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kmeans.py
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kmeans.py
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import json
import nltk
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from pprint import pprint
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans
#with open('pos_features_trained_sentences.json') as data_file:
# json_data = json.load(data_file)
featuresets = []
featuresets = [
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"1", "date_diff": "0", "response_type": "0", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False ),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "0", "byte_diff_1":"0",
}, False),
({"ip_1":"1", "date_diff": "0", "response_type": "0", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "0", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "0", "byte_diff_1":"0",
}, False),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "0", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"1",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"1",
"ip_1_1":"0", "date_diff_1": "1", "response_type_1": "0", "byte_diff_1":"0",
},False),
({"ip_1":"0", "date_diff": "1", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"1", "date_diff": "1", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"0",
},False),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"1",
},False),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"1",
"ip_1_1":"1", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"0",
},False),
({"ip_1":"1", "date_diff": "0", "response_type": "1", "byte_diff":"1",
"ip_1_1":"1", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"1",
},False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "0", "byte_diff":"0",
"ip_1_1":"1", "date_diff_1": "1", "response_type_1": "0", "byte_diff_1":"0",
},False),
({"ip_1":"0", "date_diff": "0", "response_type": "0", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
},False),
({"ip_1":"0", "date_diff": "0", "response_type": "0", "byte_diff":"1",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
},False),
({"ip_1":"0", "date_diff": "0", "response_type": "0", "byte_diff":"1",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"1",
},False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "0", "byte_diff_1":"0",
},False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"1",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"1",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"1",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"1",
}, False),
({"ip_1":"0", "date_diff": "1", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "1", "response_type_1": "1", "byte_diff_1":"0",
}, False),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True),
({"ip_1":"0", "date_diff": "0", "response_type": "1", "byte_diff":"0",
"ip_1_1":"0", "date_diff_1": "0", "response_type_1": "1", "byte_diff_1":"0",
}, True)
]
train_data,test_data = train_test_split(featuresets, test_size=0.20, train_size=0.80)
classifier = nltk.NaiveBayesClassifier.train(train_data)
print "Printing accuracy with Naive Bayes:"
print nltk.classify.accuracy(classifier, test_data)
print "Showing most informative features with Naive Bayes:"
print classifier.show_most_informative_features()
classifier = nltk.DecisionTreeClassifier.train(train_data)
print "Printing accuracy with Decision Tree:"
print nltk.classify.accuracy(classifier, test_data)
print "Printing pseudo code for depth 4:"
print(classifier.pseudocode(depth=4))
km = KMeans(n_clusters=2)
#km.fit(featuresets)
words = ['apple', 'Doppler', 'applaud', 'append', 'barker',
'baker', 'bismark', 'park', 'stake', 'steak', 'teak', 'sleek']
dist = [distance.edit_distance(words[i], words[j])
for i in range(1, len(words))
for j in range(0, i)]
labels, error, nfound = PC.kmedoids(dist, nclusters=3)
cluster = dict()
for word, label in zip(words, labels):
cluster.setdefault(label, []).append(word)
for label, grp in cluster.items():
print(grp)