genui.models.genuimodels package

Submodules

genui.models.genuimodels.algorithms module

algorithms

Created by: Martin Sicho On: 24-01-20, 15:03

class genui.models.genuimodels.algorithms.RandomForest(builder, callback=None)[source]

Bases: Algorithm

fit(X: DataFrame, y: Series)[source]

property model

name = 'RandomForest'

parameters = {'n_estimators': {'defaultValue': 100, 'type': 'integer'}}

predict(X: DataFrame)[source]

genui.models.genuimodels.bases module

bases

Created by: Martin Sicho On: 24-01-20, 15:03

class genui.models.genuimodels.bases.Algorithm(builder, callback=None)[source]

Bases: ABC

CLASSIFICATION = 'classification'

GENERATOR = 'generator'

MAP = 'map'

REGRESSION = 'regression'

classmethod attachModesToModel(model, modes)[source]

static attachToInstance(instance, items, field)[source]

property builder

deserialize(filename)[source]

django_file_formats = []

django_model = None

django_modes = []

django_parameters = []

abstract fit(X: DataFrame, y: Series)[source]

getDeserializer()[source]

classmethod getDjangoModel(corePackage=None, update=False) → Algorithm[source]

classmethod getFileFormats(attach_to=None)[source]

classmethod getModes()[source]

classmethod getParams()[source]

getSerializer()[source]

abstract property model

name = None

parameters = {}

abstract predict(X: DataFrame) → Series[source]

serialize(filename)[source]

class genui.models.genuimodels.bases.CompleteBuilder(instance, progress, *args, **kwargs)[source]: Bases: PredictionMixIn, ValidationMixIn, ProgressMixIn, ModelBuilder, ABC

class genui.models.genuimodels.bases.ModelBuilder(instance: Model, progress=None, onFit=None)[source]

Bases: ABC

build() → Model[source]

property corePackage

findAlgorithmClass(name, corePackage=None)[source]

findMetricClass(name, corePackage=None)[source]

classmethod getDjangoModel(corePackage=None, update=False)[source]

abstract getX() → DataFrame[source]

abstract getY() → Series[source]

property model: Algorithm

saveFile()[source]

class genui.models.genuimodels.bases.PredictionMixIn[source]

Bases: object

predict(X: Optional[DataFrame] = None) → Series[source]

class genui.models.genuimodels.bases.ProgressMixIn(instance, progress, *args, **kwargs)[source]

Bases: object

recordProgress()[source]

class genui.models.genuimodels.bases.ValidationMetric(builder)[source]

Bases: ABC

description = None

classmethod getDjangoModel(corePackage=None, update=False)[source]

name = None

static probasToClasses(probas)[source]

save(true_vals: ~pandas.core.series.Series, predicted_vals: ~pandas.core.series.Series, perfClass=<class 'genui.models.models.ModelPerformance'>, **kwargs)[source]

class genui.models.genuimodels.bases.ValidationMixIn[source]

Bases: object

fitAndValidate(X_train: ~pandas.core.frame.DataFrame, y_train: ~pandas.core.series.Series, X_validated: ~pandas.core.frame.DataFrame, y_validated: ~pandas.core.series.Series, y_predicted=None, perfClass=<class 'genui.models.models.ModelPerformance'>, *args, **kwargs)[source]

validate(y_validated, y_predicted, perfClass=<class 'genui.models.models.ModelPerformance'>, *args, **kwargs)[source]

genui.models.genuimodels.builders module

builders

Created by: Martin Sicho On: 24-01-20, 15:03

genui.models.genuimodels.metrics module

metrics

Created by: Martin Sicho On: 24-01-20, 15:04

class genui.models.genuimodels.metrics.MCC(builder)[source]

Bases: ValidationMetric

description = "Matthew's Correlation Coefficient"

modes = ['classification']

name = 'MCC'

class genui.models.genuimodels.metrics.MSE(builder)[source]

Bases: ValidationMetric

description = 'Mean squared error regression loss. As implemented in scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error.'

modes = ['regression']

name = 'MSE'

class genui.models.genuimodels.metrics.R2(builder)[source]

Bases: ValidationMetric

description = 'R^2 (coefficient of determination) regression score function. As implemented in scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score.'

modes = ['regression']

name = 'R2'

class genui.models.genuimodels.metrics.ROC(builder)[source]

Bases: ValidationMetric

description = 'The area under ROC curve.'

getCurve(true_vals, scores)[source]

modes = ['classification']

name = 'ROC'

save(true_vals: ~pandas.core.series.Series, predicted_vals: ~pandas.core.series.Series, perfClass=<class 'genui.models.models.ModelPerformance'>, **kwargs)[source]

Module contents

__init__.py

Created by: Martin Sicho On: 24-01-20, 14:54