def adapt(self, q, inttype=False, priority=False):
clf = Classifier()
clauses = self.clauses
int_clauses = self.int_clauses
clauses.update(int_clauses)
for i, tup in enumerate(clauses.items()):
clause = tup[0]
clf.fit(clause, clauses[clause])
if inttype and priority:
clf.fit(("how many", "number of", "who all", "how much",
"sum of", "total"), "sum of {} in {}")
return clf.predict(q)
def test_classifier_server_train_post():
data = {'hotel': ['book a hotel', 'need a nice place to stay', 'any motels near by'],
'weather': ['what is the weather like', 'is it hot outside'],
'place': ['which place is this', 'where are we', 'where are you going', 'which place is it'],
'name': ['What is your name', 'Who are you', 'What do i call you', 'what are you called']
}
server = NLUServer().serve(test=True)
clf = Classifier()
y = tuple(data.keys())
x = tuple(data.values())
server.requests.post("/models/classifier/train", json={"data": {"inputs": x, "targets": y}})
clf.fit(x, y)
r = server.requests.get("/models/classifier/predict?input=will it rain today")
assert r.text == 'weather'
def test_classifier_server_train_get():
data = {'hotel': ['book a hotel', 'need a nice place to stay', 'any motels near by'],
'weather': ['what is the weather like', 'is it hot outside'],
'place': ['which place is this', 'where are we', 'where are you going', 'which place is it'],
'name': ['What is your name', 'Who are you', 'What do i call you', 'what are you called']
}
server = NLUServer().serve(test=True)
clf = Classifier()
for y, v in data.items():
for x in v:
server.requests.get("/models/classifier/train?input={}&target={}".format(x, y))
clf.fit(x, y)
r = server.requests.get("/models/classifier/predict?input=will it rain today")
assert r.text == 'weather'
def test_classifier_serialization(self):
x_hotel = ['book a hotel', 'need a nice place to stay', 'any motels near by']
x_weather = ['what is the weather like', 'is it hot outside']
clf1 = Classifier()
clf1.fit(x_hotel, 'hotel')
clf1.fit(x_weather, 'weather')
config = clf1.serialize()
clf2 = Classifier.deserialize(config)
assert clf1.classes == clf2.classes
test_inputs = ['will it rain today', 'find a place to stay']
for test_input in test_inputs:
clf1_out = clf1.predict(test_input, return_probs=True)
clf2_out = clf2.predict(test_input, return_probs=True)
assert clf1_out == clf2_out
def test_classifier_serialization():
x_hotel = ['book a hotel', 'need a nice place to stay', 'any motels near by']
x_weather = ['what is the weather like', 'is it hot outside']
clf1 = Classifier()
clf1.fit(x_hotel, 'hotel')
clf1.fit(x_weather, 'weather')
config = clf1.serialize()
clf2 = Classifier.deserialize(config)
assert clf1.classes == clf2.classes
test_inputs = ['will it rain today', 'find a place to stay']
for test_input in test_inputs:
clf1_out = clf1.predict(test_input, return_probs=True)
clf2_out = clf2.predict(test_input, return_probs=True)
assert clf1_out == clf2_out
def test_classifier_server_get():
x_hotel = ['book a hotel', 'need a nice place to stay', 'any motels near by']
x_weather = ['what is the weather like', 'is it hot outside']
x_place = ['which place is this', 'where are we', 'where are you going', 'which place is it']
x_name = ['What is your name', 'Who are you', 'What do i call you', 'what are you called']
clf = Classifier()
clf.fit(x_hotel, 'hotel')
clf.fit(x_weather, 'weather')
clf.fit(x_place, 'place')
clf.fit(x_name, 'name')
clf_serialized = clf.serialize()
server = NLUServer(clf).serve(test=True)
r = server.requests.get("/models/classifier/predict?input=will it rain today")
assert r.text == 'weather'
r = server.requests.get("/models/classifier/config")
assert r.json() == clf_serialized
def test_classifier_basic(self):
x_hotel = ['book a hotel', 'need a nice place to stay', 'any motels near by']
x_weather = ['what is the weather like', 'is it hot outside']
clf = Classifier()
clf.fit(x_hotel, 'hotel')
clf.fit(x_weather, 'weather')
assert clf.predict('will it rain today') == 'weather'
assert clf.predict('find a place to stay') == 'hotel'
x_greetings = ['hi', 'hello', 'hello there', 'hey']
x_not_greetings = x_hotel + x_weather
clf2 = Classifier()
clf2.fit(x_greetings, 'greetings')
clf2.fit(x_not_greetings, 'not_greetings')
assert clf2.predict('flight information') == 'not_greetings'
assert clf2.predict('hey there') == 'greetings'
def test_classifier_basic():
x_hotel = ['book a hotel', 'need a nice place to stay', 'any motels near by']
x_weather = ['what is the weather like', 'is it hot outside']
clf = Classifier()
clf.fit(x_hotel, 'hotel')
clf.fit(x_weather, 'weather')
assert clf.predict('will it rain today') == 'weather'
assert clf.predict('find a place to stay') == 'hotel'
x_greetings = ['hi', 'hello', 'hello there', 'hey']
x_not_greetings = x_hotel + x_weather
clf2 = Classifier()
clf2.fit(x_greetings, 'greetings')
clf2.fit(x_not_greetings, 'not_greetings')
assert clf2.predict('flight information') == 'not_greetings'
assert clf2.predict('hey there') == 'greetings'
CONV_SAMPLES = {
'greetings': ['Hi', 'hello', 'How are you', 'hey there', 'hey'],
'taxi': ['book a cab', 'need a ride', 'find me a cab'],
'weather': [
'what is the weather in tokyo', 'weather germany',
'what is the weather like in kochi'
],
'datetime': [
'what day is today', 'todays date', 'what time is it now', 'time now',
'what is the time'
]
}
CLF = Classifier()
for key in CONV_SAMPLES:
CLF.fit(CONV_SAMPLES[key], key)
# Entitiy Extractor, gets the required entities from the input.
X_WEATHER = [
'what is the weather in tokyo', 'weather germany',
'what is the weather like in kochi'
]
Y_WEATHER = [{
'intent': 'weather',
'place': 'tokyo'
}, {
'intent': 'weather',
'place': 'germany'
}, {
'intent': 'weather',
with open("dataset.txt", "r+") as file:
text = file.readlines()
who = []
what = []
when = []
affirmation = []
unknown = []
for i in range(len(text)):
line = text[i].split(",,,")
if line[1] == " who\n":
who.append(line[0])
if line[1] == " what\n":
what.append(line[0])
if line[1] == " when\n":
when.append(line[0])
if line[1] == " affirmation\n":
affirmation.append(line[0])
if line[1] == " unknown\n":
unknown.append(line[0])
from eywa.nlu import Classifier
clf = Classifier()
clf.fit(who, 'who')
clf.fit(what, 'what')
clf.fit(when, 'when')
clf.fit(affirmation, 'affirmation')
clf.fit(unknown, 'unknown')
x = input("Enter string :")
print(clf.predict(str(x)))