immuneML.dsl.instruction_parsers package

Submodules

immuneML.dsl.instruction_parsers.ApplyGenModelParser module

class immuneML.dsl.instruction_parsers.ApplyGenModelParser.ApplyGenModelParser

Bases: object

Specification example for the ApplyGenModel instruction:

instruction_name:
    type: ApplyGenModel
    gen_examples_count: 100
    ml_config_path: ./config.zip
    reports: [data_rep1, ml_rep2]

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → ApplyGenModelInstruction

immuneML.dsl.instruction_parsers.ClusteringParser module

class immuneML.dsl.instruction_parsers.ClusteringParser.ClusteringParser

Bases: object

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → ClusteringInstruction

immuneML.dsl.instruction_parsers.ClusteringParser.make_encoder_obj(symbol_table, encoding_key, dataset_key)

immuneML.dsl.instruction_parsers.ClusteringParser.make_setting_obj(setting, valid_encodings, valid_clusterings, valid_dim_red, symbol_table, instruction)

immuneML.dsl.instruction_parsers.ClusteringParser.parse_clustering_settings(key: str, instruction: dict, symbol_table: SymbolTable) → List[ClusteringSetting]

immuneML.dsl.instruction_parsers.ClusteringParser.parse_labels(key, instruction, dataset) → LabelConfiguration

immuneML.dsl.instruction_parsers.ClusteringParser.parse_metrics(key: str, instruction: dict, symbol_table: SymbolTable) → List[str]

immuneML.dsl.instruction_parsers.ClusteringParser.parse_reports(key, instruction, symbol_table) → List[Report]

immuneML.dsl.instruction_parsers.ClusteringParser.parse_split_config(key, instruction, symbol_table) → SplitConfig

immuneML.dsl.instruction_parsers.DatasetExportParser module

class immuneML.dsl.instruction_parsers.DatasetExportParser.DatasetExportParser

Bases: object

Specification of instruction with a random datasets:

definitions:

datasets:

my_generated_dataset: # a dataset to be exported in the given format

format: RandomRepertoireDataset params:

result_path: generated_dataset/ repertoire_count: 100 sequence_count_probabilities:

100: 0.5 120: 0.5

sequence_length_probabilities:

12: 0.333 13: 0.333 14: 0.333

labels:

immune_event_1:

yes: 0.5 no: 0.5

preprocessing_sequences:

my_preprocessing:

• my_filter:

  ClonesPerRepertoireFilter:

  lower_limit: 110 upper_limit: 200

instructions:

my_instruction1: # instruction name

type: DatasetExport datasets: # list of datasets to export

• my_generated_dataset

preprocessing_sequence: my_preprocessing_sequence number_of_processes: 4 export_formats: # list of formats to export the datasets to

• AIRR

OPTIONAL_KEYS = ['preprocessing_sequence', 'number_of_processes']

REQUIRED_KEYS = ['type', 'datasets', 'export_formats']

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → DatasetExportInstruction

immuneML.dsl.instruction_parsers.ExploratoryAnalysisParser module

class immuneML.dsl.instruction_parsers.ExploratoryAnalysisParser.ExploratoryAnalysisParser

Bases: object

The specification consists of a list of analyses that need to be performed;

Each analysis is defined by a dataset identifier, a report identifier and optionally encoding and labels and are loaded into ExploratoryAnalysisUnit objects;

DSL example for ExploratoryAnalysisInstruction assuming that d1, p1, r1, r2, e1, w1 are defined previously in definitions section:

instruction_name:
    type: ExploratoryAnalysis
    number_of_processes: 4
    analyses:
        my_first_analysis: # simple analysis running a report on a dataset
            dataset: d1
            report: r1
        my_second_analysis: # more complicated analysis; including preprocessing, encoding, example weighting and running a report
            dataset: d1
            preprocessing_sequence: p1
            encoding: e1
            example_weighting: w1
            report: r2
            labels:
                - CD
                - CMV

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → ExploratoryAnalysisInstruction

immuneML.dsl.instruction_parsers.FeasibilitySummaryParser module

class immuneML.dsl.instruction_parsers.FeasibilitySummaryParser.FeasibilitySummaryParser

Bases: object

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → FeasibilitySummaryInstruction

immuneML.dsl.instruction_parsers.LabelHelper module

class immuneML.dsl.instruction_parsers.LabelHelper.LabelHelper

Bases: object

static check_label_format(labels: list, instruction_name: str, yaml_location: str)

static create_label_config(labels: list, dataset: Dataset, instruction_name: str, yaml_location: str) → LabelConfiguration

immuneML.dsl.instruction_parsers.LigoSimParser module

class immuneML.dsl.instruction_parsers.LigoSimParser.LigoSimParser

Bases: object

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → LigoSimInstruction

immuneML.dsl.instruction_parsers.LigoSimParser.get_simulation_from_symbol_table(sim_key, symbol_table, location)

immuneML.dsl.instruction_parsers.MLApplicationParser module

class immuneML.dsl.instruction_parsers.MLApplicationParser.MLApplicationParser

Bases: object

Specification example for the MLApplication instruction:

instruction_name:
    type: MLApplication
    dataset: d1
    config_path: ./config.zip
    metrics:
    - accuracy
    - precision
    - recall
    number_of_processes: 4

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path) → MLApplicationInstruction

immuneML.dsl.instruction_parsers.SubsamplingParser module

class immuneML.dsl.instruction_parsers.SubsamplingParser.SubsamplingParser

Bases: object

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → SubsamplingInstruction

immuneML.dsl.instruction_parsers.TrainGenModelParser module

class immuneML.dsl.instruction_parsers.TrainGenModelParser.TrainGenModelParser

Bases: object

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → TrainGenModelInstruction

immuneML.dsl.instruction_parsers.TrainMLModelParser module

class immuneML.dsl.instruction_parsers.TrainMLModelParser.TrainMLModelParser

Bases: object

parse(key: str, instruction: dict, symbol_table: SymbolTable, path: Path = None) → TrainMLModelInstruction

Module contents