser(sent)

        print('first three tokens:')

        for tok in it.islice(doc.token_iter(), 3):

            print(tok.norm)

            tok.write_attributes(1, include_type=False)

        # named entities are also stored contiguous tokens at the document

        # level

        self._boundary('named entities:')

        for e in doc.entities:

            print(f'{e}: cui={e[0].cui_}')

        # generate a set of features from the document as a Pandas data frame

        # and print it

        feats = 'idx i norm is_concept cui_ pref_name_ ent_'.split()

        fac = FeatureDataFrameFactory(set(feats), feats)

        df: pd.DataFrame = fac(doc)

        self._boundary('features as a Pandas data frame')

        print(df)

if (__name__ == '__main__'):

    CliHarness(

        app_config_resource='features.conf',

        app_config_context=ProgramNameConfigurator(

            None, default='features').create_section(),

        proto_args=['dump', DEFAULT_SENT],

        proto_factory_kwargs={'reload_pattern': '^features'},

    ).run()