Settings View Source Classifying fraudulent transactions

Mix.install([
  {:axon, github: "elixir-nx/axon"},
  {:nx, "~> 0.3.0", override: true},
  {:exla, "~> 0.3.0"},
  {:explorer, "~> 0.3.1"},
  {:kino, "~> 0.7.0"}
])

Nx.Defn.default_options(compiler: EXLA)
Nx.global_default_backend(EXLA.Backend)

alias Explorer.{DataFrame, Series}

introduction

Introduction

This time we will examine the Credit Card Fraud Dataset. Due to confidentiality, the original data were preprocessed by principal component analysis (PCA), and then 31 principal components were selected for the final data set. The dataset is highly imbalanced. The positive class (frauds) account for 0.172% of all transactions. Eventually, we will create a classifier which has not only great accuracy but, what is even more important, a high recall and precision - two metrics that are much more indicative of performance with imbalanced classification problems.

data-processing

Data processing

The first step is to prepare the data for training and evaluation. Please download the dataset in the CSV format from https://www.kaggle.com/mlg-ulb/creditcardfraud (this requires a Kaggla account). Once done, put the file path in the input below.

data_path_input = Kino.Input.text("Data path (CSV)")

Now, let's read the data into an Explorer.Dataframe:

data_path = Kino.Input.read(data_path_input)

df = DataFrame.from_csv!(data_path, dtypes: [{"Time", :float}])

For further processing, we will need a couple helper functions. We will group them in a module for convenience.

defmodule CredidCard.Data do
  import Nx.Defn

  def split_train_test(df, portion) do
    num_examples = DataFrame.n_rows(df)
    num_train = ceil(portion * num_examples)
    num_test = num_examples - num_train

    train = DataFrame.slice(df, 0, num_train)
    test = DataFrame.slice(df, num_train, num_test)
    {train, test}
  end

  def split_features_targets(df) do
    features = DataFrame.select(df, &(&1 == "Class"), :drop)
    targets = DataFrame.select(df, &(&1 == "Class"), :keep)
    {features, targets}
  end

  def df_to_tensor(df) do
    df
    |> DataFrame.names()
    |> Enum.map(&Series.to_tensor(df[&1]))
    |> Nx.stack(axis: 1)
  end

  defn normalize_features(tensor) do
    max =
      tensor
      |> Nx.abs()
      |> Nx.reduce_max(axes: [0], keep_axes: true)

    tensor / max
  end
end

With that, we can start converting the data into the desired format. First, we split the data into training and test data (in proportion 80% into a training set and 20% into a test set).

{train_df, test_df} = CredidCard.Data.split_train_test(df, 0.8)
{DataFrame.n_rows(train_df), DataFrame.n_rows(test_df)}

Next, we separate features from labels and convert both to tensors. In case of features we additionally normalize each of them, dividing by the maximum absolute value of that feature.

{train_features, train_targets} = CredidCard.Data.split_features_targets(train_df)
{test_features, test_targets} = CredidCard.Data.split_features_targets(test_df)

train_inputs =
  train_features
  |> CredidCard.Data.df_to_tensor()
  |> CredidCard.Data.normalize_features()

test_inputs =
  test_features
  |> CredidCard.Data.df_to_tensor()
  |> CredidCard.Data.normalize_features()

train_targets = CredidCard.Data.df_to_tensor(train_targets)
test_targets = CredidCard.Data.df_to_tensor(test_targets)

:ok

building-the-model

Building the model

Our model for predicting whether a transaction was fraudulent or not is a dense neural network. It consists of two dense layers with 256 neurons, ReLU activation functions, one dropout layer, and a dense layer with one neuron (since the problem is a binary prediction) followed by a sigmoid activation function.

model =
  Axon.input("input")
  |> Axon.dense(256)
  |> Axon.relu()
  |> Axon.dense(256)
  |> Axon.relu()
  |> Axon.dropout(rate: 0.3)
  |> Axon.dense(1)
  |> Axon.sigmoid()

training-our-model

Training our model

Now we have both data and model architecture prepared, it's time to train!

Note the disproportion in the data samples:

fraud = Nx.sum(train_targets) |> Nx.to_number()
legit = Nx.size(train_targets) - fraud

batched_train_inputs = Nx.to_batched(train_inputs, 2048)
batched_train_targets = Nx.to_batched(train_targets, 2048)
batched_train = Stream.zip(batched_train_inputs, batched_train_targets)

IO.puts("# of legit transactions (train): #{legit}")
IO.puts("# of fraudulent transactions (train): #{fraud}")
IO.puts("% fraudlent transactions (train): #{100 * (fraud / (legit + fraud))}%")

As always, we define our train loop. We are using binary cross-entropy as our loss function and Adam as the optimizer with a learning rate of 0.01. Then we immediately start the training passing our train portion of the dataset.

loss =
  &Axon.Losses.binary_cross_entropy(
    &1,
    &2,
    negative_weight: 1 / legit,
    positive_weight: 1 / fraud,
    reduction: :mean
  )

optimizer = Axon.Optimizers.adam(1.0e-2)

params =
  model
  |> Axon.Loop.trainer(loss, optimizer)
  |> Axon.Loop.run(batched_train, %{}, epochs: 30, compiler: EXLA)

:ok

model-evaluation

Model evaluation

After the training, there is only one thing left: testing. Here, we will focus on the number of true positive, true negative, false positive, and false negative values, but also on the likelihood of denying legit and fraudulent transactions.

batched_test_inputs = Nx.to_batched(test_inputs, 2048)
batched_test_targets = Nx.to_batched(test_targets, 2048)
batched_test = Stream.zip(batched_test_inputs, batched_test_targets)

summarize = fn %Axon.Loop.State{metrics: metrics} = state ->
  legit_transactions_declined = Nx.to_number(metrics["fp"])
  legit_transactions_accepted = Nx.to_number(metrics["tn"])
  fraud_transactions_accepted = Nx.to_number(metrics["fn"])
  fraud_transactions_declined = Nx.to_number(metrics["tp"])
  total_fraud = fraud_transactions_declined + fraud_transactions_accepted
  total_legit = legit_transactions_declined + legit_transactions_accepted

  fraud_denial_percent = 100 * (fraud_transactions_declined / total_fraud)
  legit_denial_percent = 100 * (legit_transactions_declined / total_legit)

  IO.write("\n")
  IO.puts("Legit Transactions Declined: #{legit_transactions_declined}")
  IO.puts("Fraudulent Transactions Caught: #{fraud_transactions_declined}")
  IO.puts("Fraudulent Transactions Missed: #{fraud_transactions_accepted}")
  IO.puts("Likelihood of catching fraud: #{fraud_denial_percent}%")
  IO.puts("Likelihood of denying legit transaction: #{legit_denial_percent}%")

  {:continue, state}
end

model
|> Axon.Loop.evaluator()
|> Axon.Loop.metric(:true_positives, "tp", :running_sum)
|> Axon.Loop.metric(:true_negatives, "tn", :running_sum)
|> Axon.Loop.metric(:false_positives, "fp", :running_sum)
|> Axon.Loop.metric(:false_negatives, "fn", :running_sum)
|> Axon.Loop.handle(:epoch_completed, summarize)
|> Axon.Loop.run(batched_test, params, compiler: EXLA)

:ok