Repository revision
16:2af1346e68c9

Repository 'keras_train_and_eval'
hg clone https://toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval

Deep learning training and evaluation tool metadata
Miscellaneous
conduct deep training and evaluation either implicitly or explicitly
keras_train_and_eval
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.11.0
1.0.11.0
echo "1.0.11.0"
True
Version lineage of this tool (guids ordered most recent to oldest)
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.11.0 (this tool)
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.10.0
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.8.4
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.8.3
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.8.2
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.8.1
keras_train_and_eval
Requirements (dependencies defined in the <requirements> tag set)
name version type
python 3.9 package
galaxy-ml 0.10.0 package
Additional information about this tool
export HDF5_USE_FILE_LOCKING='FALSE';
        #if $input_options.selected_input == 'refseq_and_interval'
        bgzip -c '$input_options.target_file' > '${target_file.element_identifier}.gz' &&
        tabix -p bed '${target_file.element_identifier}.gz' &&
        cp '$input_options.ref_genome_file' '${ref_genome_file.element_identifier}' &&
        #end if
        python '$__tool_directory__/keras_train_and_eval.py'
            --inputs '$inputs'
            --estimator '$experiment_schemes.infile_estimator'
            #if $input_options.selected_input == 'seq_fasta'
            --fasta_path '$input_options.fasta_path'
            #elif $input_options.selected_input == 'refseq_and_interval'
            --ref_seq "`pwd`/${ref_genome_file.element_identifier}"
            --interval '$input_options.interval_file'
            --targets "`pwd`/${target_file.element_identifier}.gz"
            #else
            --infile1 '$input_options.infile1'
            #end if
            --infile2 '$input_options.infile2'
            --outfile_result '$outfile_result'
            #if $save and 'save_csvlogger' in str($save)
            --outfile_history '$outfile_history'
            #end if
            #if $save and 'save_estimator' in str($save)
            --outfile_object '$outfile_object'
            #end if
            #if $save and 'save_prediction' in str($save)
            --outfile_y_true '$outfile_y_true'
            --outfile_y_preds '$outfile_y_preds'
            #end if
            #if $experiment_schemes.test_split.split_algos.shuffle == 'group'
            --groups '$experiment_schemes.test_split.split_algos.groups_selector.infile_g'
            #end if
        
    
None
False
Functional tests
name inputs outputs required files
Test-1 experiment_schemes|infile_estimator: keras_model04
experiment_schemes|hyperparams_swapping|param_set_0|sp_name: layers_1_Dense__config__kernel_initializer__config__seed
experiment_schemes|hyperparams_swapping|param_set_0|sp_value: 999
experiment_schemes|hyperparams_swapping|param_set_1|sp_name: layers_3_Dense__config__kernel_initializer__config__seed
experiment_schemes|hyperparams_swapping|param_set_1|sp_value: 999
experiment_schemes|hyperparams_swapping|param_set_2|sp_name: learning_rate
experiment_schemes|hyperparams_swapping|param_set_2|sp_value: 0.1
experiment_schemes|hyperparams_swapping|param_set_3|sp_name: optimizer
experiment_schemes|hyperparams_swapping|param_set_3|sp_value: 'adamax'
experiment_schemes|test_split|split_algos|test_size: 0.2
experiment_schemes|test_split|split_algos|random_state: 123
experiment_schemes|test_split|split_algos|shuffle: simple
experiment_schemes|val_split|split_algos|test_size: 0.2
experiment_schemes|val_split|split_algos|random_state: 456
experiment_schemes|val_split|split_algos|shuffle: simple
experiment_schemes|metrics|scoring|secondary_scoring: neg_mean_absolute_error
experiment_schemes|metrics|scoring|primary_scoring: r2
experiment_schemes|selected_exp_scheme: train_val_test
input_options|infile1: regression_X.tabular
input_options|header1: True
input_options|column_selector_options_1|selected_column_selector_option: all_columns
input_options|infile2: regression_y.tabular
input_options|header2: True
input_options|column_selector_options_2|selected_column_selector_option2: all_columns
save: save_estimator
name: value
name: value
keras_model04
regression_X.tabular
regression_y.tabular
value
Test-2 experiment_schemes|infile_estimator: keras_model04
experiment_schemes|hyperparams_swapping|param_set_0|sp_name: layers_1_Dense__config__kernel_initializer__config__seed
experiment_schemes|hyperparams_swapping|param_set_0|sp_value: 999
experiment_schemes|hyperparams_swapping|param_set_1|sp_name: layers_3_Dense__config__kernel_initializer__config__seed
experiment_schemes|hyperparams_swapping|param_set_1|sp_value: 999
experiment_schemes|hyperparams_swapping|param_set_2|sp_name: learning_rate
experiment_schemes|hyperparams_swapping|param_set_2|sp_value: 0.1
experiment_schemes|hyperparams_swapping|param_set_3|sp_name: optimizer
experiment_schemes|hyperparams_swapping|param_set_3|sp_value: 'adamax'
experiment_schemes|test_split|split_algos|group_names: test
experiment_schemes|test_split|split_algos|groups_selector|infile_g: regression_groups.tabular
experiment_schemes|test_split|split_algos|groups_selector|header_g: True
experiment_schemes|test_split|split_algos|groups_selector|column_selector_options_g|col_g: 1
experiment_schemes|test_split|split_algos|groups_selector|column_selector_options_g|selected_column_selector_option_g: by_index_number
experiment_schemes|test_split|split_algos|shuffle: group
experiment_schemes|val_split|split_algos|group_names: validation
experiment_schemes|val_split|split_algos|shuffle: group
experiment_schemes|metrics|scoring|secondary_scoring: neg_mean_absolute_error
experiment_schemes|metrics|scoring|primary_scoring: r2
experiment_schemes|selected_exp_scheme: train_val_test
input_options|infile1: regression_X.tabular
input_options|header1: True
input_options|column_selector_options_1|selected_column_selector_option: all_columns
input_options|infile2: regression_y.tabular
input_options|header2: True
input_options|column_selector_options_2|selected_column_selector_option2: all_columns
save: ['save_estimator', 'save_prediction']
name: value
name: value
name: value
keras_model04
regression_groups.tabular
regression_X.tabular
regression_y.tabular
value
Test-3 experiment_schemes|infile_estimator: pipeline10
experiment_schemes|hyperparams_swapping|param_set_0|sp_name: adaboostregressor__random_state
experiment_schemes|hyperparams_swapping|param_set_0|sp_value: 10
experiment_schemes|hyperparams_swapping|param_set_1|sp_name: adaboostregressor__base_estimator
experiment_schemes|hyperparams_swapping|param_set_1|sp_value: : sklearn_tree.ExtraTreeRegressor(random_state=0)
experiment_schemes|test_split|split_algos|test_size: 0.2
experiment_schemes|test_split|split_algos|random_state: 123
experiment_schemes|test_split|split_algos|shuffle: simple
experiment_schemes|metrics|scoring|secondary_scoring: neg_mean_absolute_error
experiment_schemes|metrics|scoring|primary_scoring: r2
experiment_schemes|selected_exp_scheme: train_val
input_options|infile1: regression_X.tabular
input_options|header1: True
input_options|column_selector_options_1|selected_column_selector_option: all_columns
input_options|infile2: regression_y.tabular
input_options|header2: True
input_options|column_selector_options_2|selected_column_selector_option2: all_columns
save:
name: value
pipeline10
regression_X.tabular
regression_y.tabular
value