def tuneSGDParametersIndividually(decentNValues):
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
"loss": [
'squared_loss', 'huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'
],
"penalty": ['none', 'l2', 'l1', 'elasticnet'],
"alpha":
np.arange(0.00001, 100, 10),
"l1_ratio":
np.arange(0, 1, 0.1),
"fit_intercept": [True, False],
"shuffle": [True, False],
"epsilon":
np.arange(0.00001, 100, 10),
"learning_rate": ['constant', 'optimal', 'invscaling']
}
threeBestParams = machineLearning.tuneParametersIndividually(
parameterGrid,
regressorName,
regressor,
jsonFileNames,
decentNValue,
2,
dataSource='reddit')
return threeBestParams
def tuneKNeighborsParametersIndividually(decentNValues):
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
"n_neighbors": np.arange(5, 100, 5),
"leaf_size": np.arange(1, 100, 5),
"weights": ['uniform', 'distance'],
"algorithm": ['ball_tree', 'kd_tree', 'brute'],
}
threeBestParams = machineLearning.tuneParametersIndividually(
parameterGrid, classifierName, classifier, jsonFileNames, decentNValue,
2)
return threeBestParams
def tuneBaggingParametersIndividually(decentNValues):
parameterGrid = {
'n_estimators': np.arange(10,500,40),
'max_samples': [100],
'max_features': [5, 10],
'bootstrap': [True],
'bootstrap_features': [True],
'warm_start': [True],
}
threeBestParams = machineLearning.tuneParametersIndividually(parameterGrid, classifierName, classifier,
jsonFileNames,
decentNValue, 2)
return threeBestParams
def tuneRandomForestParametersIndividually(decentNValues):
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
"n_estimators": np.arange(10, 500, 40),
"max_depth": np.arange(1, 14, 1),
"min_samples_split": np.arange(2, 203, 10),
"min_samples_leaf": np.arange(1, 81, 4),
"max_leaf_nodes": np.arange(2, 20, 1),
"min_weight_fraction_leaf": np.arange(0.1, 0.4, 0.1),
"max_features": ["auto", "sqrt", "log2"]
}
threeBestParams = machineLearning.tuneParametersIndividually(parameterGrid, classifierName, classifier,
jsonFileNames,
decentNValue, 2, dataSource='reddit')
return threeBestParams
def tuneBaggingRegressorParametersIndividually(decentNValues):
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
'n_estimators': np.arange(10, 500, 40),
'max_samples': [100, 500, 1000],
'max_features': [10, 5],
'bootstrap': [True],
'bootstrap_features': [True],
'warm_start': [True],
}
threeBestParams = machineLearning.tuneParametersIndividually(
parameterGrid,
classifierName,
classifier,
jsonFileNames,
decentNValue,
3,
dataSource='reddit')
return threeBestParams
# WHAT ARE THE BEST VALUES OF EACH PARAMETER?
# ______________________________________________________
#
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
"fit_intercept": [True, False],
"solver": ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
"verbose": np.arange(0, 100, 4),
"warm_start": [True, False],
"random_state": np.arange(0, 100, 4)
}
machineLearning.tuneParametersIndividually(parameterGrid, classifierName,
classifier, jsonFileNames,
decentNValue)
# ______________________________________________________
# WHAT IS THE BEST COMBINATION OF VARIABLES?
# ______________________________________________________
#
# Fill this grid with only the best parameter values, as every single combination will be run
parameterGrid = {
"C": [1000, 0.01, 10],
"fit_intercept": [False],
"solver": ['lbfgs', 'liblinear'],
"verbose": [56, 68, 84, 24],
"warm_start": [False],
"random_state": [92, 12, 20, 56]