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autogen/flaml/automl/automl.py
Mark Harley 27b2712016
Extract task class from automl (#857) 
* 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

* WIP

* WIP - Notes below

Got to the point where the methods from AutoML are pulled to
GenericTask. Started removing private markers and removing the passing
of automl to these methods. Done with decide_split_type, started on
prepare_data. Need to do the others after

* Re-add generic_task

* Fix tests: add Task.__str__

* Fix tests: test for ray.ObjectRef

* Hotwire TS_Sklearn wrapper to fix test fail

* Remove unused data size field from Task

* Fix import for CLASSIFICATION in notebook

* Update flaml/automl/data.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Fix review comments

* Fix task -> str in custom learner constructor

* Remove unused CLASSIFICATION imports

* Hotwire TS_Sklearn wrapper to fix test fail by setting
optimizer_for_horizon == False

* Revert changes to the automl_classification and pin FLAML version

* Fix imports in reverted notebook

* Fix FLAML version in automl notebooks

* Fix ml.py line endings

* Fix CLASSIFICATION task import in automl_classification notebook

* Uncomment pip install in notebook and revert import

Not convinced this will work because of installing an older version of
the package into the environment in which we're running the tests, but
let's see.

* Revert c6a5dd1a0

* Revert "Revert c6a5dd1a0"

This reverts commit e55e35adea03993de87b23f092b14c6af623d487.

* Black format model.py

* Bump version to 1.1.2 in automl_xgboost

* Add docstrings to the Task ABC

* Fix import in custom_learner

* fix 'optimize_for_horizon' for ts_sklearn

* remove debugging print statements

* Check for is_forecast() before is_classification() in decide_split_type

* Attempt to fix formatting fail

* Another attempt to fix formatting fail

* And another attempt to fix formatting fail

* Add type annotations for task arg in signatures and docstrings

* Fix formatting

* Fix linting

---------

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Kevin Chen <chenkevin.8787@gmail.com>
2023-03-11 02:39:08 +00:00

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# !
# * Copyright (c) FLAML authors. All rights reserved.
# * Licensed under the MIT License. See LICENSE file in the
# * project root for license information.
from __future__ import annotations
import time
import os
import sys
from typing import Callable, List, Union, Optional
import inspect
from functools import partial
import numpy as np
from sklearn.base import BaseEstimator
import pandas as pd
import logging
import json
from flaml.automl.state import SearchState, AutoMLState
from flaml.automl.ml import (
compute_estimator,
train_estimator,
get_estimator_class,
)
from flaml.config import (
MIN_SAMPLE_TRAIN,
MEM_THRES,
RANDOM_SEED,
SMALL_LARGE_THRES,
CV_HOLDOUT_THRESHOLD,
SPLIT_RATIO,
N_SPLITS,
SAMPLE_MULTIPLY_FACTOR,
)
from flaml.automl.data import concat
# TODO check to see when we can remove these
from flaml.automl.task.task import CLASSIFICATION, TS_FORECAST, Task
from flaml.automl.task.factory import task_factory
from flaml import tune
from flaml.automl.logger import logger, logger_formatter
from flaml.automl.training_log import training_log_reader, training_log_writer
from flaml.default import suggest_learner
from flaml.version import __version__ as flaml_version
from flaml.tune.spark.utils import check_spark, get_broadcast_data
try:
import mlflow
except ImportError:
mlflow = None
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
ray_available = True
except (ImportError, AssertionError):
ray_available = False
def size(learner_classes: dict, config: dict) -> float:
"""Size function.
Returns:
The mem size in bytes for a config.
"""
config = config.get("ml", config)
estimator = config["learner"]
learner_class = learner_classes.get(estimator)
return learner_class.size(config)
class AutoML(BaseEstimator):
"""The AutoML class.
Example:
```python
automl = AutoML()
automl_settings = {
"time_budget": 60,
"metric": 'accuracy',
"task": 'classification',
"log_file_name": 'mylog.log',
}
automl.fit(X_train = X_train, y_train = y_train, **automl_settings)
```
"""
__version__ = flaml_version
def __init__(self, **settings):
"""Constructor.
Many settings in fit() can be passed to the constructor too.
If an argument in fit() is provided, it will override the setting passed to the constructor.
If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used.
Args:
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of the Task class.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the best
model per estimator. Make sure memory is large enough if setting to True.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: A float of the training time constraint in seconds.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
hpo_method: str, default="auto" | The hyperparameter
optimization method. By default, CFO is used for sequential
search and BlendSearch is used for parallel search.
No need to set when using flaml's default search space or using
a simple customized search space. When set to 'bs', BlendSearch
is used. BlendSearch can be tried when the search space is
complex, for example, containing multiple disjoint, discontinuous
subspaces. When set to 'random', random search is used.
starting_points: A dictionary or a str to specify the starting hyperparameter
config for the estimators | default="static".
If str:
- if "data", use data-dependent defaults;
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
```python
from flaml import AutoML
automl = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl.fit(X_train, y_train)
starting_points = automl.best_config_per_estimator
new_automl = AutoML()
new_automl.fit(X_train, y_train, starting_points=starting_points)
```
seed: int or None, default=None | The random seed for hpo.
n_concurrent_trials: [Experimental] int, default=1 | The number of
concurrent trials. When n_concurrent_trials > 1, flaml performes
[parallel tuning](../../Use-Cases/Task-Oriented-AutoML#parallel-tuning)
and installation of ray or spark is required: `pip install flaml[ray]`
or `pip install flaml[spark]`. Please check
[here](https://spark.apache.org/docs/latest/api/python/getting_started/install.html)
for more details about installing Spark.
keep_search_state: boolean, default=False | Whether to keep data needed
for model search after fit(). By default the state is deleted for
space saving.
preserve_checkpoint: boolean, default=True | Whether to preserve the saved checkpoint
on disk when deleting automl. By default the checkpoint is preserved.
early_stop: boolean, default=False | Whether to stop early if the
search is considered to converge.
force_cancel: boolean, default=False | Whether to forcely cancel Spark jobs if the
search time exceeded the time budget.
append_log: boolean, default=False | Whetehr to directly append the log
records to the input log file if it exists.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
min_sample_size: int, default=MIN_SAMPLE_TRAIN | the minimal sample
size when sample=True.
use_ray: boolean or dict.
If boolean: default=False | Whether to use ray to run the training
in separate processes. This can be used to prevent OOM for large
datasets, but will incur more overhead in time.
If dict: the dict contains the keywords arguments to be passed to
[ray.tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html).
use_spark: boolean, default=False | Whether to use spark to run the training
in parallel spark jobs. This can be used to accelerate training on large models
and large datasets, but will incur more overhead in time and thus slow down
training in some cases. GPU training is not supported yet when use_spark is True.
For Spark clusters, by default, we will launch one trial per executor. However,
sometimes we want to launch more trials than the number of executors (e.g., local mode).
In this case, we can set the environment variable `FLAML_MAX_CONCURRENT` to override
the detected `num_executors`. The final number of concurrent trials will be the minimum
of `n_concurrent_trials` and `num_executors`.
free_mem_ratio: float between 0 and 1, default=0. The free memory ratio to keep during training.
metric_constraints: list, default=[] | The list of metric constraints.
Each element in this list is a 3-tuple, which shall be expressed
in the following format: the first element of the 3-tuple is the name of the
metric, the second element is the inequality sign chosen from ">=" and "<=",
and the third element is the constraint value. E.g., `('val_loss', '<=', 0.1)`.
Note that all the metric names in metric_constraints need to be reported via
the metrics_to_log dictionary returned by a customized metric function.
The customized metric function shall be provided via the `metric` key word
argument of the fit() function or the automl constructor.
Find an example in the 4th constraint type in this [doc](../../Use-Cases/Task-Oriented-AutoML#constraint).
If `pred_time_limit` is provided as one of keyword arguments to fit() function or
the automl constructor, flaml will automatically (and under the hood)
add it as an additional element in the metric_constraints. Essentially 'pred_time_limit'
specifies a constraint about the prediction latency constraint in seconds.
custom_hp: dict, default=None | The custom search space specified by user.
It is a nested dict with keys being the estimator names, and values being dicts
per estimator search space. In the per estimator search space dict,
the keys are the hyperparameter names, and values are dicts of info ("domain",
"init_value", and "low_cost_init_value") about the search space associated with
the hyperparameter (i.e., per hyperparameter search space dict). When custom_hp
is provided, the built-in search space which is also a nested dict of per estimator
search space dict, will be updated with custom_hp. Note that during this nested dict update,
the per hyperparameter search space dicts will be replaced (instead of updated) by the ones
provided in custom_hp. Note that the value for "domain" can either be a constant
or a sample.Domain object.
e.g.,
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
}
}
```
"""
self._track_iter = 0
self._state = AutoMLState()
self._state.learner_classes = {}
self._settings = settings
# no budget by default
settings["time_budget"] = settings.get("time_budget", -1)
settings["task"] = settings.get("task", "classification")
settings["n_jobs"] = settings.get("n_jobs", -1)
settings["eval_method"] = settings.get("eval_method", "auto")
settings["split_ratio"] = settings.get("split_ratio", SPLIT_RATIO)
settings["n_splits"] = settings.get("n_splits", N_SPLITS)
settings["auto_augment"] = settings.get("auto_augment", True)
settings["metric"] = settings.get("metric", "auto")
settings["estimator_list"] = settings.get("estimator_list", "auto")
settings["log_file_name"] = settings.get("log_file_name", "")
settings["max_iter"] = settings.get("max_iter") # no budget by default
settings["sample"] = settings.get("sample", True)
settings["ensemble"] = settings.get("ensemble", False)
settings["log_type"] = settings.get("log_type", "better")
settings["model_history"] = settings.get("model_history", False)
settings["log_training_metric"] = settings.get("log_training_metric", False)
settings["mem_thres"] = settings.get("mem_thres", MEM_THRES)
settings["pred_time_limit"] = settings.get("pred_time_limit", np.inf)
settings["train_time_limit"] = settings.get("train_time_limit", None)
settings["verbose"] = settings.get("verbose", 3)
settings["retrain_full"] = settings.get("retrain_full", True)
settings["split_type"] = settings.get("split_type", "auto")
settings["hpo_method"] = settings.get("hpo_method", "auto")
settings["learner_selector"] = settings.get("learner_selector", "sample")
settings["starting_points"] = settings.get("starting_points", "static")
settings["n_concurrent_trials"] = settings.get("n_concurrent_trials", 1)
settings["keep_search_state"] = settings.get("keep_search_state", False)
settings["preserve_checkpoint"] = settings.get("preserve_checkpoint", True)
settings["early_stop"] = settings.get("early_stop", False)
settings["force_cancel"] = settings.get("force_cancel", False)
settings["append_log"] = settings.get("append_log", False)
settings["min_sample_size"] = settings.get("min_sample_size", MIN_SAMPLE_TRAIN)
settings["use_ray"] = settings.get("use_ray", False)
settings["use_spark"] = settings.get("use_spark", False)
if settings["use_ray"] is not False and settings["use_spark"] is not False:
raise ValueError("use_ray and use_spark cannot be both True.")
settings["free_mem_ratio"] = settings.get("free_mem_ratio", 0)
settings["metric_constraints"] = settings.get("metric_constraints", [])
settings["cv_score_agg_func"] = settings.get("cv_score_agg_func", None)
settings["fit_kwargs_by_estimator"] = settings.get(
"fit_kwargs_by_estimator", {}
)
settings["custom_hp"] = settings.get("custom_hp", {})
settings["skip_transform"] = settings.get("skip_transform", False)
self._estimator_type = (
"classifier" if settings["task"] in CLASSIFICATION else "regressor"
)
def get_params(self, deep: bool = False) -> dict:
return self._settings.copy()
@property
def config_history(self) -> dict:
"""A dictionary of iter->(estimator, config, time),
storing the best estimator, config, and the time when the best
model is updated each time.
"""
return self._config_history
@property
def model(self):
"""An object with `predict()` and `predict_proba()` method (for
classification), storing the best trained model.
"""
return self.__dict__.get("_trained_estimator")
def best_model_for_estimator(self, estimator_name: str):
"""Return the best model found for a particular estimator.
Args:
estimator_name: a str of the estimator's name.
Returns:
An object storing the best model for estimator_name.
If `model_history` was set to False during fit(), then the returned model
is untrained unless estimator_name is the best estimator.
If `model_history` was set to True, then the returned model is trained.
"""
state = self._search_states.get(estimator_name)
return state and getattr(state, "trained_estimator", None)
@property
def best_estimator(self):
"""A string indicating the best estimator found."""
return self._best_estimator
@property
def best_iteration(self):
"""An integer of the iteration number where the best
config is found."""
return self._best_iteration
@property
def best_config(self):
"""A dictionary of the best configuration."""
state = self._search_states.get(self._best_estimator)
config = state and getattr(state, "best_config", None)
return config and AutoMLState.sanitize(config)
@property
def best_config_per_estimator(self):
"""A dictionary of all estimators' best configuration."""
return {
e: e_search_state.best_config
and AutoMLState.sanitize(e_search_state.best_config)
for e, e_search_state in self._search_states.items()
}
@property
def best_loss_per_estimator(self):
"""A dictionary of all estimators' best loss."""
return {
e: e_search_state.best_loss
for e, e_search_state in self._search_states.items()
}
@property
def best_loss(self):
"""A float of the best loss found."""
return self._state.best_loss
@property
def best_result(self):
"""Result dictionary for model trained with the best config."""
state = self._search_states.get(self._best_estimator)
return state and getattr(state, "best_result", None)
@property
def metrics_for_best_config(self):
"""Returns a float of the best loss, and a dictionary of the auxiliary metrics to log
associated with the best config. These two objects correspond to the returned
objects by the customized metric function for the config with the best loss."""
state = self._search_states.get(self._best_estimator)
return self._state.best_loss, state and getattr(state, "best_result", {}).get(
"metric_for_logging"
)
@property
def best_config_train_time(self):
"""A float of the seconds taken by training the best config."""
return getattr(
self._search_states[self._best_estimator], "best_config_train_time", None
)
def save_best_config(self, filename):
best = {
"class": self.best_estimator,
"hyperparameters": self.best_config,
}
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w") as f:
json.dump(best, f)
@property
def classes_(self):
"""A numpy array of shape (n_classes,) for class labels."""
attr = getattr(self, "_label_transformer", None)
if attr:
return attr.classes_
attr = getattr(self, "_trained_estimator", None)
if attr:
return attr.classes_
return None
@property
def n_features_in_(self):
return self._trained_estimator.n_features_in_
@property
def feature_names_in_(self):
attr = getattr(self, "_trained_estimator", None)
attr = attr and getattr(attr, "feature_names_in_", None)
if attr is not None:
return attr
return getattr(self, "_feature_names_in_", None)
@property
def feature_importances_(self):
attr = getattr(self, "_trained_estimator", None)
attr = attr and getattr(attr, "feature_importances_", None)
return attr
@property
def time_to_find_best_model(self) -> float:
"""Time taken to find best model in seconds."""
return self.__dict__.get("_time_taken_best_iter")
def score(self, X: pd.DataFrame, y: pd.Series, **kwargs):
estimator = getattr(self, "_trained_estimator", None)
if estimator is None:
logger.warning(
"No estimator is trained. Please run fit with enough budget."
)
return None
X = self._state.task.preprocess(X, self._transformer)
if self._label_transformer:
y = self._label_transformer.transform(y)
return estimator.score(X, y, **kwargs)
def predict(
self,
X: Union[np.array, pd.DataFrame, List[str], List[List[str]]],
**pred_kwargs,
):
"""Predict label from features.
Args:
X: A numpy array of featurized instances, shape n * m,
or for time series forcast tasks:
a pandas dataframe with the first column containing
timestamp values (datetime type) or an integer n for
the predict steps (only valid when the estimator is
arima or sarimax). Other columns in the dataframe
are assumed to be exogenous variables (categorical
or numeric).
**pred_kwargs: Other key word arguments to pass to predict() function of
the searched learners, such as per_device_eval_batch_size.
```python
multivariate_X_test = pd.DataFrame({
'timeStamp': pd.date_range(start='1/1/2022', end='1/07/2022'),
'categorical_col': ['yes', 'yes', 'no', 'no', 'yes', 'no', 'yes'],
'continuous_col': [105, 107, 120, 118, 110, 112, 115]
})
model.predict(multivariate_X_test)
```
Returns:
A array-like of shape n * 1: each element is a predicted
label for an instance.
"""
estimator = getattr(self, "_trained_estimator", None)
if estimator is None:
logger.warning(
"No estimator is trained. Please run fit with enough budget."
)
return None
X = self._state.task.preprocess(X, self._transformer)
y_pred = estimator.predict(X, **pred_kwargs)
if (
isinstance(y_pred, np.ndarray)
and y_pred.ndim > 1
and isinstance(y_pred, np.ndarray)
):
y_pred = y_pred.flatten()
if self._label_transformer:
return self._label_transformer.inverse_transform(
pd.Series(y_pred.astype(int))
)
else:
return y_pred
def predict_proba(self, X, **pred_kwargs):
"""Predict the probability of each class from features, only works for
classification problems.
Args:
X: A numpy array of featurized instances, shape n * m.
**pred_kwargs: Other key word arguments to pass to predict_proba() function of
the searched learners, such as per_device_eval_batch_size.
Returns:
A numpy array of shape n * c. c is the # classes. Each element at
(i, j) is the probability for instance i to be in class j.
"""
estimator = getattr(self, "_trained_estimator", None)
if estimator is None:
logger.warning(
"No estimator is trained. Please run fit with enough budget."
)
return None
X = self._state.task.preprocess(X, self._transformer)
proba = self._trained_estimator.predict_proba(X, **pred_kwargs)
return proba
def add_learner(self, learner_name, learner_class):
"""Add a customized learner.
Args:
learner_name: A string of the learner's name.
learner_class: A subclass of flaml.model.BaseEstimator.
"""
self._state.learner_classes[learner_name] = learner_class
def get_estimator_from_log(
self, log_file_name: str, record_id: int, task: Union[str, Task]
):
"""Get the estimator from log file.
Args:
log_file_name: A string of the log file name.
record_id: An integer of the record ID in the file,
0 corresponds to the first trial.
task: A string of the task type,
'binary', 'multiclass', 'regression', 'ts_forecast', 'rank',
or an instance of the Task class.
Returns:
An estimator object for the given configuration.
"""
with training_log_reader(log_file_name) as reader:
record = reader.get_record(record_id)
estimator = record.learner
config = AutoMLState.sanitize(record.config)
if isinstance(task, str):
task = task_factory(task)
estimator, _ = train_estimator(
X_train=None,
y_train=None,
config_dic=config,
task=task,
estimator_name=estimator,
estimator_class=self._state.learner_classes.get(estimator),
eval_metric="train_time",
)
return estimator
def retrain_from_log(
self,
log_file_name,
X_train=None,
y_train=None,
dataframe=None,
label=None,
time_budget=np.inf,
task: Optional[Union[str, Task]] = None,
eval_method=None,
split_ratio=None,
n_splits=None,
split_type=None,
groups=None,
n_jobs=-1,
# gpu_per_trial=0,
train_best=True,
train_full=False,
record_id=-1,
auto_augment=None,
custom_hp=None,
skip_transform=None,
preserve_checkpoint=True,
fit_kwargs_by_estimator=None,
**fit_kwargs,
):
"""Retrain from log file.
This function is intended to retrain the logged configurations.
NOTE: In some rare case, the last config is early stopped to meet time_budget and it's the best config.
But the logged config's ITER_HP (e.g., n_estimators) is not reduced.
Args:
log_file_name: A string of the log file name.
X_train: A numpy array or dataframe of training data in shape n*m.
For time series forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric).
y_train: A numpy array or series of labels in shape n*1.
dataframe: A dataframe of training data including label column.
For time series forecast tasks, dataframe must be specified and should
have at least two columns: timestamp and label, where the first
column is the timestamp column (datetime type). Other columns
in the dataframe are assumed to be exogenous variables
(categorical or numeric).
label: A str of the label column name, e.g., 'label';
Note: If X_train and y_train are provided,
dataframe and label are ignored;
If not, dataframe and label must be provided.
time_budget: A float number of the time budget in seconds.
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank',
'seq-classification', 'seq-regression', 'summarization',
or an instance of Task class.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the validation data percentage for holdout.
n_splits: An integer of the number of folds for cross-validation.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
groups: None or array-like | Group labels (with matching length to
y_train) or groups counts (with sum equal to length of y_train)
for training data.
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
train_best: A boolean of whether to train the best config in the
time budget; if false, train the last config in the budget.
train_full: A boolean of whether to train on the full data. If true,
eval_method and sample_size in the log file will be ignored.
record_id: the ID of the training log record from which the model will
be retrained. By default `record_id = -1` which means this will be
ignored. `record_id = 0` corresponds to the first trial, and
when `record_id >= 0`, `time_budget` will be ignored.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
custom_hp: dict, default=None | The custom search space specified by user
Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
domain of the custom search space can either be a value or a sample.Domain object.
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
}
}
```
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight. Below are a few examples of
estimator-specific parameters:
period: int | forecast horizon for all time series forecast tasks.
gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
only used by TransformersEstimator, XGBoostSklearnEstimator, and
TemporalFusionTransformerEstimator.
group_ids: list of strings of column names identifying a time series, only
used by TemporalFusionTransformerEstimator, required for
'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object
from PyTorchForecasting.
For other parameters to describe your dataset, refer to
[TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html).
To specify your variables, use `static_categoricals`, `static_reals`,
`time_varying_known_categoricals`, `time_varying_known_reals`,
`time_varying_unknown_categoricals`, `time_varying_unknown_reals`,
`variable_groups`. To provide more information on your data, use
`max_encoder_length`, `min_encoder_length`, `lags`.
log_dir: str, default = "lightning_logs" | Folder into which to log results
for tensorboard, only used by TemporalFusionTransformerEstimator.
max_epochs: int, default = 20 | Maximum number of epochs to run training,
only used by TemporalFusionTransformerEstimator.
batch_size: int, default = 64 | Batch size for training model, only
used by TemporalFusionTransformerEstimator.
"""
task = task or self._settings.get("task")
if isinstance(task, str):
task = task_factory(task)
eval_method = eval_method or self._settings.get("eval_method")
split_ratio = split_ratio or self._settings.get("split_ratio")
n_splits = n_splits or self._settings.get("n_splits")
split_type = split_type or self._settings.get("split_type")
auto_augment = (
self._settings.get("auto_augment") if auto_augment is None else auto_augment
)
self._state.task = task
self._estimator_type = "classifier" if task.is_classification() else "regressor"
self._state.fit_kwargs = fit_kwargs
self._state.custom_hp = custom_hp or self._settings.get("custom_hp")
self._skip_transform = (
self._settings.get("skip_transform")
if skip_transform is None
else skip_transform
)
self._state.fit_kwargs_by_estimator = (
fit_kwargs_by_estimator or self._settings.get("fit_kwargs_by_estimator")
)
self.preserve_checkpoint = (
self._settings.get("preserve_checkpoint")
if preserve_checkpoint is None
else preserve_checkpoint
)
task.validate_data(
self, self._state, X_train, y_train, dataframe, label, groups=groups
)
logger.info("log file name {}".format(log_file_name))
best_config = None
best_val_loss = float("+inf")
best_estimator = None
sample_size = None
time_used = 0.0
training_duration = 0
best = None
with training_log_reader(log_file_name) as reader:
if record_id >= 0:
best = reader.get_record(record_id)
else:
for record in reader.records():
time_used = record.wall_clock_time
if time_used > time_budget:
break
training_duration = time_used
val_loss = record.validation_loss
if val_loss <= best_val_loss or not train_best:
if val_loss == best_val_loss and train_best:
size = record.sample_size
if size > sample_size:
best = record
best_val_loss = val_loss
sample_size = size
else:
best = record
size = record.sample_size
best_val_loss = val_loss
sample_size = size
if not training_duration:
logger.warning(
f"No estimator found within time_budget={time_budget}"
)
from .model import BaseEstimator as Estimator
self._trained_estimator = Estimator()
return training_duration
if not best:
return
best_estimator = best.learner
best_config = best.config
sample_size = len(self._y_train_all) if train_full else best.sample_size
this_estimator_kwargs = self._state.fit_kwargs_by_estimator.get(best_estimator)
if this_estimator_kwargs:
this_estimator_kwargs = (
this_estimator_kwargs.copy()
) # make another shallow copy of the value (a dict obj), so user's fit_kwargs_by_estimator won't be updated
this_estimator_kwargs.update(self._state.fit_kwargs)
self._state.fit_kwargs_by_estimator[best_estimator] = this_estimator_kwargs
else:
self._state.fit_kwargs_by_estimator[best_estimator] = self._state.fit_kwargs
logger.info(
"estimator = {}, config = {}, #training instances = {}".format(
best_estimator, best_config, sample_size
)
)
# Partially copied from fit() function
# Initilize some attributes required for retrain_from_log
self._split_type = task.decide_split_type(
split_type,
self._y_train_all,
self._state.fit_kwargs,
self._state.groups,
)
eval_method = self._decide_eval_method(eval_method, time_budget)
self.modelcount = 0
self._auto_augment = auto_augment
self._prepare_data(eval_method, split_ratio, n_splits)
self._state.time_budget = -1
self._state.free_mem_ratio = 0
self._state.n_jobs = n_jobs
import os
self._state.resources_per_trial = (
{
"cpu": max(1, os.cpu_count() >> 1),
"gpu": fit_kwargs.get("gpu_per_trial", 0),
}
if self._state.n_jobs < 0
else {"cpu": self._state.n_jobs, "gpu": fit_kwargs.get("gpu_per_trial", 0)}
)
self._trained_estimator = self._state._train_with_config(
best_estimator,
best_config,
sample_size=sample_size,
)[0]
logger.info("retrain from log succeeded")
return training_duration
def _decide_eval_method(self, eval_method, time_budget):
if not isinstance(self._split_type, str):
assert eval_method in [
"auto",
"cv",
], "eval_method must be 'auto' or 'cv' for custom data splitter."
assert (
self._state.X_val is None
), "custom splitter and custom validation data can't be used together."
return "cv"
if self._state.X_val is not None:
assert eval_method in [
"auto",
"holdout",
], "eval_method must be 'auto' or 'holdout' for custom validation data."
return "holdout"
if eval_method != "auto":
assert eval_method in [
"holdout",
"cv",
], "eval_method must be 'holdout', 'cv' or 'auto'."
return eval_method
nrow, dim = self._nrow, self._ndim
if (
time_budget < 0
or nrow * dim / 0.9 < SMALL_LARGE_THRES * (time_budget / 3600)
and nrow < CV_HOLDOUT_THRESHOLD
):
# time allows or sampling can be used and cv is necessary
return "cv"
else:
return "holdout"
@property
def search_space(self) -> dict:
"""Search space.
Must be called after fit(...)
(use max_iter=0 and retrain_final=False to prevent actual fitting).
Returns:
A dict of the search space.
"""
estimator_list = self.estimator_list
if len(estimator_list) == 1:
estimator = estimator_list[0]
space = self._search_states[estimator].search_space.copy()
space["learner"] = estimator
return space
choices = []
for estimator in estimator_list:
space = self._search_states[estimator].search_space.copy()
space["learner"] = estimator
choices.append(space)
return {"ml": tune.choice(choices)}
@property
def low_cost_partial_config(self) -> dict:
"""Low cost partial config.
Returns:
A dict.
(a) if there is only one estimator in estimator_list, each key is a
hyperparameter name.
(b) otherwise, it is a nested dict with 'ml' as the key, and
a list of the low_cost_partial_configs as the value, corresponding
to each learner's low_cost_partial_config; the estimator index as
an integer corresponding to the cheapest learner is appended to the
list at the end.
"""
if len(self.estimator_list) == 1:
estimator = self.estimator_list[0]
c = self._search_states[estimator].low_cost_partial_config
return c
else:
configs = []
for estimator in self.estimator_list:
c = self._search_states[estimator].low_cost_partial_config
configs.append(c)
configs.append(
np.argmin(
[
self._state.learner_classes.get(estimator).cost_relative2lgbm()
for estimator in self.estimator_list
]
)
)
config = {"ml": configs}
return config
@property
def cat_hp_cost(self) -> dict:
"""Categorical hyperparameter cost
Returns:
A dict.
(a) if there is only one estimator in estimator_list, each key is a
hyperparameter name.
(b) otherwise, it is a nested dict with 'ml' as the key, and
a list of the cat_hp_cost's as the value, corresponding
to each learner's cat_hp_cost; the cost relative to lgbm for each
learner (as a list itself) is appended to the list at the end.
"""
if len(self.estimator_list) == 1:
estimator = self.estimator_list[0]
c = self._search_states[estimator].cat_hp_cost
return c
else:
configs = []
for estimator in self.estimator_list:
c = self._search_states[estimator].cat_hp_cost
configs.append(c)
configs.append(
[
self._state.learner_classes.get(estimator).cost_relative2lgbm()
for estimator in self.estimator_list
]
)
config = {"ml": configs}
return config
@property
def points_to_evaluate(self) -> dict:
"""Initial points to evaluate.
Returns:
A list of dicts. Each dict is the initial point for each learner.
"""
points = []
for estimator in self.estimator_list:
configs = self._search_states[estimator].init_config
for config in configs:
config["learner"] = estimator
if len(self.estimator_list) > 1:
points.append({"ml": config})
else:
points.append(config)
return points
@property
def resource_attr(self) -> Optional[str]:
"""Attribute of the resource dimension.
Returns:
A string for the sample size attribute
(the resource attribute in AutoML) or None.
"""
return "FLAML_sample_size" if self._sample else None
@property
def min_resource(self) -> Optional[float]:
"""Attribute for pruning.
Returns:
A float for the minimal sample size or None.
"""
return self._min_sample_size if self._sample else None
@property
def max_resource(self) -> Optional[float]:
"""Attribute for pruning.
Returns:
A float for the maximal sample size or None.
"""
return self._state.data_size[0] if self._sample else None
def pickle(self, output_file_name):
import pickle
estimator_to_training_function = {}
for estimator in self.estimator_list:
search_state = self._search_states[estimator]
if hasattr(search_state, "training_function"):
estimator_to_training_function[
estimator
] = search_state.training_function
del search_state.training_function
with open(output_file_name, "wb") as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
@property
def trainable(self) -> Callable[[dict], Optional[float]]:
"""Training function.
Returns:
A function that evaluates each config and returns the loss.
"""
self._state.time_from_start = 0
states = self._search_states
mem_res = self._mem_thres
def train(config: dict, state, is_report=True):
# handle spark broadcast variables
state = get_broadcast_data(state)
is_report = get_broadcast_data(is_report)
sample_size = config.get("FLAML_sample_size")
config = config.get("ml", config).copy()
if sample_size:
config["FLAML_sample_size"] = sample_size
estimator = config["learner"]
# check memory constraints before training
if states[estimator].learner_class.size(config) <= mem_res:
del config["learner"]
config.pop("_choice_", None)
result = AutoMLState._compute_with_config_base(
config, state=state, estimator=estimator, is_report=is_report
)
else:
# If search algorithm is not in flaml, it does not handle the config constraint, should also tune.report before return
result = {
"pred_time": 0,
"wall_clock_time": None,
"metric_for_logging": np.inf,
"val_loss": np.inf,
"trained_estimator": None,
}
if is_report is True:
tune.report(**result)
return result
if self._use_ray is not False:
from ray.tune import with_parameters
return with_parameters(
train,
state=self._state,
)
elif self._use_spark:
from flaml.tune.spark.utils import with_parameters
return with_parameters(train, state=self._state, is_report=False)
else:
return partial(
train,
state=self._state,
)
@property
def metric_constraints(self) -> list:
"""Metric constraints.
Returns:
A list of the metric constraints.
"""
return self._metric_constraints
def _prepare_data(self, eval_method, split_ratio, n_splits):
self._state.task.prepare_data(
self._state,
self._X_train_all,
self._y_train_all,
self._auto_augment,
eval_method,
self._split_type,
split_ratio,
n_splits,
self._df,
self._sample_weight_full,
)
self.data_size_full = len(self._state.y_train_all)
def fit(
self,
X_train=None,
y_train=None,
dataframe=None,
label=None,
metric=None,
task: Optional[Union[str, Task]] = None,
n_jobs=None,
# gpu_per_trial=0,
log_file_name=None,
estimator_list=None,
time_budget=None,
max_iter=None,
sample=None,
ensemble=None,
eval_method=None,
log_type=None,
model_history=None,
split_ratio=None,
n_splits=None,
log_training_metric=None,
mem_thres=None,
pred_time_limit=None,
train_time_limit=None,
X_val=None,
y_val=None,
sample_weight_val=None,
groups_val=None,
groups=None,
verbose=None,
retrain_full=None,
split_type=None,
learner_selector=None,
hpo_method=None,
starting_points=None,
seed=None,
n_concurrent_trials=None,
keep_search_state=None,
preserve_checkpoint=True,
early_stop=None,
force_cancel=None,
append_log=None,
auto_augment=None,
min_sample_size=None,
use_ray=None,
use_spark=None,
free_mem_ratio=0,
metric_constraints=None,
custom_hp=None,
cv_score_agg_func=None,
skip_transform=None,
fit_kwargs_by_estimator=None,
**fit_kwargs,
):
"""Find a model for a given task.
Args:
X_train: A numpy array or a pandas dataframe of training data in
shape (n, m). For time series forecsat tasks, the first column of X_train
must be the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, X_train can be a ray.ObjectRef.
y_train: A numpy array or a pandas series of labels in shape (n, ).
dataframe: A dataframe of training data including label column.
For time series forecast tasks, dataframe must be specified and must have
at least two columns, timestamp and label, where the first
column is the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, dataframe can be a ray.ObjectRef.
label: A str of the label column name for, e.g., 'label';
Note: If X_train and y_train are provided,
dataframe and label are ignored;
If not, dataframe and label must be provided.
metric: A string of the metric name or a function,
e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted',
'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following input arguments:
```python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None,
config=None, groups_test=None, groups_train=None,
):
return metric_to_minimize, metrics_to_log
```
which returns a float number as the minimization objective,
and a dictionary as the metrics to log. E.g.,
```python
def custom_metric(
X_val, y_val, estimator, labels,
X_train, y_train, weight_val=None, weight_train=None,
*args,
):
from sklearn.metrics import log_loss
import time
start = time.time()
y_pred = estimator.predict_proba(X_val)
pred_time = (time.time() - start) / len(X_val)
val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
alpha = 0.5
return val_loss * (1 + alpha) - alpha * train_loss, {
"val_loss": val_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
```
task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast_regression',
'ts_forecast_classification', 'rank', 'seq-classification',
'seq-regression', 'summarization', or an instance of Task class
n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging,
set it to be an empty string "".
estimator_list: A list of strings for estimator names, or 'auto'.
e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']```.
time_budget: A float number of the time budget in seconds.
Use -1 if no time limit.
max_iter: An integer of the maximal number of iterations.
NOTE: when both time_budget and max_iter are unspecified,
only one model will be trained per estimator.
sample: A boolean of whether to sample the training data during
search.
ensemble: boolean or dict | default=False. Whether to perform
ensemble after search. Can be a dict with keys 'passthrough'
and 'final_estimator' to specify the passthrough and
final_estimator in the stacker. The dict can also contain
'n_jobs' as the key to specify the number of jobs for the stacker.
eval_method: A string of resampling strategy, one of
['auto', 'cv', 'holdout'].
split_ratio: A float of the valiation data percentage for holdout.
n_splits: An integer of the number of folds for cross - validation.
log_type: A string of the log type, one of
['better', 'all'].
'better' only logs configs with better loss than previos iters
'all' logs all the tried configs.
model_history: A boolean of whether to keep the trained best
model per estimator. Make sure memory is large enough if setting to True.
Default value is False: best_model_for_estimator would return a
untrained model for non-best learner.
log_training_metric: A boolean of whether to log the training
metric for each model.
mem_thres: A float of the memory size constraint in bytes.
pred_time_limit: A float of the prediction latency constraint in seconds.
It refers to the average prediction time per row in validation data.
train_time_limit: None or a float of the training time constraint in seconds.
X_val: None or a numpy array or a pandas dataframe of validation data.
y_val: None or a numpy array or a pandas series of validation labels.
sample_weight_val: None or a numpy array of the sample weight of
validation data of the same shape as y_val.
groups_val: None or array-like | group labels (with matching length
to y_val) or group counts (with sum equal to length of y_val)
for validation data. Need to be consistent with groups.
groups: None or array-like | Group labels (with matching length to
y_train) or groups counts (with sum equal to length of y_train)
for training data.
verbose: int, default=3 | Controls the verbosity, higher means more
messages.
retrain_full: bool or str, default=True | whether to retrain the
selected model on the full training data when using holdout.
True - retrain only after search finishes; False - no retraining;
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For time series forecast tasks, must be "auto" or 'time'.
For ranking task, must be "auto" or 'group'.
hpo_method: str, default="auto" | The hyperparameter
optimization method. By default, CFO is used for sequential
search and BlendSearch is used for parallel search.
No need to set when using flaml's default search space or using
a simple customized search space. When set to 'bs', BlendSearch
is used. BlendSearch can be tried when the search space is
complex, for example, containing multiple disjoint, discontinuous
subspaces. When set to 'random', random search is used.
starting_points: A dictionary or a str to specify the starting hyperparameter
config for the estimators | default="data".
If str:
- if "data", use data-dependent defaults;
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
```python
from flaml import AutoML
automl = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl.fit(X_train, y_train)
starting_points = automl.best_config_per_estimator
new_automl = AutoML()
new_automl.fit(X_train, y_train, starting_points=starting_points)
```
seed: int or None, default=None | The random seed for hpo.
n_concurrent_trials: [Experimental] int, default=1 | The number of
concurrent trials. When n_concurrent_trials > 1, flaml performes
[parallel tuning](../../Use-Cases/Task-Oriented-AutoML#parallel-tuning)
and installation of ray or spark is required: `pip install flaml[ray]`
or `pip install flaml[spark]`. Please check
[here](https://spark.apache.org/docs/latest/api/python/getting_started/install.html)
for more details about installing Spark.
keep_search_state: boolean, default=False | Whether to keep data needed
for model search after fit(). By default the state is deleted for
space saving.
preserve_checkpoint: boolean, default=True | Whether to preserve the saved checkpoint
on disk when deleting automl. By default the checkpoint is preserved.
early_stop: boolean, default=False | Whether to stop early if the
search is considered to converge.
force_cancel: boolean, default=False | Whether to forcely cancel the PySpark job if overtime.
append_log: boolean, default=False | Whetehr to directly append the log
records to the input log file if it exists.
auto_augment: boolean, default=True | Whether to automatically
augment rare classes.
min_sample_size: int, default=MIN_SAMPLE_TRAIN | the minimal sample
size when sample=True.
use_ray: boolean or dict.
If boolean: default=False | Whether to use ray to run the training
in separate processes. This can be used to prevent OOM for large
datasets, but will incur more overhead in time.
If dict: the dict contains the keywords arguments to be passed to
[ray.tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html).
use_spark: boolean, default=False | Whether to use spark to run the training
in parallel spark jobs. This can be used to accelerate training on large models
and large datasets, but will incur more overhead in time and thus slow down
training in some cases.
free_mem_ratio: float between 0 and 1, default=0. The free memory ratio to keep during training.
metric_constraints: list, default=[] | The list of metric constraints.
Each element in this list is a 3-tuple, which shall be expressed
in the following format: the first element of the 3-tuple is the name of the
metric, the second element is the inequality sign chosen from ">=" and "<=",
and the third element is the constraint value. E.g., `('precision', '>=', 0.9)`.
Note that all the metric names in metric_constraints need to be reported via
the metrics_to_log dictionary returned by a customized metric function.
The customized metric function shall be provided via the `metric` key word argument
of the fit() function or the automl constructor.
Find examples in this [test](https://github.com/microsoft/FLAML/tree/main/test/automl/test_constraints.py).
If `pred_time_limit` is provided as one of keyword arguments to fit() function or
the automl constructor, flaml will automatically (and under the hood)
add it as an additional element in the metric_constraints. Essentially 'pred_time_limit'
specifies a constraint about the prediction latency constraint in seconds.
custom_hp: dict, default=None | The custom search space specified by user
Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
domain of the custom search space can either be a value of a sample.Domain object.
```python
custom_hp = {
"transformer_ms": {
"model_path": {
"domain": "albert-base-v2",
},
"learning_rate": {
"domain": tune.choice([1e-4, 1e-5]),
}
}
}
```
cv_score_agg_func: customized cross-validation scores aggregate function. Default to average metrics across folds. If specificed, this function needs to
have the following input arguments:
* val_loss_folds: list of floats, the loss scores of each fold;
* log_metrics_folds: list of dicts/floats, the metrics of each fold to log.
This function should return the final aggregate result of all folds. A float number of the minimization objective, and a dictionary as the metrics to log or None.
E.g.,
```python
def cv_score_agg_func(val_loss_folds, log_metrics_folds):
metric_to_minimize = sum(val_loss_folds)/len(val_loss_folds)
metrics_to_log = None
for single_fold in log_metrics_folds:
if metrics_to_log is None:
metrics_to_log = single_fold
elif isinstance(metrics_to_log, dict):
metrics_to_log = {k: metrics_to_log[k] + v for k, v in single_fold.items()}
else:
metrics_to_log += single_fold
if metrics_to_log:
n = len(val_loss_folds)
metrics_to_log = (
{k: v / n for k, v in metrics_to_log.items()}
if isinstance(metrics_to_log, dict)
else metrics_to_log / n
)
return metric_to_minimize, metrics_to_log
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
For TransformersEstimator, available fit_kwargs can be found from
[TrainingArgumentsForAuto](nlp/huggingface/training_args).
e.g.,
```python
fit_kwargs_by_estimator = {
"transformer": {
"output_dir": "test/data/output/",
"fp16": False,
},
"tft": {
"max_encoder_length": 1,
"min_encoder_length": 1,
"static_categoricals": [],
"static_reals": [],
"time_varying_known_categoricals": [],
"time_varying_known_reals": [],
"time_varying_unknown_categoricals": [],
"time_varying_unknown_reals": [],
"variable_groups": {},
"lags": {},
}
}
```
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight. Below are a few examples of
estimator-specific parameters:
period: int | forecast horizon for all time series forecast tasks.
gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
only used by TransformersEstimator, XGBoostSklearnEstimator, and
TemporalFusionTransformerEstimator.
group_ids: list of strings of column names identifying a time series, only
used by TemporalFusionTransformerEstimator, required for
'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object
from PyTorchForecasting.
For other parameters to describe your dataset, refer to
[TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html).
To specify your variables, use `static_categoricals`, `static_reals`,
`time_varying_known_categoricals`, `time_varying_known_reals`,
`time_varying_unknown_categoricals`, `time_varying_unknown_reals`,
`variable_groups`. To provide more information on your data, use
`max_encoder_length`, `min_encoder_length`, `lags`.
log_dir: str, default = "lightning_logs" | Folder into which to log results
for tensorboard, only used by TemporalFusionTransformerEstimator.
max_epochs: int, default = 20 | Maximum number of epochs to run training,
only used by TemporalFusionTransformerEstimator.
batch_size: int, default = 64 | Batch size for training model, only
used by TemporalFusionTransformerEstimator.
"""
self._state._start_time_flag = self._start_time_flag = time.time()
task = task or self._settings.get("task")
if isinstance(task, str):
task = task_factory(task, X_train, y_train)
self._state.task = task
self._estimator_type = "classifier" if task.is_classification() else "regressor"
time_budget = time_budget or self._settings.get("time_budget")
n_jobs = n_jobs or self._settings.get("n_jobs")
gpu_per_trial = fit_kwargs.get("gpu_per_trial", 0)
eval_method = eval_method or self._settings.get("eval_method")
split_ratio = split_ratio or self._settings.get("split_ratio")
n_splits = n_splits or self._settings.get("n_splits")
auto_augment = (
self._settings.get("auto_augment") if auto_augment is None else auto_augment
)
metric = metric or self._settings.get("metric")
estimator_list = estimator_list or self._settings.get("estimator_list")
log_file_name = (
self._settings.get("log_file_name")
if log_file_name is None
else log_file_name
)
max_iter = self._settings.get("max_iter") if max_iter is None else max_iter
sample_is_none = sample is None
if sample_is_none:
sample = self._settings.get("sample")
ensemble = self._settings.get("ensemble") if ensemble is None else ensemble
log_type = log_type or self._settings.get("log_type")
model_history = (
self._settings.get("model_history")
if model_history is None
else model_history
)
log_training_metric = (
self._settings.get("log_training_metric")
if log_training_metric is None
else log_training_metric
)
mem_thres = mem_thres or self._settings.get("mem_thres")
pred_time_limit = pred_time_limit or self._settings.get("pred_time_limit")
train_time_limit = train_time_limit or self._settings.get("train_time_limit")
self._metric_constraints = metric_constraints or self._settings.get(
"metric_constraints"
)
if np.isfinite(pred_time_limit):
self._metric_constraints.append(("pred_time", "<=", pred_time_limit))
verbose = self._settings.get("verbose") if verbose is None else verbose
retrain_full = (
self._settings.get("retrain_full") if retrain_full is None else retrain_full
)
split_type = split_type or self._settings.get("split_type")
hpo_method = hpo_method or self._settings.get("hpo_method")
learner_selector = learner_selector or self._settings.get("learner_selector")
no_starting_points = starting_points is None
if no_starting_points:
starting_points = self._settings.get("starting_points")
n_concurrent_trials = n_concurrent_trials or self._settings.get(
"n_concurrent_trials"
)
keep_search_state = (
self._settings.get("keep_search_state")
if keep_search_state is None
else keep_search_state
)
self.preserve_checkpoint = (
self._settings.get("preserve_checkpoint")
if preserve_checkpoint is None
else preserve_checkpoint
)
early_stop = (
self._settings.get("early_stop") if early_stop is None else early_stop
)
force_cancel = (
self._settings.get("force_cancel") if force_cancel is None else force_cancel
)
# no search budget is provided?
no_budget = time_budget < 0 and max_iter is None and not early_stop
append_log = (
self._settings.get("append_log") if append_log is None else append_log
)
min_sample_size = min_sample_size or self._settings.get("min_sample_size")
use_ray = self._settings.get("use_ray") if use_ray is None else use_ray
use_spark = self._settings.get("use_spark") if use_spark is None else use_spark
if use_spark and use_ray is not False:
raise ValueError("use_spark and use_ray cannot be both True.")
elif use_spark:
spark_available, spark_error_msg = check_spark()
if not spark_available:
raise spark_error_msg
old_level = logger.getEffectiveLevel()
self.verbose = verbose
logger.setLevel(50 - verbose * 10)
if not logger.handlers:
# Add the console handler.
_ch = logging.StreamHandler(stream=sys.stdout)
_ch.setFormatter(logger_formatter)
logger.addHandler(_ch)
if not use_ray and not use_spark and n_concurrent_trials > 1:
if ray_available:
logger.warning(
"n_concurrent_trials > 1 is only supported when using Ray or Spark. "
"Ray installed, setting use_ray to True. If you want to use Spark, set use_spark to True."
)
use_ray = True
else:
spark_available, _ = check_spark()
if spark_available:
logger.warning(
"n_concurrent_trials > 1 is only supported when using Ray or Spark. "
"Spark installed, setting use_spark to True. If you want to use Ray, set use_ray to True."
)
use_spark = True
else:
logger.warning(
"n_concurrent_trials > 1 is only supported when using Ray or Spark. "
"Neither Ray nor Spark installed, setting n_concurrent_trials to 1."
)
n_concurrent_trials = 1
self._state.n_jobs = n_jobs
self._n_concurrent_trials = n_concurrent_trials
self._early_stop = early_stop
self._use_spark = use_spark
self._force_cancel = force_cancel
self._use_ray = use_ray
# use the following condition if we have an estimation of average_trial_time and average_trial_overhead
# self._use_ray = use_ray or n_concurrent_trials > ( average_trial_time + average_trial_overhead) / (average_trial_time)
if self._use_ray is not False:
import ray
n_cpus = (
ray.is_initialized()
and ray.available_resources()["CPU"]
or os.cpu_count()
)
self._state.resources_per_trial = (
# when using gpu, default cpu is 1 per job; otherwise, default cpu is n_cpus / n_concurrent_trials
(
{
"cpu": max(int((n_cpus - 2) / 2 / n_concurrent_trials), 1),
"gpu": gpu_per_trial,
}
if gpu_per_trial == 0
else {"cpu": 1, "gpu": gpu_per_trial}
)
if n_jobs < 0
else {"cpu": n_jobs, "gpu": gpu_per_trial}
)
if isinstance(X_train, ray.ObjectRef):
X_train = ray.get(X_train)
elif isinstance(dataframe, ray.ObjectRef):
dataframe = ray.get(dataframe)
else:
# TODO: Integrate with Spark
self._state.resources_per_trial = (
{"cpu": n_jobs} if n_jobs > 0 else {"cpu": 1}
)
self._state.free_mem_ratio = (
self._settings.get("free_mem_ratio")
if free_mem_ratio is None
else free_mem_ratio
)
self._state.task = task
self._state.log_training_metric = log_training_metric
self._state.fit_kwargs = fit_kwargs
custom_hp = custom_hp or self._settings.get("custom_hp")
self._skip_transform = (
self._settings.get("skip_transform")
if skip_transform is None
else skip_transform
)
fit_kwargs_by_estimator = fit_kwargs_by_estimator or self._settings.get(
"fit_kwargs_by_estimator"
)
self._state.fit_kwargs_by_estimator = (
fit_kwargs_by_estimator.copy()
) # shallow copy of fit_kwargs_by_estimator
self._state.weight_val = sample_weight_val
task.validate_data(
self,
self._state,
X_train,
y_train,
dataframe,
label,
X_val,
y_val,
groups_val,
groups,
)
self._search_states = {} # key: estimator name; value: SearchState
self._random = np.random.RandomState(RANDOM_SEED)
self._seed = seed if seed is not None else 20
self._learner_selector = learner_selector
logger.info(f"task = {task}")
self._split_type = self._state.task.decide_split_type(
split_type,
self._y_train_all,
self._state.fit_kwargs,
self._state.groups,
)
logger.info(f"Data split method: {self._split_type}")
eval_method = self._decide_eval_method(eval_method, time_budget)
self._state.eval_method = eval_method
logger.info("Evaluation method: {}".format(eval_method))
self._state.cv_score_agg_func = cv_score_agg_func or self._settings.get(
"cv_score_agg_func"
)
self._retrain_in_budget = retrain_full == "budget" and (
eval_method == "holdout" and self._state.X_val is None
)
self._auto_augment = auto_augment
_sample_size_from_starting_points = {}
if isinstance(starting_points, dict):
for _estimator, _point_per_estimator in starting_points.items():
sample_size = (
_point_per_estimator
and isinstance(_point_per_estimator, dict)
and _point_per_estimator.get("FLAML_sample_size")
)
if sample_size:
_sample_size_from_starting_points[_estimator] = sample_size
elif _point_per_estimator and isinstance(_point_per_estimator, list):
_sample_size_set = set(
[
config["FLAML_sample_size"]
for config in _point_per_estimator
if "FLAML_sample_size" in config
]
)
if _sample_size_set:
_sample_size_from_starting_points[_estimator] = min(
_sample_size_set
)
if len(_sample_size_set) > 1:
logger.warning(
"Using the min FLAML_sample_size of all the provided starting points for estimator {}. (Provided FLAML_sample_size are: {})".format(
_estimator, _sample_size_set
)
)
if not sample and isinstance(starting_points, dict):
assert (
not _sample_size_from_starting_points
), "When subsampling is disabled, do not include FLAML_sample_size in the starting point."
self._min_sample_size = _sample_size_from_starting_points or min_sample_size
self._min_sample_size_input = min_sample_size
self._prepare_data(eval_method, split_ratio, n_splits)
# TODO pull this to task as decide_sample_size
if isinstance(self._min_sample_size, dict):
self._sample = {
(
k,
sample
and not task.is_rank()
and eval_method != "cv"
and (
self._min_sample_size[k] * SAMPLE_MULTIPLY_FACTOR
< self._state.data_size[0]
),
)
for k in self._min_sample_size.keys()
}
else:
self._sample = (
sample
and not task.is_rank()
and eval_method != "cv"
and (
self._min_sample_size * SAMPLE_MULTIPLY_FACTOR
< self._state.data_size[0]
)
)
metric = task.default_metric(metric)
self._state.metric = metric
# TODO pull this to task
def is_to_reverse_metric(metric, task):
if metric.startswith("ndcg"):
return True, f"1-{metric}"
if metric in [
"r2",
"accuracy",
"roc_auc",
"roc_auc_ovr",
"roc_auc_ovo",
"roc_auc_weighted",
"roc_auc_ovr_weighted",
"roc_auc_ovo_weighted",
"f1",
"ap",
"micro_f1",
"macro_f1",
]:
return True, f"1-{metric}"
if task.is_nlp():
from flaml.automl.ml import huggingface_metric_to_mode
if (
metric in huggingface_metric_to_mode
and huggingface_metric_to_mode[metric] == "max"
):
return True, f"-{metric}"
return False, None
if isinstance(metric, str):
is_reverse, reverse_metric = is_to_reverse_metric(metric, task)
if is_reverse:
error_metric = reverse_metric
else:
error_metric = metric
else:
error_metric = "customized metric"
logger.info(f"Minimizing error metric: {error_metric}")
estimator_list = task.default_estimator_list(estimator_list)
# When no search budget is specified
if no_budget:
max_iter = len(estimator_list)
self._learner_selector = "roundrobin"
if sample_is_none:
self._sample = False
if no_starting_points:
starting_points = "data"
logger.warning(
"No search budget is provided via time_budget or max_iter."
" Training only one model per estimator."
" Zero-shot AutoML is used for certain tasks and estimators."
" To tune hyperparameters for each estimator,"
" please provide budget either via time_budget or max_iter."
)
elif max_iter is None:
# set to a large number
max_iter = 1000000
self._state.retrain_final = (
retrain_full is True
and eval_method == "holdout"
and (X_val is None or self._use_ray is not False)
or eval_method == "cv"
and (max_iter > 0 or retrain_full is True)
or max_iter == 1
)
# add custom learner
for estimator_name in estimator_list:
if estimator_name not in self._state.learner_classes:
self.add_learner(
estimator_name,
get_estimator_class(self._state.task, estimator_name),
)
# set up learner search space
if isinstance(starting_points, str) and starting_points.startswith("data"):
from flaml.default import suggest_config
location = starting_points[5:]
starting_points = {}
for estimator_name in estimator_list:
try:
configs = suggest_config(
self._state.task,
self._X_train_all,
self._y_train_all,
estimator_name,
location,
k=1,
)
starting_points[estimator_name] = [
x["hyperparameters"] for x in configs
]
except FileNotFoundError:
pass
try:
learner = suggest_learner(
self._state.task,
self._X_train_all,
self._y_train_all,
estimator_list=estimator_list,
location=location,
)
if learner != estimator_list[0]:
estimator_list.remove(learner)
estimator_list.insert(0, learner)
except FileNotFoundError:
pass
self._state.time_budget = time_budget
starting_points = {} if starting_points == "static" else starting_points
for estimator_name in estimator_list:
estimator_class = self._state.learner_classes[estimator_name]
estimator_class.init()
this_estimator_kwargs = self._state.fit_kwargs_by_estimator.get(
estimator_name
)
if this_estimator_kwargs:
# make another shallow copy of the value (a dict obj), so user's fit_kwargs_by_estimator won't be updated
this_estimator_kwargs = this_estimator_kwargs.copy()
this_estimator_kwargs.update(
self._state.fit_kwargs
) # update the shallow copy of fit_kwargs to fit_kwargs_by_estimator
self._state.fit_kwargs_by_estimator[
estimator_name
] = this_estimator_kwargs # set self._state.fit_kwargs_by_estimator[estimator_name] to the update, so only self._state.fit_kwargs_by_estimator will be updated
else:
self._state.fit_kwargs_by_estimator[
estimator_name
] = self._state.fit_kwargs
self._search_states[estimator_name] = SearchState(
learner_class=estimator_class,
data_size=self._state.data_size,
task=self._state.task,
starting_point=starting_points.get(estimator_name),
period=self._state.fit_kwargs.get(
"period"
), # NOTE: this is after kwargs is updated to fit_kwargs_by_estimator
custom_hp=custom_hp and custom_hp.get(estimator_name),
max_iter=max_iter / len(estimator_list)
if self._learner_selector == "roundrobin"
else max_iter,
budget=self._state.time_budget,
)
logger.info("List of ML learners in AutoML Run: {}".format(estimator_list))
self.estimator_list = estimator_list
self._active_estimators = estimator_list.copy()
self._ensemble = ensemble
self._max_iter = max_iter
self._mem_thres = mem_thres
self._pred_time_limit = pred_time_limit
self._state.train_time_limit = train_time_limit
self._log_type = log_type
self.split_ratio = split_ratio
self._state.model_history = model_history
self._hpo_method = (
hpo_method
if hpo_method != "auto"
else (
"bs"
if n_concurrent_trials > 1
or (self._use_ray is not False or self._use_spark)
and len(estimator_list) > 1
else "cfo"
)
)
if log_file_name:
with training_log_writer(log_file_name, append_log) as save_helper:
self._training_log = save_helper
self._search()
else:
self._training_log = None
self._search()
if self._best_estimator:
logger.info("fit succeeded")
logger.info(
f"Time taken to find the best model: {self._time_taken_best_iter}"
)
if (
self._hpo_method in ("cfo", "bs")
and self._state.time_budget > 0
and (self._time_taken_best_iter >= self._state.time_budget * 0.7)
and not all(
state.search_alg and state.search_alg.searcher.is_ls_ever_converged
for state in self._search_states.values()
)
):
logger.warning(
"Time taken to find the best model is {0:.0f}% of the "
"provided time budget and not all estimators' hyperparameter "
"search converged. Consider increasing the time budget.".format(
self._time_taken_best_iter / self._state.time_budget * 100
)
)
if not keep_search_state:
# release space
del self._X_train_all, self._y_train_all, self._state.kf
del self._state.X_train, self._state.X_train_all, self._state.X_val
del self._state.y_train, self._state.y_train_all, self._state.y_val
del (
self._sample_weight_full,
self._state.fit_kwargs_by_estimator,
self._state.fit_kwargs,
) # NOTE: this is after kwargs is updated to fit_kwargs_by_estimator
del self._state.groups, self._state.groups_all, self._state.groups_val
logger.setLevel(old_level)
def _search_parallel(self):
if self._use_ray is not False:
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest import ConcurrencyLimiter
else:
from ray.tune.search import ConcurrencyLimiter
import ray
except (ImportError, AssertionError):
raise ImportError(
"use_ray=True requires installation of ray. "
"Please run pip install flaml[ray]"
)
else:
from flaml.tune.searcher.suggestion import ConcurrencyLimiter
if self._hpo_method in ("cfo", "grid"):
from flaml import CFO as SearchAlgo
elif "bs" == self._hpo_method:
from flaml import BlendSearch as SearchAlgo
elif "random" == self._hpo_method:
from flaml import RandomSearch as SearchAlgo
elif "optuna" == self._hpo_method:
if self._use_ray is not False:
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo
else:
from ray.tune.search.optuna import OptunaSearch as SearchAlgo
except (ImportError, AssertionError):
from flaml.tune.searcher.suggestion import (
OptunaSearch as SearchAlgo,
)
else:
from flaml.tune.searcher.suggestion import OptunaSearch as SearchAlgo
else:
raise NotImplementedError(
f"hpo_method={self._hpo_method} is not recognized. "
"'auto', 'cfo' and 'bs' are supported."
)
space = self.search_space
self._state.time_from_start = time.time() - self._start_time_flag
time_budget_s = (
self._state.time_budget - self._state.time_from_start
if self._state.time_budget >= 0
else None
)
if self._hpo_method != "optuna":
min_resource = self.min_resource
if isinstance(min_resource, dict):
_min_resource_set = set(min_resource.values())
min_resource_all_estimator = min(_min_resource_set)
if len(_min_resource_set) > 1:
logger.warning(
"Using the min FLAML_sample_size of all the provided starting points as the starting sample size in the case of parallel search."
)
else:
min_resource_all_estimator = min_resource
search_alg = SearchAlgo(
metric="val_loss",
space=space,
low_cost_partial_config=self.low_cost_partial_config,
points_to_evaluate=self.points_to_evaluate,
cat_hp_cost=self.cat_hp_cost,
resource_attr=self.resource_attr,
min_resource=min_resource_all_estimator,
max_resource=self.max_resource,
config_constraints=[
(partial(size, self._state.learner_classes), "<=", self._mem_thres)
],
metric_constraints=self.metric_constraints,
seed=self._seed,
time_budget_s=time_budget_s,
num_samples=self._max_iter,
allow_empty_config=True,
)
else:
# if self._hpo_method is optuna, sometimes the search space and the initial config dimension do not match
# need to remove the extra keys from the search space to be consistent with the initial config
converted_space = SearchAlgo.convert_search_space(space)
removed_keys = set(space.keys()).difference(converted_space.keys())
new_points_to_evaluate = []
for idx in range(len(self.points_to_evaluate)):
r = self.points_to_evaluate[idx].copy()
for each_key in removed_keys:
r.pop(each_key)
new_points_to_evaluate.append(r)
search_alg = SearchAlgo(
metric="val_loss",
mode="min",
points_to_evaluate=[
p for p in new_points_to_evaluate if len(p) == len(converted_space)
],
)
search_alg = ConcurrencyLimiter(search_alg, self._n_concurrent_trials)
resources_per_trial = self._state.resources_per_trial
if self._use_spark:
# use spark as parallel backend
analysis = tune.run(
self.trainable,
search_alg=search_alg,
config=space,
metric="val_loss",
mode="min",
time_budget_s=time_budget_s,
num_samples=self._max_iter,
verbose=max(self.verbose - 2, 0),
use_ray=False,
use_spark=True,
force_cancel=self._force_cancel,
# raise_on_failed_trial=False,
# keep_checkpoints_num=1,
# checkpoint_score_attr="min-val_loss",
)
else:
# use ray as parallel backend
analysis = ray.tune.run(
self.trainable,
search_alg=search_alg,
config=space,
metric="val_loss",
mode="min",
resources_per_trial=resources_per_trial,
time_budget_s=time_budget_s,
num_samples=self._max_iter,
verbose=max(self.verbose - 2, 0),
raise_on_failed_trial=False,
keep_checkpoints_num=1,
checkpoint_score_attr="min-val_loss",
**self._use_ray if isinstance(self._use_ray, dict) else {},
)
# logger.info([trial.last_result for trial in analysis.trials])
trials = sorted(
(
trial
for trial in analysis.trials
if trial.last_result
and trial.last_result.get("wall_clock_time") is not None
),
key=lambda x: x.last_result["wall_clock_time"],
)
for self._track_iter, trial in enumerate(trials):
result = trial.last_result
better = False
if result:
config = result["config"]
estimator = config.get("ml", config)["learner"]
search_state = self._search_states[estimator]
search_state.update(result, 0)
wall_time = result.get("wall_clock_time")
if wall_time is not None:
self._state.time_from_start = wall_time
self._iter_per_learner[estimator] += 1
if search_state.sample_size == self._state.data_size[0]:
if not self._fullsize_reached:
self._fullsize_reached = True
if search_state.best_loss < self._state.best_loss:
self._state.best_loss = search_state.best_loss
self._best_estimator = estimator
self._config_history[self._track_iter] = (
self._best_estimator,
config,
self._time_taken_best_iter,
)
self._trained_estimator = search_state.trained_estimator
self._best_iteration = self._track_iter
self._time_taken_best_iter = self._state.time_from_start
better = True
self._search_states[estimator].best_config = config
if better or self._log_type == "all":
self._log_trial(search_state, estimator)
def _log_trial(self, search_state, estimator):
if self._training_log:
self._training_log.append(
self._iter_per_learner[estimator],
search_state.metric_for_logging,
search_state.trial_time,
self._state.time_from_start,
search_state.val_loss,
search_state.config,
estimator,
search_state.sample_size,
)
if mlflow is not None and mlflow.active_run():
with mlflow.start_run(nested=True):
mlflow.log_metric("iter_counter", self._track_iter)
if (search_state.metric_for_logging is not None) and (
"intermediate_results" in search_state.metric_for_logging
):
for each_entry in search_state.metric_for_logging[
"intermediate_results"
]:
with mlflow.start_run(nested=True):
mlflow.log_metrics(each_entry)
mlflow.log_metric(
"iter_counter", self._iter_per_learner[estimator]
)
del search_state.metric_for_logging["intermediate_results"]
if search_state.metric_for_logging:
mlflow.log_metrics(search_state.metric_for_logging)
mlflow.log_metric("trial_time", search_state.trial_time)
mlflow.log_metric("wall_clock_time", self._state.time_from_start)
mlflow.log_metric("validation_loss", search_state.val_loss)
mlflow.log_params(search_state.config)
mlflow.log_param("learner", estimator)
mlflow.log_param("sample_size", search_state.sample_size)
mlflow.log_metric("best_validation_loss", search_state.best_loss)
mlflow.log_param("best_config", search_state.best_config)
mlflow.log_param("best_learner", self._best_estimator)
def _search_sequential(self):
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest import ConcurrencyLimiter
else:
from ray.tune.search import ConcurrencyLimiter
except (ImportError, AssertionError):
from flaml.tune.searcher.suggestion import ConcurrencyLimiter
if self._hpo_method in ("cfo", "grid"):
from flaml import CFO as SearchAlgo
elif "optuna" == self._hpo_method:
try:
from ray import __version__ as ray_version
assert ray_version >= "1.10.0"
if ray_version.startswith("1."):
from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo
else:
from ray.tune.search.optuna import OptunaSearch as SearchAlgo
except (ImportError, AssertionError):
from flaml.tune.searcher.suggestion import OptunaSearch as SearchAlgo
elif "bs" == self._hpo_method:
from flaml import BlendSearch as SearchAlgo
elif "random" == self._hpo_method:
from flaml.tune.searcher import RandomSearch as SearchAlgo
elif "cfocat" == self._hpo_method:
from flaml.tune.searcher.cfo_cat import CFOCat as SearchAlgo
else:
raise NotImplementedError(
f"hpo_method={self._hpo_method} is not recognized. "
"'cfo' and 'bs' are supported."
)
est_retrain_time = next_trial_time = 0
best_config_sig = None
better = True # whether we find a better model in one trial
for self._track_iter in range(self._max_iter):
if self._estimator_index is None:
estimator = self._active_estimators[0]
else:
estimator = self._select_estimator(self._active_estimators)
if not estimator:
break
logger.info(f"iteration {self._track_iter}, current learner {estimator}")
search_state = self._search_states[estimator]
self._state.time_from_start = time.time() - self._start_time_flag
time_left = self._state.time_budget - self._state.time_from_start
budget_left = (
time_left
if not self._retrain_in_budget
or better
or (not self.best_estimator)
or self._search_states[self.best_estimator].sample_size
< self._state.data_size[0]
else time_left - est_retrain_time
)
if not search_state.search_alg:
search_state.training_function = partial(
AutoMLState._compute_with_config_base,
state=self._state,
estimator=estimator,
)
search_space = search_state.search_space
if self._sample:
resource_attr = "FLAML_sample_size"
min_resource = (
self._min_sample_size[estimator]
if isinstance(self._min_sample_size, dict)
and estimator in self._min_sample_size
else self._min_sample_size_input
)
max_resource = self._state.data_size[0]
else:
resource_attr = min_resource = max_resource = None
learner_class = self._state.learner_classes.get(estimator)
if "grid" == self._hpo_method: # for synthetic exp only
points_to_evaluate = []
space = search_space
keys = list(space.keys())
domain0, domain1 = space[keys[0]], space[keys[1]]
for x1 in range(domain0.lower, domain0.upper + 1):
for x2 in range(domain1.lower, domain1.upper + 1):
points_to_evaluate.append(
{
keys[0]: x1,
keys[1]: x2,
}
)
self._max_iter_per_learner = len(points_to_evaluate)
low_cost_partial_config = None
else:
points_to_evaluate = search_state.init_config.copy()
low_cost_partial_config = search_state.low_cost_partial_config
time_budget_s = (
min(budget_left, self._state.train_time_limit or np.inf)
if self._state.time_budget >= 0
else None
)
if self._hpo_method in ("bs", "cfo", "grid", "cfocat", "random"):
algo = SearchAlgo(
metric="val_loss",
mode="min",
space=search_space,
points_to_evaluate=points_to_evaluate,
low_cost_partial_config=low_cost_partial_config,
cat_hp_cost=search_state.cat_hp_cost,
resource_attr=resource_attr,
min_resource=min_resource,
max_resource=max_resource,
config_constraints=[
(learner_class.size, "<=", self._mem_thres)
],
metric_constraints=self.metric_constraints,
seed=self._seed,
allow_empty_config=True,
time_budget_s=time_budget_s,
num_samples=self._max_iter,
)
else:
# if self._hpo_method is optuna, sometimes the search space and the initial config dimension do not match
# need to remove the extra keys from the search space to be consistent with the initial config
converted_space = SearchAlgo.convert_search_space(search_space)
removed_keys = set(search_space.keys()).difference(
converted_space.keys()
)
new_points_to_evaluate = []
for idx in range(len(points_to_evaluate)):
r = points_to_evaluate[idx].copy()
for each_key in removed_keys:
r.pop(each_key)
new_points_to_evaluate.append(r)
points_to_evaluate = new_points_to_evaluate
algo = SearchAlgo(
metric="val_loss",
mode="min",
space=search_space,
points_to_evaluate=[
p for p in points_to_evaluate if len(p) == len(search_space)
],
)
search_state.search_alg = ConcurrencyLimiter(algo, max_concurrent=1)
# search_state.search_alg = algo
else:
search_space = None
if self._hpo_method in ("bs", "cfo", "cfocat"):
search_state.search_alg.searcher.set_search_properties(
metric=None,
mode=None,
metric_target=self._state.best_loss,
)
start_run_time = time.time()
analysis = tune.run(
search_state.training_function,
search_alg=search_state.search_alg,
time_budget_s=time_budget_s,
verbose=max(self.verbose - 3, 0),
use_ray=False,
use_spark=False,
)
time_used = time.time() - start_run_time
better = False
if analysis.trials:
result = analysis.trials[-1].last_result
search_state.update(result, time_used=time_used)
if self._estimator_index is None:
# update init eci estimate
eci_base = search_state.init_eci
self._eci.append(search_state.estimated_cost4improvement)
for e in self.estimator_list[1:]:
self._eci.append(
self._search_states[e].init_eci / eci_base * self._eci[0]
)
self._estimator_index = 0
min_budget = max(10 * self._eci[0], sum(self._eci))
max_budget = 10000 * self._eci[0]
if search_state.sample_size:
ratio = search_state.data_size[0] / search_state.sample_size
min_budget *= ratio
max_budget *= ratio
logger.info(
f"Estimated sufficient time budget={max_budget:.0f}s."
f" Estimated necessary time budget={min_budget:.0f}s."
)
wall_time = result.get("wall_clock_time")
if wall_time is not None:
self._state.time_from_start = wall_time
# logger.info(f"{self._search_states[estimator].sample_size}, {data_size}")
if search_state.sample_size == self._state.data_size[0]:
self._iter_per_learner_fullsize[estimator] += 1
self._fullsize_reached = True
self._iter_per_learner[estimator] += 1
if search_state.best_loss < self._state.best_loss:
best_config_sig = estimator + search_state.get_hist_config_sig(
self.data_size_full, search_state.best_config
)
self._state.best_loss = search_state.best_loss
self._best_estimator = estimator
est_retrain_time = (
search_state.est_retrain_time(self.data_size_full)
if (best_config_sig not in self._retrained_config)
else 0
)
self._config_history[self._track_iter] = (
estimator,
search_state.best_config,
self._state.time_from_start,
)
if self._trained_estimator:
self._trained_estimator.cleanup()
del self._trained_estimator
self._trained_estimator = None
if not self._state.retrain_final:
self._trained_estimator = search_state.trained_estimator
self._best_iteration = self._track_iter
self._time_taken_best_iter = self._state.time_from_start
better = True
next_trial_time = search_state.time2eval_best
if (
search_state.trained_estimator
and not self._state.model_history
and search_state.trained_estimator != self._trained_estimator
):
search_state.trained_estimator.cleanup()
if better or self._log_type == "all":
self._log_trial(search_state, estimator)
logger.info(
" at {:.1f}s,\testimator {}'s best error={:.4f},\tbest estimator {}'s best error={:.4f}".format(
self._state.time_from_start,
estimator,
search_state.best_loss,
self._best_estimator,
self._state.best_loss,
)
)
if (
self._hpo_method in ("cfo", "bs")
and all(
state.search_alg
and state.search_alg.searcher.is_ls_ever_converged
for state in self._search_states.values()
)
and (
self._state.time_from_start
> self._warn_threshold * self._time_taken_best_iter
)
):
logger.warning(
"All estimator hyperparameters local search has "
"converged at least once, and the total search time "
f"exceeds {self._warn_threshold} times the time taken "
"to find the best model."
)
if self._early_stop:
logger.warning("Stopping search as early_stop is set to True.")
break
self._warn_threshold *= 10
else:
logger.info(f"stop trying learner {estimator}")
if self._estimator_index is not None:
self._active_estimators.remove(estimator)
self._estimator_index -= 1
search_state.search_alg.searcher._is_ls_ever_converged = True
if (
self._retrain_in_budget
and best_config_sig
and est_retrain_time
and not better
and self._search_states[self._best_estimator].sample_size
== self._state.data_size[0]
and (
est_retrain_time
<= self._state.time_budget - self._state.time_from_start
<= est_retrain_time + next_trial_time
)
):
state = self._search_states[self._best_estimator]
self._trained_estimator, retrain_time = self._state._train_with_config(
self._best_estimator,
state.best_config,
self.data_size_full,
)
logger.info(
"retrain {} for {:.1f}s".format(self._best_estimator, retrain_time)
)
self._retrained_config[
best_config_sig
] = state.best_config_train_time = retrain_time
est_retrain_time = 0
self._state.time_from_start = time.time() - self._start_time_flag
if (
self._state.time_from_start >= self._state.time_budget >= 0
or not self._active_estimators
):
break
if self._ensemble and self._best_estimator:
time_left = self._state.time_budget - self._state.time_from_start
time_ensemble = self._search_states[self._best_estimator].time2eval_best
if time_left < time_ensemble < 2 * time_left:
break
def _search(self):
# initialize the search_states
self._eci = []
self._state.best_loss = float("+inf")
self._state.time_from_start = 0
self._estimator_index = None
self._best_iteration = 0
self._time_taken_best_iter = 0
self._config_history = {}
self._max_iter_per_learner = 10000
self._iter_per_learner = dict([(e, 0) for e in self.estimator_list])
self._iter_per_learner_fullsize = dict([(e, 0) for e in self.estimator_list])
self._fullsize_reached = False
self._trained_estimator = None
self._best_estimator = None
self._retrained_config = {}
self._warn_threshold = 10
self._selected = None
self.modelcount = 0
if self._max_iter < 2 and self.estimator_list and self._state.retrain_final:
# when max_iter is 1, no need to search
self.modelcount = self._max_iter
self._max_iter = 0
self._best_estimator = estimator = self.estimator_list[0]
self._selected = state = self._search_states[estimator]
state.best_config_sample_size = self._state.data_size[0]
state.best_config = state.init_config[0] if state.init_config else {}
elif self._use_ray is False and self._use_spark is False:
self._search_sequential()
else:
self._search_parallel()
# Add a checkpoint for the current best config to the log.
if self._training_log:
self._training_log.checkpoint()
self._state.time_from_start = time.time() - self._start_time_flag
if self._best_estimator:
self._selected = self._search_states[self._best_estimator]
self.modelcount = sum(
search_state.total_iter for search_state in self._search_states.values()
)
if self._trained_estimator:
logger.info(f"selected model: {self._trained_estimator.model}")
estimators = []
if self._ensemble and self._state.task in (
"binary",
"multiclass",
"regression",
):
search_states = list(
x for x in self._search_states.items() if x[1].best_config
)
search_states.sort(key=lambda x: x[1].best_loss)
estimators = [
(
x[0],
x[1].learner_class(
task=self._state.task,
n_jobs=self._state.n_jobs,
**AutoMLState.sanitize(x[1].best_config),
),
)
for x in search_states[:2]
]
estimators += [
(
x[0],
x[1].learner_class(
task=self._state.task,
n_jobs=self._state.n_jobs,
**AutoMLState.sanitize(x[1].best_config),
),
)
for x in search_states[2:]
if x[1].best_loss < 4 * self._selected.best_loss
]
logger.info(
[(estimator[0], estimator[1].params) for estimator in estimators]
)
if len(estimators) > 1:
if self._state.task.is_classification():
from sklearn.ensemble import StackingClassifier as Stacker
else:
from sklearn.ensemble import StackingRegressor as Stacker
if self._use_ray is not False:
import ray
n_cpus = (
ray.is_initialized()
and ray.available_resources()["CPU"]
or os.cpu_count()
)
elif self._use_spark:
from flaml.tune.spark.utils import get_n_cpus
n_cpus = get_n_cpus()
else:
n_cpus = os.cpu_count()
ensemble_n_jobs = (
-self._state.n_jobs # maximize total parallelization degree
if abs(self._state.n_jobs)
== 1 # 1 and -1 correspond to min/max parallelization
else max(1, int(n_cpus / 2 / self._state.n_jobs))
# the total degree of parallelization = parallelization degree per estimator * parallelization degree of ensemble
)
if isinstance(self._ensemble, dict):
final_estimator = self._ensemble.get(
"final_estimator", self._trained_estimator
)
passthrough = self._ensemble.get("passthrough", True)
ensemble_n_jobs = self._ensemble.get("n_jobs", ensemble_n_jobs)
else:
final_estimator = self._trained_estimator
passthrough = True
stacker = Stacker(
estimators,
final_estimator,
n_jobs=ensemble_n_jobs,
passthrough=passthrough,
)
sample_weight_dict = (
(self._sample_weight_full is not None)
and {"sample_weight": self._sample_weight_full}
or {}
)
for e in estimators:
e[1].__class__.init()
import joblib
try:
logger.info("Building ensemble with tuned estimators")
stacker.fit(
self._X_train_all,
self._y_train_all,
**sample_weight_dict, # NOTE: _search is after kwargs is updated to fit_kwargs_by_estimator
)
logger.info(f"ensemble: {stacker}")
self._trained_estimator = stacker
self._trained_estimator.model = stacker
except ValueError as e:
if passthrough:
logger.warning(
"Using passthrough=False for ensemble because the data contain categorical features."
)
stacker = Stacker(
estimators,
final_estimator,
n_jobs=self._state.n_jobs,
passthrough=False,
)
stacker.fit(
self._X_train_all,
self._y_train_all,
**sample_weight_dict, # NOTE: _search is after kwargs is updated to fit_kwargs_by_estimator
)
logger.info(f"ensemble: {stacker}")
self._trained_estimator = stacker
self._trained_estimator.model = stacker
else:
raise e
except joblib.externals.loky.process_executor.TerminatedWorkerError:
logger.error(
"No enough memory to build the ensemble."
" Please try increasing available RAM, decreasing n_jobs for ensemble, or disabling ensemble."
)
elif self._state.retrain_final:
# reset time budget for retraining
if self._max_iter > 1:
self._state.time_budget = -1
if (
self._state.task in TS_FORECAST
or self._trained_estimator is None
or self._trained_estimator.model is None
or (
self._state.time_budget < 0
or self._state.time_budget - self._state.time_from_start
> self._selected.est_retrain_time(self.data_size_full)
)
and self._selected.best_config_sample_size
== self._state.data_size[0]
):
state = self._search_states[self._best_estimator]
(
self._trained_estimator,
retrain_time,
) = self._state._train_with_config(
self._best_estimator,
state.best_config,
self.data_size_full,
)
logger.info(
"retrain {} for {:.1f}s".format(
self._best_estimator, retrain_time
)
)
state.best_config_train_time = retrain_time
if self._trained_estimator:
logger.info(f"retrained model: {self._trained_estimator.model}")
else:
logger.info("not retraining because the time budget is too small.")
def __del__(self):
if (
hasattr(self, "_trained_estimator")
and self._trained_estimator
and hasattr(self._trained_estimator, "cleanup")
):
if self.preserve_checkpoint is False:
self._trained_estimator.cleanup()
del self._trained_estimator
def _select_estimator(self, estimator_list):
if self._learner_selector == "roundrobin":
self._estimator_index += 1
if self._estimator_index == len(estimator_list):
self._estimator_index = 0
return estimator_list[self._estimator_index]
min_estimated_cost, selected = np.Inf, None
inv = []
untried_exists = False
for i, estimator in enumerate(estimator_list):
if estimator in self._search_states and (
self._search_states[estimator].sample_size
): # sample_size=None meaning no result
search_state = self._search_states[estimator]
if (
self._state.time_budget >= 0
and self._search_states[estimator].time2eval_best
> self._state.time_budget - self._state.time_from_start
or self._iter_per_learner_fullsize[estimator]
>= self._max_iter_per_learner
):
inv.append(0)
continue
estimated_cost = search_state.estimated_cost4improvement
if (
search_state.sample_size < self._state.data_size[0]
and self._state.time_budget >= 0
):
estimated_cost = min(
estimated_cost,
search_state.time2eval_best
* min(
SAMPLE_MULTIPLY_FACTOR,
self._state.data_size[0] / search_state.sample_size,
),
)
gap = search_state.best_loss - self._state.best_loss
if gap > 0 and not self._ensemble:
delta_loss = (
search_state.best_loss_old - search_state.best_loss
) or search_state.best_loss
delta_time = (
search_state.total_time_used - search_state.time_best_found_old
) or 1e-10
speed = delta_loss / delta_time
if speed:
estimated_cost = max(2 * gap / speed, estimated_cost)
estimated_cost = estimated_cost or 1e-9
inv.append(1 / estimated_cost)
else:
estimated_cost = self._eci[i]
inv.append(0)
untried_exists = True
if estimated_cost < min_estimated_cost:
min_estimated_cost = estimated_cost
selected = estimator
if untried_exists or not selected:
state = self._search_states.get(selected)
if not (state and state.sample_size):
return selected
s = sum(inv)
p = self._random.rand()
q = 0
for i in range(len(inv)):
if inv[i]:
q += inv[i] / s
if p < q:
return estimator_list[i]
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