/
automl.py
1305 lines (1230 loc) · 58.6 KB
/
automl.py
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'''!
* Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the
* project root for license information.
'''
import time
import warnings
from functools import partial
import numpy as np
from scipy.sparse import issparse
from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold, \
RepeatedKFold, GroupKFold
from sklearn.utils import shuffle
import pandas as pd
import os
import contextlib
from .ml import compute_estimator, train_estimator, get_estimator_class, \
get_classification_objective
from .config import (
MIN_SAMPLE_TRAIN, MEM_THRES, RANDOM_SEED,
SMALL_LARGE_THRES, CV_HOLDOUT_THRESHOLD, SPLIT_RATIO, N_SPLITS,
SAMPLE_MULTIPLY_FACTOR)
from .data import concat
from . import tune
from .training_log import training_log_reader, training_log_writer
import logging
logger = logging.getLogger(__name__)
logger_formatter = logging.Formatter(
'[%(name)s: %(asctime)s] {%(lineno)d} %(levelname)s - %(message)s',
'%m-%d %H:%M:%S')
try:
import mlflow
except ImportError:
mlflow = None
class SearchState:
@property
def search_space(self):
return self._search_space_domain
@property
def estimated_cost4improvement(self):
return max(self.time_best_found - self.time_best_found_old,
self.total_time_used - self.time_best_found)
def __init__(self, learner_class, data_size, task):
self.init_eci = learner_class.cost_relative2lgbm()
self._search_space_domain = {}
self.init_config = {}
self.low_cost_partial_config = {}
self.cat_hp_cost = {}
self.data_size = data_size
search_space = learner_class.search_space(
data_size=data_size, task=task)
for name, space in search_space.items():
assert 'domain' in space
self._search_space_domain[name] = space['domain']
if 'init_value' in space:
self.init_config[name] = space['init_value']
if 'low_cost_init_value' in space:
self.low_cost_partial_config[name] = space[
'low_cost_init_value']
if 'cat_hp_cost' in space:
self.cat_hp_cost[name] = space['cat_hp_cost']
self._hp_names = list(self._search_space_domain.keys())
self.search_alg = None
self.best_loss = self.best_loss_old = np.inf
self.total_time_used = 0
self.total_iter = 0
self.base_eci = None
self.time_best_found = 0
self.time2eval_best = 0
self.time2eval_best_old = 0
self.trained_estimator = None
self.sample_size = None
self.trial_time = 0
def update(self, analysis, time_used, save_model_history=False):
if not analysis.trials:
return
result = analysis.trials[-1].last_result
if result:
config = result['config']
if config and 'FLAML_sample_size' in config:
self.sample_size = config['FLAML_sample_size']
else:
self.sample_size = self.data_size
obj = result['val_loss']
train_loss = result['train_loss']
time2eval = result['time2eval']
trained_estimator = result[
'trained_estimator']
else:
obj, time2eval, trained_estimator = np.inf, 0.0, None
train_loss = config = None
self.trial_time = time2eval
self.total_time_used += time_used
self.total_iter += 1
if self.base_eci is None:
self.base_eci = time_used
if (obj is not None) and (self.best_loss is None or obj < self.best_loss):
self.best_loss_old = self.best_loss if self.best_loss < np.inf \
else 2 * obj
self.best_loss = obj
self.time_best_found_old = self.time_best_found
self.time_best_found = self.total_time_used
self.iter_best_found = self.total_iter
self.best_config = config
self.best_config_sample_size = self.sample_size
self.best_config_train_time = time_used
if time2eval:
self.time2eval_best_old = self.time2eval_best
self.time2eval_best = time2eval
if self.trained_estimator and trained_estimator and \
self.trained_estimator != trained_estimator and \
not save_model_history:
self.trained_estimator.cleanup()
if trained_estimator:
self.trained_estimator = trained_estimator
self.train_loss, self.val_loss, self.config = train_loss, obj, config
def get_hist_config_sig(self, sample_size, config):
config_values = tuple([config[k] for k in self._hp_names])
config_sig = str(sample_size) + '_' + str(config_values)
return config_sig
def est_retrain_time(self, retrain_sample_size):
assert self.best_config_sample_size is not None, \
'need to first get best_config_sample_size'
return (self.time2eval_best * retrain_sample_size
/ self.best_config_sample_size)
class AutoMLState:
def _prepare_sample_train_data(self, sample_size):
full_size = len(self.y_train)
sampled_weight = None
if sample_size <= full_size:
if isinstance(self.X_train, pd.DataFrame):
sampled_X_train = self.X_train.iloc[:sample_size]
else:
sampled_X_train = self.X_train[:sample_size]
sampled_y_train = self.y_train[:sample_size]
weight = self.fit_kwargs.get('sample_weight')
if weight is not None:
sampled_weight = weight[:sample_size]
else:
sampled_X_train = concat(self.X_train, self.X_val)
sampled_y_train = np.concatenate([self.y_train, self.y_val])
weight = self.fit_kwargs.get('sample_weight')
if weight is not None:
sampled_weight = np.concatenate([weight, self.weight_val])
return sampled_X_train, sampled_y_train, sampled_weight
def _compute_with_config_base(self,
estimator,
config_w_resource):
compute_start_time = time.time()
if 'FLAML_sample_size' in config_w_resource:
sample_size = int(config_w_resource['FLAML_sample_size'])
else:
sample_size = self.data_size
sampled_X_train, sampled_y_train, sampled_weight = \
self._prepare_sample_train_data(sample_size)
if sampled_weight is not None:
weight = self.fit_kwargs['sample_weight']
self.fit_kwargs['sample_weight'] = sampled_weight
else:
weight = None
config = config_w_resource.copy()
if 'FLAML_sample_size' in config:
del config['FLAML_sample_size']
time_left = self.time_budget - self.time_from_start
budget = time_left if sample_size == self.data_size else \
time_left / 2 * sample_size / self.data_size
trained_estimator, val_loss, train_loss, time2eval, _ = \
compute_estimator(
sampled_X_train,
sampled_y_train,
self.X_val,
self.y_val,
self.weight_val,
budget,
self.kf,
config,
self.task,
estimator,
self.eval_method,
self.metric,
self.best_loss,
self.n_jobs,
self.learner_classes.get(estimator),
self.log_training_metric,
self.fit_kwargs)
result = {
'total_time': time.time() - compute_start_time,
'time2eval': time2eval,
'train_loss': train_loss,
'val_loss': val_loss,
'trained_estimator': trained_estimator
}
with open(os.devnull, "w") as f, contextlib.redirect_stdout(f):
tune.report(**result)
if sampled_weight is not None:
self.fit_kwargs['sample_weight'] = weight
def _train_with_config(
self, estimator, config_w_resource, sample_size=None
):
config = config_w_resource.copy()
if 'FLAML_sample_size' in config:
if not sample_size:
sample_size = config['FLAML_sample_size']
del config['FLAML_sample_size']
assert sample_size is not None
sampled_X_train, sampled_y_train, sampled_weight = \
self._prepare_sample_train_data(sample_size)
if sampled_weight is not None:
weight = self.fit_kwargs['sample_weight']
self.fit_kwargs['sample_weight'] = sampled_weight
else:
weight = None
budget = None if self.time_budget is None else (
self.time_budget - self.time_from_start)
estimator, train_time = train_estimator(
sampled_X_train,
sampled_y_train,
config,
self.task,
estimator,
self.n_jobs,
self.learner_classes.get(estimator),
budget,
self.fit_kwargs)
if sampled_weight is not None:
self.fit_kwargs['sample_weight'] = weight
return estimator, train_time
class AutoML:
'''The AutoML class
Example:
.. code-block:: python
automl = AutoML()
automl_settings = {
"time_budget": 60,
"metric": 'accuracy',
"task": 'classification',
"log_file_name": 'test/mylog.log',
}
automl.fit(X_train = X_train, y_train = y_train,
**automl_settings)
'''
from .version import __version__
def __init__(self):
self._track_iter = 0
self._state = AutoMLState()
self._state.learner_classes = {}
@property
def model_history(self):
'''A dictionary of iter->model, storing the models when
the best model is updated each time.
'''
return self._model_history
@property
def config_history(self):
'''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.
'''
if self._trained_estimator:
return self._trained_estimator
else:
return None
def best_model_for_estimator(self, estimator_name):
'''Return the best model found for a particular estimator
Args:
estimator_name: a str of the estimator's name
Returns:
An object with `predict()` and `predict_proba()` method (for
classification), storing the best trained model for estimator_name.
'''
if estimator_name in self._search_states:
state = self._search_states[estimator_name]
if hasattr(state, 'trained_estimator'):
return state.trained_estimator
return 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.'''
return self._search_states[self._best_estimator].best_config
@property
def best_loss(self):
'''A float of the best loss found
'''
return self._state.best_loss
@property
def best_config_train_time(self):
'''A float of the seconds taken by training the
best config.'''
return self._search_states[self._best_estimator].best_config_train_time
@property
def classes_(self):
'''A list of n_classes elements for class labels.'''
if self._label_transformer:
return self._label_transformer.classes_.tolist()
if self._trained_estimator:
return self._trained_estimator.classes_.tolist()
return None
def predict(self, X_test):
'''Predict label from features.
Args:
X_test: A numpy array of featurized instances, shape n * m.
Returns:
A numpy array of shape n * 1 - - each element is a predicted class
label for an instance.
'''
if self._trained_estimator is None:
warnings.warn(
"No estimator is trained. Please run fit with enough budget.")
return None
X_test = self._preprocess(X_test)
y_pred = self._trained_estimator.predict(X_test)
if y_pred.ndim > 1:
y_pred = y_pred.flatten()
if self._label_transformer:
return self._label_transformer.inverse_transform(pd.Series(
y_pred))
else:
return y_pred
def predict_proba(self, X_test):
'''Predict the probability of each class from features, only works for
classification problems.
Args:
X_test: A numpy array of featurized instances, shape n * m.
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.
'''
X_test = self._preprocess(X_test)
proba = self._trained_estimator.predict_proba(X_test)
return proba
def _preprocess(self, X):
if issparse(X):
X = X.tocsr()
if self._transformer:
X = self._transformer.transform(X)
return X
def _validate_data(self, X_train_all, y_train_all, dataframe, label,
X_val=None, y_val=None):
if X_train_all is not None and y_train_all is not None:
if not (isinstance(X_train_all, np.ndarray) or issparse(X_train_all)
or isinstance(X_train_all, pd.DataFrame)):
raise ValueError(
"X_train_all must be a numpy array, a pandas dataframe, "
"or Scipy sparse matrix.")
if not (isinstance(y_train_all, np.ndarray)
or isinstance(y_train_all, pd.Series)):
raise ValueError(
"y_train_all must be a numpy array or a pandas series.")
if X_train_all.size == 0 or y_train_all.size == 0:
raise ValueError("Input data must not be empty.")
if isinstance(y_train_all, np.ndarray):
y_train_all = y_train_all.flatten()
if X_train_all.shape[0] != y_train_all.shape[0]:
raise ValueError(
"# rows in X_train must match length of y_train.")
self._df = isinstance(X_train_all, pd.DataFrame)
self._nrow, self._ndim = X_train_all.shape
X, y = X_train_all, y_train_all
elif dataframe is not None and label is not None:
if not isinstance(dataframe, pd.DataFrame):
raise ValueError("dataframe must be a pandas DataFrame")
if label not in dataframe.columns:
raise ValueError("label must a column name in dataframe")
self._df = True
X = dataframe.drop(columns=label)
self._nrow, self._ndim = X.shape
y = dataframe[label]
else:
raise ValueError(
"either X_train+y_train or dataframe+label are required")
if issparse(X_train_all):
self._transformer = self._label_transformer = False
self._X_train_all, self._y_train_all = X, y
else:
from .data import DataTransformer
self._transformer = DataTransformer()
self._X_train_all, self._y_train_all = \
self._transformer.fit_transform(X, y, self._state.task)
self._label_transformer = self._transformer.label_transformer
self._sample_weight_full = self._state.fit_kwargs.get('sample_weight')
if X_val is not None and y_val is not None:
if not (isinstance(X_val, np.ndarray) or issparse(X_val)
or isinstance(X_val, pd.DataFrame)):
raise ValueError(
"X_val must be None, a numpy array, a pandas dataframe, "
"or Scipy sparse matrix.")
if not (isinstance(y_val, np.ndarray)
or isinstance(y_val, pd.Series)):
raise ValueError(
"y_val must be None, a numpy array or a pandas series.")
if X_val.size == 0 or y_val.size == 0:
raise ValueError(
"Validation data are expected to be nonempty. "
"Use None for X_val and y_val if no validation data.")
if isinstance(y_val, np.ndarray):
y_val = y_val.flatten()
if X_val.shape[0] != y_val.shape[0]:
raise ValueError("# rows in X_val must match length of y_val.")
if self._transformer:
self._state.X_val = self._transformer.transform(X_val)
else:
self._state.X_val = X_val
if self._label_transformer:
self._state.y_val = self._label_transformer.transform(y_val)
else:
self._state.y_val = y_val
else:
self._state.X_val = self._state.y_val = None
def _prepare_data(self,
eval_method,
split_ratio,
n_splits):
X_val, y_val = self._state.X_val, self._state.y_val
if issparse(X_val):
X_val = X_val.tocsr()
X_train_all, y_train_all = \
self._X_train_all, self._y_train_all
if issparse(X_train_all):
X_train_all = X_train_all.tocsr()
if self._state.task != 'regression' and self._state.fit_kwargs.get(
'sample_weight') is None:
# logger.info(f"label {pd.unique(y_train_all)}")
label_set, counts = np.unique(y_train_all, return_counts=True)
# augment rare classes
rare_threshld = 20
rare = counts < rare_threshld
rare_label, rare_counts = label_set[rare], counts[rare]
for i, label in enumerate(rare_label):
count = rare_count = rare_counts[i]
rare_index = y_train_all == label
n = len(y_train_all)
while count < rare_threshld:
if self._df:
X_train_all = concat(X_train_all,
X_train_all.iloc[:n].loc[rare_index])
else:
X_train_all = concat(X_train_all,
X_train_all[:n][rare_index, :])
if isinstance(y_train_all, pd.Series):
y_train_all = concat(y_train_all,
y_train_all.iloc[:n].loc[rare_index])
else:
y_train_all = np.concatenate([y_train_all,
y_train_all[:n][rare_index]])
count += rare_count
logger.debug(
f"class {label} augmented from {rare_count} to {count}")
if 'sample_weight' in self._state.fit_kwargs:
X_train_all, y_train_all, self._state.fit_kwargs[
'sample_weight'] = shuffle(
X_train_all, y_train_all,
self._state.fit_kwargs['sample_weight'],
random_state=RANDOM_SEED)
elif hasattr(self._state, 'groups') and self._state.groups is not None:
X_train_all, y_train_all, self._state.groups = shuffle(
X_train_all, y_train_all, self._state.groups,
random_state=RANDOM_SEED)
else:
X_train_all, y_train_all = shuffle(
X_train_all, y_train_all, random_state=RANDOM_SEED)
if self._df:
X_train_all.reset_index(drop=True, inplace=True)
if isinstance(y_train_all, pd.Series):
y_train_all.reset_index(drop=True, inplace=True)
X_train, y_train = X_train_all, y_train_all
if X_val is None:
# if eval_method = holdout, make holdout data
if self._state.task != 'regression' and eval_method == 'holdout':
# for classification, make sure the labels are complete in both
# training and validation data
label_set, first = np.unique(y_train_all, return_index=True)
rest = []
last = 0
first.sort()
for i in range(len(first)):
rest.extend(range(last, first[i]))
last = first[i] + 1
rest.extend(range(last, len(y_train_all)))
X_first = X_train_all.iloc[first] if self._df else X_train_all[
first]
X_rest = X_train_all.iloc[rest] if self._df else X_train_all[rest]
y_rest = y_train_all.iloc[rest] if isinstance(
y_train_all, pd.Series) else y_train_all[rest]
stratify = y_rest if self._split_type == 'stratified' else \
None
if 'sample_weight' in self._state.fit_kwargs:
X_train, X_val, y_train, y_val, weight_train, weight_val = \
train_test_split(
X_rest,
y_rest,
self._state.fit_kwargs['sample_weight'][rest],
test_size=split_ratio,
random_state=RANDOM_SEED)
weight1 = self._state.fit_kwargs['sample_weight'][first]
self._state.weight_val = concat(weight1, weight_val)
self._state.fit_kwargs['sample_weight'] = concat(
weight1, weight_train)
else:
X_train, X_val, y_train, y_val = train_test_split(
X_rest,
y_rest,
test_size=split_ratio,
stratify=stratify,
random_state=RANDOM_SEED)
X_train = concat(X_first, X_train)
y_train = concat(
label_set, y_train) if self._df else np.concatenate(
[label_set, y_train])
X_val = concat(X_first, X_val)
y_val = concat(label_set, y_val) if self._df else \
np.concatenate([label_set, y_val])
elif eval_method == 'holdout' and self._state.task == 'regression':
if 'sample_weight' in self._state.fit_kwargs:
X_train, X_val, y_train, y_val, self._state.fit_kwargs[
'sample_weight'], self._state.weight_val = \
train_test_split(
X_train_all,
y_train_all,
self._state.fit_kwargs['sample_weight'],
test_size=split_ratio,
random_state=RANDOM_SEED)
else:
X_train, X_val, y_train, y_val = train_test_split(
X_train_all,
y_train_all,
test_size=split_ratio,
random_state=RANDOM_SEED)
self._state.data_size = X_train.shape[0]
if X_val is None:
self.data_size_full = self._state.data_size
else:
self.data_size_full = self._state.data_size + X_val.shape[0]
self._state.X_train, self._state.y_train, self._state.X_val, \
self._state.y_val = (X_train, y_train, X_val, y_val)
if hasattr(self._state, 'groups') and self._state.groups is not None:
logger.info("Using GroupKFold")
assert len(self._state.groups) == y_train_all.size, \
"the length of groups must match the number of examples"
assert len(np.unique(self._state.groups)) >= n_splits, \
"the number of groups must be equal or larger than n_splits"
self._state.kf = GroupKFold(n_splits)
self._state.kf.groups = self._state.groups
elif self._split_type == "stratified":
logger.info("Using StratifiedKFold")
assert y_train_all.size >= n_splits, (
f"{n_splits}-fold cross validation"
f" requires input data with at least {n_splits} examples.")
assert y_train_all.size >= 2 * n_splits, (
f"{n_splits}-fold cross validation with metric=r2 "
f"requires input data with at least {n_splits*2} examples.")
self._state.kf = RepeatedStratifiedKFold(
n_splits=n_splits, n_repeats=1, random_state=RANDOM_SEED)
else:
logger.info("Using RepeatedKFold")
self._state.kf = RepeatedKFold(
n_splits=n_splits, n_repeats=1, random_state=RANDOM_SEED)
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, record_id, 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', 'multi', or 'regression'
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 = record.config
estimator, _ = train_estimator(
None, None, config, task, estimator,
estimator_class=self._state.learner_classes.get(estimator))
return estimator
def retrain_from_log(self,
log_file_name,
X_train=None,
y_train=None,
dataframe=None,
label=None,
time_budget=0,
task='classification',
eval_method='auto',
split_ratio=SPLIT_RATIO,
n_splits=N_SPLITS,
split_type="stratified",
n_jobs=1,
train_best=True,
train_full=False,
record_id=-1,
**fit_kwargs):
'''Retrain from log file
Args:
time_budget: A float number of the time budget in seconds
log_file_name: A string of the log file name
X_train: A numpy array of training data in shape n*m
y_train: A numpy array of labels in shape n*1
task: A string of the task type, e.g.,
'classification', 'regression'
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
n_jobs: An integer of the number of threads for training
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.
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight
'''
self._state.task = task
self._state.fit_kwargs = fit_kwargs
self._validate_data(X_train, y_train, dataframe, label)
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.total_search_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:
from .model import BaseEstimator as Estimator
self._trained_estimator = Estimator()
self._trained_estimator.model = None
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
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._state.task = task
if self._state.task == 'classification':
self._state.task = get_classification_objective(
len(np.unique(self._y_train_all)))
assert split_type in ["stratified", "uniform"]
self._split_type = split_type
else:
self._split_type = "uniform"
if record_id >= 0:
eval_method = 'cv'
elif eval_method == 'auto':
eval_method = self._decide_eval_method(time_budget)
self.modelcount = 0
self._prepare_data(eval_method, split_ratio, n_splits)
self._state.time_budget = None
self._state.n_jobs = n_jobs
self._trained_estimator = self._state._train_with_config(
best_estimator, best_config, sample_size)[0]
return training_duration
def _decide_eval_method(self, time_budget):
if self._state.X_val is not None:
return 'holdout'
nrow, dim = self._nrow, self._ndim
if 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'
def fit(self,
X_train=None,
y_train=None,
dataframe=None,
label=None,
metric='auto',
task='classification',
n_jobs=-1,
log_file_name='default.log',
estimator_list='auto',
time_budget=60,
max_iter=1000000,
sample=True,
ensemble=False,
eval_method='auto',
log_type='better',
model_history=False,
split_ratio=SPLIT_RATIO,
n_splits=N_SPLITS,
log_training_metric=False,
mem_thres=MEM_THRES,
X_val=None,
y_val=None,
sample_weight_val=None,
groups=None,
verbose=1,
retrain_full=True,
split_type="stratified",
learner_selector='sample',
hpo_method=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)
y_train: A numpy array or a pandas series of labels in shape (n,)
dataframe: A dataframe of training data including label column
label: A str of the label column name
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', 'f1', 'micro_f1', 'macro_f1',
'log_loss', 'mae', 'mse', 'r2'
if passing a customized metric function, the function needs to
have the follwing signature:
.. code-block:: python
def custom_metric(
X_test, y_test, estimator, labels,
X_train, y_train, weight_test=None, weight_train=None
):
return metric_to_minimize, metrics_to_log
which returns a float number as the minimization objective,
and a tuple of floats as the metrics to log
task: A string of the task type, e.g.,
'classification', 'regression'
n_jobs: An integer of the number of threads for training
log_file_name: A string of the log file name
estimator_list: A list of strings for estimator names, or 'auto'
e.g.,
.. code-block:: python
['lgbm', 'xgboost', 'catboost', 'rf', 'extra_tree']
time_budget: A float number of the time budget in seconds
max_iter: An integer of the maximal number of iterations
sample: A boolean of whether to sample the training data during
search
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 history of best
models in the history property. 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
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
groups: None or an array-like of shape (n,) | Group labels for the
samples used while splitting the dataset into train/valid set
verbose: int, default=1 | Controls the verbosity, higher means more
messages
**fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such sample_weight
'''
self._start_time_flag = time.time()
self._state.task = task
self._state.log_training_metric = log_training_metric
self._state.fit_kwargs = fit_kwargs
self._state.weight_val = sample_weight_val
self._state.groups = groups
self._validate_data(X_train, y_train, dataframe, label, X_val, y_val)
self._search_states = {} # key: estimator name; value: SearchState
self._random = np.random.RandomState(RANDOM_SEED)
self._learner_selector = learner_selector
old_level = logger.getEffectiveLevel()
self.verbose = verbose
if verbose == 0:
logger.setLevel(logging.WARNING)
if self._state.task == 'classification':
self._state.task = get_classification_objective(
len(np.unique(self._y_train_all)))
assert split_type in ["stratified", "uniform"]
self._split_type = split_type
else:
self._split_type = "uniform"
if eval_method == 'auto' or self._state.X_val is not None:
eval_method = self._decide_eval_method(time_budget)
self._state.eval_method = eval_method
if (not mlflow or not mlflow.active_run()) and not logger.handlers:
# Add the console handler.
_ch = logging.StreamHandler()
_ch.setFormatter(logger_formatter)
logger.addHandler(_ch)
logger.info("Evaluation method: {}".format(eval_method))
self._retrain_full = retrain_full and (
eval_method == 'holdout' and self._state.X_val is None)
self._prepare_data(eval_method, split_ratio, n_splits)
self._sample = sample and eval_method != 'cv' and (
MIN_SAMPLE_TRAIN * SAMPLE_MULTIPLY_FACTOR < self._state.data_size)
if 'auto' == metric:
if 'binary' in self._state.task:
metric = 'roc_auc'
elif 'multi' in self._state.task:
metric = 'log_loss'
else:
metric = 'r2'
self._state.metric = metric
if metric in ['r2', 'accuracy', 'roc_auc', 'f1', 'ap', 'micro_f1', 'macro_f1']:
error_metric = f"1-{metric}"
elif isinstance(metric, str):
error_metric = metric
else:
error_metric = 'customized metric'
logger.info(f'Minimizing error metric: {error_metric}')
if 'auto' == estimator_list:
estimator_list = ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree']
if 'regression' != self._state.task:
estimator_list += ['lrl1']
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
for estimator_name in estimator_list:
estimator_class = self._state.learner_classes[estimator_name]
estimator_class.init()
self._search_states[estimator_name] = SearchState(
learner_class=estimator_class,
data_size=self._state.data_size, task=self._state.task,
)
logger.info("List of ML learners in AutoML Run: {}".format(
estimator_list))
self._hpo_method = hpo_method or 'cfo'
with training_log_writer(log_file_name) as save_helper:
self._training_log = save_helper
self._state.time_budget = time_budget
self.estimator_list = estimator_list
self._ensemble = ensemble
self._max_iter = max_iter
self._mem_thres = mem_thres
self._log_type = log_type
self.split_ratio = split_ratio
self._save_model_history = model_history
self._state.n_jobs = n_jobs
self._search()
logger.info("fit succeeded")
if verbose == 0:
logger.setLevel(old_level)
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._model_history = {}
self._config_history = {}
self._max_iter_per_learner = 1000000 # TODO
self._iter_per_learner = dict([(e, 0) for e in self.estimator_list])
self._fullsize_reached = False
self._trained_estimator = None
self._best_estimator = None
self._retrained_config = {}
est_retrain_time = next_trial_time = 0
best_config_sig = None
# use ConcurrencyLimiter to limit the amount of concurrency when
# using a search algorithm
better = True # whether we find a better model in one trial
if self._ensemble:
self.best_model = {}
try:
from ray.tune.suggest import ConcurrencyLimiter
except ImportError:
from .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.tune.suggest.optuna import OptunaSearch as SearchAlgo
except ImportError:
from .searcher.suggestion import OptunaSearch as SearchAlgo
elif 'bs' == self._hpo_method: