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MachineLearning.py
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MachineLearning.py
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import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, f1_score
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import KFold, cross_validate
df = pd.read_csv("datasete.csv", encoding='latin-1')
sf = pd.read_csv("testdata.csv", encoding='latin-1')
vectorizer = CountVectorizer()
import sys, json, numpy as np
def machine_learn():
lines = sys.stdin.readlines()
labels = set(df.posi)
dataneg = df[df.posi.str.contains("neg")]
datapos = df[df.posi.str.contains("pos")]
dataneg2 = sf[sf.posi.str.contains("neg")]
datapos2 = sf[sf.posi.str.contains("pos")]
data = [dataneg, datapos]
data2 = [dataneg2,datapos2]
dataall= pd.concat(data)
dataall2 = pd.concat(data2)
from sklearn.utils import shuffle
dataall = shuffle(dataall)
dataall2 = shuffle(dataall2)
word_data = dataall.word
word_test = dataall2.word
d = {'neg':0, 'pos':1}
y = dataall.posi.replace(d)
z = dataall2.posi.replace(d)
vectorizer = CountVectorizer(ngram_range=(1,1), analyzer='word')
x = vectorizer.fit(word_data)
pipeline = Pipeline([
('bow',CountVectorizer(ngram_range=(1,1), analyzer='word')),
('tfidf', TfidfTransformer()),
('classifier', MultinomialNB())
])
data_x = vectorizer.fit_transform(word_data)
unseen_tfidf = vectorizer.transform(word_test)
km = KMeans(30)
kmresult = km.fit(data_x).predict(unseen_tfidf)
classifier = MultinomialNB()
kf = KFold(n_splits=20)
scoring = ['precision_macro','recall_macro','f1_macro','f1_micro','accuracy','f1_weighted']
scores = cross_validate(classifier, data_x, y, scoring=scoring, cv=kf, return_train_score=False)
return json.loads(kmresult)
def main():
#create a numpy array
np = machine_learn
#return the sum to the output stream
print (np)
#start process
if __name__ == '__main__':
main()