Define general, pre processing and learn block identification settings.

cml/shared/settings.py

+"""Settings module.
+
+"""
+from itertools import starmap
+from dataclasses import dataclass
+from configparser import ConfigParser
+
+
+# TODO (dmt): Set default values
+class Settings:
+    INPUT_FILE: str = ""
+    LEARN_DIR: str = ""
+    MAX_LEARN_DIR: int = 0
+    USE_EXISTING_MODELS: bool = False
+    SET_FEATURES: str = ""
+    SET_TARGETS: str = ""
+    SORT_TIME_STAMP: bool = False
+    CUT_TIME_STAMP: bool = False
+    BLOCK_SIZE: int = 0
+    MAX_BLOCKS: int = 0
+    STACK_ITERATIONS: int = 0
+    LEARN_BLOCK_MINIMUM: int = 0
+    SIGMA_ZETA_CUTOFF: float = 0.0
+
+
+@dataclass
+class GeneralSettings:
+    input_file: str
+    learn_dir: str
+    max_learn_dir: int
+    use_existing_models: bool
+
+
+@dataclass
+class PreprocessingSettings:
+    set_features: str
+    set_targets: str
+    sort_time_stamp: bool
+    cut_time_stamp: bool
+
+
+@dataclass
+class BlockProcessingSettings:
+    block_size = int
+    max_blocks = int
+    stack_iterations = int
+    learn_block_minimum = int
+    sigma_zeta_cutoff = float
+
+
+def specific_settings_factory(settings_type: str):
+    types = {
+        "general": starmap(
+            GeneralSettings, [(Settings.INPUT_FILE,
+                               Settings.LEARN_DIR,
+                               Settings.MAX_LEARN_DIR,
+                               Settings.USE_EXISTING_MODELS)]),
+        "preprocessing": starmap(
+            PreprocessingSettings, [(Settings.SET_FEATURES,
+                                     Settings.SET_TARGETS,
+                                     Settings.SORT_TIME_STAMP,
+                                     Settings.CUT_TIME_STAMP)]),
+        "block_processing": starmap(
+            BlockProcessingSettings, [(Settings.BLOCK_SIZE,
+                                       Settings.MAX_BLOCKS,
+                                       Settings.STACK_ITERATIONS,
+                                       Settings.LEARN_BLOCK_MINIMUM,
+                                       Settings.SIGMA_ZETA_CUTOFF)])
+    }
+
+    return next(types[settings_type])
+
+
+def read_settings(path: str):
+    try:
+        config = ConfigParser(path)
+        configure_main_settings_class(config)
+    except AttributeError as e:
+        # TODO (dmt): Implement proper error handling.
+        pass
+
+
+def configure_main_settings_class(config):
+
+    default = config["DEFAULT"]
+    Settings.INPUT_FILE = default["input_file"]
+    Settings.LEARN_DIR = default["learn_dir"]
+    Settings.MAX_LEARN_DIR = default["max_learn_dir"]
+    Settings.USE_EXISTING_MODELS = default["use_existing_models"]
+
+    preprocessing = config["PREPROCESSING"]
+    Settings.SET_FEATURES = preprocessing["set_features"]
+    Settings.SET_TARGETS = preprocessing["set_targets"]
+    Settings.SORT_TIME_STAMP = preprocessing["sort_time_stamp"]
+    Settings.CUT_TIME_STAMP = preprocessing["cut_time_stamp"]
+
+    block_processing = config["BLOCK_PROCESSING"]
+    Settings.BLOCK_SIZE = block_processing["block_size"]
+    Settings.MAX_BLOCKS = block_processing["max_blocks"]
+    Settings.STACK_ITERATIONS = block_processing["stack_iterations"]
+    Settings.LEARN_BLOCK_MINIMUM = block_processing["learn_block_minimum"]
+    Settings.SIGMA_ZETA_CUTOFF = block_processing["sigma_zeta_cutoff"]