I have a trained autoencoder model which have developed by using tensor flow. Also, I have created a data frame from a dictionary. the dictionary is like below
{'step': '4', 'type': 'CASH_IN', 'amount': '100000', 'nameOrig': 'C1666544295', 'oldBalanceOrig': '1096835345345', 'newBalanceOrig': '61652353534545.23', 'nameDest': 'M1979787155', 'oldBalanceDest': '1.2312322342353452e+21', 'newBalacneDest': '827467862345345400'}
After I convert this dictionary I entered into the below function,
def get_predictions_for_live_transactions(train_set, model_path):
x_lables = train_set[['type', 'nameOrig', 'nameDest']].copy()
x_lables = pd.DataFrame(x_lables)
# print('here', x_lables)
train_set = train_set.drop(['nameOrig', 'nameDest', 'step'], axis=1)
# print(train_set['type'].values[0])
# exit()
if train_set['type'].values[0] == 'DEBIT':
train_set['is_CASH_IN'] = 0
train_set['is_CASH_OUT'] = 0
train_set['is_DEBIT'] = 1
train_set['is_PAYMENT'] = 0
train_set['is_TRANSFER'] = 0
elif train_set['type'].values[0] == 'PAYMENT':
train_set['is_CASH_IN'] = 0
train_set['is_CASH_OUT'] = 0
train_set['is_DEBIT'] = 0
train_set['is_PAYMENT'] = 1
train_set['is_TRANSFER'] = 0
elif train_set['type'].values[0] == 'TRANSFER':
train_set['is_CASH_IN'] = 0
train_set['is_CASH_OUT'] = 0
train_set['is_DEBIT'] = 0
train_set['is_PAYMENT'] = 0
train_set['is_TRANSFER'] = 1
elif train_set['type'].values[0] == 'CASH_OUT':
train_set['is_CASH_IN'] = 0
train_set['is_CASH_OUT'] = 1
train_set['is_DEBIT'] = 0
train_set['is_PAYMENT'] = 0
train_set['is_TRANSFER'] = 0
elif train_set['type'].values[0] == 'CASH_IN':
train_set['is_CASH_IN'] = 1
train_set['is_CASH_OUT'] = 0
train_set['is_DEBIT'] = 0
train_set['is_PAYMENT'] = 0
train_set['is_TRANSFER'] = 0
else:
pass
print(train_set)
# print(train_set.columns)
# exit()
train_set = train_set.drop(['type'], axis=1)
new_df = pd.concat([x_lables, train_set], axis=1)
new_df = new_df.drop(['is_CASH_IN', 'is_CASH_OUT', 'is_DEBIT', 'is_PAYMENT', 'is_TRANSFER'], axis=1) # here chaged
# print(train_set.columns.values)
# exit()
saver = None
sess = tf.Session()
num_input = 10
result_errors = []
try:
saver = tf.train.import_meta_graph(model_path + 'general.meta')
print("successfully loaded the model")
saver.restore(sess, tf.train.latest_checkpoint(model_path))
graph = tf.get_default_graph()
# print(train_set)
np_scaled = min_max_scaler.fit_transform(train_set)
# print(np_scaled)
train_set = pd.DataFrame(np_scaled)
print(train_set.iloc[0])
# exit()
for i in range(len(train_set)):
print(i)
# exit()
X = graph.get_tensor_by_name("x:0")
# print(X)
decorder_op = graph.get_tensor_by_name("decoder_op:0")
# g = sess.run(decorder_op, feed_dict={X: train_set.iloc[i].values.reshape(1, num_input)})
feed_dict_testing = {X: train_set.iloc[i].values.reshape(1, num_input)}
g = sess.run(decorder_op, feed_dict=feed_dict_testing)
# print(g)
error = np.sum(abs(train_set.iloc[i].values - g))
# print(error)
result_errors.append(error)
new_df['result_errors'] = result_errors
return new_df
except IOError as e:
print('No Trained Model Found at %s. \nPlease run train.py first', model_path)
exit()
Once I put that into this function to enter it to the autoencoder model it gives below the message.
It says that the data frame is empty. Then I checked the data frame by printing it. It gives me bellow result,
After that, I have checked the result which is giving by the min_max_schaler.It has given below result,
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
I don't have an idea of why this problem is coming to me. Please help me to fix this issue.
I have checked below questions but it not worked for me yet.
