first commit

Author: root

qst.py

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+import nltk
+import pandas as pd
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.cross_validation import train_test_split
+# import and instantiate a Multinomial Naive Bayes model
+from sklearn.naive_bayes import MultinomialNB
+from sklearn import metrics
+from sklearn.datasets import load_iris
+iris = load_iris()
+
+
+def qst_type(test_qst):
+	path = 'qstns.tsv'
+	sent = pd.read_table(path, header=None, names=['qtype', 'sentance'])
+	sent.shape
+	sent['label_num'] = sent.qtype.map({'whqst':0, 'statement':1,'yesOrNo':2})
+	X = iris.data
+	y = iris.target
+	X = sent.sentance
+	y = sent.label_num
+	sent.head(5)
+
+
+	X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
+
+	vect = CountVectorizer()
+	vect.fit(X_train)
+	X_train_dtm = vect.transform(X_train)
+	X_train_dtm = vect.fit_transform(X_train)
+	X_train_dtm
+	X_test_dtm = vect.transform(X_test)
+	X_test_dtm
+
+
+	nb = MultinomialNB()
+	nb.fit(X_train_dtm, y_train)
+	y_pred_class = nb.predict(X_test_dtm)
+	#a=y_pred_class.tostring()
+	#print (a)
+	#metrics.accuracy_score(y_test, y_pred_class)
+
+	
+	test_qst_dtm = vect.transform(test_qst)
+	test_qst_dtm
+	y_pred_class = nb.predict(test_qst_dtm)
+	a = y_pred_class
+	if a == 0:
+		c = "wh_questions"
+		return(c)
+	elif a == 1:
+		c = ["statement"]
+	elif a == 2:
+		c == ["yes or no question"]
+
+	return(a)
+
+#Just edit the question here and try type of qustions
+test_qst = ["Tell me city which is capital of India"]
+q_type = qst_type(test_qst)
+print(q_type)

qstns.tsv

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+whqst	what is your name?
+whqst	where are you?
+whqst	when will you come
+whqst	who are you
+whqst	who is this
+whqst	how do you do
+whqst	how are you
+whqst	how is  you
+whqst	how can i
+yesOrNo	do you know who
+yesOrNo	do you know why
+yesOrNo	do you know what
+yesOrNo	can do this for me
+yesOrNo	do it or not
+yesOrNo	is this your
+yesOrNo	is that your
+statement	yes i  am
+statement	i know how it is
+