MomentFMClassifier

MomentFMClassifier

class MomentFMClassifier(pretrained_model_name_or_path='AutonLab/MOMENT-1-large', head_dropout=0.1, batch_size=32, eval_batch_size=32, epochs=1, max_lr=0.0001, device='auto', pct_start=0.3, max_norm=5.0, train_val_split=0.2, config=None, to_cpu_after_fit=False)[source]

Interface for classification with the deep learning time series model momentfm.

MomentFM is a collection of open source foundation models for the general purpose of time series analysis. The Moment Foundation Model is a pre-trained model that is capable of accomplishing various time series tasks, such as:

• Classification

This interface with MomentFM focuses on the classification task, in which the foundation model uses a user fine tuned ‘classification head’ to classify a time series. This model does NOT have zero shot capabilities and requires fine-tuning to achieve performance on user inputted data.

NOTE: This model can only handle time series with a sequence length of 512 or less.

For more information: see https://github.com/moment-timeseries-foundation-model/moment

For information regarding licensing and use of the momentfm model please visit: https://huggingface.co/AutonLab/MOMENT-1-large

pretrained_model_name_or_pathstr

Path to the pretrained Momentfm model. Default is AutonLab/MOMENT-1-large

head_dropoutfloat

Dropout value of classification head of the model. Values range between [0.0, 1.0] Default = 0.1

batch_sizeint

size of batches to train the model on default = 32

eval_batch_sizeint or “all”

size of batches to evaluate the model on. If the string “all” is specified, then we process the entire validation set as a single batch default = 32

epochsint

Number of epochs to fit tune the model on default = 1

max_lrfloat

Maximum learning rate that the learning rate scheduler will use default = 1e-4

devicestr

torch device to use default = “auto” If set to auto, it will automatically use whatever device that accelerate detects.

pct_startfloat

percentage of total iterations where the learning rate rises during one epoch default = 0.3

max_normfloat

Float value used to clip gradients during training default = 5.0

train_val_splitfloat

float value between 0 and 1 to determine portions of training and validation splits default = 0.2

configdict, default = {}

If desired, user can pass in a config detailing all momentfm parameters that they wish to set in dictionary form, so that parameters do not need to be individually set. If a parameter inside a config is a duplicate of one already passed in individually, it will be overwritten.

to_cpu_after_fitbool, default = False

After fitting and training, will return the momentfm model to the cpu.

Attributes:

is_fitted

Whether fit has been called.

References

Paper: https://arxiv.org/abs/2402.03885 Github: https://github.com/moment-timeseries-foundation-model/moment/tree/main

Examples

>>> from sktime.classification.foundation_models.momentfm import (
...     MomentFMClassifier,
... )
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_type = "numpy3d")
>>> X_test, _ = load_unit_test(split="test", return_type = "numpy3d")
>>> classifier = MomentFMClassifier(epochs=1, batch_size=16)
>>> classifier.fit(X_train, y_train)
>>> y_pred = classifier.predict(X_test)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X, y)	Fit time series classifier to training data.
fit_predict(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
fit_predict_proba(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict(X)	Predicts labels for sequences in X.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(X, y)	Scores predicted labels against ground truth labels on X.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.