src.predictive_model.regression package

Subpackages

Submodules

src.predictive_model.regression.apps module

class src.predictive_model.regression.apps.RegressionConfig(app_name, app_module)

Bases: src.predictive_model.apps.PredictiveModelConfig

name = 'src.predictive_model.regression'

src.predictive_model.regression.custom_regression_models module

class src.predictive_model.regression.custom_regression_models.NNRegressor(**kwargs)

Bases: sklearn.base.RegressorMixin

Neural Network regressor, implements the same methods as the sklearn models to make it simple to add

fit(train_data, targets)

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 dataset
• targets (ndarray) – encoded target dataset

Return type:

None

predict(test_data)

returns predictive_model predictions

parses the encoded test dataset, then returns the predictive_model predictions

Parameters:test_data (DataFrame) – encoded test dataset Return type:ndarray Returns:predictive_model predictions

reset()

placeholder to allow use with other sklearn algorithms

Return type:None

src.predictive_model.regression.methods_default_config module

src.predictive_model.regression.methods_default_config.regression_lasso()

src.predictive_model.regression.methods_default_config.regression_linear()

src.predictive_model.regression.methods_default_config.regression_nn()

src.predictive_model.regression.methods_default_config.regression_random_forest()

src.predictive_model.regression.methods_default_config.regression_xgboost()

src.predictive_model.regression.models module

class src.predictive_model.regression.models.Lasso(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, regression_ptr, alpha, fit_intercept, normalize)

Bases: src.predictive_model.regression.models.Regression

exception DoesNotExist

Bases: src.predictive_model.regression.models.DoesNotExist

exception MultipleObjectsReturned

Bases: src.predictive_model.regression.models.MultipleObjectsReturned

alpha

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

fit_intercept

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

normalize

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

regression_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 a ForwardOneToOneDescriptor instance.

regression_ptr_id

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

to_dict()

class src.predictive_model.regression.models.Linear(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, regression_ptr, fit_intercept, normalize)

Bases: src.predictive_model.regression.models.Regression

exception DoesNotExist

Bases: src.predictive_model.regression.models.DoesNotExist

exception MultipleObjectsReturned

Bases: src.predictive_model.regression.models.MultipleObjectsReturned

fit_intercept

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

normalize

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

regression_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 a ForwardOneToOneDescriptor instance.

regression_ptr_id

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

to_dict()

class src.predictive_model.regression.models.NeuralNetwork(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, regression_ptr, n_hidden_layers, n_hidden_units, activation, n_epochs, dropout_rate)

Bases: src.predictive_model.regression.models.Regression

exception DoesNotExist

Bases: src.predictive_model.regression.models.DoesNotExist

exception MultipleObjectsReturned

Bases: src.predictive_model.regression.models.MultipleObjectsReturned

activation

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

dropout_rate

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

get_activation_display(*, field=<django.db.models.fields.CharField: activation>)

n_epochs

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

n_hidden_layers

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

n_hidden_units

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

regression_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 a ForwardOneToOneDescriptor instance.

regression_ptr_id

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

to_dict()

class src.predictive_model.regression.models.RandomForest(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, regression_ptr, n_estimators, max_features, max_depth)

Bases: src.predictive_model.regression.models.Regression

exception DoesNotExist

Bases: src.predictive_model.regression.models.DoesNotExist

exception MultipleObjectsReturned

Bases: src.predictive_model.regression.models.MultipleObjectsReturned

max_depth

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

max_features

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

n_estimators

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

random_state = 21

regression_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 a ForwardOneToOneDescriptor instance.

regression_ptr_id

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

to_dict()

class src.predictive_model.regression.models.Regression(*args, **kwargs)

Bases: src.predictive_model.models.PredictiveModel

Container of Regression to be shown in frontend

exception DoesNotExist

Bases: src.predictive_model.models.DoesNotExist

exception MultipleObjectsReturned

Bases: src.predictive_model.models.MultipleObjectsReturned

static init(configuration)

lasso

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 a ReverseOneToOneDescriptor instance.

linear

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 a ReverseOneToOneDescriptor instance.

neuralnetwork

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 a ReverseOneToOneDescriptor instance.

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 a ForwardOneToOneDescriptor instance.

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.

randomforest

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 a ReverseOneToOneDescriptor instance.

xgboost

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 a ReverseOneToOneDescriptor instance.

class src.predictive_model.regression.models.RegressionMethods

Bases: enum.Enum

An enumeration.

LASSO = 'lasso'

LINEAR = 'linear'

NN = 'nn'

RANDOM_FOREST = 'randomForest'

XGBOOST = 'xgboost'

class src.predictive_model.regression.models.XGBoost(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, regression_ptr, max_depth, n_estimators)

Bases: src.predictive_model.regression.models.Regression

exception DoesNotExist

Bases: src.predictive_model.regression.models.DoesNotExist

exception MultipleObjectsReturned

Bases: src.predictive_model.regression.models.MultipleObjectsReturned

max_depth

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

n_estimators

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

regression_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 a ForwardOneToOneDescriptor instance.

regression_ptr_id

A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.

to_dict()

src.predictive_model.regression.regression module

regression methods and functionalities

src.predictive_model.regression.regression.regression(training_df, test_df, clusterer, job)

main regression entry point

train and tests the regressor 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

src.predictive_model.regression.regression.regression_single_log(input_df, model)

single log regression

classifies a single log using the provided TODO: complete

Parameters:

• input_df (DataFrame) – input DataFrame
• model (dict) – TODO: complete

Return type:

DataFrame

Returns:

predictive_model scores

Module contents