gobbli.model.mixin module¶
Mixins which can be applied to classes derived from BaseModel.
-
class
gobbli.model.mixin.
EmbedMixin
[source]¶ Bases:
object
Apply to a model which can be used to generate embeddings from data.
-
embed
(embed_input, embed_dir_name=None)[source]¶ Generates embeddings using a model and the params in the given
gobbli.io.EmbedInput
.- Parameters
embed_input¶ (
EmbedInput
) – Contains various parameters needed to determine how to generate embeddings and what data to generate embeddings for.embed_dir_name¶ (
Optional
[str
]) – Optional name to store embedding input and output under. The directory is always created under the model’sdata_dir
. If a name is not given, a unique name is generated via a UUID. If a name is given, that directory must not already exist.
- Return type
- Returns
Output of training.
-
embed_dir
()[source]¶ The directory to be used for data related to embedding (weights, embeddings, etc)
- Return type
Path
- Returns
Path to the embedding data directory.
-
abstract property
logger
¶
-
-
class
gobbli.model.mixin.
PredictMixin
[source]¶ Bases:
object
Apply to a model which can be used to predict on new data.
-
abstract property
logger
¶
-
predict
(predict_input, predict_dir_name=None)[source]¶ Runs prediction on new data using params containing in the given
gobbli.io.PredictInput
.- Parameters
predict_input¶ (
PredictInput
) – Contains various parameters needed to determine how to run prediction and what data to predict for.predict_dir_name¶ (
Optional
[str
]) – Optional name to store prediction input and output under. The directory is always created under the model’sdata_dir
. If a name is not given, a unique name is generated via a UUID. If a name is given, that directory must not already exist.
- Return type
-
abstract property
-
class
gobbli.model.mixin.
TrainMixin
[source]¶ Bases:
object
Apply to a model which can be trained in some way.
-
abstract property
logger
¶
-
train
(train_input, train_dir_name=None)[source]¶ Trains a model using params in the given
gobbli.io.TrainInput
. The training process varies depending on the model, but in general, it includes the following steps:Update weights using the training dataset
Evaluate performance on the validation dataset.
Repeat for a number of epochs.
When finished, report loss/accuracy and return the trained weights.
- Parameters
train_input¶ (
TrainInput
) – Contains various parameters needed to determine how to train and what data to train on.train_dir_name¶ (
Optional
[str
]) – Optional name to store training input and output under. The directory is always created under the model’sdata_dir
. If a name is not given, a unique name is generated via a UUID. If a name is given, that directory must not already exist.
- Return type
- Returns
Output of training.
-
abstract property