eegyolk.ml module

Copyright 2022 Netherlands eScience Center and Utrecht University. Licensed under the Apache License, version 2.0. See LICENSE for details.

This file contains functions designed to load and run ML models on a specific object type (from other repro work).

class eegyolk.ml.Dummy(loader, verbose=0, use_joblib=True)

Bases: Regression

best_fit()
fit()
class eegyolk.ml.Emrvr(loader, verbose, use_joblib)

Bases: Regression

parameters = {'emrvr__degree': [3, 4, 5, 6, 7], 'emrvr__epsilon': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'emrvr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'emrvr__kernel': ['poly', 'rbf', 'sigmoid']}
class eegyolk.ml.Lsv(loader, verbose, use_joblib)

Bases: Regression

gs_parameters = {'linearsvr__C': [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8], 'linearsvr__epsilon': [1.5, 2, 2.5, 3]}
parameters = {'linearsvr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'linearsvr__epsilon': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>}
class eegyolk.ml.RandomForest(loader, verbose, use_joblib)

Bases: Regression

fit()
parameters = {'max_features': ['sqrt', 'log2', 15, 30, 40, 50, 60, 70, 80, 90, 100, 150, 250, None], 'min_samples_leaf': [1, 2, 3, 4, 5, 10, 20, 30, 40, 50]}
class eegyolk.ml.Regression(loader, verbose=0, use_joblib=True)

Bases: object

best_fit()
dump(result)
fit()
load_model(kind='')
predict(kind='')
scorer()
xy_train()
class eegyolk.ml.Regressions(loader, verbose=0, use_joblib=True)

Bases: object

algorithm(name)
class eegyolk.ml.Sgd(loader, verbose, use_joblib)

Bases: Regression

gs_parameters = {'sgdregressor__alpha': [0.001, 0.0015, 0.002, 0.0025, 0.003], 'sgdregressor__epsilon': [2.5, 3, 3.5, 4, 4.5, 5], 'sgdregressor__loss': ['huber', 'epsilon_insensitive']}
parameters = {'sgdregressor__alpha': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'sgdregressor__epsilon': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'sgdregressor__loss': ['huber', 'epsilon_insensitive']}
class eegyolk.ml.Svr(loader, verbose, use_joblib)

Bases: Regression

gs_parameters = {'svr__C': [10, 12.5, 15, 17.5, 20, 22.5, 25, 27.5, 30, 32.5, 35, 37.5, 40, 42.5, 45, 47.5, 50], 'svr__epsilon': [1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5]}
parameters = {'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'svr__epsilon': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>, 'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object>}