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44ddf9e104
* Refactor into automl subpackage Moved some of the packages into an automl subpackage to tidy before the task-based refactor. This is in response to discussions with the group and a comment on the first task-based PR. Only changes here are moving subpackages and modules into the new automl, fixing imports to work with this structure and fixing some dependencies in setup.py. * Fix doc building post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Remove vw from test deps as this is breaking the build * Move default back to the top-level I'd moved this to automl as that's where it's used internally, but had missed that this is actually part of the public interface so makes sense to live where it was. * Re-add top level modules with deprecation warnings flaml.data, flaml.ml and flaml.model are re-added to the top level, being re-exported from flaml.automl for backwards compatability. Adding a deprecation warning so that we can have a planned removal later. * Fix model.py line-endings * Pin pytorch-lightning to less than 1.8.0 We're seeing strange lightning related bugs from pytorch-forecasting since the release of lightning 1.8.0. Going to try constraining this to see if we have a fix. * Fix the lightning version pin Was optimistic with setting it in the 1.7.x range, but that isn't compatible with python 3.6 * Remove lightning version pin * Revert dependency version changes * Minor change to retrigger the build * Fix line endings in ml.py and model.py Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com>
118 lines
3.3 KiB
Python
118 lines
3.3 KiB
Python
from typing import Dict, Any
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import numpy as np
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from flaml.automl.data import (
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SUMMARIZATION,
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SEQREGRESSION,
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SEQCLASSIFICATION,
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MULTICHOICECLASSIFICATION,
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TOKENCLASSIFICATION,
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)
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def load_default_huggingface_metric_for_task(task):
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if task == SEQCLASSIFICATION:
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return "accuracy"
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elif task == SEQREGRESSION:
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return "r2"
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elif task == SUMMARIZATION:
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return "rouge1"
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elif task == MULTICHOICECLASSIFICATION:
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return "accuracy"
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elif task == TOKENCLASSIFICATION:
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return "seqeval"
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def is_a_list_of_str(this_obj):
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return (isinstance(this_obj, list) or isinstance(this_obj, np.ndarray)) and all(
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isinstance(x, str) for x in this_obj
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)
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def _clean_value(value: Any) -> str:
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if isinstance(value, float):
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return "{:.5}".format(value)
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else:
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return str(value).replace("/", "_")
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def format_vars(resolved_vars: Dict) -> str:
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"""Formats the resolved variable dict into a single string."""
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out = []
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for path, value in sorted(resolved_vars.items()):
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if path[0] in ["run", "env", "resources_per_trial"]:
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continue # TrialRunner already has these in the experiment_tag
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pieces = []
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last_string = True
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for k in path[::-1]:
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if isinstance(k, int):
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pieces.append(str(k))
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elif last_string:
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last_string = False
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pieces.append(k)
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pieces.reverse()
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out.append(_clean_value("_".join(pieces)) + "=" + _clean_value(value))
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return ",".join(out)
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counter = 0
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def date_str():
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from datetime import datetime
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return datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
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def _generate_dirname(experiment_tag, trial_id):
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generated_dirname = f"train_{str(trial_id)}_{experiment_tag}"
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generated_dirname = generated_dirname[:130]
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generated_dirname += f"_{date_str()}"
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return generated_dirname.replace("/", "_")
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def get_logdir_name(dirname, local_dir):
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import os
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local_dir = os.path.expanduser(local_dir)
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logdir = os.path.join(local_dir, dirname)
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return logdir
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class Counter:
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counter = 0
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@staticmethod
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def get_trial_fold_name(local_dir, trial_config, trial_id):
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Counter.counter += 1
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experiment_tag = "{0}_{1}".format(
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str(Counter.counter), format_vars(trial_config)
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)
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logdir = get_logdir_name(
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_generate_dirname(experiment_tag, trial_id=trial_id), local_dir
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)
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return logdir
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class LabelEncoderforTokenClassification:
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def fit_transform(self, y):
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# if the labels are tokens, convert them to ids
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if any(isinstance(id, str) for id in y[0]):
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self.label_list = sorted(list(set().union(*y)))
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self._tokenlabel_to_id = {
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self.label_list[id]: id for id in range(len(self.label_list))
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}
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y = y.apply(lambda sent: [self._tokenlabel_to_id[token] for token in sent])
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# if the labels are not tokens, they must be ids
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else:
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assert all(
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isinstance(id, (int, np.integer)) for id in y[0]
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), "The labels must either be tokens or ids"
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return y
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def transform(self, y):
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if hasattr(self, "_tokenlabel_to_id"):
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y = y.apply(lambda sent: [self._tokenlabel_to_id[token] for token in sent])
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return y
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