Utility Scripts

In addition to the main script, run_experiment, SKLL comes with a number of helpful utility scripts that can be used to prepare feature files and perform other routine tasks. Each is described briefly below.

compute_eval_from_predictions

Compute evaluation metrics from prediction files after you have run an experiment.

Positional Arguments

examples_file

SKLL input file with labeled examples

predictions_file

file with predictions from SKLL

metric_names

metrics to compute

Optional Arguments

--version

Show program’s version number and exit.


filter_features

Filter feature file to remove (or keep) any instances with the specified IDs or labels. Can also be used to remove/keep feature columns.

Positional Arguments

infile

Input feature file (ends in .arff, .csv, .jsonlines, .megam, .ndj, or .tsv)

outfile

Output feature file (must have same extension as input file)

Optional Arguments

-f <feature <feature ...>>, --feature <feature <feature ...>>

A feature in the feature file you would like to keep. If unspecified, no features are removed.

-I <id <id ...>>, --id <id <id ...>>

An instance ID in the feature file you would like to keep. If unspecified, no instances are removed based on their IDs.

-i, --inverse

Instead of keeping features and/or examples in lists, remove them.

--id_col <id_col>

Name of the column which contains the instance IDs in ARFF, CSV, or TSV files. (default: id)

-L <label <label ...>>, --label <label <label ...>>

A label in the feature file you would like to keep. If unspecified, no instances are removed based on their labels.

-l <label_col>, --label_col <label_col>

Name of the column which contains the class labels in ARFF, CSV, or TSV files. For ARFF files, this must be the final column to count as the label. (default: y)

-db, --drop-blanks

Drop all lines/rows that have any blank values. (default: False)

-rb <replacement>, --replace-blanks-with <replacement>

Specifies a new value with which to replace blank values in all columns in the file. To replace blanks differently in each column, use the SKLL Reader API directly. (default: None)

-q, --quiet

Suppress printing of "Loading..." messages.

--version

Show program’s version number and exit.


generate_predictions

Loads a trained model and outputs predictions based on input feature files. Useful if you want to reuse a trained model as part of a larger system without creating configuration files. Offers the following modes of operation:

  • For non-probabilistic classification and regression, generate the predictions.
  • For probabilistic classification, generate either the most likely labels or the probabilities for each class label.
  • For binary probablistic classification, generate the positive class label only if its probability exceeds the given threshold. The positive class label is either read from the model file or inferred the same way as a SKLL learner would.

Positional Arguments

model_file

Model file to load and use for generating predictions.

input_file(s)

One or more csv file(s), jsonlines file(s), or megam file(s) (with or without the label column), with the appropriate suffix.

Optional Arguments

-i <id_col>, --id_col <id_col>

Name of the column which contains the instance IDs in ARFF, CSV, or TSV files. (default: id)

-l <label_col>, --label_col <label_col>

Name of the column which contains the labels in ARFF, CSV, or TSV files. For ARFF files, this must be the final column to count as the label. (default: y)

-o <path>, --output_file <path>

Path to output TSV file. If not specified, predictions will be printed to stdout. For probabilistic binary classification, the probability of the positive class will always be in the last column.

-p, --predict_labels

If the model does probabilistic classification, output the class label with the highest probability instead of the class probabilities.

-q, --quiet

Suppress printing of "Loading..." messages.

-t <threshold>, --threshold <threshold>

If the model does binary probabilistic classification, return the positive class label only if it meets/exceeds the given threshold and the other class label otherwise.

--version

Show program’s version number and exit.


join_features

Combine multiple feature files into one larger file.

Positional Arguments

infile ...

Input feature files (ends in .arff, .csv, .jsonlines, .megam, .ndj, or .tsv)

outfile

Output feature file (must have same extension as input file)

Optional Arguments

-l <label_col>, --label_col <label_col>

Name of the column which contains the labels in ARFF, CSV, or TSV files. For ARFF files, this must be the final column to count as the label. (default: y)

-q, --quiet

Suppress printing of "Loading..." messages.

--version

Show program’s version number and exit.


plot_learning_curves

Generate learning curve plots from a learning curve output TSV file.

Positional Arguments

tsv_file

Input learning Curve TSV output file.

output_dir

Output directory to store the learning curve plots.


skll_convert

Convert between .arff, .csv., .jsonlines, .libsvm, .megam, and .tsv formats.

Positional Arguments

infile

Input feature file (ends in .arff, .csv, .jsonlines, .libsvm, .megam, .ndj, or .tsv)

outfile

Output feature file (ends in .arff, .csv, .jsonlines, .libsvm, .megam, .ndj, or .tsv)

Optional Arguments

-l <label_col>, --label_col <label_col>

Name of the column which contains the labels in ARFF, CSV, or TSV files. For ARFF files, this must be the final column to count as the label. (default: y)

-q, --quiet

Suppress printing of "Loading..." messages.

--arff_regression

Create ARFF files for regression, not classification.

--arff_relation ARFF_RELATION

Relation name to use for ARFF file. (default: skll_relation)

--no_labels

Used to indicate that the input data has no labels.

--reuse_libsvm_map REUSE_LIBSVM_MAP

If you want to output multiple files that use the same mapping from labels and features to numbers when writing libsvm files, you can specify an existing .libsvm file to reuse the mapping from.

--version

Show program’s version number and exit.


summarize_results

Creates an experiment summary TSV file from a list of JSON files generated by run_experiment.

Positional Arguments

summary_file

TSV file to store summary of results.

json_file

JSON results file generated by run_experiment.

Optional Arguments

-a, --ablation

The results files are from an ablation run.

--version

Show program’s version number and exit.