merge output

import pandas as pd

from nltk.stem import WordNetLemmatizer

def get_dom(v):

    global vad

    try:

        return round(vad.loc[v],2)['Dominance']

    except KeyError:

        return None

df = pd.read_csv('vdberg_output.csv')

lemmatizer = WordNetLemmatizer()

df['lemma'] = df.verb.apply(lemmatizer.lemmatize)

vad = pd.read_csv('data/NRC-VAD-Lexicon.txt', delimiter='\t', index_col=0)

df['dom'] = df.lemma.apply(get_dom)

grs = df.groupby('same')

# symptoms of agency

for n, gr in grs:

    print(n)

    print(gr)

    print('\n', gr.dom.mean())

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

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

#print([i for i in sent.index if i > 1400])

data = sent.iloc[:]

print(max(sent.index))

exit(0)

data = sent.loc[11237:11243]

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")

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

for i,r in data.iterrows():

    names = ner(r.s)

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

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

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

    #print('roles', roles)

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

        print(verbs)

        print()

        verb_df.arg = verb_df.arg.str[2:]

        verb_df.to_csv('vdberg_output.csv')

,arg,same,verb

Sessions,ARG0,False,defended

Sessions,ARG0,False,saying

Sessions,ARG2,False,asked

Sessions,ARG1,False,‘