Overview
On this put up we are going to prepare an autoencoder to detect bank card fraud. We may even show the best way to prepare Keras fashions within the cloud utilizing CloudML.
The premise of our mannequin would be the Kaggle Credit score Card Fraud Detection dataset, which was collected throughout a analysis collaboration of Worldline and the Machine Studying Group of ULB (Université Libre de Bruxelles) on massive knowledge mining and fraud detection.
The dataset incorporates bank card transactions by European cardholders revamped a two day interval in September 2013. There are 492 frauds out of 284,807 transactions. The dataset is very unbalanced, the constructive class (frauds) account for under 0.172% of all transactions.
Studying the information
After downloading the information from Kaggle, you possibly can learn it in to R with read_csv():
The enter variables encompass solely numerical values that are the results of a PCA transformation. As a way to protect confidentiality, no extra details about the unique options was offered. The options V1, …, V28 have been obtained with PCA. There are nonetheless 2 options (Time and Quantity) that weren’t remodeled.
Time is the seconds elapsed between every transaction and the primary transaction within the dataset. Quantity is the transaction quantity and could possibly be used for cost-sensitive studying. The Class variable takes worth 1 in case of fraud and 0 in any other case.
Autoencoders
Since solely 0.172% of the observations are frauds, we now have a extremely unbalanced classification drawback. With this sort of drawback, conventional classification approaches normally don’t work very effectively as a result of we now have solely a really small pattern of the rarer class.
An autoencoder is a neural community that’s used to study a illustration (encoding) for a set of information, usually for the aim of dimensionality discount. For this drawback we are going to prepare an autoencoder to encode non-fraud observations from our coaching set. Since frauds are imagined to have a distinct distribution then regular transactions, we count on that our autoencoder may have greater reconstruction errors on frauds then on regular transactions. Which means we are able to use the reconstruction error as a amount that signifies if a transaction is fraudulent or not.
If you wish to study extra about autoencoders, place to begin is that this video from Larochelle on YouTube and Chapter 14 from the Deep Studying e-book by Goodfellow et al.
Visualization
For an autoencoder to work effectively we now have a robust preliminary assumption: that the distribution of variables for regular transactions is totally different from the distribution for fraudulent ones. Let’s make some plots to confirm this. Variables have been remodeled to a [0,1] interval for plotting.
We will see that distributions of variables for fraudulent transactions are very totally different then from regular ones, aside from the Time variable, which appears to have the very same distribution.
Preprocessing
Earlier than the modeling steps we have to do some preprocessing. We’ll cut up the dataset into prepare and check units after which we are going to Min-max normalize our knowledge (that is completed as a result of neural networks work significantly better with small enter values). We may even take away the Time variable because it has the very same distribution for regular and fraudulent transactions.
Based mostly on the Time variable we are going to use the primary 200,000 observations for coaching and the remaining for testing. That is good follow as a result of when utilizing the mannequin we wish to predict future frauds based mostly on transactions that occurred earlier than.
Now let’s work on normalization of inputs. We created 2 capabilities to assist us. The primary one will get descriptive statistics concerning the dataset which can be used for scaling. Then we now have a perform to carry out the min-max scaling. It’s necessary to notice that we utilized the identical normalization constants for coaching and check units.
library(purrr)
#' Will get descriptive statistics for each variable within the dataset.
get_desc <- perform(x) {
map(x, ~record(
min = min(.x),
max = max(.x),
imply = imply(.x),
sd = sd(.x)
))
}
#' Given a dataset and normalization constants it is going to create a min-max normalized
#' model of the dataset.
normalization_minmax <- perform(x, desc) {
map2_dfc(x, desc, ~(.x - .y$min)/(.y$max - .y$min))
}Now let’s create normalized variations of our datasets. We additionally remodeled our knowledge frames to matrices since that is the format anticipated by Keras.
We’ll now outline our mannequin in Keras, a symmetric autoencoder with 4 dense layers.
library(keras)
mannequin <- keras_model_sequential()
mannequin %>%
layer_dense(models = 15, activation = "tanh", input_shape = ncol(x_train)) %>%
layer_dense(models = 10, activation = "tanh") %>%
layer_dense(models = 15, activation = "tanh") %>%
layer_dense(models = ncol(x_train))
abstract(mannequin)___________________________________________________________________________________
Layer (kind) Output Form Param #
===================================================================================
dense_1 (Dense) (None, 15) 450
___________________________________________________________________________________
dense_2 (Dense) (None, 10) 160
___________________________________________________________________________________
dense_3 (Dense) (None, 15) 165
___________________________________________________________________________________
dense_4 (Dense) (None, 29) 464
===================================================================================
Complete params: 1,239
Trainable params: 1,239
Non-trainable params: 0
___________________________________________________________________________________We’ll then compile our mannequin, utilizing the imply squared error loss and the Adam optimizer for coaching.
mannequin %>% compile(
loss = "mean_squared_error",
optimizer = "adam"
)Coaching the mannequin
We will now prepare our mannequin utilizing the match() perform. Coaching the mannequin within reason quick (~ 14s per epoch on my laptop computer). We’ll solely feed to our mannequin the observations of regular (non-fraudulent) transactions.
We’ll use callback_model_checkpoint() to be able to save our mannequin after every epoch. By passing the argument save_best_only = TRUE we are going to carry on disk solely the epoch with smallest loss worth on the check set.
We may even use callback_early_stopping() to cease coaching if the validation loss stops lowering for five epochs.
checkpoint <- callback_model_checkpoint(
filepath = "mannequin.hdf5",
save_best_only = TRUE,
interval = 1,
verbose = 1
)
early_stopping <- callback_early_stopping(persistence = 5)
mannequin %>% match(
x = x_train[y_train == 0,],
y = x_train[y_train == 0,],
epochs = 100,
batch_size = 32,
validation_data = record(x_test[y_test == 0,], x_test[y_test == 0,]),
callbacks = record(checkpoint, early_stopping)
)Practice on 199615 samples, validate on 84700 samples
Epoch 1/100
199615/199615 [==============================] - 17s 83us/step - loss: 0.0036 - val_loss: 6.8522e-04d from inf to 0.00069, saving mannequin to mannequin.hdf5
Epoch 2/100
199615/199615 [==============================] - 17s 86us/step - loss: 4.7817e-04 - val_loss: 4.7266e-04d from 0.00069 to 0.00047, saving mannequin to mannequin.hdf5
Epoch 3/100
199615/199615 [==============================] - 19s 94us/step - loss: 3.7753e-04 - val_loss: 4.2430e-04d from 0.00047 to 0.00042, saving mannequin to mannequin.hdf5
Epoch 4/100
199615/199615 [==============================] - 19s 94us/step - loss: 3.3937e-04 - val_loss: 4.0299e-04d from 0.00042 to 0.00040, saving mannequin to mannequin.hdf5
Epoch 5/100
199615/199615 [==============================] - 19s 94us/step - loss: 3.2259e-04 - val_loss: 4.0852e-04 enhance
Epoch 6/100
199615/199615 [==============================] - 18s 91us/step - loss: 3.1668e-04 - val_loss: 4.0746e-04 enhance
...After coaching we are able to get the ultimate loss for the check set by utilizing the consider() fucntion.
loss <- consider(mannequin, x = x_test[y_test == 0,], y = x_test[y_test == 0,])
loss loss
0.0003534254 Tuning with CloudML
We could possibly get higher outcomes by tuning our mannequin hyperparameters. We will tune, for instance, the normalization perform, the educational price, the activation capabilities and the dimensions of hidden layers. CloudML makes use of Bayesian optimization to tune hyperparameters of fashions as described in this weblog put up.
We will use the cloudml package deal to tune our mannequin, however first we have to put together our challenge by making a coaching flag for every hyperparameter and a tuning.yml file that may inform CloudML what parameters we wish to tune and the way.
The total script used for coaching on CloudML may be discovered at https://github.com/dfalbel/fraud-autoencoder-example. An important modifications to the code have been including the coaching flags:
FLAGS <- flags(
flag_string("normalization", "minmax", "One in all minmax, zscore"),
flag_string("activation", "relu", "One in all relu, selu, tanh, sigmoid"),
flag_numeric("learning_rate", 0.001, "Optimizer Studying Price"),
flag_integer("hidden_size", 15, "The hidden layer dimension")
)We then used the FLAGS variable contained in the script to drive the hyperparameters of the mannequin, for instance:
mannequin %>% compile(
optimizer = optimizer_adam(lr = FLAGS$learning_rate),
loss = 'mean_squared_error',
)We additionally created a tuning.yml file describing how hyperparameters must be diverse throughout coaching, in addition to what metric we needed to optimize (on this case it was the validation loss: val_loss).
tuning.yml
trainingInput:
scaleTier: CUSTOM
masterType: standard_gpu
hyperparameters:
purpose: MINIMIZE
hyperparameterMetricTag: val_loss
maxTrials: 10
maxParallelTrials: 5
params:
- parameterName: normalization
kind: CATEGORICAL
categoricalValues: [zscore, minmax]
- parameterName: activation
kind: CATEGORICAL
categoricalValues: [relu, selu, tanh, sigmoid]
- parameterName: learning_rate
kind: DOUBLE
minValue: 0.000001
maxValue: 0.1
scaleType: UNIT_LOG_SCALE
- parameterName: hidden_size
kind: INTEGER
minValue: 5
maxValue: 50
scaleType: UNIT_LINEAR_SCALEWe describe the kind of machine we wish to use (on this case a standard_gpu occasion), the metric we wish to reduce whereas tuning, and the the utmost variety of trials (i.e. variety of mixtures of hyperparameters we wish to check). We then specify how we wish to fluctuate every hyperparameter throughout tuning.
You’ll be able to study extra concerning the tuning.yml file on the Tensorflow for R documentation and at Google’s official documentation on CloudML.
Now we’re able to ship the job to Google CloudML. We will do that by operating:
library(cloudml)
cloudml_train("prepare.R", config = "tuning.yml")The cloudml package deal takes care of importing the dataset and putting in any R package deal dependencies required to run the script on CloudML. In case you are utilizing RStudio v1.1 or greater, it is going to additionally assist you to monitor your job in a background terminal. You may also monitor your job utilizing the Google Cloud Console.
After the job is completed we are able to accumulate the job outcomes with:
This can copy the information from the job with one of the best val_loss efficiency on CloudML to your native system and open a report summarizing the coaching run.
Since we used a callback to avoid wasting mannequin checkpoints throughout coaching, the mannequin file was additionally copied from Google CloudML. Recordsdata created throughout coaching are copied to the “runs” subdirectory of the working listing from which cloudml_train() known as. You’ll be able to decide this listing for the newest run with:
[1] runs/cloudml_2018_01_23_221244595-03You may also record all earlier runs and their validation losses with:
ls_runs(order = metric_val_loss, lowering = FALSE) run_dir metric_loss metric_val_loss
1 runs/2017-12-09T21-01-11Z 0.2577 0.1482
2 runs/2017-12-09T21-00-11Z 0.2655 0.1505
3 runs/2017-12-09T19-59-44Z 0.2597 0.1402
4 runs/2017-12-09T19-56-48Z 0.2610 0.1459
Use View(ls_runs()) to view all columnsIn our case the job downloaded from CloudML was saved to runs/cloudml_2018_01_23_221244595-03/, so the saved mannequin file is offered at runs/cloudml_2018_01_23_221244595-03/mannequin.hdf5. We will now use our tuned mannequin to make predictions.
Making predictions
Now that we skilled and tuned our mannequin we’re able to generate predictions with our autoencoder. We have an interest within the MSE for every commentary and we count on that observations of fraudulent transactions may have greater MSE’s.
First, let’s load our mannequin.
mannequin <- load_model_hdf5("runs/cloudml_2018_01_23_221244595-03/mannequin.hdf5",
compile = FALSE)Now let’s calculate the MSE for the coaching and check set observations.
measure of mannequin efficiency in extremely unbalanced datasets is the Space Beneath the ROC Curve (AUC). AUC has a pleasant interpretation for this drawback, it’s the likelihood {that a} fraudulent transaction may have greater MSE then a traditional one. We will calculate this utilizing the Metrics package deal, which implements all kinds of frequent machine studying mannequin efficiency metrics.
[1] 0.9546814
[1] 0.9403554To make use of the mannequin in follow for making predictions we have to discover a threshold (okay) for the MSE, then if if (MSE > okay) we think about that transaction a fraud (in any other case we think about it regular). To outline this worth it’s helpful to have a look at precision and recall whereas various the edge (okay).
possible_k <- seq(0, 0.5, size.out = 100)
precision <- sapply(possible_k, perform(okay) {
predicted_class <- as.numeric(mse_test > okay)
sum(predicted_class == 1 & y_test == 1)/sum(predicted_class)
})
qplot(possible_k, precision, geom = "line")
+ labs(x = "Threshold", y = "Precision")
recall <- sapply(possible_k, perform(okay) {
predicted_class <- as.numeric(mse_test > okay)
sum(predicted_class == 1 & y_test == 1)/sum(y_test)
})
qplot(possible_k, recall, geom = "line")
+ labs(x = "Threshold", y = "Recall")
place to begin can be to decide on the edge with most precision however we may additionally base our determination on how a lot cash we’d lose from fraudulent transactions.
Suppose every handbook verification of fraud prices us $1 but when we don’t confirm a transaction and it’s a fraud we are going to lose this transaction quantity. Let’s discover for every threshold worth how a lot cash we’d lose.
cost_per_verification <- 1
lost_money <- sapply(possible_k, perform(okay) {
predicted_class <- as.numeric(mse_test > okay)
sum(cost_per_verification * predicted_class + (predicted_class == 0) * y_test * df_test$Quantity)
})
qplot(possible_k, lost_money, geom = "line") + labs(x = "Threshold", y = "Misplaced Cash")
We will discover one of the best threshold on this case with:
[1] 0.005050505If we would have liked to manually confirm all frauds, it might price us ~$13,000. Utilizing our mannequin we are able to cut back this to ~$2,500.