merge

from allennlp.predictors.predictor import Predictor

import pandas as pd

import logging, re

import time, os

from argparse import ArgumentParser

def load_nameslab():

    namfn = 'data/names_labeled.csv'

    df = pd.read_csv(namfn, index_col=0)

    df = df.replace({'l': 'From the Left', 'r': 'From the Right', 'c':'From the Center'})

    return df, df.name_bias.to_dict()

def load_flipper():

    datafn = 'data/webis_bias_flipper.csv'

    df = pd.read_csv(datafn, error_bad_lines=False)

    df = df.fillna('n.a.')

    n = df.shape[0]

    cols = [i for i in df.columns]

    print('Loaded {0}/6472 rows w/ columns: {1}'.format(n, cols))

    return df

namlabs_df, namlabs = load_nameslab()

def crude_ner(sent):

    global namlabs

    nampat = '\\b' + '\\b|\\b'.join([i for i in namlabs]) + '\\b'

    m = re.search(nampat, sent)

    return bool(m)

def ner(t):

    global allen_ner

    o = allen_ner.predict(sentence=t)

    return o

### load flipper

#flipper = load_flipper()

#flipper['text'] = flipper.original_title +'. '+ flipper.original_body

### load sent_df

def hardcoded_clean(names):

    names.w = names.w.str.replace('\ufeff', '')

    names.w = names.w.str.replace('—', '')

    names.w = names.w.str.replace('‘s', '')

    names.w = names.w.str.replace('2.', '')

    names.w = names.w.str.replace('’a', '')

    return names

parser = ArgumentParser()

parser.add_argument("-s", "--start_i", type=int, default=157584, help="which i to start from")

#parser.add_argument("-pre", "--preprocess", action="store_true", default=False, help="whether to preprocess or not")

args = parser.parse_args()

sent = pd.read_csv('data/sent_df.csv')

sent = sent[sent.s.apply(crude_ner) == True]

#print(max(sent.index))

#vdberg_output_11315.csv.csv.csv.csv 64210

over = [i for i in sent.index if i > args.start_i] #99.000 #130562

start_i = over[0]

data = sent.loc[start_i:]

srl_pred = Predictor.from_path("https://s3-us-west-2.amazonaws.com/allennlp/models/srl-model-2018.05.25.tar.gz")

allen_ner = Predictor.from_path("https://s3-us-west-2.amazonaws.com/allennlp/models/ner-model-2018.12.18.tar.gz")

times = []

verb_df = pd.DataFrame(columns=['arg', 'verb', 'same', 'id'])

for i, r in data.iterrows():

    t0 = time.time()

    tag_out = 'outputs/srl_ner_tagged_{}.csv'.format(i)

    proc_out = 'outputs/vdberg_output_{}.csv'.format(i)

    if not os.path.isfile(tag_out):

        print('Processing: {}, {}'.format(i, r.s))

        # perform ner and srl

        names = ner(r.s)

        roles = srl_pred.predict(sentence=r.s)['verbs']

        tagged = [names['words'], names['tags']] + [gr['tags'] for gr in roles]

        verbs = [gr['verb'] for gr in roles]

        tagged = pd.DataFrame(zip(*tagged), columns=['w', 'n'] + verbs)

        tagged.to_csv(tag_out)

        if not os.path.isfile(proc_out):

            persons = tagged[tagged.n.str.endswith('PER')]

            names = persons[persons.w.apply(crude_ner) == True].copy()

            names = hardcoded_clean(names)

            # convert to desired output format

            verbs = names.set_index(['w']).iloc[:,1:]

            if not verbs.empty:

                verbs = verbs.copy().stack().reset_index(level=1, name='arg').rename(columns={'level_1':'verb'})

                verbs = verbs.replace({'O':None}).dropna()

                verbs['same'] = namlabs_df.loc[verbs.index].name_bias.values == r.bias

                verbs['id'] = [i] * len(verbs)

                print('Found {} verbs'.format(len(verbs)))

                verbs.to_csv(proc_out)

                verb_df = verb_df.append(verbs)

    t1 = time.time()

    total_n = t1-t0

    times.append(total_n)

    print('Took {} seconds'.format(sum(times)/len(times)))

verb_df.to_csv('vdberg_output.csv')

#grs = verb_df.groupby('same')

#for n, gr in grs:

#    print(str(n).upper())

#    for c in gr:

#        print(gr[c].value_counts().head(10))

#        print()

#    print('---')