import nltk
import pandas as pd
from sklearn.neural_network import MLPClassifier
from nltk.tokenize import word_tokenize
from gensim.models import Word2Vec
nltk.download('punkt')

# w pliku train.tsv w kolumnach 25706, 58881, 73761 trzeba zamienic w tekscie tabulator na 4 spacje
train = pd.read_csv('train/train.tsv', sep='\t', names=['y', 'x'], header=None)

print(train["y"][0], train["x"][0])

# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
slowa = []
for tekst in train["x"]:
    pom = []
    for slowo in word_tokenize(tekst):
        pom.append(slowo.lower())
    slowa.append(pom)
print(slowa[0])

# https://radimrehurek.com/gensim/models/word2vec.html
model = Word2Vec(sentences=slowa, vector_size=100, window=5, min_count=1, workers=4)
model.save("word2vec.model")

wektor = model.wv['przyjmujący']
print(wektor)

podobne = model.wv.most_similar('przyjmujący', topn=5)
print(podobne)

teksty = []
for tekst in train["x"]:
    pom = None
    for slowo in word_tokenize(tekst):
        wektor = model.wv[slowo.lower()]
        if pom is None:
            pom = wektor
        else:
            pom = pom + wektor
    teksty.append(wektor)
print(teksty[0])

X = teksty
y = train["y"]

clf = MLPClassifier()   # activation="tanh"
clf.fit(X, y)

# w pliku in.tsv w kolumnach 1983, 5199 trzeba zamienic w tekscie tabulator na 4 spacje
test = pd.read_csv('test-A/in.tsv', sep='\t', names=['x'], header=None)

print(test["x"][0])

# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
slowa = []
for tekst in test["x"]:
    pom = []
    for slowo in word_tokenize(tekst):
        pom.append(slowo.lower())
    slowa.append(pom)
print(slowa[0])

teksty = []
for tekst in test["x"]:
    pom = None
    for slowo in word_tokenize(tekst):
        wektor = None
        try:
            wektor = model.wv[slowo.lower()]
        except KeyError:
            pass
        if wektor is not None:
            if pom is None:
                pom = wektor
            else:
                pom = pom + wektor
    teksty.append(wektor)
print(teksty[0])

przewidywania = clf.predict(teksty)
print(przewidywania)

with open("test-A/out.tsv", "w", encoding="utf-8") as uwu:
    for p in przewidywania:
        uwu.write(str(p)+"\n")

### dev-0

# w pliku in.tsv w kolumnach 1983, 5199 trzeba zamienic w tekscie tabulator na 4 spacje
dev_in = pd.read_csv('dev-0/in.tsv', sep='\t', names=['x'], header=None)

print(dev_in["x"][0])

dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['y'], header=None)

print(dev_expected["y"][0])

# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
slowa = []
for tekst in dev_in["x"]:
    pom = []
    for slowo in word_tokenize(tekst):
        pom.append(slowo.lower())
    slowa.append(pom)
print(slowa[0])

teksty = []
for tekst in test["x"]:
    pom = None
    for slowo in word_tokenize(tekst):
        wektor = None
        try:
            wektor = model.wv[slowo.lower()]
        except KeyError:
            pass
        if wektor is not None:
            if pom is None:
                pom = wektor
            else:
                pom = pom + wektor
    teksty.append(wektor)
print(teksty[0])

przewidywania = clf.predict(teksty)
print(przewidywania)

with open("dev-0/out.tsv", "w", encoding="utf-8") as uwu:
    for p in przewidywania:
        uwu.write(str(p)+"\n")

for i in range(len(przewidywania)):
    print(przewidywania[i], dev_expected["y"][i])