def runFineTunedKNeighbors():
bestRegressor = KNeighborsRegressor(
n_neighbors=65,
leaf_size=46,
weights='uniform',
algorithm='kd_tree',
)
machineLearning.runRegressor(bestRegressor, "kNeighborsTwitter",
jsonFileNames)
def runFineTunedBagging():
bestRegressor = BaggingRegressor(
n_estimators=100,
max_samples=100,
max_features=5,
bootstrap=True,
bootstrap_features=True,
warm_start=True
)
machineLearning.runRegressor(bestRegressor, "bagging", jsonFileNames)
def runFineTunedBaggingRegressor():
bestRegressor = BaggingRegressor(n_estimators=100,
max_samples=100,
max_features=5,
bootstrap=True,
bootstrap_features=True,
warm_start=True)
machineLearning.runRegressor(bestRegressor,
"BaggingRegressorReddit",
jsonFileNames,
dataSource='reddit')
def runFineTunedKNeighbors():
bestRegressor = KNeighborsRegressor(
n_neighbors=85,
leaf_size=46,
weights="uniform",
algorithm="ball_tree",
)
machineLearning.runRegressor(bestRegressor,
"kNeighborsReddit",
jsonFileNames,
dataSource='reddit')
def runFineTunedRandomForest():
bestRegressor = RandomForestRegressor(
n_estimators=490,
max_depth=3,
min_samples_split=202,
min_samples_leaf=77,
max_leaf_nodes=5,
min_weight_fraction_leaf=.1,
max_features="sqrt",
random_state=43,
)
machineLearning.runRegressor(bestRegressor, "randomForest", jsonFileNames, dataSource='reddit')
def runFineTunedSGD():
bestRegressor = SGDRegressor(
loss = 'huber',
penalty = 'l2',
alpha = 20.00001,
l1_ratio = 0.0,
fit_intercept = True,
shuffle = False,
epsilon = 1.0000000000000001e-05,
learning_rate = 'invscaling'
)
machineLearning.runRegressor(bestRegressor, "SGD", jsonFileNames)
def runFineTunedSGD():
bestRegressor = SGDRegressor(loss='huber',
penalty='l1',
alpha=90.000010000000003,
l1_ratio=0.0,
fit_intercept=True,
shuffle=True,
epsilon=10.00001,
learning_rate='invscaling')
machineLearning.runRegressor(bestRegressor,
"SGD",
jsonFileNames,
dataSource='reddit')
def runFineTunedRandomForest():
bestRegressor = RandomForestRegressor(
n_estimators=250,
max_depth=3,
min_samples_split=202,
min_samples_leaf=33,
max_leaf_nodes=7,
min_weight_fraction_leaf=.1,
max_features="sqrt",
random_state=43,
)
machineLearning.runRegressor(bestRegressor, "randomForestPercentAverage",
jsonFileNames)
# ______________________________________________________
#
# 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]
}
machineLearning.tuneParameters(parameterGrid, classifierName, classifier,
jsonFileNames, decentNValue)
# ______________________________________________________
# RUN THE MODEL WITH BEST PARAMETERS
# ______________________________________________________
#
bestClassifier = LogisticRegression(C=0.01,
fit_intercept=False,
solver='lbfgs',
verbose=84,
warm_start=False,
random_state=92)
machineLearning.runRegressor(bestClassifier, "LogisticRegression",
jsonFileNames)
# "max_depth": [2, 3],
# "min_samples_split": [74, 130],
# "min_samples_leaf": [35, 59],
# "max_leaf_nodes": [2, 8],
# "min_weight_fraction_leaf": [0.2, 0.4],
# "max_features": ["auto", "log2"]
'priors': [None]
}
machineLearning.tuneParameters(parameterGrid, classifierName, classifier, jsonFileNames, decentNValue)
# ______________________________________________________
# RUN THE MODEL WITH BEST PARAMETERS
# ______________________________________________________
#
bestClassifier = GaussianNB(
# n_estimators=15,
# max_depth=2,
# min_samples_split=120,
# min_samples_leaf=15,
# max_leaf_nodes=80,
# min_weight_fraction_leaf=.2,
# random_state=0,
# max_features="auto",
priors=None
)
machineLearning.runRegressor(bestClassifier, "GaussNB", jsonFileNames)
# 'bootstrap_features': [True],
# 'oob_score': [False],
# 'warm_start': [True],
# 'n_jobs': [-1],
# 'random_state': [0],
# 'verbose': [1]
# }
# #
# machineLearning.tuneParameters(parameterGrid, classifierName, classifier, jsonFileNames, decentNValue)
#
# # ______________________________________________________
# # RUN THE MODEL WITH BEST PARAMETERS
# # ______________________________________________________
# #
#
bestClassifier = BaggingClassifier(base_estimator=None,
n_estimators=50,
max_samples=1000,
max_features=5,
bootstrap=True,
bootstrap_features=True,
oob_score=False,
warm_start=True,
n_jobs=-1,
random_state=0,
verbose=1)
#
machineLearning.runRegressor(bestClassifier, "BaggingClassifier",
jsonFileNames)
#