models#

Provides Models.

class AveragePredictor(time_series_params: TimeSeriesConfig, model_params: ModelConfig)#

Bases: Model

Defines a model, which predicts the average of the train data.

Inits the AveragePredictor.

Parameters:

time_series_params – parameters of the time series that influence the training and archicture of the model.
model_params – configuration for the model.

property name: str#

Returns the models name.

Returns:: The models name.

predict(data: ndarray[Any, dtype[float64]]) → ndarray[Any, dtype[float64]]#

Predicts the next timestamps for every row.

Parameters:: data – 3 dimensional numpy array. First dimension contains time-series. Second dimension contains time steps of a time-series. Third dimension contains the attributes at a single timestep.
Returns:: A 3 dimensional numpy array, with the predicted values.

Example

>>> import numpy as np
>>> from simba_ml.prediction.time_series.models import average_predictor
>>> from simba_ml.prediction.time_series.config import time_series_config
>>> train = np.array([[[1,2], [1,2]], [[2,5], [2,6]], [[10, 11], [12,12]]])

>>> train.shape
(3, 2, 2)
>>> model_config = average_predictor.AveragePredictorConfig()
>>> ts_config = time_series_config.TimeSeriesConfig()
>>> ts_config.input_length = 2
>>> model = average_predictor.AveragePredictor(ts_config, model_config)
>>> model.train(train=train)
>>> model.avg
5.5
>>> test_input = np.array([[[10, 10], [20, 20]], [[15, 15], [15, 16]]])
>>> print(test_input)
[[[10 10]
  [20 20]]

 [[15 15]
  [15 16]]]
>>> print(model.predict(test_input))
[[[5.5 5.5]]

 [[5.5 5.5]]]

train(train: list[numpy.ndarray[Any, numpy.dtype[numpy.float64]]]) → None#

Trains the model with the given data.

Parameters:: train – data, that can be used for training.

validate_prediction_input(data: ndarray[Any, dtype[float64]]) → None#

Validates the input of the predict function.

Parameters:: data – a single dataframe containing the input data, where the output will be predicted.
Raises:: ValueError – if data has incorrect shape (row length does not equal )

class LastValuePredictor(time_series_params: TimeSeriesConfig, model_params: ModelConfig)#

Bases: Model

Defines a model, which predicts the previous value.

Inits the model.

Parameters:

time_series_params – Time-series parameters that affect the training and architecture of models
model_params – configuration for the model.

Raises:

TypeError – if input_length or output_length is not an integer.

property name: str#

Returns the models name.

Returns:: The models name.

predict(data: ndarray[Any, dtype[float64]]) → ndarray[Any, dtype[float64]]#

Predicts the next timestamps for every row.

Parameters:: data – 3 dimensional numpy array. First dimension contains time-series. Second dimension contains time steps of a time-series. Third dimension contains the attributes at a single timestep.
Returns:: A 3 dimensional numpy array, with the predicted values.

Example

>>> import numpy as np
>>> from simba_ml.prediction.time_series.models import last_value_predictor
>>> from simba_ml.prediction.time_series.config import time_series_config
>>> train = np.array([[[1,2], [1,2]], [[2,5], [2,6]], [[10, 11], [12,12]]])
>>> train.shape
(3, 2, 2)
>>> model_config = last_value_predictor.LastValuePredictorConfig()
>>> ts_config = time_series_config.TimeSeriesConfig()
>>> ts_config.input_length = 2
>>> model = last_value_predictor.LastValuePredictor(ts_config, model_config)
>>> model.train(train=train)
>>> test_input = np.array([[[10, 10], [20, 20]], [[15, 15], [15, 16]]])
>>> print(test_input)
[[[10 10]
  [20 20]]

 [[15 15]
  [15 16]]]
>>> print(model.predict(test_input))
[[[20 20]]

 [[15 16]]]

train(train: list[numpy.ndarray[Any, numpy.dtype[numpy.float64]]]) → None#

Trains the model with the given data.

Parameters:: train – data, that can be used for training.

validate_prediction_input(data: ndarray[Any, dtype[float64]]) → None#

Validates the input of the predict function.

Parameters:: data – a single dataframe containing the input data, where the output will be predicted.
Raises:: ValueError – if data has incorrect shape (row length does not equal )

class Model(time_series_params: TimeSeriesConfig, model_params: ModelConfig)#

Bases: ABC

Defines the abstract model.

Inits the model.

Parameters:

time_series_params – Time-series parameters that affect the training and architecture of models
model_params – configuration for the model.

Raises:

TypeError – if input_length or output_length is not an integer.

property name: str#

Returns the models name.

Returns:: The models name.

abstract predict(data: ndarray[Any, dtype[float64]]) → ndarray[Any, dtype[float64]]#

Predicts the next timesteps.

Parameters:: data – 3 dimensional numpy array. First dimension contains time-series. Second dimension contains time steps of a time-series. Third dimension contains the attributes at a single timestep.

abstract train(train: list[numpy.ndarray[Any, numpy.dtype[numpy.float64]]]) → None#

Trains the model with the given data.

Parameters:: train – training data.

validate_prediction_input(data: ndarray[Any, dtype[float64]]) → None#

Validates the input of the predict function.

Parameters:: data – a single dataframe containing the input data, where the output will be predicted.
Raises:: ValueError – if data has incorrect shape (row length does not equal )

`simba_ml.prediction.time_series.models.average_predictor`	Provides a model, which predicts the average of the train data.
`simba_ml.prediction.time_series.models.factory`	Factory for creating a models.
`simba_ml.prediction.time_series.models.keras`	Registers the keras models as plugins.
`simba_ml.prediction.time_series.models.last_value_predictor`	Provides a model, which predicts the last given input value.
`simba_ml.prediction.time_series.models.model`	Provides an abstract Model.
`simba_ml.prediction.time_series.models.model_to_transfer_learning_model`	Converts a model to a transfer learning model.
`simba_ml.prediction.time_series.models.pytorch_lightning`	Registers the pytorch lightning models as plugins.
`simba_ml.prediction.time_series.models.sk_learn`	Provides the sklearn models.
`simba_ml.prediction.time_series.models.transfer_learning_factory`	Factory for creating a transfer learning models.
`simba_ml.prediction.time_series.models.transfer_learning_model`	Provides an abstract Model.