def get_cost(self, word_1: str, word_2: str):
return osa(word_1.lower(), word_2.lower(),
transpose_costs=self.transpose_costs,
substitute_costs=self.substitute_costs,
insert_costs=self.insert_costs,
delete_costs=self.delete_costs,
)
def get_cost(self, word_1: str, word_2: str):
return osa(
word_1.lower(),
word_2.lower(),
transpose_costs=self.transpose_costs,
substitute_costs=self.substitute_costs,
insert_costs=self.insert_costs,
delete_costs=self.delete_costs,
)
def test_osa(self):
self.assertEqual(osa('1234', '1234'), 0.0)
self.assertEqual(osa('', '1234'), 4.0)
self.assertEqual(osa('1234', ''), 4.0)
self.assertEqual(osa('', ''), 0.0)
self.assertEqual(osa('1234', '12'), 2.0)
self.assertEqual(osa('1234', '14'), 2.0)
self.assertEqual(osa('1111', '1'), 3.0)
def test_osa(self):
self.assertEqual(osa('1234', '1234'), 0.0)
self.assertEqual(osa('', '1234'), 4.0)
self.assertEqual(osa('1234', ''), 4.0)
self.assertEqual(osa('', ''), 0.0)
self.assertEqual(osa('1234', '12'), 2.0)
self.assertEqual(osa('1234', '14'), 2.0)
self.assertEqual(osa('1111', '1'), 3.0)
def find_closest_string_weighted(string):
print("string: " + string)
with open('../approaches/edit_distance/cleaned_bucket_data.json',
encoding="ASCII") as f:
data = json.load(f)
# find first letter of every word in the string
words = string.split()
letters = [word[0] for word in words]
# get corresponding buckets
first_letter = string[0]
products = []
for bucket in data:
if bucket[0][0] == first_letter:
products += bucket
# remove non-ascii characters
cleaned_products = []
for entry in products:
cleaned_entry = ""
for character in entry:
if ord(character) <= 128:
cleaned_entry += character
cleaned_products.append(cleaned_entry)
insert_costs = np.full(
128, .3, dtype=np.float64
) # make an array of all 1's of size 128, the number of ASCII characters
transpose_costs = np.full((128, 128), .7, dtype=np.float64)
delete_costs = np.full(128, 1.2, dtype=np.float64)
closest_distance = 999999
closest_string = None
for line in cleaned_products:
distance = osa(string.lower(),
line.lower(),
insert_costs=insert_costs,
transpose_costs=transpose_costs,
delete_costs=delete_costs)
if closest_distance is None or distance < closest_distance:
closest_distance = distance
closest_string = line.lower()
print("closest_string: " + closest_string)
return closest_string
def predict(searched=None,
internal_call=False,
receipt=None,
receipt_titles=None):
'''
important variables:
tokenizer - tokenizer for tokenizing text
lstm_model - trained lstm model
le - label encoder to decode output
'''
# lstm
K.clear_session()
lstm_path = "../approaches/LSTM/"
with open(lstm_path + "pickled/tokenizer_300k_1epoch.pickle",
'rb') as handle:
tokenizer = pickle.load(handle)
lstm_model = load_model(lstm_path + "models/lstm_300k_epochs_1.h5")
le = preprocessing.LabelEncoder()
le.classes_ = np.load(lstm_path +
"pickled/labelencoder_classes_300k_1epoch.npy")
tokenizer.oov_token = None
# edit distance
with open('../approaches/edit_distance/cleaned_bucket_data.json',
encoding="ASCII") as f:
data = json.load(f)
# gbdt
gbdt_path = "../approaches/gbdt/"
gbdt_model = pickle.load(open(gbdt_path + "models/gbdt_model.sav", "rb"))
gbdt_le = preprocessing.LabelEncoder()
gbdt_le.classes_ = np.load(gbdt_path +
"pickled/labelencoder_gbdt_classes.npy")
## if internal call ##
if internal_call:
# lstm
encoded_x = tokenizer.texts_to_sequences([searched])
padded = pad_sequences(encoded_x, 25)
lstm_preds = lstm_model.predict(padded)
pred_labels = [[np.argmax(x)] for x in lstm_preds]
lstm_preds = le.inverse_transform(pred_labels)
# gbdt
df = get_dataframe([searched.upper()])
gbdt_preds = gbdt_model.predict(df.drop(columns=['x', 'y'], axis=1))
gbdt_preds = gbdt_le.inverse_transform(gbdt_preds)[0]
#=========================================================
# edit distance
# find first letter of every word in the string
words = searched.split()
letters = [word[0] for word in words]
# get corresponding buckets
first_letter = searched[0]
products = []
for bucket in data:
if bucket[0][0].lower() == first_letter.lower():
products += bucket
# remove non-ascii characters
cleaned_products = []
for entry in products:
cleaned_entry = ""
for character in entry:
if ord(character) <= 128:
cleaned_entry += character
cleaned_products.append(cleaned_entry)
insert_costs = np.full(
128, .3, dtype=np.float64
) # make an array of all 1's of size 128, the number of ASCII characters
transpose_costs = np.full((128, 128), .7, dtype=np.float64)
delete_costs = np.full(128, 1.2, dtype=np.float64)
closest_distance = 999999
closest_string = None
for line in cleaned_products:
distance = osa(searched.lower(),
line.lower(),
insert_costs=insert_costs,
transpose_costs=transpose_costs,
delete_costs=delete_costs)
if closest_distance is None or distance < closest_distance:
closest_distance = distance
closest_string = line.lower()
edit_distance_pred = closest_string
return (lstm_preds, edit_distance_pred, gbdt_preds)
else:
print('receipt titles = ', receipt_titles)
encoded_x = tokenizer.texts_to_sequences(receipt_titles)
padded = pad_sequences(encoded_x, 25)
lstm_preds = lstm_model.predict(padded)
pred_labels = [[np.argmax(x)] for x in lstm_preds]
lstm_preds = le.inverse_transform(pred_labels)
# gbdt
titles = []
for title in receipt_titles:
titles.append(title.upper())
df = get_dataframe(titles)
gbdt_preds = gbdt_model.predict(df.drop(columns=['x', 'y'], axis=1))
gbdt_preds = gbdt_le.inverse_transform(gbdt_preds)
#=========================================================
# edit distance
edit_distance_preds = []
for title in receipt_titles[:]:
# find first letter of every word in the string
words = title.split()
letters = [word[0] for word in words]
# get corresponding buckets
first_letter = title[0]
products = []
for bucket in data:
if bucket[0][0] == first_letter:
products += bucket
# remove non-ascii characters
cleaned_products = []
for entry in products:
cleaned_entry = ""
for character in entry:
if ord(character) <= 128:
cleaned_entry += character
cleaned_products.append(cleaned_entry)
insert_costs = np.full(
128, .3, dtype=np.float64
) # make an array of all 1's of size 128, the number of ASCII characters
transpose_costs = np.full((128, 128), .7, dtype=np.float64)
delete_costs = np.full(128, 1.2, dtype=np.float64)
closest_distance = 999999
closest_string = None
for line in cleaned_products:
distance = osa(title.lower(),
line.lower(),
insert_costs=insert_costs,
transpose_costs=transpose_costs,
delete_costs=delete_costs)
if closest_distance is None or distance < closest_distance:
closest_distance = distance
closest_string = line.lower()
edit_distance_preds.append(closest_string)
global manual_search
global text
global receipt_titles_global
global lstm_output
global edit_distance_output
global gbdt_output
manual_search = False
text = receipt
receipt_titles_global = receipt_titles
lstm_output = lstm_preds
edit_distance_output = edit_distance_preds
gbdt_output = gbdt_preds
print("edit_distance_output " + str(edit_distance_output))
return render_template("index.html",
manual_search=False,
text=receipt,
receipt_titles=receipt_titles,
lstm_output=lstm_preds,
edit_distance_output=edit_distance_preds,
gbdt_output=gbdt_preds)
def _osa(self, x, y):
return osa(x, y, self.iw, self.dw, self.sw, self.tw)
def _osa(self, x, y):
return osa(x, y, self.iw, self.dw, self.sw, self.tw)
