Deepak Mallubhotla
a731a81c6a
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275 lines
7.4 KiB
Python
275 lines
7.4 KiB
Python
import argparse
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import pathlib
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# import csv
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import deepdog
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import deepdog.direct_monte_carlo.compose_filter
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import deepdog.indexify
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import deepdog.direct_monte_carlo
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import logging
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import tantri.cli
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import tantri.cli.file_importer
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import tantri.dipoles.types
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import typing
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import json
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import kalpaa
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import kalpaa.common
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_logger = logging.getLogger(__name__)
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# LOG_PATTERN = "%(asctime)s | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s"
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# JOBS = list(range(18))
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# TOOD move to json file and read
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# COSTS = [10.0, 5.0, 1.0, 0.5, 0.1, 0.06]
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# DOTS_DICT = {
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# "dot1": "dot1",
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# "dot2": "dot1,dot2",
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# "line": "dot1,dot2,line",
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# "triangle1": "dot1,dot2,triangle1",
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# "triangle2": "dot1,dot2,triangle2",
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# "uprise1": "dot1,dot2,uprise1",
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# "uprise2": "dot1,dot2,uprise2",
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# }
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def enumify_orientation_string(
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orientation_string: str,
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) -> tantri.dipoles.types.Orientation:
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canonical_orientation_string = orientation_string.upper()
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if canonical_orientation_string in ["FIXEDZ", "Z"]:
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return tantri.dipoles.types.Orientation.Z
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if canonical_orientation_string in ["FIXEDXY", "XY"]:
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return tantri.dipoles.types.Orientation.XY
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if canonical_orientation_string in ["FREE", "RANDOM"]:
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return tantri.dipoles.types.Orientation.RANDOM
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else:
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raise ValueError(
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f"Could not find match for orientation_string {orientation_string=}"
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)
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class Stage02Runner:
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def __init__(self, config: kalpaa.Config):
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self.config = config
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_logger.info(f"Initialising Stage02 runner with {config=}")
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with config.absify(config.general_config.indexes_json_name).open(
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"r"
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) as indexify_json_file:
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indexify_spec = json.load(indexify_json_file)
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indexify_data = indexify_spec["indexes"]
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if "seed_spec" in indexify_spec:
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seed_spec = indexify_spec["seed_spec"]
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indexify_data[seed_spec["field_name"]] = list(
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range(seed_spec["num_seeds"])
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)
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_logger.info(f"loading indexifier with data {indexify_data=}")
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self.indexifier = deepdog.indexify.Indexifier(indexify_data)
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self.dots = tantri.cli.file_importer.read_dots_json_file(
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self.config.absify(self.config.general_config.dots_json_name)
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)
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_logger.info(f"Got dots {self.dots=}")
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def _dots_to_include(self, current_dot: str) -> typing.Sequence[str]:
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if current_dot == "dot1":
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return ["dot1"]
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if current_dot == "dot2":
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return ["dot1", "dot2"]
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else:
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return ["dot1", "dot2", current_dot]
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def run_in_subdir(self, subdir: pathlib.Path):
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with kalpaa.common.new_cd(subdir):
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_logger.debug(f"Running inside {subdir=}")
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# TODO hardcoding that we're executing every job index.
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num_jobs = len(self.indexifier)
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_logger.debug(f"Have {num_jobs=}")
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seed_index = 0
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for job_index in range(num_jobs):
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for cost in self.config.deepdog_config.costs_to_try:
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for dot in self.dots:
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seed_index += 1
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combined_dot_name = ",".join(
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[d for d in self._dots_to_include(dot.label)]
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)
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trial_name = (
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f"{dot.label}-{combined_dot_name}-{cost}-{job_index}"
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)
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_logger.info(f"Working on {trial_name=}")
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_logger.debug(f"Have {seed_index=}")
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self.single_run_in_subdir(
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job_index, cost, dot.label, trial_name, seed_index
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)
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def single_run_in_subdir(
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self,
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job_index: int,
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cost: float,
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dot_name: str,
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trial_name: str,
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seed_index: int,
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):
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# _logger.info(f"Got job index {job_index}")
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# NOTE This guy runs inside subdirs, obviously. In something like <kalpa>/out/z-10-2/dipoles
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# we had job_index, trial_name, args let's see what we need
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_logger.debug(
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f"run_analysis() being called with ({job_index=}, {cost=}, {dot_name=}, {trial_name=}, {seed_index=})"
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)
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_logger.info(
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f"Have {self.config.generation_config.tantri_configs} as our tantri_configs"
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)
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num_tantri_configs = len(self.config.generation_config.tantri_configs)
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binned_datas = [
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kalpaa.read_dots_and_binned(
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self.config.get_dots_json_path(),
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pathlib.Path("..")
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/ kalpaa.common.tantri_binned_output_name(tantri_index),
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)
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for tantri_index in range(num_tantri_configs)
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]
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dot_names = self._dots_to_include(dot_name)
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_logger.debug(f"Got dot names {dot_names}")
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models = []
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indexes = self.indexifier.indexify(job_index)
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_logger.debug(f"indexes are {indexes}")
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log_magnitude = indexes["magnitudes"]
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avg_filled = indexes["occupancies"]
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orientation = enumify_orientation_string(indexes["orientations"])
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# we are just finding matches given a single seed so don't need to change this
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seed = seed_index
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# TODO find way to store this as a global config file
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occupancies_dict = {
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1: (500, 1000),
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2: (250, 2000),
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3: (250, 2000),
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5: (100, 5000),
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10: (50, 10000),
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16: (50, 10000),
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17: (50, 10000),
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31: (50, 10000),
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56: (25, 20000),
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100: (5, 100000),
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}
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mccount, mccountcycles = occupancies_dict[avg_filled]
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model_params = kalpaa.ReducedModelParams(
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count=avg_filled, log_magnitude=log_magnitude, orientation=orientation
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)
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models.append(kalpaa.get_model(model_params))
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_logger.info(f"have {len(models)} models to look at")
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if len(models) == 1:
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_logger.info(f"only one model, name: {models[0][0]}")
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deepdog_config = deepdog.direct_monte_carlo.DirectMonteCarloConfig(
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monte_carlo_count_per_cycle=mccount,
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monte_carlo_cycles=mccountcycles,
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target_success=self.config.deepdog_config.target_success,
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max_monte_carlo_cycles_steps=self.config.deepdog_config.max_monte_carlo_cycles_steps,
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monte_carlo_seed=seed,
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write_successes_to_file=True,
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tag=trial_name,
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write_bayesrun_file=True,
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)
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_logger.info(f"{deepdog_config=}")
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stdev_cost_function_filters = [
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b.stdev_cost_function_filter(
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dot_names, cost, self.config.deepdog_config.use_log_noise
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)
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for b in binned_datas
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]
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_logger.debug(f"{stdev_cost_function_filters=}")
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combining_filter = deepdog.direct_monte_carlo.compose_filter.ComposedDMCFilter(
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stdev_cost_function_filters
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)
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run = deepdog.direct_monte_carlo.DirectMonteCarloRun(
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model_name_pairs=models,
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filter=combining_filter,
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config=deepdog_config,
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)
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results = run.execute()
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_logger.info(results)
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def run(self):
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"""Going to iterate over every folder in out_dir, and execute the subdir stuff inside dirs like <kalpa>/out/z-10-2/dipoles"""
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out_dir_path = self.config.get_out_dir_path()
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subdirs = [child for child in out_dir_path.iterdir() if child.is_dir]
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# _logger.info(f"Going to execute within each of the directories in {subdirs=}")
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for subdir in subdirs:
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# skip try finally for now just blow up if problem
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_logger.debug(f"Running for {subdir=}")
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dipoles_dir = subdir / "dipoles"
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dipoles_dir.mkdir(exist_ok=True, parents=False)
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self.run_in_subdir(subdir / "dipoles")
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def parse_args():
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parser = argparse.ArgumentParser(
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"Stage02 Runner", formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--log-file",
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type=str,
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help="A filename for logging to, if not provided will only log to stderr",
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default=None,
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)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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tantri_configs = [
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kalpaa.TantriConfig(31415, 100, 5, 100000),
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kalpaa.TantriConfig(314, 100, 0.00005, 100000),
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]
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generation_config = kalpaa.GenerationConfig(tantri_configs=tantri_configs)
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config = kalpaa.Config(generation_config=generation_config)
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kalpaa.common.set_up_logging(config, args.log_file)
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_logger.info("Generating our data, for the following iterations")
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_logger.info(config)
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runner = Stage02Runner(config)
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runner.run()
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if __name__ == "__main__":
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main()
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