Source code for immuneML.api.galaxy.build_yaml_from_arguments

import argparse
import glob
import itertools as it
import logging
import sys
import warnings
from pathlib import Path

import yaml

from immuneML.util.ReadsType import ReadsType
from immuneML.encodings.kmer_frequency.sequence_encoding.SequenceEncodingType import SequenceEncodingType
from immuneML.ml_methods.MLMethod import MLMethod
from immuneML.reports.ml_reports.CoefficientPlottingSetting import CoefficientPlottingSetting
from immuneML.util.PathBuilder import PathBuilder
from immuneML.util.ReflectionHandler import ReflectionHandler

[docs] def get_sequence_enc_type(sequence_type, position_type, gap_type): if sequence_type == "complete": encoding_type = SequenceEncodingType.IDENTITY else: if position_type == "positional": if gap_type == "gapped": encoding_type = SequenceEncodingType.IMGT_GAPPED_KMER else: encoding_type = SequenceEncodingType.IMGT_CONTINUOUS_KMER else: if gap_type == "gapped": encoding_type = SequenceEncodingType.GAPPED_KMER else: encoding_type = SequenceEncodingType.CONTINUOUS_KMER return
[docs] def build_encodings_specs(args): encodings = dict() for i in range(len(args.sequence_type)): enc_name = f"encoding_{i + 1}" enc_spec = dict() enc_spec["sequence_encoding"] = get_sequence_enc_type(args.sequence_type[i], None if args.position_type is None else args.position_type[i], None if args.gap_type is None else args.gap_type[i]) enc_spec["reads"] = args.reads[i] if args.sequence_type[i] == "subsequence": if args.gap_type[i] == "gapped": enc_spec["k_left"] = args.k_left[i] enc_spec["k_right"] = args.k_right[i] enc_spec["min_gap"] = args.min_gap[i] enc_spec["max_gap"] = args.max_gap[i] else: enc_spec["k"] = args.k[i] encodings[enc_name] = {"KmerFrequency": enc_spec} return encodings
[docs] def get_ml_method_spec(ml_method_class, model_selection_n_folds=5): if ml_method_class == "LogisticRegression" or ml_method_class == "SimpleLogisticRegression": ml_spec = { "logistic_regression": { "LogisticRegression": { "penalty": ["l1"], "C": [0.01, 0.1, 1, 10, 100], "class_weight": ["balanced"], "show_warnings": False }, "model_selection_cv": True, "model_selection_n_folds": model_selection_n_folds } } elif ml_method_class == "RandomForestClassifier": ml_spec = { "random_forest": { "RandomForestClassifier": { "n_estimators": [10, 50, 100], "class_weight": ["balanced"], "show_warnings": False }, "model_selection_cv": True, "model_selection_n_folds": model_selection_n_folds } } elif ml_method_class == "SVM": ml_spec = { "support_vector_machine": { "SVC": { "penalty": ["l1"], "dual": False, "C": [0.01, 0.1, 1, 10, 100], "class_weight": ["balanced"], "show_warnings": False }, "model_selection_cv": True, "model_selection_n_folds": model_selection_n_folds } } elif ml_method_class == "KNN": ml_spec = { "k_nearest_neighbors": { "KNN": { "n_neighbors": [3, 5, 7], "show_warnings": False }, "model_selection_cv": True, "model_selection_n_folds": model_selection_n_folds } } else: ml_spec = {ml_method_class: ml_method_class} return ml_spec
[docs] def build_ml_methods_specs(args): ml_methods_spec = dict() for method in args.ml_methods: ml_methods_spec.update(get_ml_method_spec(method)) return ml_methods_spec
[docs] def build_settings_specs(enc_names, ml_names): return [{"encoding": enc_name, "ml_method": ml_name} for enc_name, ml_name in it.product(enc_names, ml_names)]
[docs] def discover_dataset_params(): dataset = glob.glob("*.iml_dataset") assert len(dataset) > 0, "no .iml_dataset file was present in the current working directory" assert len(dataset) < 2, "multiple .iml_dataset files were present in the current working directory" dataset_path = dataset[0] dataset_name = dataset_path.rsplit('.iml_dataset', 1)[0] return {"path": dataset_path}
[docs] def build_labels(labels_str): labels = labels_str.split(",") return [label.strip().strip("'\"") for label in labels]
[docs] def build_specs(args): specs = { "definitions": { "datasets": { "dataset": { "format": "ImmuneML", "params": None } }, "encodings": dict(), "ml_methods": dict(), "reports": { "coefficients": { "Coefficients": { "coefs_to_plot": [], "n_largest": [25] } }, "benchmark": "MLSettingsPerformance" } }, "instructions": { "inst1": { "type": "TrainMLModel", "settings": [], "assessment": { "split_strategy": "random", "split_count": None, "training_percentage": None, "reports": { "models": ["coefficients"] } }, "selection": { "split_strategy": "random", "split_count": 1, "training_percentage": 0.7, }, "labels": [], "dataset": "dataset", "strategy": "GridSearch", "metrics": [], "number_of_processes": 10, "reports": ["benchmark"], "optimization_metric": "accuracy", 'refit_optimal_model': True } } } enc_specs = build_encodings_specs(args) ml_specs = build_ml_methods_specs(args) settings_specs = build_settings_specs(enc_specs.keys(), ml_specs.keys()) dataset_params = discover_dataset_params() labels = build_labels(args.labels) specs["definitions"]["datasets"]["dataset"]["params"] = dataset_params specs["definitions"]["encodings"] = enc_specs specs["definitions"]["ml_methods"] = ml_specs specs["instructions"]["inst1"]["settings"] = settings_specs specs["instructions"]["inst1"]["assessment"]["split_count"] = args.split_count specs["instructions"]["inst1"]["assessment"]["training_percentage"] = args.training_percentage / 100 specs["instructions"]["inst1"]["labels"] = labels return specs
[docs] def check_arguments(args): assert 100 >= args.training_percentage >= 10, "training_percentage must range between 10 and 100" assert args.split_count >= 1, "The minimal split_count is 1." encoding_err = "When multiple encodings are used, fields must still be of equal length, add 'NA' variables where necessary" assert len(args.sequence_type) == len(args.reads), encoding_err assert args.position_type is None or len(args.sequence_type) == len(args.position_type), encoding_err assert args.gap_type is None or len(args.sequence_type) == len(args.gap_type), encoding_err assert args.k is None or len(args.sequence_type) == len(args.k), encoding_err assert args.k_left is None or len(args.sequence_type) == len(args.k_left), encoding_err assert args.k_right is None or len(args.sequence_type) == len(args.k_right), encoding_err assert args.min_gap is None or len(args.sequence_type) == len(args.min_gap), encoding_err assert args.max_gap is None or len(args.sequence_type) == len(args.max_gap), encoding_err
[docs] def parse_commandline_arguments(args): ReflectionHandler.get_classes_by_partial_name("", "ml_methods/") ml_method_names = [cl.__name__ for cl in ReflectionHandler.all_nonabstract_subclasses(MLMethod)] + ["SimpleLogisticRegression"] parser = argparse.ArgumentParser(description="tool for building immuneML Galaxy YAML from arguments") parser.add_argument("-o", "--output_path", required=True, help="Output location for the generated yaml file (directiory).") parser.add_argument("-f", "--file_name", default="specs.yaml", help="Output file name for the yaml file. Default name is 'specs.yaml' if not specified.") parser.add_argument("-l", "--labels", required=True, help="Which metadata labels should be predicted for the dataset (separated by comma).") parser.add_argument("-m", "--ml_methods", nargs="+", choices=ml_method_names, required=True, help="Which machine learning methods should be applied.") parser.add_argument("-t", "--training_percentage", type=float, required=True, help="The percentage of data used for training.") parser.add_argument("-c", "--split_count", type=int, required=True, help="The number of times to repeat the training process with a different random split of the data.") parser.add_argument("-s", "--sequence_type", choices=["complete", "subsequence"], default=["subsequence"], nargs="+", help="Whether complete CDR3 sequences are used, or k-mer subsequences.") parser.add_argument("-p", "--position_type", choices=["invariant", "positional"], nargs="+", help="Whether IMGT-positional information is used for k-mers, or the k-mer positions are position-invariant.") parser.add_argument("-g", "--gap_type", choices=["gapped", "ungapped"], nargs="+", help="Whether the k-mers contain gaps.") parser.add_argument("-k", "--k", type=int, nargs="+", help="K-mer size.") parser.add_argument("-kl", "--k_left", type=int, nargs="+", help="Length before gap when k-mers are used.") parser.add_argument("-kr", "--k_right", type=int, nargs="+", help="Length after gap when k-mers are used.") parser.add_argument("-gi", "--min_gap", type=int, nargs="+", help="Minimal gap length when gapped k-mers are used.") parser.add_argument("-ga", "--max_gap", type=int, nargs="+", help="Maximal gap length when gapped k-mers are used.") parser.add_argument("-r", "--reads", choices=[ReadsType.UNIQUE.value, ReadsType.ALL.value], nargs="+", default=[ReadsType.UNIQUE.value], help="Whether k-mer counts should be scaled by unique clonotypes or all observed receptor sequences") return parser.parse_args(args)
[docs] def main(args): logging.basicConfig(filename="build_yaml_from_args_log.txt", level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s') warnings.showwarning = lambda message, category, filename, lineno, file=None, line=None: logging.warning(message) parsed_args = parse_commandline_arguments(args) check_arguments(parsed_args) specs = build_specs(parsed_args) output_location = Path(parsed_args.output_path) / parsed_args.file_name with"w") as file: yaml.dump(specs, file) return output_location
if __name__ == "__main__": main(sys.argv[1:])