From 105f7ed121cc02447aa3b3d385f04fb0753ebf39 Mon Sep 17 00:00:00 2001 From: Max West Date: Fri, 1 Nov 2024 15:38:33 -0700 Subject: [PATCH 1/3] squashed all commits --- data/tests/general_hot_features_day_2.csv | 1000 +++++++++++++++++ src/resspect/__init__.py | 11 + src/resspect/loop_configuration.py | 18 +- src/resspect/time_domain_configuration.py | 146 +++ src/resspect/time_domain_loop.py | 528 +++------ .../test_time_domain_configuration.py | 139 +++ tests/resspect/test_time_domain_loop.py | 30 + 7 files changed, 1494 insertions(+), 378 deletions(-) create mode 100644 data/tests/general_hot_features_day_2.csv create mode 100644 src/resspect/time_domain_configuration.py create mode 100644 tests/resspect/test_time_domain_configuration.py create mode 100644 tests/resspect/test_time_domain_loop.py diff --git a/data/tests/general_hot_features_day_2.csv b/data/tests/general_hot_features_day_2.csv new file mode 100644 index 00000000..be3c00c1 --- /dev/null +++ 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"RandomSampling", + "UncSamplingEntropy", + "UncSamplingLeastConfident", + "UncSamplingMargin", + "QBDMI", + "QBDEntropy", +] + from .bazin import * from .build_plasticc_canonical import * from .build_plasticc_metadata import * @@ -38,6 +48,7 @@ from .scripts.make_metrics_plots import make_metrics_plots as make_metrics_plots from .scripts.run_loop import run_loop as run_loop from .scripts.run_time_domain import run_time_domain as run_time_domain +from .time_domain_configuration import * from .time_domain_plasticc import * from .time_domain_snpcc import * from .time_domain_loop import * diff --git a/src/resspect/loop_configuration.py b/src/resspect/loop_configuration.py index 0e3d2b39..1e6f3e58 100644 --- a/src/resspect/loop_configuration.py +++ b/src/resspect/loop_configuration.py @@ -1,7 +1,9 @@ -from dataclasses import dataclass, make_dataclass, asdict +from dataclasses import dataclass, asdict import json from os import path +from resspect import VALID_STRATEGIES + @dataclass class LoopConfiguration: """Configuration for the `resspect.learn_loop` function. @@ -141,19 +143,9 @@ def __post_init__(self): raise ValueError("cannot save predictions, no `pred_dir` was provided.") if not path.isdir(self.pred_dir): raise ValueError("provided `pred_dir` does not exist/is not a directory.") - - # check strategy - self.valid_strategies = [ - "UncSampling", - "RandomSampling", - "UncSamplingEntropy", - "UncSamplingLeastConfident", - "UncSamplingMargin", - "QBDMI", - "QBDEntropy", - ] - if self.strategy not in self.valid_strategies: + # check strategy + if self.strategy not in VALID_STRATEGIES: raise ValueError(f"{self.strategy} is not a valid strategy.") if "QBD" in self.strategy and not self.classifier_bootstrap: raise ValueError("Bootstrap must be true when using disagreement strategy") diff --git a/src/resspect/time_domain_configuration.py b/src/resspect/time_domain_configuration.py new file mode 100644 index 00000000..9ea2a20c --- /dev/null +++ b/src/resspect/time_domain_configuration.py @@ -0,0 +1,146 @@ +from dataclasses import dataclass, asdict +import json +from os import path + +from resspect import VALID_STRATEGIES + +@dataclass +class TimeDomainConfiguration: + """ + Atributes + ---------- + days: list + List of 2 elements. First and last day of observations since the + beginning of the survey. + output_metrics_file: str + Full path to output file to store metrics for each loop. + output_queried_file: str + Full path to output file to store the queried sample. + path_to_features_dir: str + Complete path to directory holding features files for all days. + strategy: str + Query strategy. Options are (all can be run with budget): + "UncSampling", + "UncSamplingEntropy", + "UncSamplingLeastConfident", + "UncSamplingMargin", + "QBDMI", + "QBDEntropy", + "RandomSampling", + fname_pattern: str + List of strings. Set the pattern for filename, except day of + survey. If file name is 'day_1_vx.csv' -> ['day_', '_vx.csv']. + path_to_ini_files: dict (optional) + Path to initial full light curve files. + Possible keywords are: "train", "test" and "validation". + At least "train" is mandatory. + batch: int (optional) + Size of batch to be queried in each loop. Default is 1. + canonical: bool (optional) + If True, restrict the search to the canonical sample. + classifier: str (optional) + Machine Learning algorithm. + Currently 'RandomForest', 'GradientBoostedTrees', + 'KNN', 'MLP', 'SVM' and 'NB' are implemented. + Default is 'RandomForest'. + clf_bootstrap: bool (default: False) + If true build a boostrap ensemble of the classifier. + budgets: tuple of floats (default: None) + Budgets for each of the telescopes + ia_frac: float in [0,1] (optional) + Fraction of Ia required in initial training sample. + Default is 0.5. + nclass + Number of classes to consider in the classification + Currently only nclass == 2 is implemented. + path_to_canonical: str (optional) + Path to canonical sample features files. + It is only used if "strategy==canonical". + queryable: bool (optional) + If True, allow queries only on objects flagged as queryable. + Default is True. + query_thre: float (optional) + Percentile threshold for query. Default is 1. + save_samples: bool (optional) + If True, save training and test samples to file. + Default is False. + save_full_query: bool (optional) + If True, save complete queried sample to file. + Otherwise, save only first element. Default is False. + sep_files: bool (optional) + If True, consider samples separately read + from independent files. Default is False. + survey: str (optional) + Name of survey to be analyzed. Accepts 'DES' or 'LSST'. + Default is LSST. + initial_training: str or int (optional) + Choice of initial training sample. + If 'original': begin from the train sample flagged in the file + eilf 'previous': read training and queried from previous run. + If int: choose the required number of samples at random, + ensuring that at least half are SN Ia + Default is 'original'. + feature_extraction_method: str (optional) + Feature extraction method. The current implementation only + accepts method=='Bazin' or 'photometry'. + Default is 'Bazin'. + """ + days: list + output_metrics_file: str + output_queried_file: str + path_to_features_dir: str + strategy: str + fname_pattern: list + path_to_ini_files: dict + batch: int = 1 + canonical: bool = False + classifier: str = "RandomForest" + clf_bootstrap: bool = False + budgets: tuple = None + nclass: int = 2 + ia_frac: float = 0.5 + path_to_canonical: str = "" + queryable: bool = True + query_thre: float = 1.0 + save_samples: bool = False + sep_files: bool = False + survey: str = "LSST" + initial_training: str = "original" + feature_extraction_method: str = "Bazin" + save_full_query: bool = False + + def __post_init__(self): + # file checking + if not path.isdir(self.path_to_features_dir): + raise ValueError("`path_to_features` must be an existing directory.") + + # check strategy + if self.strategy not in VALID_STRATEGIES: + raise ValueError(f"{self.strategy} is not a valid strategy.") + if "QBD" in self.strategy and not self.clf_bootstrap: + raise ValueError("Bootstrap must be true when using disagreement strategy") + + for key in self.path_to_ini_files.keys(): + if not path.isfile(self.path_to_ini_files[key]): + raise ValueError(f"{key} does not point to existing file.") + + def to_dict(self): + """converts configurations elements into a dict.""" + return asdict(self) + + @classmethod + def from_dict(cls, lc_dict): + """creates a `LoopConfiguration` instance from a dict.""" + return cls(**lc_dict) + + def to_json(self, file_path): + """write out the `LoopConfiguration` as a json file.""" + with open(file_path, 'w') as fp: + json.dump(self.to_dict(), fp) + + @classmethod + def from_json(cls, file_path): + """read a `LoopConfiguration` generated json file and instantiate.""" + with open(file_path) as fp: + lc_dict = json.load(fp) + return cls(**lc_dict) \ No newline at end of file diff --git a/src/resspect/time_domain_loop.py b/src/resspect/time_domain_loop.py index fb50a210..a78c09e7 100644 --- a/src/resspect/time_domain_loop.py +++ b/src/resspect/time_domain_loop.py @@ -25,16 +25,11 @@ from resspect import DataBase from resspect.tom_client import TomClient +from resspect.time_domain_configuration import TimeDomainConfiguration - -def load_dataset(file_names_dict: dict, survey_name: str = 'DES', - initial_training: Union[str, int] = 'original', - ia_training_fraction: float = 0.5, is_queryable: bool = False, - is_separate_files: bool = False, samples_list: list = [None], - is_load_build_samples: bool = True, - number_of_classes: int = 2, - feature_extraction_method: str = 'Bazin', - is_save_samples: bool = False) -> DataBase: +def load_dataset( + file_names_dict: dict, config: TimeDomainConfiguration, samples_list: list = [None], is_load_build_samples: bool = True +) -> DataBase: """ Reads a data sample from file. @@ -44,38 +39,12 @@ def load_dataset(file_names_dict: dict, survey_name: str = 'DES', Path to light curve features file. #if "sep_files == True", dictionary keywords must contain identify #different samples: ['train', 'test','validation', 'pool', None] - ia_training_fraction: float in [0,1] (optional) - Fraction of Ia required in initial training sample. - Only used if "initial_training" is a number. Default is 0.5. - initial_training: str or int (optional) - Choice of initial training sample. - If 'original': begin from the train sample flagged in the file - elif int: choose the required number of samples at random, - ensuring that at least "ia_frac" are SN Ia. - Default is 'original'. - is_queryable: bool (optional) - If True, allow queries only on objects flagged as queryable. - Default is True. - is_separate_files: bool (optional) - If True, consider samples separately read - from independent files. Default is False. - survey_name: str (optional) - Name of survey to be analyzed. Accepts 'DES' or 'LSST'. - Default is DES. + config: TimeDomainConfiguration + The configuration blob. samples_list: list (optional) If None, sample is given by a column within the given file. else, read independent files for 'train' and 'test'. Default is None. - number_of_classes - Number of classes to consider in the classification - Currently only nclass == 2 is implemented. - feature_extraction_method: str (optional) - Feature extraction method. The current implementation only - accepts method=='Bazin', 'photometry', or 'Malanchev'. - Default is 'Bazin'. - is_save_samples: bool (optional) - If True, save training and test samples to file. - Default is False. is_load_build_samples if database.build_samples method should be called """ @@ -84,25 +53,26 @@ def load_dataset(file_names_dict: dict, survey_name: str = 'DES', database_class = DataBase() for sample in samples_list: database_class.load_features( - file_names_dict[sample], survey=survey_name, sample=sample, - feature_extractor=feature_extraction_method) + file_names_dict[sample], + survey=config.survey, + sample=sample, + feature_extractor=config.feature_extraction_method + ) if is_load_build_samples: database_class.build_samples( - initial_training=initial_training, nclass=number_of_classes, - Ia_frac=ia_training_fraction, queryable=is_queryable, - save_samples=is_save_samples, sep_files=is_separate_files, - survey=survey_name) + initial_training=config.initial_training, + nclass=config.nclass, + Ia_frac=config.ia_frac, + queryable=config.queryable, + save_samples=config.save_samples, + sep_files=config.sep_files, + survey=config.survey + ) return database_class def _load_first_loop_and_full_data( - first_loop_file_name: str, initial_light_curve_file_name: dict, - survey_name: str = 'DES', - initial_training: Union[str, int] = 'original', - ia_training_fraction: float = 0.5, is_queryable: bool = False, - is_separate_files: bool = False, number_of_classes: int = 2, - feature_extraction_method: str = 'Bazin', - is_save_samples: bool = False) -> Tuple[DataBase, DataBase]: + first_loop_file_name: str, config: TimeDomainConfiguration) -> Tuple[DataBase, DataBase]: """ Loads first loop and initial light curve training data @@ -112,70 +82,34 @@ def _load_first_loop_and_full_data( Path to light curve features file. #if "sep_files == True", dictionary keywords must contain identify #different samples: ['train', 'test','validation', 'pool'] - initial_light_curve_file_name - Path to initial full light curve files. - Possible keywords are: "train", "test" and "validation". - At least "train" is mandatory. - survey_name - Name of survey to be analyzed. Accepts 'DES' or 'LSST'. - Default is LSST. - initial_training - Choice of initial training sample. - If 'original': begin from the train sample flagged in the file - eilf 'previous': read training and queried from previous run. - If int: choose the required number of samples at random, - ensuring that at least half are SN Ia - Default is 'original'. - ia_training_fraction - Fraction of Ia required in initial training sample. - Default is 0.5. - is_queryable - If True, allow queries only on objects flagged as queryable. - Default is True. - is_separate_files - If True, consider samples separately read - from independent files. Default is False. - number_of_classes - Number of classes to consider in the classification - Currently only number_of_classes == 2 is implemented. - feature_extraction_method: str (optional) - Feature extraction method. The current implementation only - accepts method=='Bazin', 'photometry', or 'Malanchev'. - Default is 'Bazin'. - is_save_samples - If True, save training and test samples to file. - Default is False. + config + The `TimeDomainConfiguration` dataclass that + contains all the runtime configuration options. """ - if not is_separate_files: + if not config.sep_files: first_loop_file_name = {None: first_loop_file_name} first_loop_data = load_dataset( file_names_dict=first_loop_file_name, - survey_name=survey_name, is_separate_files=is_separate_files, - initial_training=0, ia_training_fraction=ia_training_fraction, - is_queryable=is_queryable, - feature_extraction_method=feature_extraction_method) - light_curve_file_name = {None: initial_light_curve_file_name['train']} + config=config, + ) + light_curve_file_name = {None: config.path_to_ini_files['train']} light_curve_data = load_dataset( file_names_dict=light_curve_file_name, - survey_name=survey_name, is_separate_files=is_separate_files, - initial_training=initial_training, - ia_training_fraction=ia_training_fraction, - is_queryable=is_queryable, - feature_extraction_method=feature_extraction_method) + config=config, + ) else: first_loop_file_name = {'pool': first_loop_file_name} first_loop_data = load_dataset( file_names_dict=first_loop_file_name, - survey_name=survey_name, samples_list=['pool'], - number_of_classes=number_of_classes, - feature_extraction_method=feature_extraction_method, - is_save_samples=is_save_samples, is_queryable=is_queryable, - is_separate_files=is_separate_files) + config=config, + samples_list=['pool'], + ) light_curve_data = load_dataset( - file_names_dict=initial_light_curve_file_name, + file_names_dict=config.path_to_ini_files, + config=config, samples_list=['train', 'test', 'validation'], - feature_extraction_method=feature_extraction_method, - survey_name=survey_name, is_load_build_samples=False) + is_load_build_samples=False, + ) return first_loop_data, light_curve_data @@ -362,8 +296,7 @@ def _run_classification_and_evaluation( def _get_indices_of_objects_to_be_queried( - database_class: DataBase, strategy: str, budgets: tuple, - is_queryable: bool, query_threshold: float, batch: int) -> list: + database_class: DataBase,budgets: tuple, config: TimeDomainConfiguration) -> list: """ Finds indices of objects to be queried @@ -371,32 +304,23 @@ def _get_indices_of_objects_to_be_queried( ---------- database_class An instance of DataBase class - strategy - Query strategy. Options are (all can be run with budget): - "UncSampling", - "UncSamplingEntropy", - "UncSamplingLeastConfident", - "UncSamplingMargin", - "QBDMI", - "QBDEntropy", - "RandomSampling", budgets Budgets for each of the telescopes - is_queryable - If True, allow queries only on objects flagged as queryable. - Default is True. - query_threshold - Percentile threshold for query. Default is 1. - batch - Size of batch to be queried in each loop. Default is 1. + config + The `TimeDomainConfiguration` dataclass that + contains all the runtime configuration options. """ if budgets: object_indices = database_class.make_query_budget( - budgets=budgets, strategy=strategy) + budgets=budgets, strategy=config.strategy + ) else: object_indices = database_class.make_query( - strategy=strategy, batch=batch, queryable=is_queryable, - query_threshold=query_threshold) + strategy=config.strategy, + batch=config.batch, + queryable=config.queryable, + query_thre=config.query_thre, + ) return list(object_indices) @@ -460,9 +384,7 @@ def _save_metrics_and_queried_sample( def _load_next_day_data( - next_day_features_file_name: str, is_separate_files: bool, - is_queryable: bool, survey_name: str, ia_training_fraction: float, - is_save_samples: bool, feature_extraction_method: str='Bazin'): + next_day_features_file_name: str, config: TimeDomainConfiguration): """ Loads features of next day @@ -486,19 +408,14 @@ def _load_next_day_data( If True, save training and test samples to file. Default is False. """ - if is_separate_files: + if config.sep_files: next_day_features_file_name = {'pool': next_day_features_file_name} next_day_data = load_dataset( - next_day_features_file_name, survey_name, samples_list=['pool'], - is_separate_files=is_separate_files, is_queryable=is_queryable, - is_save_samples=is_save_samples, - feature_extraction_method=feature_extraction_method) + next_day_features_file_name, config, samples_list=['pool'], + ) else: next_day_features_file_name = {None: next_day_features_file_name} - next_day_data = load_dataset( - next_day_features_file_name, survey_name, is_queryable=is_queryable, - initial_training=0, ia_training_fraction=ia_training_fraction, - feature_extraction_method=feature_extraction_method) + next_day_data = load_dataset(next_day_features_file_name, config) return next_day_data @@ -724,12 +641,13 @@ def _update_light_curve_data_for_next_epoch( # TODO: Too many arguments. Refactor and update docs def process_next_day_loop( - light_curve_data: DataBase, next_day_features_file_name: str, - is_separate_files: bool, is_queryable: bool, survey_name: str, - ia_training_fraction: float, id_key_name: str, - light_curve_train_ids: np.ndarray, is_save_samples: bool, - canonical_data: DataBase, strategy: str, - feature_extraction_method: str='Bazin') -> DataBase: + light_curve_data: DataBase, + next_day_features_file_name: str, + id_key_name: str, + light_curve_train_ids: np.ndarray, + canonical_data: DataBase, + config: TimeDomainConfiguration, +) -> DataBase: """ Runs next day active learning loop @@ -739,64 +657,58 @@ def process_next_day_loop( next day light curve data next_day_features_file_name path to next day features file name - is_separate_files - If True, consider samples separately read - from independent files. Default is False. - is_queryable - If True, allow queries only on objects flagged as queryable. - Default is True. - survey_name - Name of survey to be analyzed. Accepts 'DES' or 'LSST'. - Default is LSST. - ia_training_fraction - Fraction of Ia required in initial training sample. - Default is 0.5. id_key_name object identification key name light_curve_train_ids light curve data metadata values - is_save_samples - If True, save training and test samples to file. - Default is False. canonical_data canonical strategy light curve data - strategy - Query strategy. Options are (all can be run with budget): - "UncSampling", "UncSamplingEntropy", "UncSamplingLeastConfident", - "UncSamplingMargin", "QBDMI", "QBDEntropy", "RandomSampling" - feature_extraction_method: str (optional) - Feature extraction method. The current implementation only - accepts method=='Bazin' or 'photometry'. - Default is 'Bazin'. + config + The `TimeDomainConfiguration` dataclass that + contains all the runtime configuration options. """ - next_day_data = _load_next_day_data( - next_day_features_file_name, is_separate_files, is_queryable, - survey_name, ia_training_fraction, is_save_samples, - feature_extraction_method=feature_extraction_method) + next_day_data = _load_next_day_data(next_day_features_file_name, config) for metadata_value in light_curve_data.train_metadata[id_key_name].values: next_day_pool_metadata = next_day_data.pool_metadata[id_key_name].values if metadata_value in next_day_pool_metadata: - next_day_pool_metadata_indices = list( - next_day_pool_metadata).index(metadata_value) + next_day_pool_metadata_indices = list(next_day_pool_metadata).index(metadata_value) if metadata_value not in light_curve_train_ids: - light_curve_train_metadata = light_curve_data.train_metadata[ - id_key_name].values + light_curve_train_metadata = light_curve_data.train_metadata[id_key_name].values light_curve_data = _remove_old_training_features( - light_curve_data, light_curve_train_metadata, - metadata_value) + light_curve_data, + light_curve_train_metadata, + metadata_value + ) if light_curve_data.queryable_ids.shape[0] > 0: light_curve_data = _update_queried_sample( - light_curve_data, next_day_data, id_key_name, - metadata_value) + light_curve_data, + next_day_data, + id_key_name, + metadata_value + ) light_curve_data = _update_training_data_with_new_features( - light_curve_data, next_day_data, metadata_value, id_key_name) + light_curve_data, + next_day_data, + metadata_value, + id_key_name + ) next_day_data = _update_next_day_pool_data( - next_day_data, next_day_pool_metadata_indices) + next_day_data, + next_day_pool_metadata_indices + ) next_day_data = _update_next_day_val_and_test_data( - next_day_data, metadata_value, id_key_name) + next_day_data, + metadata_value, + id_key_name + ) light_curve_data = _update_light_curve_data_for_next_epoch( - light_curve_data, next_day_data, canonical_data, is_queryable, strategy, - is_separate_files) + light_curve_data, + next_day_data, + canonical_data, + config.queryable, + config.strategy, + config.sep_files + ) return light_curve_data @@ -816,15 +728,13 @@ def submit_queries_to_TOM(username, passwordfile, objectids: list, priorities: l # TODO: Too many arguments. Refactor and update docs def run_time_domain_active_learning_loop( - light_curve_data: DataBase, learning_days: list, - classifier: str, is_classifier_bootstrap: bool, strategy: str, - budgets: tuple, is_queryable: bool, query_threshold: float, batch: int, - output_metric_file_name: str, output_queried_file_name: str, - is_save_full_query: bool, id_key_name: str, - light_curve_train_ids: np.ndarray, canonical_data: DataBase, - is_separate_files: bool, path_to_features_directory: str, - fname_pattern: list, survey_name: str, ia_training_fraction: float, - is_save_samples: bool, feature_extraction_method: str='Bazin', **kwargs: dict): + light_curve_data: DataBase, + id_key_name: str, + light_curve_train_ids: np.ndarray, + canonical_data: DataBase, + config: TimeDomainConfiguration, + **kwargs: dict +): """ Runs time domain active learning loop @@ -832,63 +742,15 @@ def run_time_domain_active_learning_loop( ---------- light_curve_data light curve learning data - learning_days - List of 2 elements. First and last day of observations since the - beginning of the survey. - classifier - Machine Learning algorithm. - Currently 'RandomForest', 'GradientBoostedTrees', - 'KNN', 'MLP', 'SVM' and 'NB' are implemented. - Default is 'RandomForest'. - is_classifier_bootstrap - If true build a boostrap ensemble of the classifier. - strategy - Query strategy. Options are (all can be run with budget): - "UncSampling", "UncSamplingEntropy", "UncSamplingLeastConfident", - "UncSamplingMargin", "QBDMI", "QBDEntropy", "RandomSampling" - budgets - Budgets for each of the telescopes - is_queryable - If True, allow queries only on objects flagged as queryable. - Default is True. - query_threshold - Percentile threshold for query. Default is 1. - batch - Size of batch to be queried in each loop. Default is 1. - output_metric_file_name - Full path to output file to store metrics for each loop. - output_queried_file_name - Full path to output file to store the queried sample. - is_save_full_query - If True, save complete queried sample to file. - Otherwise, save only first element. Default is False. id_key_name object identification key name light_curve_train_ids light curve data metadata values canonical_data canonical strategy light curve data - is_separate_files - If True, consider samples separately read - from independent files. Default is False. - path_to_features_directory - Complete path to directory holding features files for all days. - fname_pattern - List of strings. Set the pattern for filename, except day of - survey. If file name is 'day_1_vx.csv' -> ['day_', '_vx.csv'] - survey_name - Name of survey to be analyzed. Accepts 'DES' or 'LSST'. - Default is LSST. - ia_training_fraction - Fraction of Ia required in initial training sample. - Default is 0.5. - is_save_samples - If True, save training and test samples to file. - Default is False. - feature_extraction_method: str (optional) - Feature extraction method. The current implementation only - accepts method=='Bazin' or 'photometry'. - Default is 'Bazin'. + config + The `TimeDomainConfiguration` dataclass that + contains all the runtime configuration options. kwargs All keywords required by the classifier function. @@ -896,170 +758,106 @@ def run_time_domain_active_learning_loop( ------- """ - learning_days = [int(each_day) for each_day in learning_days] + learning_days = [int(each_day) for each_day in config.days] # create dictionary with budgets - if budgets is not None and len(budgets) not in [2, len(np.arange(learning_days[0], learning_days[1]))]: + if config.budgets is not None and len(config.budgets) not in [2, len(np.arange(config.days[0], config.days[1]))]: raise ValueError('There must be 1 budget per telescope or ' + \ '1 budget per telescope per night!') budgets_dict = {} - for epoch in range(learning_days[0], learning_days[-1] - 1): - budgets_dict[epoch] = budgets + for epoch in range(config.days[0], config.days[-1] - 1): + budgets_dict[epoch] = config.budgets for epoch in progressbar.progressbar( range(learning_days[0], learning_days[-1] - 1)): if light_curve_data.pool_features.shape[0] > 0: light_curve_data = _run_classification_and_evaluation( - light_curve_data, classifier, is_classifier_bootstrap, **kwargs) + light_curve_data, config.classifier, config.clf_bootstrap, **kwargs + ) if light_curve_data.queryable_ids.shape[0] > 0: object_indices = _get_indices_of_objects_to_be_queried( - light_curve_data, strategy, budgets_dict[epoch], is_queryable, - query_threshold, batch) + light_curve_data, + budgets_dict[epoch], + config, + ) light_curve_data = _update_samples_with_object_indices( - light_curve_data, object_indices, is_queryable, epoch) + light_curve_data, object_indices, config.queryable, epoch + ) _save_metrics_and_queried_sample( - light_curve_data, epoch - learning_days[0], - output_metric_file_name, output_queried_file_name, len(object_indices), epoch, - is_save_full_query) - next_day_features_file_name = ( - path_to_features_directory + fname_pattern[0] + str(epoch + 1) - + fname_pattern[1]) + light_curve_data, epoch - learning_days[0], + config.output_metrics_file, + config.output_queried_file, + len(object_indices), + epoch, + config. save_full_query, + ) + next_day_features_file_name = f"{config.fname_pattern[0]}{epoch + 1}{config.fname_pattern[1]}" + next_day_features_file_path = os.path.join(config.path_to_features_dir, next_day_features_file_name) light_curve_data = process_next_day_loop( - light_curve_data, next_day_features_file_name, is_separate_files, - is_queryable, survey_name, ia_training_fraction, id_key_name, - light_curve_train_ids, is_save_samples, canonical_data, strategy, - feature_extraction_method=feature_extraction_method) + light_curve_data, + next_day_features_file_path, + id_key_name, + light_curve_train_ids, + canonical_data, + config, + ) # TODO: Too many arguments. Refactor and update docs -def time_domain_loop(days: list, output_metrics_file: str, - output_queried_file: str, path_to_features_dir: str, - strategy: str, fname_pattern: list, - path_to_ini_files: dict, batch: int = 1, - canonical: bool = False, classifier: str = 'RandomForest', - clf_bootstrap: bool = False, budgets: tuple = None, - nclass: int = 2, ia_frac: float = 0.5, - path_to_canonical: str = "", queryable: bool = True, - query_thre: float = 1.0, save_samples: bool = False, - sep_files: bool = False, survey: str = 'LSST', - initial_training: str = 'original', - feature_extraction_method: str = 'Bazin', - save_full_query: bool = False, **kwargs): +def time_domain_loop(config, **kwargs): """ Perform the active learning loop. All results are saved to file. Parameters ---------- - days: list - List of 2 elements. First and last day of observations since the - beginning of the survey. - output_metrics_file: str - Full path to output file to store metrics for each loop. - output_queried_file: str - Full path to output file to store the queried sample. - path_to_features_dir: str - Complete path to directory holding features files for all days. - strategy: str - Query strategy. Options are (all can be run with budget): - "UncSampling", - "UncSamplingEntropy", - "UncSamplingLeastConfident", - "UncSamplingMargin", - "QBDMI", - "QBDEntropy", - "RandomSampling", - fname_pattern: str - List of strings. Set the pattern for filename, except day of - survey. If file name is 'day_1_vx.csv' -> ['day_', '_vx.csv']. - path_to_ini_files: dict (optional) - Path to initial full light curve files. - Possible keywords are: "train", "test" and "validation". - At least "train" is mandatory. - batch: int (optional) - Size of batch to be queried in each loop. Default is 1. - canonical: bool (optional) - If True, restrict the search to the canonical sample. - classifier: str (optional) - Machine Learning algorithm. - Currently 'RandomForest', 'GradientBoostedTrees', - 'KNN', 'MLP', 'SVM' and 'NB' are implemented. - Default is 'RandomForest'. - clf_bootstrap: bool (default: False) - If true build a boostrap ensemble of the classifier. - budgets: tuple of floats (default: None) - Budgets for each of the telescopes - ia_frac: float in [0,1] (optional) - Fraction of Ia required in initial training sample. - Default is 0.5. - nclass - Number of classes to consider in the classification - Currently only nclass == 2 is implemented. - path_to_canonical: str (optional) - Path to canonical sample features files. - It is only used if "strategy==canonical". - queryable: bool (optional) - If True, allow queries only on objects flagged as queryable. - Default is True. - query_thre: float (optional) - Percentile threshold for query. Default is 1. - save_samples: bool (optional) - If True, save training and test samples to file. - Default is False. - save_full_query: bool (optional) - If True, save complete queried sample to file. - Otherwise, save only first element. Default is False. - sep_files: bool (optional) - If True, consider samples separately read - from independent files. Default is False. - survey: str (optional) - Name of survey to be analyzed. Accepts 'DES' or 'LSST'. - Default is LSST. - initial_training: str or int (optional) - Choice of initial training sample. - If 'original': begin from the train sample flagged in the file - eilf 'previous': read training and queried from previous run. - If int: choose the required number of samples at random, - ensuring that at least half are SN Ia - Default is 'original'. - feature_extraction_method: str (optional) - Feature extraction method. The current implementation only - accepts method=='Bazin' or 'photometry'. - Default is 'Bazin'. + config + The `TimeDomainConfiguration` dataclass that + contains all the runtime configuration options. """ # load features for the first obs day - first_loop_file_name = os.path.join( - path_to_features_dir, - fname_pattern[0] + str(days[0]) + fname_pattern[1]) + first_loop_file_name = f"{config.fname_pattern[0]}{config.days[0]}{config.fname_pattern[1]}" + first_loop_file_path = os.path.join( + config.path_to_features_dir, + first_loop_file_name + ) first_loop_data, light_curve_data = _load_first_loop_and_full_data( - first_loop_file_name, path_to_ini_files, survey, initial_training, - ia_frac, queryable, sep_files, nclass, is_save_samples=save_samples, - feature_extraction_method = feature_extraction_method) + first_loop_file_path, config + ) # get keyword for obj identification id_key_name = light_curve_data.identify_keywords() light_curve_train_ids = light_curve_data.train_metadata[id_key_name].values first_loop_data, light_curve_data = _update_data_by_remove_repeated_ids( - first_loop_data, light_curve_data, id_key_name) + first_loop_data, light_curve_data, id_key_name + ) light_curve_data = _update_light_curve_data_val_and_test_data( - light_curve_data, first_loop_data, sep_files, initial_training, - queryable) + light_curve_data, + first_loop_data, + config.sep_files, + config.initial_training, + config.queryable + ) light_curve_data = _update_queryable_ids( - light_curve_data, id_key_name, queryable) + light_curve_data, id_key_name, config.queryable + ) light_curve_data, canonical_data = _update_canonical_ids( - light_curve_data, path_to_canonical, canonical) + light_curve_data, config.path_to_canonical, config.canonical + ) light_curve_data = _update_initial_train_meta_data_header( - first_loop_data, light_curve_data) + first_loop_data, light_curve_data + ) run_time_domain_active_learning_loop( - light_curve_data, days, classifier, clf_bootstrap, - strategy, budgets, queryable, query_thre, batch, output_metrics_file, - output_queried_file, save_full_query, id_key_name, - light_curve_train_ids, canonical_data, sep_files, path_to_features_dir, - fname_pattern, survey, ia_frac, save_samples, - feature_extraction_method=feature_extraction_method, **kwargs) + light_curve_data, + id_key_name, + light_curve_train_ids, + canonical_data, + config, + **kwargs + ) def main(): return None diff --git a/tests/resspect/test_time_domain_configuration.py b/tests/resspect/test_time_domain_configuration.py new file mode 100644 index 00000000..8065113a --- /dev/null +++ b/tests/resspect/test_time_domain_configuration.py @@ -0,0 +1,139 @@ +import os +import pytest +import tempfile + +from resspect import TimeDomainConfiguration + +def test_create_time_domain_configuration(): + """test file path checks and other validation steps.""" + with tempfile.TemporaryDirectory() as dir_name: + real_file_path = os.path.join(dir_name, "test.txt") + f = open(real_file_path, "w") + f.write("out of all the files that exist, this is one of them.") + f.close() + ini_files = { + "train": real_file_path + } + + TimeDomainConfiguration( + days=[1], + strategy="RandomSampling", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + ) + + # bad path_to_features + with pytest.raises(ValueError): + TimeDomainConfiguration( + days=[1], + strategy="RandomSampling", + path_to_features_dir="/not/real/dir", + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + ) + + # bad path_to_ini_files + bad_ini_files = { + "train": "/not/real/dir/bad_file.csv" + } + with pytest.raises(ValueError): + TimeDomainConfiguration( + days=[1], + strategy="RandomSampling", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=bad_ini_files, + ) + + # bad strategies + with pytest.raises(ValueError): + TimeDomainConfiguration( + days=[1], + strategy="idk whatever", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + ) + TimeDomainConfiguration( + days=[1], + strategy="QBDMI", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + clf_bootstrap=True + ) + with pytest.raises(ValueError): + TimeDomainConfiguration( + days=[1], + strategy="QBDMI", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + clf_bootstrap=False + ) + +def test_to_and_from_dict(): + """dump `LoopConfiguration` to a dict and recreate.""" + with tempfile.TemporaryDirectory() as dir_name: + real_file_path = os.path.join(dir_name, "test.txt") + f = open(real_file_path, "w") + f.write("out of all the files that exist, this is one of them.") + f.close() + ini_files = { + "train": real_file_path + } + + tdc1 = TimeDomainConfiguration( + days=[1], + strategy="RandomSampling", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + ) + d = tdc1.to_dict() + + tdc2 = TimeDomainConfiguration.from_dict(d) + + assert tdc1 == tdc2 + +def test_to_and_from_json(): + """dump `LoopConfiguration` to a dict and recreate.""" + with tempfile.TemporaryDirectory() as dir_name: + real_file_path = os.path.join(dir_name, "test.txt") + f = open(real_file_path, "w") + f.write("out of all the files that exist, this is one of them.") + f.close() + ini_files = { + "train": real_file_path + } + + tdc1 = TimeDomainConfiguration( + days=[1], + strategy="RandomSampling", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + ) + json_path = os.path.join(dir_name, "test.json") + tdc1.to_json(json_path) + + tdc2 = TimeDomainConfiguration.from_json(json_path) + + assert tdc1 == tdc2 diff --git a/tests/resspect/test_time_domain_loop.py b/tests/resspect/test_time_domain_loop.py new file mode 100644 index 00000000..ce5847df --- /dev/null +++ b/tests/resspect/test_time_domain_loop.py @@ -0,0 +1,30 @@ +import os +import pytest +import tempfile + +from resspect.time_domain_loop import time_domain_loop +from resspect.time_domain_configuration import TimeDomainConfiguration + +def test_can_run_time_domain_loop(test_data_path): + """Test that learn_loop can load data and run.""" + with tempfile.TemporaryDirectory() as dir_name: + ini_file = { + "train": os.path.join(test_data_path, "general_hot_features_day_2.csv") + } + + td_config = TimeDomainConfiguration( + days = [2], + strategy="RandomSampling", + path_to_features_dir=test_data_path, + output_metrics_file=os.path.join(dir_name,"just_a_name.csv"), + output_queried_file=os.path.join(dir_name,"just_other_name.csv"), + fname_pattern=["general_hot_features_day_", ".csv"], + path_to_ini_files=ini_file, + sep_files=False, + feature_extraction_method="Malanchev" + ) + + time_domain_loop(td_config) + +if __name__ == '__main__': + pytest.main() \ No newline at end of file From 2112298c21c2bd3f0fdb4177129e0fe8e9986a53 Mon Sep 17 00:00:00 2001 From: Max West Date: Thu, 14 Nov 2024 12:21:09 -0800 Subject: [PATCH 2/3] abstract out Configuration utility functions --- src/resspect/__init__.py | 1 + src/resspect/base_configuration.py | 28 ++++++++++++++++++ src/resspect/loop_configuration.py | 27 ++--------------- src/resspect/time_domain_configuration.py | 27 ++--------------- .../test_time_domain_configuration.py | 29 ++++++++++++++++++- 5 files changed, 63 insertions(+), 49 deletions(-) create mode 100644 src/resspect/base_configuration.py diff --git a/src/resspect/__init__.py b/src/resspect/__init__.py index 1bd19170..5f6c7031 100644 --- a/src/resspect/__init__.py +++ b/src/resspect/__init__.py @@ -24,6 +24,7 @@ "QBDEntropy", ] +from .base_configuration import * from .bazin import * from .build_plasticc_canonical import * from .build_plasticc_metadata import * diff --git a/src/resspect/base_configuration.py b/src/resspect/base_configuration.py new file mode 100644 index 00000000..eff6df40 --- /dev/null +++ b/src/resspect/base_configuration.py @@ -0,0 +1,28 @@ +from dataclasses import dataclass, asdict +import json + +@dataclass +class BaseConfiguration: + def __init__(self): + raise NotImplementedError("Please use one of the extended classes.") + + def to_dict(self): + """converts configurations elements into a dict.""" + return asdict(self) + + @classmethod + def from_dict(cls, lc_dict): + """creates a `BaseConfiguration` instance from a dict.""" + return cls(**lc_dict) + + def to_json(self, file_path): + """write out the `BaseConfiguration` as a json file.""" + with open(file_path, 'w') as fp: + json.dump(self.to_dict(), fp) + + @classmethod + def from_json(cls, file_path): + """read a `BaseConfiguration` generated json file and instantiate.""" + with open(file_path) as fp: + lc_dict = json.load(fp) + return cls(**lc_dict) \ No newline at end of file diff --git a/src/resspect/loop_configuration.py b/src/resspect/loop_configuration.py index 1e6f3e58..94b04f62 100644 --- a/src/resspect/loop_configuration.py +++ b/src/resspect/loop_configuration.py @@ -2,10 +2,10 @@ import json from os import path -from resspect import VALID_STRATEGIES +from resspect import VALID_STRATEGIES, BaseConfiguration @dataclass -class LoopConfiguration: +class LoopConfiguration(BaseConfiguration): """Configuration for the `resspect.learn_loop` function. Attributes @@ -148,25 +148,4 @@ def __post_init__(self): if self.strategy not in VALID_STRATEGIES: raise ValueError(f"{self.strategy} is not a valid strategy.") if "QBD" in self.strategy and not self.classifier_bootstrap: - raise ValueError("Bootstrap must be true when using disagreement strategy") - - def to_dict(self): - """converts configurations elements into a dict.""" - return asdict(self) - - @classmethod - def from_dict(cls, lc_dict): - """creates a `LoopConfiguration` instance from a dict.""" - return cls(**lc_dict) - - def to_json(self, file_path): - """write out the `LoopConfiguration` as a json file.""" - with open(file_path, 'w') as fp: - json.dump(self.to_dict(), fp) - - @classmethod - def from_json(cls, file_path): - """read a `LoopConfiguration` generated json file and instantiate.""" - with open(file_path) as fp: - lc_dict = json.load(fp) - return cls(**lc_dict) \ No newline at end of file + raise ValueError("Bootstrap must be true when using disagreement strategy") \ No newline at end of file diff --git a/src/resspect/time_domain_configuration.py b/src/resspect/time_domain_configuration.py index 9ea2a20c..6520a94c 100644 --- a/src/resspect/time_domain_configuration.py +++ b/src/resspect/time_domain_configuration.py @@ -2,10 +2,10 @@ import json from os import path -from resspect import VALID_STRATEGIES +from resspect import VALID_STRATEGIES, BaseConfiguration @dataclass -class TimeDomainConfiguration: +class TimeDomainConfiguration(BaseConfiguration): """ Atributes ---------- @@ -122,25 +122,4 @@ def __post_init__(self): for key in self.path_to_ini_files.keys(): if not path.isfile(self.path_to_ini_files[key]): - raise ValueError(f"{key} does not point to existing file.") - - def to_dict(self): - """converts configurations elements into a dict.""" - return asdict(self) - - @classmethod - def from_dict(cls, lc_dict): - """creates a `LoopConfiguration` instance from a dict.""" - return cls(**lc_dict) - - def to_json(self, file_path): - """write out the `LoopConfiguration` as a json file.""" - with open(file_path, 'w') as fp: - json.dump(self.to_dict(), fp) - - @classmethod - def from_json(cls, file_path): - """read a `LoopConfiguration` generated json file and instantiate.""" - with open(file_path) as fp: - lc_dict = json.load(fp) - return cls(**lc_dict) \ No newline at end of file + raise ValueError(f"{key} does not point to existing file.") \ No newline at end of file diff --git a/tests/resspect/test_time_domain_configuration.py b/tests/resspect/test_time_domain_configuration.py index 8065113a..f4e138e8 100644 --- a/tests/resspect/test_time_domain_configuration.py +++ b/tests/resspect/test_time_domain_configuration.py @@ -2,7 +2,7 @@ import pytest import tempfile -from resspect import TimeDomainConfiguration +from resspect import TimeDomainConfiguration, LoopConfiguration def test_create_time_domain_configuration(): """test file path checks and other validation steps.""" @@ -137,3 +137,30 @@ def test_to_and_from_json(): tdc2 = TimeDomainConfiguration.from_json(json_path) assert tdc1 == tdc2 + +def test_incompatibility(): + """Ensure that we fail gracefully if we try to generate another BaseConfiguration + class from a TimeDomainConfiguration json.""" + with tempfile.TemporaryDirectory() as dir_name: + real_file_path = os.path.join(dir_name, "test.txt") + f = open(real_file_path, "w") + f.write("out of all the files that exist, this is one of them.") + f.close() + ini_files = { + "train": real_file_path + } + + tdc = TimeDomainConfiguration( + days=[1], + strategy="RandomSampling", + path_to_features_dir=dir_name, + output_metrics_file=os.path.join(dir_name, "blah.txt"), + output_queried_file=os.path.join(dir_name, "blah2.txt"), + fname_pattern=["blah_", ".csv"], + path_to_ini_files=ini_files, + ) + json_path = os.path.join(dir_name, "test.json") + tdc.to_json(json_path) + + with pytest.raises(TypeError): + LoopConfiguration.from_json(json_path) From 7ef79a2c14f06d85d2783c434fb89c252ee44069 Mon Sep 17 00:00:00 2001 From: Max West Date: Thu, 14 Nov 2024 12:22:15 -0800 Subject: [PATCH 3/3] remove unused imports --- src/resspect/loop_configuration.py | 3 +-- src/resspect/time_domain_configuration.py | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/src/resspect/loop_configuration.py b/src/resspect/loop_configuration.py index 94b04f62..babc6f09 100644 --- a/src/resspect/loop_configuration.py +++ b/src/resspect/loop_configuration.py @@ -1,5 +1,4 @@ -from dataclasses import dataclass, asdict -import json +from dataclasses import dataclass from os import path from resspect import VALID_STRATEGIES, BaseConfiguration diff --git a/src/resspect/time_domain_configuration.py b/src/resspect/time_domain_configuration.py index 6520a94c..efd80bfa 100644 --- a/src/resspect/time_domain_configuration.py +++ b/src/resspect/time_domain_configuration.py @@ -1,5 +1,4 @@ -from dataclasses import dataclass, asdict -import json +from dataclasses import dataclass from os import path from resspect import VALID_STRATEGIES, BaseConfiguration