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54 lines
3 KiB
YAML
54 lines
3 KiB
YAML
evaluation:
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data_dirs: null # List of Paths
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output_dir: null # output filename is output_dir / experiment_name
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experiment_name: null
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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
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aggregate_groups: # groups over which to aggregate values and compute mean/std. Default: [engine.seed]
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- engine.seed
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depth: 1 # the depth of the trial dirs relative to the given data_dirs
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checkpoints: [best, last] # which model checkpoint to use
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output_types: [pdf, png, csv] # choose all you want from {csv, pdf, png} and put it in brackets
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verbose: False # debug prints
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column_split_key: optimizer.name # if set, will split the dataframe and plot it in columns. Default: optimizer.name
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column_split_order: null # sets the order in which the columns are plotted.
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# keeping the values on null -> automatically figure it out if possible, or let matplotlib decide
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plot:
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x_axis: # indices on x axis (same order as order of subigures given in data_dirs)
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- optimizer.weight_decay
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y_axis: # indices on y axis (same order as order of subigures given in data_dirs)
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- optimizer.learning_rate
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metric: null # is automatically chosen from task name, this will overwrite it
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limits: null # sets the limits for the colormap, 2 ints, order does not matter, leave empty for automatic
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std: True # show std over aggregated values
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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
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# format:
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# string, how many digits to display, expects two values seperated by a dot (e.g. "2.3")
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# to make accuracy -> percent use a '2' in front of the dot
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# to display 3 digits after the decimal point, write a '3' behind the dot
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format: null # for example {"2.0", "2.1", "2.3", "0.2", ...}
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single_file: true # if true, save all heatmaps in one file. 'split_groups' are represented as rows.
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plotstyle:
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tight_layout: True
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text:
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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
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# general font
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font:
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family: "serif" # matplotlib {serif, sans-serif, cursive, fantasy, monospace}
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size: 14
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# the font in the tiles of the matrix
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matrix_font:
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size: 12
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scale: 1.0 # scales *figsize* argument by this value, useful for ".png"
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color_palette: "rocket"
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dpi: 300
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# the name of the files storing the hyperparameters of the experiments and the scores
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experiment_files:
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best_model: results_best_model.json
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last_model: results_final_model.json
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config: config.yaml
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