# +
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp'
# ress or implied.
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import warnings
from bigdl.chronos.model.arima import ARIMABuilder, ARIMAModel
from bigdl.chronos.autots.utils import recalculate_n_sampling
# -
[docs]class AutoARIMA:
def __init__(self,
p=2,
q=2,
seasonal=True,
P=1,
Q=1,
m=7,
metric='mse',
metric_mode=None,
logs_dir="/tmp/auto_arima_logs",
cpus_per_trial=1,
name="auto_arima",
remote_dir=None,
load_dir=None,
**arima_config
):
"""
Create an automated ARIMA Model.
User need to specify either the exact value or the search space of
the ARIMA model hyperparameters. For details of the ARIMA model hyperparameters, refer to
https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.ARIMA.html#pmdarima.arima.ARIMA.
:param p: Int or hp sampling function from an integer space for hyperparameter p
of the ARIMA model.
For hp sampling, see bigdl.chronos.orca.automl.hp for more details.
e.g. hp.randint(0, 3).
:param q: Int or hp sampling function from an integer space for hyperparameter q
of the ARIMA model.
e.g. hp.randint(0, 3).
:param seasonal: Bool or hp sampling function from an integer space for whether to add
seasonal components to the ARIMA model.
e.g. hp.choice([True, False]).
:param P: Int or hp sampling function from an integer space for hyperparameter P
of the ARIMA model.
For hp sampling, see bigdl.chronos.orca.automl.hp for more details.
e.g. hp.randint(0, 3).
:param Q: Int or hp sampling function from an integer space for hyperparameter Q
of the ARIMA model.
e.g. hp.randint(0, 3).
:param m: Int or hp sampling function from an integer space for hyperparameter p
of the ARIMA model.
e.g. hp.choice([4, 7, 12, 24, 365]).
:param metric: String or customized evaluation metric function.
If string, metric is the evaluation metric name to optimize, e.g. "mse".
If callable function, it signature should be func(y_true, y_pred), where y_true and
y_pred are numpy ndarray. The function should return a float value as evaluation result.
:param metric_mode: One of ["min", "max"]. "max" means greater metric value is better.
You have to specify metric_mode if you use a customized metric function.
You don't have to specify metric_mode if you use the built-in metric in
bigdl.orca.automl.metrics.Evaluator.
:param logs_dir: Local directory to save logs and results. It defaults to
"/tmp/auto_arima_logs"
:param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
:param name: name of the AutoARIMA. It defaults to "auto_arima"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
:param arima_config: Other ARIMA hyperparameters.
"""
if load_dir:
self.best_model = ARIMAModel()
self.best_model.restore(load_dir)
try:
from bigdl.orca.automl.auto_estimator import AutoEstimator
self.search_space = {
"p": p,
"q": q,
"seasonal": seasonal,
"P": P,
"Q": Q,
"m": m,
}
self.metric = metric
self.metric_mode = metric_mode
model_builder = ARIMABuilder()
self.auto_est = AutoEstimator(model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial={
"cpu": cpus_per_trial},
remote_dir=remote_dir,
name=name)
except ImportError:
warnings.warn("You need to install `bigdl-orca[automl]` to use `fit` function.")
[docs] def fit(self,
data,
epochs=1,
validation_data=None,
metric_threshold=None,
n_sampling=1,
search_alg=None,
search_alg_params=None,
scheduler=None,
scheduler_params=None,
):
"""
Automatically fit the model and search for the best hyperparameters.
:param data: Training data, A 1-D numpy array.
:param epochs: Max number of epochs to train in each trial. Defaults to 1.
If you have also set metric_threshold, a trial will stop if either it has been
optimized to the metric_threshold or it has been trained for {epochs} epochs.
:param validation_data: Validation data. A 1-D numpy array.
:param metric_threshold: a trial will be terminated when metric threshold is met
:param n_sampling: Number of trials to evaluate in total. Defaults to 1.
If hp.grid_search is in search_space, the grid will be run n_sampling of trials
and round up n_sampling according to hp.grid_search.
If this is -1, (virtually) infinite samples are generated
until a stopping condition is met.
:param search_alg: str, all supported searcher provided by ray tune
(i.e."variant_generator", "random", "ax", "dragonfly", "skopt",
"hyperopt", "bayesopt", "bohb", "nevergrad", "optuna", "zoopt" and
"sigopt")
:param search_alg_params: extra parameters for searcher algorithm besides search_space,
metric and searcher mode
:param scheduler: str, all supported scheduler provided by ray tune
:param scheduler_params: parameters for scheduler
"""
n_sampling = recalculate_n_sampling(self.search_space,
n_sampling) if n_sampling != -1 else -1
self.auto_est.fit(data=data,
validation_data=validation_data,
metric=self.metric,
metric_mode=self.metric_mode,
metric_threshold=metric_threshold,
n_sampling=n_sampling,
search_space=self.search_space,
search_alg=search_alg,
search_alg_params=search_alg_params,
scheduler=scheduler,
scheduler_params=scheduler_params
)
[docs] def get_best_model(self):
"""
Get the best ARIMA model.
"""
return self.auto_est.get_best_model()