Index

Symbols

--arff_regression skll_convert command line option --arff_relation ARFF_RELATION skll_convert command line option --id_col <id_col> filter_features command line option --k <k> print_model_weights command line option --no_labels skll_convert command line option --reuse_libsvm_map REUSE_LIBSVM_MAP skll_convert command line option --sign {positive,negative,all} print_model_weights command line option --sort_by_labels print_model_weights command line option --version compute_eval_from_predictions command line option filter_features command line option generate_predictions command line option join_features command line option print_model_weights command line option run_experiment command line option skll_convert command line option summarize_results command line option -a <num_features>, --ablation <num_features> run_experiment command line option -a, --ablation summarize_results command line option -A, --ablation_all run_experiment command line option -db, --drop-blanks filter_features command line option -f <feature <feature ...>>, --feature <feature <feature ...>> filter_features command line option -I <id <id ...>>, --id <id <id ...>> filter_features command line option

-i <id_col>, --id_col <id_col> generate_predictions command line option -i, --inverse filter_features command line option -k, --keep-models run_experiment command line option -L <label <label ...>>, --label <label <label ...>> filter_features command line option -l <label_col>, --label_col <label_col> filter_features command line option generate_predictions command line option join_features command line option skll_convert command line option -l, --local run_experiment command line option -m <machines>, --machines <machines> run_experiment command line option -o <path>, --output_file <path> generate_predictions command line option -p, --predict_labels generate_predictions command line option -q <queue>, --queue <queue> run_experiment command line option -q, --quiet filter_features command line option generate_predictions command line option join_features command line option skll_convert command line option -r, --resume run_experiment command line option -rb <replacement>, --replace-blanks-with <replacement> filter_features command line option -t <threshold>, --threshold <threshold> generate_predictions command line option -v, --verbose run_experiment command line option

A

ARFFReader (class in skll.data.readers)

ARFFWriter (class in skll.data.writers)

C

CLASSIFICATION_ONLY_METRICS (in module skll.utils.constants) compute_eval_from_predictions command line option --version examples_file metric_names predictions_file

correlation() (in module skll.metrics) CORRELATION_METRICS (in module skll.utils.constants) cross_validate() (skll.Learner method) (skll.learner.Learner method) CSVReader (class in skll.data.readers) CSVWriter (class in skll.data.writers)

D

DelimitedReader (class in skll.data.readers)

DictListReader (class in skll.data.readers)

E

evaluate() (skll.Learner method) (skll.learner.Learner method)

examples_file compute_eval_from_predictions command line option

F

f1_score_least_frequent() (in module skll.metrics) FeatureSet (class in skll) (class in skll.data.featureset) filter() (skll.data.featureset.FeatureSet method) (skll.FeatureSet method) filter_features command line option --id_col <id_col> --version -I <id <id ...>>, --id <id <id ...>> -L <label <label ...>>, --label <label <label ...>> -db, --drop-blanks -f <feature <feature ...>>, --feature <feature <feature ...>> -i, --inverse -l <label_col>, --label_col <label_col> -q, --quiet -rb <replacement>, --replace-blanks-with <replacement> infile outfile

filtered_iter() (skll.data.featureset.FeatureSet method) (skll.FeatureSet method) fix_json() (in module skll.config) for_path() (skll.data.readers.Reader class method) (skll.data.writers.Writer class method) from_data_frame() (skll.data.featureset.FeatureSet static method) (skll.FeatureSet static method) from_file() (skll.Learner class method) (skll.learner.Learner class method)

G

generate_learning_curve_plots() (in module skll.experiments) generate_predictions command line option --version -i <id_col>, --id_col <id_col> -l <label_col>, --label_col <label_col> -o <path>, --output_file <path> -p, --predict_labels -q, --quiet -t <threshold>, --threshold <threshold> input_file(s) model_file

get_skll_logger() (in module skll.utils.logging)

H

has_labels (skll.data.featureset.FeatureSet attribute) (skll.FeatureSet attribute)

I

infile filter_features command line option skll_convert command line option

infile ... join_features command line option input_file(s) generate_predictions command line option

J

join_features command line option --version -l <label_col>, --label_col <label_col> -q, --quiet infile ... outfile

json_file summarize_results command line option

K

kappa() (in module skll.metrics)

L

Learner (class in skll) (class in skll.learner) learning_curve() (skll.Learner method) (skll.learner.Learner method) LibSVMReader (class in skll.data.readers) LibSVMWriter (class in skll.data.writers)

load() (skll.Learner method) (skll.learner.Learner method) load_custom_learner() (in module skll.learner) load_cv_folds() (in module skll.config) load_featureset() (in module skll.experiments) locate_file() (in module skll.config)

M

metric_names compute_eval_from_predictions command line option model (skll.Learner attribute) (skll.learner.Learner attribute) model_file generate_predictions command line option print_model_weights command line option

model_kwargs (skll.Learner attribute) (skll.learner.Learner attribute) model_params (skll.Learner attribute) (skll.learner.Learner attribute) model_type (skll.Learner attribute) (skll.learner.Learner attribute)

N

NDJReader (class in skll.data.readers)

NDJWriter (class in skll.data.writers)

O

outfile filter_features command line option join_features command line option skll_convert command line option

output_dir plot_learning_curves command line option

P

plot_learning_curves command line option output_dir tsv_file predict() (skll.Learner method) (skll.learner.Learner method) predictions_file compute_eval_from_predictions command line option print_model_weights command line option --k <k> --sign {positive,negative,all} --sort_by_labels --version model_file

PROBABILISTIC_METRICS (in module skll.utils.constants) probability (skll.Learner attribute) (skll.learner.Learner attribute)

R

read() (skll.data.readers.DictListReader method) (skll.data.readers.Reader method) Reader (class in skll.data.readers) register_custom_metric() (in module skll.metrics) REGRESSION_ONLY_METRICS (in module skll.utils.constants) run_configuration() (in module skll) (in module skll.experiments) run_experiment command line option --version -A, --ablation_all -a <num_features>, --ablation <num_features> -k, --keep-models -l, --local -m <machines>, --machines <machines> -q <queue>, --queue <queue> -r, --resume -v, --verbose

S

safe_float() (in module skll.data.readers) save() (skll.Learner method) (skll.learner.Learner method) skll.data.featureset (module) skll.data.readers (module) skll.data.writers (module) skll.experiments (module) skll.learner (module) skll.metrics (module) skll_convert command line option --arff_regression --arff_relation ARFF_RELATION --no_labels --reuse_libsvm_map REUSE_LIBSVM_MAP --version -l <label_col>, --label_col <label_col> -q, --quiet infile outfile

split_by_ids() (skll.data.featureset.FeatureSet static method) (skll.FeatureSet static method) split_with_quotes() (skll.data.readers.ARFFReader static method) summarize_results command line option --version -a, --ablation json_file summary_file summary_file summarize_results command line option

T

train() (skll.Learner method) (skll.learner.Learner method) tsv_file plot_learning_curves command line option

TSVReader (class in skll.data.readers) TSVWriter (class in skll.data.writers)

U

UNWEIGHTED_KAPPA_METRICS (in module skll.utils.constants)

use_score_func() (in module skll.metrics)

W

WEIGHTED_KAPPA_METRICS (in module skll.utils.constants)

write() (skll.data.writers.Writer method) Writer (class in skll.data.writers)