AutoTS (deprecated)¶
Warning
The API in this page will be deprecated soon. Please refer to our new AutoTS API.
AutoTSTrainer¶
AutoTSTrainer trains a time series pipeline (including data processing, feature engineering, and model) with AutoML.
- class zoo.chronos.autots.deprecated.forecast.AutoTSTrainer[source]¶
The Automated Time Series Forecast Trainer
Initialize the AutoTS Trainer.
- Parameters
horizon – steps to look forward
dt_col – the datetime column
target_col – the target column to forecast
extra_features_col – extra feature columns
- fit()[source]¶
Fit a time series forecasting pipeline w/ automl
- Parameters
train_df – the input dataframe (as pandas.dataframe)
validation_df – the validation dataframe (as pandas.dataframe)
recipe – the configuration of searching
metric – the evaluation metric to optimize
uncertainty – whether to enable uncertainty calculation (will output an uncertainty sigma)
upload_dir – Optional URI to sync training results and checkpoints. We only support hdfs URI for now.
:return a TSPipeline
TSPipeline¶
A pipeline for time series forecasting.
- class zoo.chronos.autots.deprecated.forecast.TSPipeline[source]¶
Bases:
object
A pipeline for time series forecasting.
Initialize an emtpy TSPipeline. Usually it is not called by user directly. A TSPipeline is either obtained from AutoTrainer.fit or TSPipeline.load
- save(pipeline_file)[source]¶
Save the pipeline to a file
- Parameters
pipeline_file – the file path
- Returns
- static load(pipeline_file)[source]¶
Load pipeline from a file
- Parameters
pipeline_file – the pipeline file
- Returns
a TSPipeline object
- fit(input_df, validation_df=None, uncertainty: bool = False, epochs=1, **user_config)[source]¶
Incremental Fitting
- Parameters
input_df – the input dataframe
validation_df – the validation dataframe
uncertainty – whether to calculate uncertainty
epochs – number of epochs to train
user_config – user configurations
- Returns
Recipe¶
Recipe is used for search configuration for AutoTSTrainer.
- class zoo.chronos.autots.deprecated.config.recipe.SmokeRecipe[source]¶
A very simple Recipe for smoke test that runs one epoch and one iteration with only 1 random sample.
- class zoo.chronos.autots.deprecated.config.recipe.MTNetSmokeRecipe[source]¶
A very simple Recipe for smoke test that runs one epoch and one iteration with only 1 random sample.
- class zoo.chronos.autots.deprecated.config.recipe.TCNSmokeRecipe[source]¶
A very simple Recipe for smoke test that runs one epoch and one iteration with only 1 random sample.
- class zoo.chronos.autots.deprecated.config.recipe.PastSeqParamHandler[source]¶
Bases:
object
Utility to handle PastSeq Param
- class zoo.chronos.autots.deprecated.config.recipe.GridRandomRecipe[source]¶
A recipe involves both grid search and random search.
Constructor. :param num_rand_samples: number of hyper-param configurations sampled randomly :param look_back: the length to look back, either a tuple with 2 int values,
which is in format is (min len, max len), or a single int, which is a fixed length to look back.
- Parameters
training_iteration – no. of iterations for training (n epochs) in trials
epochs – no. of epochs to train in each iteration
- class zoo.chronos.autots.deprecated.config.recipe.LSTMSeq2SeqRandomRecipe[source]¶
A recipe involves both grid search and random search, only for Seq2SeqPytorch. Note: This recipe is specifically designed for third-party model searching, rather than TimeSequencePredictor.
Constructor. set the param to a list for grid search. set the param to a tuple with length = 2 for random search.
- Parameters
input_feature_num – (int) no. of input feature
output_feature_num – (int) no. of ouput feature
future_seq_len – (int) no. of steps to be predicted (i.e. horizon)
num_rand_samples – (int) number of hyper-param configurations sampled randomly
epochs – (int) no. of epochs to train in each iteration
training_iteration – (int) no. of iterations for training (n epochs) in trials
batch_size – (tuple|list) grid search candidates for batch size
lr – (tuple|list) learning rate
lstm_hidden_dim – (tuple|list) lstm hidden dim for both encoder and decoder
lstm_layer_num – (tuple|list) no. of lstm layer for both encoder and decoder
dropout – (tuple|list) dropout for lstm layer
teacher_forcing – (list) if to use teacher forcing machanism during training
- class zoo.chronos.autots.deprecated.config.recipe.LSTMGridRandomRecipe[source]¶
A recipe involves both grid search and random search, only for LSTM.
Constructor.
- Parameters
lstm_1_units – random search candidates for num of lstm_1_units
lstm_2_units – grid search candidates for num of lstm_1_units
batch_size – grid search candidates for batch size
num_rand_samples – number of hyper-param configurations sampled randomly
look_back – the length to look back, either a tuple with 2 int values, which is in format is (min len, max len), or a single int, which is a fixed length to look back.
training_iteration – no. of iterations for training (n epochs) in trials
epochs – no. of epochs to train in each iteration
- class zoo.chronos.autots.deprecated.config.recipe.Seq2SeqRandomRecipe[source]¶
A recipe involves both grid search and random search, only for LSTM.
Constructor.
- Parameters
lstm_1_units – random search candidates for num of lstm_1_units
lstm_2_units – grid search candidates for num of lstm_1_units
batch_size – grid search candidates for batch size
num_rand_samples – number of hyper-param configurations sampled randomly
look_back – the length to look back, either a tuple with 2 int values, which is in format is (min len, max len), or a single int, which is a fixed length to look back.
training_iteration – no. of iterations for training (n epochs) in trials
epochs – no. of epochs to train in each iteration
- class zoo.chronos.autots.deprecated.config.recipe.MTNetGridRandomRecipe[source]¶
Grid+Random Recipe for MTNet
Constructor.
- Parameters
num_rand_samples – number of hyper-param configurations sampled randomly
training_iteration – no. of iterations for training (n epochs) in trials
epochs – no. of epochs to train in each iteration
time_step – random search candidates for model param “time_step”
long_num – random search candidates for model param “long_num”
ar_size – random search candidates for model param “ar_size”
batch_size – grid search candidates for batch size
cnn_height – random search candidates for model param “cnn_height”
cnn_hid_size – random search candidates for model param “cnn_hid_size”
- class zoo.chronos.autots.deprecated.config.recipe.TCNGridRandomRecipe[source]¶
Grid+Random Recipe for TCN
Constructor.
- Parameters
num_rand_samples – number of hyper-param configurations sampled randomly
training_iteration – no. of iterations for training (n epochs) in trials
batch_size – grid search candidates for batch size
hidden_size – grid search candidates for hidden size of each layer
levels – the number of layers
kernel_size – the kernel size of each layer
dropout – dropout rate (1 - keep probability)
lr – learning rate
- class zoo.chronos.autots.deprecated.config.recipe.RandomRecipe[source]¶
Pure random sample Recipe. Often used as baseline.
Constructor.
- Parameters
num_rand_samples – number of hyper-param configurations sampled randomly
- :param look_back:the length to look back, either a tuple with 2 int values,
which is in format is (min len, max len), or a single int, which is a fixed length to look back.
- Parameters
reward_metric – the rewarding metric value, when reached, stop trial
training_iteration – no. of iterations for training (n epochs) in trials
epochs – no. of epochs to train in each iteration
- class zoo.chronos.autots.deprecated.config.recipe.BayesRecipe[source]¶
A Bayes search Recipe. (Experimental)
Constructor.
- Parameters
num_samples – number of hyper-param configurations sampled
look_back – the length to look back, either a tuple with 2 int values, which is in format is (min len, max len), or a single int, which is a fixed length to look back.
reward_metric – the rewarding metric value, when reached, stop trial
training_iteration – no. of iterations for training (n epochs) in trials
epochs – no. of epochs to train in each iteration
- class zoo.chronos.autots.deprecated.config.recipe.XgbRegressorGridRandomRecipe(num_rand_samples=1, n_estimators=[8, 15], max_depth=[10, 15], n_jobs=- 1, tree_method='hist', random_state=2, seed=0, lr=(0.0001, 0.1), subsample=0.8, colsample_bytree=0.8, min_child_weight=[1, 2, 3], gamma=0, reg_alpha=0, reg_lambda=1)[source]¶
Bases:
zoo.chronos.autots.deprecated.config.base.Recipe
Grid + Random Recipe for XGBoost Regressor.
Constructor. For XGBoost hyper parameters, refer to https://xgboost.readthedocs.io/en/latest/python/python_api.html for details.
- Parameters
num_rand_samples – number of hyper-param configurations sampled randomly
n_estimators – number of gradient boosted trees.
max_depth – max tree depth
n_jobs – number of parallel threads used to run xgboost.
tree_method – specify which tree method to use.
random_state – random number seed.
seed – seed used to generate the folds
lr – learning rate
subsample – subsample ratio of the training instance
colsample_bytree – subsample ratio of columns when constructing each tree.
min_child_weight – minimum sum of instance weight(hessian) needed in a child.
gamma – minimum loss reduction required to make a further partition on a leaf node of the tree.
reg_alpha – L1 regularization term on weights (xgb’s alpha).
reg_lambda – L2 regularization term on weights (xgb’s lambda).
- class zoo.chronos.autots.deprecated.config.recipe.XgbRegressorSkOptRecipe(num_rand_samples=10, n_estimators_range=(50, 1000), max_depth_range=(2, 15), lr=(0.0001, 0.1), min_child_weight=[1, 2, 3])[source]¶
Bases:
zoo.chronos.autots.deprecated.config.base.Recipe
A recipe using SkOpt search algorithm for XGBoost Regressor.
Constructor.
- Parameters
num_rand_samples – number of hyper-param configurations sampled randomly
n_estimators_range – range of number of gradient boosted trees.
max_depth_range – range of max tree depth
lr – learning rate
min_child_weight – minimum sum of instance weight(hessian) needed in a child.