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Source: triage/experiments/multicore.py#L0

insert_into_table

insert_into_table(insert_statements, feature_generator_factory, db_connection_string)

build_matrix

build_matrix(build_tasks, planner_factory, db_connection_string)

train_model

train_model(train_tasks, trainer_factory, db_connection_string)

test_and_evaluate

test_and_evaluate(model_ids, predictor_factory, evaluator_factory, indiv_importance_factory, \
    test_store, db_connection_string, split_def, train_matrix_columns, config)

MultiCoreExperiment

The Base class for all Experiments.

MultiCoreExperiment.all_as_of_times

All 'as of times' in experiment config

Used for label and feature generation.

Returns: (list) of datetimes

MultiCoreExperiment.all_label_windows

All train and test label windows

Returns: (list) label windows, in string form as they appeared in the experiment config

MultiCoreExperiment.collate_aggregations

collate Aggregation objects used by this experiment.

Returns: (list) of collate.Aggregation objects

MultiCoreExperiment.feature_dicts

Feature dictionaries, representing the feature tables and columns configured in this experiment after computing feature groups.

Returns: (list) of dicts, keys being feature table names and values being lists of feature names

MultiCoreExperiment.feature_table_tasks

All feature table query tasks specified by this Experiment

Returns: (dict) keys are group table names, values are themselves dicts, each with keys for different stages of table creation (prepare, inserts, finalize) and with values being lists of SQL commands

MultiCoreExperiment.full_matrix_definitions

Full matrix definitions

Returns: (list) temporal and feature information for each matrix

MultiCoreExperiment.master_feature_dictionary

All possible features found in the database. Not all features will necessarily end up in matrices

Returns: (list) of dicts, keys being feature table names and values being lists of feature names

MultiCoreExperiment.matrix_build_tasks

Tasks for all matrices that need to be built as a part of this Experiment.

Each task contains arguments understood by Architect.build_matrix

Returns: (list) of dicts

MultiCoreExperiment.split_definitions

Temporal splits based on the experiment's configuration

Returns: (dict) temporal splits

Example:

{
  'beginning_of_time': {datetime},
  'modeling_start_time': {datetime},
  'modeling_end_time': {datetime},
  'train_matrix': {
  'matrix_start_time': {datetime},
  'matrix_end_time': {datetime},
  'as_of_times': [list of {datetime}s]
  },
  'test_matrices': [list of matrix defs similar to train_matrix]
}

MultiCoreExperiment.__init__

__init__(self, n_processes=1, n_db_processes=1, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

MultiCoreExperiment.build_matrices

build_matrices(self)

Generate labels, features, and matrices

MultiCoreExperiment.catwalk

catwalk(self)

Train, test, and evaluate models

MultiCoreExperiment.parallelize

parallelize(self, partially_bound_function, tasks, n_processes, chunksize=1)

MultiCoreExperiment.parallelize_with_success_count

parallelize_with_success_count(self, partially_bound_function, tasks, n_processes, chunksize=1)