src.predictive_model.time_series_prediction package¶
Subpackages¶
Submodules¶
src.predictive_model.time_series_prediction.TimeSeriesPredictorMixin module¶
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class
src.predictive_model.time_series_prediction.TimeSeriesPredictorMixin.
TimeSeriesPredictorMixin
¶ Bases:
object
src.predictive_model.time_series_prediction.apps module¶
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class
src.predictive_model.time_series_prediction.apps.
TimeSeriesPredictionConfig
(app_name, app_module)¶ Bases:
src.predictive_model.apps.PredictiveModelConfig
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name
= 'src.predictive_model.time_series_prediction'¶
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src.predictive_model.time_series_prediction.custom_time_series_prediction_models module¶
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class
src.predictive_model.time_series_prediction.custom_time_series_prediction_models.
RNNTimeSeriesPredictor
(**kwargs)¶ Bases:
src.predictive_model.time_series_prediction.TimeSeriesPredictorMixin.TimeSeriesPredictorMixin
Recurrent Neural Network Time Series predictor, implements the same methods as the sklearn models to make it simple to add. This architecture is of the seq2seq type, taking as input a sequence (0…t) and outputting a sequence (1…t+1)
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fit
(train_data)¶ creates and fits the predictive_model
first the encoded data is parsed, then the predictive_model created and then trained
Parameters: train_data ( DataFrame
) – encoded training datasetReturn type: None
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predict
(test_data)¶ returns predictive_model predictions
parses the encoded test dataset, then returns the predictive_model predictions
Parameters: test_data ( DataFrame
) – encoded test datasetReturn type: ndarray
Returns: predictive_model predictions
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src.predictive_model.time_series_prediction.methods_default_config module¶
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src.predictive_model.time_series_prediction.methods_default_config.
time_series_prediction_rnn
()¶
src.predictive_model.time_series_prediction.models module¶
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class
src.predictive_model.time_series_prediction.models.
RecurrentNeuralNetwork
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, timeseriesprediction_ptr, n_units, rnn_type, n_epochs)¶ Bases:
src.predictive_model.time_series_prediction.models.TimeSeriesPrediction
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exception
DoesNotExist
¶ Bases:
src.predictive_model.time_series_prediction.models.DoesNotExist
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exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.time_series_prediction.models.MultipleObjectsReturned
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get_rnn_type_display
(*, field=<django.db.models.fields.CharField: rnn_type>)¶
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n_epochs
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
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n_units
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
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rnn_type
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
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timeseriesprediction_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
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timeseriesprediction_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
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to_dict
()¶
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exception
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class
src.predictive_model.time_series_prediction.models.
TimeSeriesPrediction
(*args, **kwargs)¶ Bases:
src.predictive_model.models.PredictiveModel
Container of Classification to be shown in frontend
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exception
DoesNotExist
¶ Bases:
src.predictive_model.models.DoesNotExist
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exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.models.MultipleObjectsReturned
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static
init
(configuration)¶
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predictivemodel_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
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predictivemodel_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
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recurrentneuralnetwork
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
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exception
src.predictive_model.time_series_prediction.time_series_prediction module¶
time series prediction methods and functionalities
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src.predictive_model.time_series_prediction.time_series_prediction.
time_series_prediction
(training_df, test_df, clusterer, job)¶ main time series prediction entry point
train and tests the time series predictor using the provided data
Parameters: - clusterer (
Clustering
) – - training_df (
DataFrame
) – training DataFrame - test_df (
DataFrame
) – testing DataFrame - job (
Job
) – job configuration
Return type: (<class ‘dict’>, <class ‘dict’>)
Returns: predictive_model scores and split
- clusterer (
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src.predictive_model.time_series_prediction.time_series_prediction.
time_series_prediction_single_log
(input_df, model)¶ single log time series prediction
time series predicts a single log using the provided TODO: complete
Parameters: - input_df (
DataFrame
) – input DataFrame - model (
dict
) – TODO: complete
Return type: dict
Returns: predictive_model scores
- input_df (