evaluation:
  data_dirs: null                # List of Paths
  output_dir: null               # output filename is output_dir / experiment_name
  experiment_name: null
  split_groups: false            # {True, False, [param.a, param.b, ...]} create additional plots where the data is grouped by the given parameter; True to detect all params with multiple unique values
  aggregate_groups:              # groups over which to aggregate values and compute mean/std. Default: [engine.seed]
    - engine.seed
  depth: 1                       # the depth of the trial dirs relative to the given data_dirs
  checkpoints: [best, last]      # which model checkpoint to use
  output_types: [pdf, png, csv]  # choose all you want from {csv, pdf, png} and put it in brackets
  verbose: False                 # debug prints
  column_split_key: optimizer.name  # if set, will split the dataframe and plot it in columns. Default: optimizer.name
  column_split_order: null        # sets the order in which the columns are plotted.

  # keeping the values on null -> automatically figure it out if possible, or let matplotlib decide
  plot:
    x_axis:                      # indices on x axis (same order as order of subigures given in data_dirs)
      - optimizer.weight_decay
    y_axis:                      # indices on y axis (same order as order of subigures given in data_dirs)
      - optimizer.learning_rate
    metric: null                 # is automatically chosen from task name, this will overwrite it
    limits: null                 # sets the limits for the colormap, 2 ints, order does not matter, leave empty for automatic
    std: True                    # show std over aggregated values
    aggfunc: std                 # for example {std, var, sem} which function to use to aggregate over the seeds; will only be used when 'std' is set to true
    # format:
    #  string, how many digits to display, expects two values seperated by a dot (e.g. "2.3")
    #  to make accuracy -> percent use a '2' in front of the dot
    #  to display 3 digits after the decimal point, write a '3' behind the dot
    format: null                 # for example {"2.0", "2.1", "2.3", "0.2", ...}
    single_file: true            # if true, save all heatmaps in one file. 'split_groups' are represented as rows.

  plotstyle:
    tight_layout: True
    text:
      usetex: True               # you can give latex code in the yaml: $\sqrt{\pi \cdot \sigma}$ but some cluster dont have it installed# the font in the tiles of the matrix

    # general font
    font:
      family: "serif"            # matplotlib {serif, sans-serif, cursive, fantasy, monospace}
      size: 14

    # the font in the tiles of the matrix
    matrix_font:
      size: 12

    scale: 1.0                   # scales *figsize* argument by this value, useful for ".png"
    color_palette: "rocket"
    dpi: 300

  # the name of the files storing the hyperparameters of the experiments and the scores
  experiment_files:
    best_model: results_best_model.json
    last_model: results_final_model.json
    config: config.yaml