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Titanic

livebooks/axon/titanic.livemd

Titanic

Mix.install([
  {:csv, "~> 3.0"},
  {:exla, "~> 0.6"},
  {:axon, "~> 0.6"},
  {:kino, "~> 0.12"},
  {:kino_vega_lite, "~> 0.1"}
])

データの準備

Kaggle のタイタニックデータをダウンロードする

https://www.kaggle.com/competitions/titanic/data

train_data_input = Kino.Input.file("train data")
test_data_input = Kino.Input.file("test data")

前処理

[header_org | rows_org] =
  train_data_input
  |> Kino.Input.read()
  |> Map.get(:file_ref)
  |> Kino.Input.file_path()
  |> File.stream!()
  |> CSV.decode!()
  |> Enum.to_list()
header =
  header_org
  |> Enum.map(fn column_name ->
    column_name
    |> String.downcase()
    |> String.to_atom()
  end)
train_data =
  header
  |> Enum.with_index()
  |> Enum.into(%{}, fn {key, index} ->
    {
      key,
      Enum.map(rows_org, &Enum.at(&1, index))
    }
  end)
Kino.DataTable.new(train_data)
id_list = train_data.passengerid
label_tensor =
  train_data.survived
  |> Enum.map(fn datum ->
    datum
    |> String.to_integer()
    |> Nx.tensor(type: :f32)
    |> Nx.new_axis(-1)
    |> Nx.new_axis(-1)
  end)
inputs = Map.drop(train_data, [:passengerid, :survived])
dropped_inputs = Map.drop(inputs, [:cabin, :name, :ticket])
train_data.age
|> Enum.filter(&(&1 == ""))
|> Enum.count()
count_missings = fn data ->
  data
  |> Enum.into(%{}, fn {key, list} ->
    {
      key,
      list
      |> Enum.filter(&(&1 == ""))
      |> Enum.count()
    }
  end)
  |> Enum.filter(fn {_, count} -> count > 0 end)
end
count_missings.(train_data)
filled_inputs =
  dropped_inputs
  |> Map.put(
    :age,
    dropped_inputs.age
    |> Enum.map(fn
      "" -> "0"
      others -> others
    end)
  )
  |> Map.put(
    :embarked,
    dropped_inputs.embarked
    |> Enum.map(fn
      "" -> "S"
      others -> others
    end)
  )

Kino.DataTable.new(filled_inputs)
sex_map =
  filled_inputs.sex
  |> Enum.uniq()
  |> Enum.sort()
  |> Enum.with_index()
  |> Enum.into(%{})
embarked_map =
  filled_inputs.embarked
  |> Enum.uniq()
  |> Enum.sort()
  |> Enum.with_index()
  |> Enum.into(%{})
categorised_inputs =
  filled_inputs
  |> Map.put(
    :sex,
    filled_inputs.sex
    |> Enum.map(&sex_map[&1])
  )
  |> Map.put(
    :embarked,
    filled_inputs.embarked
    |> Enum.map(&embarked_map[&1])
  )
parsed_inputs =
  [:age, :fare, :parch, :pclass, :sibsp]
  |> Enum.reduce(categorised_inputs, fn column_name, acc ->
    acc
    |> Map.put(
      column_name,
      acc
      |> Map.get(column_name)
      |> Enum.map(fn value ->
        value
        |> Float.parse()
        |> elem(0)
      end)
    )
  end)

Kino.DataTable.new(parsed_inputs)
input_tensor_list =
  parsed_inputs
  |> Map.values()
  |> Nx.tensor()
  |> Nx.transpose()
  |> Nx.to_batched(1)
  |> Enum.to_list()
defmodule PreProcess do
  def load_csv(kino_input) do
    [header_org | rows_org] =
      kino_input
      |> Kino.Input.read()
      |> Map.get(:file_ref)
      |> Kino.Input.file_path()
      |> File.stream!()
      |> CSV.decode!()
      |> Enum.to_list()

    header =
      header_org
      |> Enum.map(fn column_name ->
        column_name
        |> String.downcase()
        |> String.to_atom()
      end)

    header
    |> Enum.with_index()
    |> Enum.into(%{}, fn {key, index} ->
      {
        key,
        Enum.map(rows_org, &Enum.at(&1, index))
      }
    end)
  end

  def fill_empty(data, fill_map) do
    fill_map
    |> Enum.reduce(data, fn {column_name, fill_value}, acc ->
      acc
      |> Map.put(
        column_name,
        data
        |> Map.get(column_name)
        |> Enum.map(fn
          "" -> fill_value
          others -> others
        end)
      )
    end)
  end

  def to_number(data, columns_names) do
    columns_names
    |> Enum.reduce(data, fn column_name, acc ->
      conversion_map =
        data
        |> Map.get(column_name)
        |> Enum.uniq()
        |> Enum.sort()
        |> Enum.with_index()
        |> Enum.into(%{})

      acc
      |> Map.put(
        column_name,
        data
        |> Map.get(column_name)
        |> Enum.map(&conversion_map[&1])
      )
    end)
  end

  def to_tensor(data, columns_names) do
    columns_names
    |> Enum.reduce(data, fn column_name, acc ->
      acc
      |> Map.put(
        column_name,
        acc
        |> Map.get(column_name)
        |> Enum.map(fn value ->
          value
          |> Float.parse()
          |> elem(0)
        end)
      )
    end)
    |> Map.values()
    |> Nx.tensor()
    |> Nx.transpose()
    |> Nx.to_batched(1)
    |> Enum.to_list()
  end

  def process(kino_input, id_key, label_key) do
    data_org = load_csv(kino_input)

    id_list = Map.get(data_org, id_key)

    label_list =
      if Map.has_key?(data_org, label_key) do
        data_org
        |> Map.get(label_key)
        |> Enum.map(fn datum ->
          datum
          |> String.to_integer()
          |> Nx.tensor(type: :f32)
          |> Nx.new_axis(-1)
          |> Nx.new_axis(-1)
        end)
      else
        nil
      end

    inputs =
      data_org
      |> Map.drop([id_key, label_key, :cabin, :name, :ticket])
      |> fill_empty(%{age: "0", embarked: "S", fare: "0"})
      |> to_number([:sex, :embarked])
      |> to_tensor([:age, :fare, :parch, :pclass, :sibsp])

    {id_list, label_list, inputs}
  end
end
{
  train_id_list,
  train_label_list,
  train_inputs
} = PreProcess.process(train_data_input, :passengerid, :survived)
{
  test_id_list,
  test_label_list,
  test_inputs
} = PreProcess.process(test_data_input, :passengerid, :survived)

モデルの定義

model =
  Axon.input("input", shape: {nil, 7})
  |> Axon.dense(48, activation: :tanh)
  |> Axon.dropout(rate: 0.2)
  |> Axon.dense(48, activation: :tanh)
  |> Axon.dense(1, activation: :sigmoid)

学習

train_data = Enum.zip(train_inputs, train_label_list)
loss_plot =
  VegaLite.new(width: 300)
  |> VegaLite.mark(:line)
  |> VegaLite.encode_field(:x, "step", type: :quantitative)
  |> VegaLite.encode_field(:y, "loss", type: :quantitative)
  |> Kino.VegaLite.new()

acc_plot =
  VegaLite.new(width: 300)
  |> VegaLite.mark(:line)
  |> VegaLite.encode_field(:x, "step", type: :quantitative)
  |> VegaLite.encode_field(:y, "accuracy", type: :quantitative)
  |> Kino.VegaLite.new()

Kino.Layout.grid([loss_plot, acc_plot], columns: 2)
trained_state =
  model
  |> Axon.Loop.trainer(:mean_squared_error, Axon.Optimizers.adam(0.0005))
  |> Axon.Loop.metric(:accuracy, "accuracy")
  |> Axon.Loop.kino_vega_lite_plot(loss_plot, "loss", event: :epoch_completed)
  |> Axon.Loop.kino_vega_lite_plot(acc_plot, "accuracy", event: :epoch_completed)
  |> Axon.Loop.run(train_data, %{}, epochs: 50, compiler: EXLA)

テストデータに対する推論

test_inputs
|> List.first()
|> then(&Axon.predict(model, trained_state, &1))
results =
  test_inputs
  |> Nx.concatenate()
  |> then(&Axon.predict(model, trained_state, &1))
  |> Nx.to_flat_list()
  |> Enum.map(&round(&1))
results_csv =
  results
  |> then(&[test_id_list, &1])
  |> Enum.zip()
  |> Enum.map(fn {id, label} ->
    [id, "#{label}"]
  end)
  |> then(&[["PassengerId", "Survived"] | &1])
  |> CSV.encode()
  |> Enum.to_list()
  |> Enum.join()
Kino.Download.new(fn -> results_csv end, filename: "result.csv")