import numpy as np
import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.lancaster import LancasterStemmer
import nltk
import re
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.models import Sequential, load_model
from keras.layers import Dense, GRU, LSTM, Bidirectional, Embedding, Dropout
from keras.callbacks import ModelCheckpointDataset link: https://raw.githubusercontent.com/PolyAI-LDN/task-specific-datasets/master/banking_data/train.csv
Intent = "category"
Sentence = "text"def load_dataset(filename, Sentence, Intent):
df = pd.read_csv(filename, names = [Sentence, Intent])
intent = df[Intent]
unique_intent = list(set(intent))
sentences = list(df[Sentence])
return (df, intent, unique_intent, sentences)df, intent, unique_intent, sentences = load_dataset("Dataset.csv", "text", "category")print(df.head(10)) text category
0 text category
1 I am still waiting on my card? card_arrival
2 What can I do if my card still hasn't arrived ... card_arrival
3 I have been waiting over a week. Is the card s... card_arrival
4 Can I track my card while it is in the process... card_arrival
5 How do I know if I will get my card, or if it ... card_arrival
6 When did you send me my new card? card_arrival
7 Do you have info about the card on delivery? card_arrival
8 What do I do if I still have not received my n... card_arrival
9 Does the package with my card have tracking? card_arrival
import seaborn as sns
import tkinter
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
%matplotlib inline
sns.countplot(x=Intent, data=df)<AxesSubplot:xlabel='category', ylabel='count'>
print(sentences[:5])['text', 'I am still waiting on my card?', "What can I do if my card still hasn't arrived after 2 weeks?", 'I have been waiting over a week. Is the card still coming?', 'Can I track my card while it is in the process of delivery?']
nltk.download("stopwords")
nltk.download("punkt")[nltk_data] Downloading package stopwords to
[nltk_data] /home/shiningflash/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data] /home/shiningflash/nltk_data...
[nltk_data] Package punkt is already up-to-date!
True
#define stemmer
stemmer = LancasterStemmer()def cleaning(sentences):
words = []
for s in sentences:
clean = re.sub(r'[^ a-z A-Z 0-9]', " ", s)
w = word_tokenize(clean)
words.append([i.lower() for i in w])
return words cleaned_words = cleaning(sentences)
print(len(cleaned_words))
print(cleaned_words[:2]) 10004
[['text'], ['i', 'am', 'still', 'waiting', 'on', 'my', 'card']]
def create_tokenizer(words, filters = '!"#$%&()*+,-./:;<=>?@[\]^_`{|}~'):
token = Tokenizer(filters = filters)
token.fit_on_texts(words)
return tokendef max_length(words):
return(len(max(words, key = len)))word_tokenizer = create_tokenizer(cleaned_words)
vocab_size = len(word_tokenizer.word_index) + 1
max_length = max_length(cleaned_words)
print("Vocab Size = %d and Maximum length = %d" % (vocab_size, max_length))Vocab Size = 2343 and Maximum length = 84
def encoding_doc(token, words):
return(token.texts_to_sequences(words))encoded_doc = encoding_doc(word_tokenizer, cleaned_words)def padding_doc(encoded_doc, max_length):
return(pad_sequences(encoded_doc, maxlen = max_length, padding = "post"))padded_doc = padding_doc(encoded_doc, max_length)print("Shape of padded docs = ",padded_doc.shape)Shape of padded docs = (10004, 84)
#tokenizer with filter changed
output_tokenizer = create_tokenizer(unique_intent, filters = '!"#$%&()*+,-/:;<=>?@[\]^`{|}~')output_tokenizer.word_index{'verify_source_of_funds': 1,
'topping_up_by_card': 2,
'balance_not_updated_after_cheque_or_cash_deposit': 3,
'visa_or_mastercard': 4,
'request_refund': 5,
'extra_charge_on_statement': 6,
'wrong_amount_of_cash_received': 7,
'atm_support': 8,
'card_payment_fee_charged': 9,
'pending_card_payment': 10,
'transfer_fee_charged': 11,
'getting_spare_card': 12,
'wrong_exchange_rate_for_cash_withdrawal': 13,
'top_up_by_cash_or_cheque': 14,
'category': 15,
'transfer_not_received_by_recipient': 16,
'top_up_by_bank_transfer_charge': 17,
'unable_to_verify_identity': 18,
'refund_not_showing_up': 19,
'pending_transfer': 20,
'pin_blocked': 21,
'top_up_by_card_charge': 22,
'country_support': 23,
'getting_virtual_card': 24,
'pending_cash_withdrawal': 25,
'card_payment_wrong_exchange_rate': 26,
'declined_cash_withdrawal': 27,
'fiat_currency_support': 28,
'lost_or_stolen_phone': 29,
'transfer_into_account': 30,
'passcode_forgotten': 31,
'terminate_account': 32,
'verify_top_up': 33,
'top_up_failed': 34,
'declined_transfer': 35,
'verify_my_identity': 36,
'card_acceptance': 37,
'direct_debit_payment_not_recognised': 38,
'declined_card_payment': 39,
'beneficiary_not_allowed': 40,
'cash_withdrawal_charge': 41,
'card_swallowed': 42,
'automatic_top_up': 43,
'supported_cards_and_currencies': 44,
'compromised_card': 45,
'card_arrival': 46,
'virtual_card_not_working': 47,
'change_pin': 48,
'transfer_timing': 49,
'reverted_card_payment': 50,
'get_disposable_virtual_card': 51,
'balance_not_updated_after_bank_transfer': 52,
'exchange_charge': 53,
'card_not_working': 54,
'lost_or_stolen_card': 55,
'card_payment_not_recognised': 56,
'why_verify_identity': 57,
'contactless_not_working': 58,
'disposable_card_limits': 59,
'activate_my_card': 60,
'card_about_to_expire': 61,
'apple_pay_or_google_pay': 62,
'card_delivery_estimate': 63,
'exchange_rate': 64,
'edit_personal_details': 65,
'card_linking': 66,
'get_physical_card': 67,
'cash_withdrawal_not_recognised': 68,
'order_physical_card': 69,
'transaction_charged_twice': 70,
'cancel_transfer': 71,
'top_up_reverted': 72,
'age_limit': 73,
'failed_transfer': 74,
'receiving_money': 75,
'top_up_limits': 76,
'exchange_via_app': 77,
'pending_top_up': 78}
encoded_output = encoding_doc(output_tokenizer, intent)encoded_output = np.array(encoded_output).reshape(len(encoded_output), 1)encoded_output.shape(10004, 1)
def one_hot(encode):
o = OneHotEncoder(sparse = False)
return(o.fit_transform(encode))output_one_hot = one_hot(encoded_output)output_one_hot.shape(10004, 78)
from sklearn.model_selection import train_test_splittrain_X, val_X, train_Y, val_Y = train_test_split(padded_doc, output_one_hot, shuffle = True, test_size = 0.2)print("Shape of train_X = %s and train_Y = %s" % (train_X.shape, train_Y.shape))
print("Shape of val_X = %s and val_Y = %s" % (val_X.shape, val_Y.shape))Shape of train_X = (8003, 84) and train_Y = (8003, 78)
Shape of val_X = (2001, 84) and val_Y = (2001, 78)
from livelossplot.tf_keras import PlotLossesCallback
from livelossplot import PlotLossesKerasdef create_model(vocab_size, max_length):
model = Sequential()
model.add(Embedding(vocab_size, 128, input_length = max_length, trainable = False))
model.add(Bidirectional(GRU(128)))
model.add(Dense(32, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(78, activation = "softmax"))
return modelmodel = create_model(vocab_size, max_length)
model.compile(loss = "categorical_crossentropy", optimizer = "adam", metrics = ["accuracy"])
model.summary()Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 84, 128) 299904
_________________________________________________________________
bidirectional (Bidirectional (None, 256) 198144
_________________________________________________________________
dense (Dense) (None, 32) 8224
_________________________________________________________________
dropout (Dropout) (None, 32) 0
_________________________________________________________________
dense_1 (Dense) (None, 78) 2574
=================================================================
Total params: 508,846
Trainable params: 208,942
Non-trainable params: 299,904
_________________________________________________________________
filename = 'model.h5'
checkpoint = ModelCheckpoint(filename,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')hist = model.fit(train_X, train_Y,
epochs = 100,
batch_size = 32,
validation_data = (val_X, val_Y),
callbacks = [PlotLossesKeras(), checkpoint])
accuracy
training (min: 0.018, max: 0.807, cur: 0.803)
validation (min: 0.025, max: 0.694, cur: 0.681)
Loss
training (min: 0.562, max: 4.331, cur: 0.568)
validation (min: 1.468, max: 4.558, cur: 2.376)
Epoch 00100: val_loss did not improve from 1.46752
251/251 [==============================] - 27s 107ms/step - loss: 0.5683 - accuracy: 0.8031 - val_loss: 2.3763 - val_accuracy: 0.6812
def create_model(vocab_size, max_length):
model = Sequential()
model.add(Embedding(vocab_size, 128, input_length = max_length, trainable = False))
model.add(Bidirectional(LSTM(128)))
model.add(Dense(32, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(78, activation = "softmax"))
return model
model_lstm = create_model(vocab_size, max_length)
model_lstm.compile(loss = "categorical_crossentropy", optimizer = "adam", metrics = ["accuracy"])
model_lstm.summary()Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 84, 128) 299904
_________________________________________________________________
bidirectional_1 (Bidirection (None, 256) 263168
_________________________________________________________________
dense_2 (Dense) (None, 32) 8224
_________________________________________________________________
dropout_1 (Dropout) (None, 32) 0
_________________________________________________________________
dense_3 (Dense) (None, 78) 2574
=================================================================
Total params: 573,870
Trainable params: 273,966
Non-trainable params: 299,904
_________________________________________________________________
filename = 'model.h5'
checkpoint = ModelCheckpoint(filename,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
hist = model_lstm.fit(train_X, train_Y,
epochs = 100,
batch_size = 32,
validation_data = (val_X, val_Y),
callbacks = [PlotLossesKeras(), checkpoint])
accuracy
training (min: 0.017, max: 0.774, cur: 0.768)
validation (min: 0.034, max: 0.711, cur: 0.688)
Loss
training (min: 0.651, max: 4.339, cur: 0.695)
validation (min: 1.337, max: 4.285, cur: 1.775)
Epoch 00100: val_loss did not improve from 1.33676
251/251 [==============================] - 27s 108ms/step - loss: 0.6949 - accuracy: 0.7677 - val_loss: 1.7749 - val_accuracy: 0.6882
model_lstm = load_model("model.h5")def predictions(text):
clean = re.sub(r'[^ a-z A-Z 0-9]', " ", text)
test_word = word_tokenize(clean)
test_word = [w.lower() for w in test_word]
test_ls = word_tokenizer.texts_to_sequences(test_word)
#Check for unknown words
if [] in test_ls:
test_ls = list(filter(None, test_ls))
test_ls = np.array(test_ls).reshape(1, len(test_ls))
x = padding_doc(test_ls, max_length)
pred = model_lstm.predict(x)
return preddef get_final_output(pred, classes):
predictions = pred[0]
classes = np.array(classes)
ids = np.argsort(-predictions)
classes = classes[ids]
predictions = -np.sort(-predictions)
for i in range(pred.shape[1]):
print("%s has confidence = %s" % (classes[i], (predictions[i])))
return classes[0]text = "I am still waiting on my card?"
pred = predictions(text)
result = get_final_output(pred, unique_intent)
print('\nans: {}\n'.format(result))card_arrival has confidence = 0.5400851
lost_or_stolen_card has confidence = 0.13254714
card_not_working has confidence = 0.099424854
contactless_not_working has confidence = 0.05694259
compromised_card has confidence = 0.02806151
card_about_to_expire has confidence = 0.024988813
card_delivery_estimate has confidence = 0.024908025
declined_card_payment has confidence = 0.023217054
card_linking has confidence = 0.019666543
activate_my_card has confidence = 0.019631721
card_swallowed has confidence = 0.015140002
reverted_card_payment? has confidence = 0.011102826
atm_support has confidence = 0.0015607645
declined_cash_withdrawal has confidence = 0.0007894527
lost_or_stolen_phone has confidence = 0.00065130007
card_payment_not_recognised has confidence = 0.00042219783
cash_withdrawal_not_recognised has confidence = 0.0002289453
card_acceptance has confidence = 0.0001782307
declined_transfer has confidence = 0.00015152607
pin_blocked has confidence = 9.299157e-05
passcode_forgotten has confidence = 5.496411e-05
order_physical_card has confidence = 3.1216285e-05
unable_to_verify_identity has confidence = 2.856871e-05
transaction_charged_twice has confidence = 1.5831929e-05
beneficiary_not_allowed has confidence = 1.5821604e-05
country_support has confidence = 1.3478491e-05
pending_card_payment has confidence = 1.0269475e-05
why_verify_identity has confidence = 9.724789e-06
receiving_money has confidence = 6.8202044e-06
direct_debit_payment_not_recognised has confidence = 5.4109573e-06
virtual_card_not_working has confidence = 2.912666e-06
supported_cards_and_currencies has confidence = 2.4180426e-06
pending_cash_withdrawal has confidence = 2.1170988e-06
topping_up_by_card has confidence = 2.0330606e-06
request_refund has confidence = 1.1766249e-06
transfer_not_received_by_recipient has confidence = 1.0827664e-06
card_payment_fee_charged has confidence = 1.0126768e-06
visa_or_mastercard has confidence = 6.679286e-07
terminate_account has confidence = 5.908272e-07
Refund_not_showing_up has confidence = 3.619633e-07
top_up_failed has confidence = 3.1810998e-07
cancel_transfer has confidence = 2.8714103e-07
verify_top_up has confidence = 2.622885e-07
category has confidence = 2.5423205e-07
getting_spare_card has confidence = 1.889249e-07
transfer_fee_charged has confidence = 1.5563825e-07
age_limit has confidence = 1.2111296e-07
pending_top_up has confidence = 6.930501e-08
get_physical_card has confidence = 5.6756654e-08
getting_virtual_card has confidence = 5.3940063e-08
top_up_by_cash_or_cheque has confidence = 4.5198856e-08
failed_transfer has confidence = 1.5451935e-08
edit_personal_details has confidence = 1.4790701e-08
exchange_rate has confidence = 1.3748348e-08
wrong_amount_of_cash_received has confidence = 1.2084236e-08
top_up_reverted has confidence = 1.031402e-08
change_pin has confidence = 7.595862e-09
verify_my_identity has confidence = 5.57865e-09
balance_not_updated_after_bank_transfer has confidence = 2.6290028e-09
verify_source_of_funds has confidence = 2.390937e-09
automatic_top_up has confidence = 1.8031706e-09
card_payment_wrong_exchange_rate has confidence = 1.7656182e-09
top_up_by_card_charge has confidence = 1.2948175e-09
wrong_exchange_rate_for_cash_withdrawal has confidence = 9.181001e-10
fiat_currency_support has confidence = 8.16949e-10
balance_not_updated_after_cheque_or_cash_deposit has confidence = 6.584936e-10
transfer_timing has confidence = 4.593117e-10
apple_pay_or_google_pay has confidence = 2.880023e-10
top_up_by_bank_transfer_charge has confidence = 2.7394342e-10
cash_withdrawal_charge has confidence = 1.2649745e-10
exchange_charge has confidence = 9.780896e-11
extra_charge_on_statement has confidence = 8.268214e-11
transfer_into_account has confidence = 5.788913e-11
get_disposable_virtual_card has confidence = 4.7355724e-11
pending_transfer has confidence = 1.560852e-11
exchange_via_app has confidence = 5.856411e-12
disposable_card_limits has confidence = 1.6419753e-12
top_up_limits has confidence = 7.180127e-14
ans: card_arrival
def get_only_final_output(pred, classes):
predictions = pred[0]
classes = np.array(classes)
ids = np.argsort(-predictions)
classes = classes[ids]
predictions = -np.sort(-predictions)
return classes[0]def get_intent(text):
pred = predictions(text)
result = get_only_final_output(pred, unique_intent)
print('ans: {}'.format(result))get_intent("I am still waiting on my card?")ans: card_arrival
get_intent("What are you exchange rates?")ans: exchange_rate
get_intent("Which countries are represented?")ans: country_support