import os
import time

from ....utils import load_data as ld
from ....utils.model_helpers import LemmaTokenizer
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
import sklearn.feature_extraction.text
import sklearn.multiclass
import sklearn.linear_model
from nltk import word_tokenize
from nltk.stem import WordNetLemmatizer

np.random.seed(1029384756)

df = ld.load_data()

crime_df = df.ix[df.loc[:, 'OEMC':'TASR'].any(1), :]
crime_df = crime_df.append(df.ix[~df['relevant'], :].sample(n=3000, axis=0))

vectorizer = sklearn.feature_extraction.text.CountVectorizer(tokenizer=LemmaTokenizer(),
                                                             binary=True)

clf = sklearn.multiclass.OneVsRestClassifier(
    sklearn.linear_model.LogisticRegression()
)

X = vectorizer.fit_transform(crime_df['bodytext'].values)
Y = crime_df.loc[:, 'OEMC':'TASR'].values

clf.fit(X, Y)

from ...tag import Tagger

tagger = Tagger(clf=clf, vectorizer=vectorizer)

print(tagger.tagtext_proba(('This is an article about drugs and'
                            ' gangs. Copyright Kevin Rose.')))

import pickle

curr_time = time.strftime("%Y%m%d-%H%M%S")

with open(os.path.join(os.path.split(__file__)[0],
                       'model-' + curr_time + '.pkl'), 'wb') as f:
    pickle.dump(clf, f)
with open(os.path.join(os.path.split(__file__)[0],
                       'vectorizer-' + curr_time + '.pkl'), 'wb') as f:
    pickle.dump(vectorizer, f)

import os
import time

from ....utils import load_data as ld
from ....utils.model_helpers import LemmaTokenizer
import numpy as np
import pandas as pd
import sklearn
import sklearn.feature_extraction.text
import sklearn.multiclass
import sklearn.linear_model
from nltk import word_tokenize
from nltk.stem import WordNetLemmatizer

np.random.seed(1029384756)

df = ld.load_data()

crime_df = df.ix[df.loc[:, 'OEMC':'TASR'].any(1), :]
crime_df = crime_df.append(df.ix[~df['relevant'], :].sample(n=3000, axis=0))

vectorizer = sklearn.feature_extraction.text.CountVectorizer(tokenizer=LemmaTokenizer(),
                                                             binary=True)

clf = sklearn.multiclass.OneVsRestClassifier(
    sklearn.linear_model.LogisticRegression()
)

X = vectorizer.fit_transform(crime_df['bodytext'].values)
Y = crime_df.loc[:, 'OEMC':'TASR'].values

clf.fit(X, Y)

from ...tag import Tagger

tagger = Tagger(clf=clf, vectorizer=vectorizer)

print(tagger.tagtext_proba(('This is an article about drugs and'
                            ' gangs. Copyright Kevin Rose.')))

import pickle

curr_time = time.strftime("%Y%m%d-%H%M%S")

with open(os.path.join(os.path.split(__file__)[0],
                       'model-' + curr_time + '.pkl'), 'wb') as f:
    pickle.dump(clf, f)
with open(os.path.join(os.path.split(__file__)[0],
                       'vectorizer-' + curr_time + '.pkl'), 'wb') as f:
    pickle.dump(vectorizer, f)

remove unnecessary matplotlib imports