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Results Processing

Results%20Processing.livemd

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Alexis Brodeur
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Results Processing

Dependencies

Mix.install([
  {:exla, "~> 0.1.0-dev", github: "elixir-nx/nx", sparse: "exla", override: true},
  {:nx, "~> 0.1.0-dev", github: "elixir-nx/nx", sparse: "nx", override: true},
  {:scholar, "~> 0.1.0", github: "elixir-nx/scholar", branch: "main"},
  {:explorer, "~> 0.1.0-dev", github: "elixir-nx/explorer", branch: "main"},
  {:vega_lite, "~> 0.1.3"},
  {:kino, "~> 0.5.2"}
])
alias Explorer.DataFrame
alias Explorer.Series
alias VegaLite, as: Vl

Feed Forward Neural Network

losses_df =
  "FFNN.losses.csv"
  |> DataFrame.read_csv!()
  |> DataFrame.mutate(
    epoch: &Series.add(&1["epoch"], 1),
    attempt: &Series.add(&1["attempt"], 1)
  )
Vl.new(width: 800, height: 600)
|> Vl.data_from_series(DataFrame.to_map(losses_df))
|> Vl.mark(:line)
|> Vl.encode_field(:x, "epoch", type: :ordinal, title: "Epoch")
|> Vl.encode_field(:y, "loss", type: :quantitative, title: "Loss")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
results_df =
  "FFNN.results.csv"
  |> DataFrame.read_csv!()
  |> DataFrame.mutate(attempt: &Series.add(&1["attempt"], 1))

make_colors = fn serie ->
  results_df
  |> Access.get(serie)
  |> Series.to_list()
  |> Enum.map(fn _ -> serie end)
  |> Series.from_list()
end

attempt = Series.concat(results_df["attempt"], results_df["attempt"])

metrics = results_df["accuracy"]
colors = make_colors.("accuracy")

metrics = Series.concat(metrics, results_df["precision"])
colors = Series.concat(colors, make_colors.("precision"))

results_df = DataFrame.from_columns(attempt: attempt, color: colors, metric: metrics)
Vl.new(width: 800, height: 600)
|> Vl.data_from_series(DataFrame.to_map(results_df))
|> Vl.mark(:bar)
|> Vl.encode_field(:x, "attempt", type: :ordinal, title: "Attempt")
|> Vl.encode_field(:y, "metric", type: :quantitative, title: "Value")
|> Vl.encode_field(:color, "color", type: :nominal, title: "Metric")
|> Vl.encode_field(:x_offset, "color", type: :nominal, title: "Metric")

Auto Encoder

losses_df =
  "AE.losses.csv"
  |> DataFrame.read_csv!()
  |> DataFrame.mutate(
    epoch: &Series.add(&1["epoch"], 1),
    attempt: &Series.add(&1["attempt"], 1)
  )
Vl.new(width: 800, height: 600)
|> Vl.mark(:line)
|> Vl.data_from_series(DataFrame.to_map(losses_df))
|> Vl.encode_field(:x, "epoch", type: :ordinal, title: "Epoch")
|> Vl.encode_field(:y, "loss", type: :quantitative, title: "Loss")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
Vl.new(width: 800, height: 600)
|> Vl.mark(:line)
|> Vl.data_from_series(DataFrame.to_map(losses_df))
|> Vl.encode_field(:x, "epoch", type: :ordinal, title: "Epoch")
|> Vl.encode_field(:y, "mae", type: :quantitative, title: "Mean Absolute Error")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
results_df =
  "AE.results.csv"
  |> DataFrame.read_csv!()
  |> DataFrame.mutate(attempt: &Series.add(&1["attempt"], 1))
Vl.new(width: 800, height: 600)
|> Vl.mark(:bar)
|> Vl.data_from_series(DataFrame.to_map(results_df))
|> Vl.encode_field(:x, "threshold", type: :ordinal, title: "Threshold")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
|> Vl.encode_field(:y, "fp", type: :quantitative, title: "False Positives")
|> Vl.encode_field(:x_offset, "attempt", type: :nominal, title: "Attempt")
Vl.new(width: 800, height: 600)
|> Vl.mark(:bar)
|> Vl.data_from_series(DataFrame.to_map(results_df))
|> Vl.encode_field(:x, "threshold", type: :ordinal, title: "Threshold")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
|> Vl.encode_field(:y, "tp", type: :quantitative, title: "True Positives")
|> Vl.encode_field(:x_offset, "attempt", type: :nominal, title: "Attempt")

GAN

losses_df =
  "GAN.losses.csv"
  |> DataFrame.read_csv!()
  |> DataFrame.mutate(
    epoch: &Series.add(&1["epoch"], 1),
    attempt: &Series.add(&1["attempt"], 1)
  )
Vl.new(width: 800, height: 600)
|> Vl.mark(:line)
|> Vl.data_from_series(DataFrame.to_map(losses_df))
|> Vl.encode_field(:x, "epoch", type: :ordinal, title: "Epoch")
|> Vl.encode_field(:y, "g_loss", type: :quantitative, title: "Generator Loss")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
Vl.new(width: 800, height: 600)
|> Vl.mark(:line)
|> Vl.data_from_series(DataFrame.to_map(losses_df))
|> Vl.encode_field(:x, "epoch", type: :ordinal, title: "Epoch")
|> Vl.encode_field(:y, "d_loss", type: :quantitative, title: "Discriminator Loss")
|> Vl.encode_field(:color, "attempt", type: :nominal, title: "Attempt")
mean_losses_df =
  losses_df
  |> DataFrame.group_by(["epoch"])
  |> DataFrame.summarise(d_loss: [:mean], g_loss: [:mean])
make_colors = fn serie ->
  mean_losses_df
  |> Access.get(serie)
  |> Series.to_list()
  |> Enum.map(fn _ -> serie end)
  |> Series.from_list()
end

epoch = Series.concat(mean_losses_df["epoch"], mean_losses_df["epoch"])

color = make_colors.("g_loss_mean")
metric = mean_losses_df["g_loss_mean"]

color = Series.concat(color, make_colors.("d_loss_mean"))
metric = Series.concat(metric, mean_losses_df["d_loss_mean"])

mean_losses_df = DataFrame.from_columns(epoch: epoch, color: color, metric: metric)
Vl.new(width: 800, height: 600)
|> Vl.data_from_series(DataFrame.to_map(mean_losses_df))
|> Vl.mark(:line)
|> Vl.encode_field(:x, "epoch", type: :nominal, title: "Epoch")
|> Vl.encode_field(:y, "metric", type: :quantitative, title: "Metric")
|> Vl.encode_field(:color, "color", type: :nominal, title: "Metric")
results_df =
  "GAN.results.csv"
  |> DataFrame.read_csv!()
  |> DataFrame.mutate(attempt: &Series.add(&1["attempt"], 1))

make_colors = fn serie ->
  results_df
  |> Access.get(serie)
  |> Series.to_list()
  |> Enum.map(fn _ -> serie end)
  |> Series.from_list()
end

attempt = Series.concat(results_df["attempt"], results_df["attempt"])

metrics = results_df["accuracy"]
colors = make_colors.("accuracy")

metrics = Series.concat(metrics, results_df["precision"])
colors = Series.concat(colors, make_colors.("precision"))

results_df = DataFrame.from_columns(attempt: attempt, color: colors, metric: metrics)
Vl.new(width: 800, height: 600)
|> Vl.data_from_series(DataFrame.to_map(results_df))
|> Vl.mark(:bar)
|> Vl.encode_field(:x, "attempt", type: :ordinal, title: "Attempt")
|> Vl.encode_field(:y, "metric", type: :quantitative, title: "Value")
|> Vl.encode_field(:color, "color", type: :nominal, title: "Metric")
|> Vl.encode_field(:x_offset, "color", type: :nominal, title: "Metric")

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