def perform(self):
# TODO: Clean up the older alpha stuff?
max_depths = np.arange(1, 51, 1)
params = {'DT__criterion': ['gini', 'entropy'], 'DT__max_depth': max_depths,
'DT__class_weight': ['balanced', None]} # , 'DT__max_leaf_nodes': max_leaf_nodes}
complexity_param = {'name': 'DT__max_depth', 'display_name': 'Max Depth', 'values': max_depths}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'criterion': 'gini', 'max_depth': 5, 'class_weight': 'balanced'}
#
# Dataset 2:
best_params = {'criterion': 'entropy', 'max_depth': 14, 'class_weight': 'balanced'}
learner = learners.DTLearner(random_state=self._details.seed)
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name,
learner, 'DT', 'DT', params,
complexity_param=complexity_param, seed=self._details.seed,
threads=self._details.threads,
best_params=best_params,
verbose=self._verbose)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/Boosting.py
# Search for good alphas
alphas = np.arange(1, 11)
max_depths = np.arange(1, 41, 1) # np.arange(1, 11)
base = learners.DTLearner(criterion='gini', class_weight='balanced', random_state=self._details.seed)
of_base = learners.DTLearner(criterion='gini', class_weight='balanced', random_state=self._details.seed)
booster = learners.BoostingLearner(algorithm='SAMME', learning_rate=1, base_estimator=base,
random_state=self._details.seed)
of_booster = learners.BoostingLearner(algorithm='SAMME', learning_rate=1, base_estimator=of_base,
random_state=self._details.seed)
# TODO: No 90 here?
params = {'Boost__n_estimators': [1, 2, 5, 10, 20, 30, 45, 60, 80, 100],
'Boost__base_estimator__max_depth': max_depths}
iteration_params = {'Boost__n_estimators': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]}
of_params = {'Boost__base_estimator__max_depth': 100, 'Boost__n_estimators': 50}
complexity_param = {'name': 'Boost__n_estimators', 'display_name': 'Estimator count', 'x_scale': 'log',
'values': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]}
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name, booster,
'Boost', 'Boost', params, complexity_param=complexity_param,
seed=self._details.seed, threads=self._details.threads, verbose=self._verbose)
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name,
of_booster, 'Boost_OF', 'Boost', of_params, seed=self._details.seed,
iteration_params=iteration_params, threads=self._details.threads,
verbose=self._verbose, iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/DT.py
max_depths = np.arange(1, 25, 1)
params = {
'DT__criterion': ['gini', 'entropy'],
'DT__max_depth': max_depths,
'DT__class_weight': ['balanced']
}
complexity_param = {
'name': 'DT__max_depth',
'display_name': 'Max Depth',
'values': max_depths
}
learner = learners.DTLearner(random_state=self._details.seed)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'DT',
'DT',
params,
complexity_param=complexity_param,
seed=self._details.seed,
threads=self._details.threads,
verbose=self._verbose)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/KNN.py
params = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 51, 3),
'KNN__weights': ['uniform']
}
complexity_param = {
'name': 'KNN__n_neighbors',
'display_name': 'Neighbor count',
'values': np.arange(1, 51, 1)
}
best_params = None
if self._details.ds_best_params is not None and 'KNN' in self._details.ds_best_params:
best_params = self._details.ds_best_params['KNN']
learner = learners.KNNLearner(n_jobs=self._details.threads)
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'KNN',
'KNN',
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
def perform(self):
# TODO: Clean up the older alpha stuff?
max_depths = np.arange(1, 51, 1)
params = {
'DT__criterion': ['gini', 'entropy'],
'DT__max_depth': max_depths,
'DT__class_weight': ['balanced', None]
} # , 'DT__max_leaf_nodes': max_leaf_nodes}
complexity_param = {
'name': 'DT__max_depth',
'display_name': 'Max Depth',
'values': max_depths
}
best_params = None
learner = learners.DTLearner(random_state=self._details.seed)
if self._details.ds_best_params is not None and 'DT' in self._details.ds_best_params:
best_params = self._details.ds_best_params['DT']
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'DT',
'DT',
params,
complexity_param=complexity_param,
seed=self._details.seed,
threads=self._details.threads,
best_params=best_params,
verbose=self._verbose)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/KNN.py
params = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 51, 3),
'KNN__weights': ['uniform']
}
complexity_param = {
'name': 'KNN__n_neighbors',
'display_name': 'Neighbor count',
'values': np.arange(1, 51, 1)
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
params_wine = {
'metric': 'manhattan',
'n_neighbors': 22,
'weights': 'uniform'
}
if self._details.ds_name == "wine-qual":
for k in params.keys():
if k in params_wine.keys():
params[k] = [params_wine.get(k)]
#
# Dataset 2:
params_enhancer = {
'metric': 'manhattan',
'n_neighbors': 7,
'weights': 'uniform'
}
if self._details.ds_name == "enhancer-b":
for k in params.keys():
if k in params_enhancer.keys():
params[k] = [params_enhancer.get(k)]
learner = learners.KNNLearner(n_jobs=self._details.threads)
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'KNN',
'KNN',
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
def perform(self):
# TODO: Clean up the older alpha stuff?
max_depths = np.arange(1, 51, 1)
params = {
"DT__criterion": ["gini", "entropy"],
"DT__max_depth": max_depths,
"DT__class_weight": ["balanced", None]
} # , "DT__max_leaf_nodes": max_leaf_nodes}
complexity_param = {
"name": "DT__max_depth",
"display_name": "Max Depth",
"values": max_depths
}
# max_leaf_nodes = np.arange(10, 200, 10)
# params = {"DT__criterion": ["gini", "entropy"],
# "DT__class_weight": ["balanced", None], "DT__max_leaf_nodes": max_leaf_nodes}
# complexity_param = {
# "name": "DT__max_leaf_nodes", "display_name": "Max Leaf Nodes", "values": max_leaf_nodes}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# Seed: 2702306879, 3882803657
# best_params = {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 11}
#
# Dataset 2:
# best_params = {"criterion": "entropy", "max_depth": 4, "class_weight": "balanced"}
learner = learners.DTLearner(random_state=self._details.seed)
if best_params is not None:
learner.set_params(**best_params)
self.log(
"Best parameters are provided, GridSearchCV will is skipped")
else:
self.log(
"Best parameters are not provided, GridSearchCV is scheduled")
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"DT",
"DT",
params,
complexity_param=complexity_param,
seed=self._details.seed,
threads=self._details.threads,
best_params=best_params,
verbose=self._verbose)
def perform(self):
# TODO: Clean up the older alpha stuff?
max_depths = np.arange(1, 50, 1)
params = None
complexity_param = None
if self._details.ds_name == "poisonous_mushrooms":
params = {
"DT__criterion": ["gini"],
"DT__max_depth": max_depths,
} # , 'DT__max_leaf_nodes': max_leaf_nodes}
complexity_param = {
"name": "DT__max_depth",
"display_name": "Max Depth",
"values": max_depths,
}
elif self._details.ds_name == "spam":
params = {
"DT__criterion": ["gini"],
"DT__max_depth": max_depths,
} # , 'DT__max_leaf_nodes': max_leaf_nodes}
complexity_param = {
"name": "DT__max_depth",
"display_name": "Max Depth",
"values": max_depths,
}
best_params = None
# if self._details.ds_name == "poisonous_mushrooms":
# best_params = {"criterion": "gini", "max_depth": 7}
# elif self._details.ds_name == "spam":
# best_params = {"criterion": "gini", "max_depth": 50}
learner = learners.DTLearner(random_state=self._details.seed)
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"DT",
"DT",
params,
complexity_param=complexity_param,
seed=self._details.seed,
threads=self._details.threads,
best_params=best_params,
verbose=self._verbose,
)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/Boosting.py
max_depths = np.arange(1, 11, 1)
# NOTE: Criterion may need to be adjusted here depending on the dataset
base = learners.DTLearner(criterion='entropy', class_weight='balanced', max_depth=10,
random_state=self._details.seed)
of_base = learners.DTLearner(criterion='entropy', class_weight='balanced', random_state=self._details.seed)
booster = learners.BoostingLearner(algorithm='SAMME', learning_rate=1, base_estimator=base,
random_state=self._details.seed)
of_booster = learners.BoostingLearner(algorithm='SAMME', learning_rate=1, base_estimator=of_base,
random_state=self._details.seed)
params = {'Boost__n_estimators': [1, 2, 5, 10, 20, 30, 45, 60, 80, 90, 100],
'Boost__learning_rate': [(2**x)/100 for x in range(7)]+[1],
'Boost__base_estimator__max_depth': max_depths}
iteration_details = {
'params': {'Boost__n_estimators': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]}
}
of_params = {'Boost__base_estimator__max_depth': None}
#complexity_param = {'name': 'Boost__learning_rate', 'display_name': 'Learning rate', 'x_scale': 'log',
# 'values': [(2**x)/100 for x in range(7)]+[1]}
complexity_param = {'name': 'Boost__n_estimators', 'display_name': 'N_estimators', 'x_scale': 'linear',
'values': [1, 2, 5, 10, 20, 30, 45, 60, 80, 90, 100]}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'base_estimator__max_depth': 4, 'learning_rate': 0.32, 'n_estimators': 20}
#
# Dataset 2:
best_params = {'base_estimator__max_depth': 5, 'learning_rate': 0.64, 'n_estimators': 45}
if best_params is not None:
booster.set_params(**best_params)
of_booster.set_params(**best_params)
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name, booster,
'Boost', 'Boost', params, complexity_param=complexity_param,
iteration_details=iteration_details, best_params=best_params,
seed=self._details.seed, threads=self._details.threads, verbose=self._verbose)
# TODO: This should turn OFF regularization
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name,
of_booster, 'Boost_OF', 'Boost', of_params, seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params, threads=self._details.threads,
verbose=self._verbose, iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/SVM.py
alphas = [10**-x for x in np.arange(1, 9.01, 1 / 2)]
samples = self._details.ds.features.shape[0]
gamma_fracs = np.arange(0.2, 2.1, 0.2)
params = {
'SVM__alpha': alphas,
'SVM__max_iter': [int((1e6 / samples) / .8) + 1],
'SVM__gamma_frac': gamma_fracs
}
complexity_param = {
'name': 'SVM__gamma_frac',
'display_name': 'Gamma Fraction',
'values': gamma_fracs
}
iteration_params = {'SVM__max_iter': [2**x for x in range(12)]}
learner = learners.SVMLearner(tol=None)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF',
'SVM',
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_params=iteration_params,
threads=self._details.threads,
verbose=self._verbose)
of_params = best_params.copy()
of_params['SVM__alpha'] = 1e-16
learner = learners.SVMLearner(n_jobs=self._details.threads)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF_OF',
'SVM',
of_params,
seed=self._details.seed,
iteration_params=iteration_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/Boosting.py
max_depths = np.arange(1, 11, 1)
# NOTE: Criterion may need to be adjusted here depending on the dataset
base = learners.DTLearner(criterion='gini', class_weight='balanced', max_depth=7,
random_state=self._details.seed)
of_base = learners.DTLearner(criterion='gini', class_weight='balanced', random_state=self._details.seed)
booster = learners.BoostingLearner(algorithm='SAMME', learning_rate=1, base_estimator=base,
random_state=self._details.seed)
of_booster = learners.BoostingLearner(algorithm='SAMME', learning_rate=1, base_estimator=of_base,
random_state=self._details.seed)
params = {'Boost__n_estimators': [1, 2, 5, 10, 20, 30, 45, 60, 80, 90, 100],
'Boost__learning_rate': [(2**x)/100 for x in range(7)]+[1],
'Boost__base_estimator__max_depth': max_depths}
iteration_details = {
'params': {'Boost__n_estimators': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]}
}
of_params = {'Boost__base_estimator__max_depth': None}
complexity_param = {'name': 'Boost__learning_rate', 'display_name': 'Learning rate', 'x_scale': 'log',
'values': [(2**x)/100 for x in range(7)]+[1]}
best_params = None
if self._details.ds_best_params is not None and 'Boost' in self._details.ds_best_params:
best_params = self._details.ds_best_params['Boost']
if best_params is not None:
booster.set_params(**best_params)
of_booster.set_params(**best_params)
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name, booster,
'Boost', 'Boost', params, complexity_param=complexity_param,
iteration_details=iteration_details, best_params=best_params,
seed=self._details.seed, threads=self._details.threads, verbose=self._verbose)
# TODO: This should turn OFF regularization
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name,
of_booster, 'Boost_OF', 'Boost', of_params, seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params, threads=self._details.threads,
verbose=self._verbose, iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/KNN.py
n_neighbors = np.arange(1, 51, 3)
params = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': n_neighbors,
'KNN__weights': ['uniform']
}
complexity_param = {
'name': 'KNN__n_neighbors',
'display_name': 'Neighbor count',
'values': n_neighbors
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'metric': 'manhattan', 'n_neighbors': 3, 'weights': 'distance'}
#
# Dataset 1:
# best_params = {'metric': 'chebyshev', 'n_neighbors': 13, 'weights': 'uniform'}
learner = learners.KNNLearner(n_jobs=self._details.threads)
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'KNN',
'KNN',
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# Search for good alphas
alphas = [10 ** -x for x in np.arange(-1, 5.01, 1 / 2)]
# TODO: Allow for tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
hiddens = [(h,) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
params = {'MLP__activation': ['relu', 'tanh'], 'MLP__alpha': alphas,
'MLP__hidden_layer_sizes': hiddens}
timing_params = {'MLP__early_stopping': True}
iteration_params = {'MLP__max_iter':
[2 ** x for x in range(11)] + [2000, 2200, 2400, 2600, 2800,
3000]}
complexity_param = {'name': 'MLP__alpha', 'display_name': 'Alpha', 'x_scale': 'log',
'values': alphas}
learner = learners.ANNLearner(tol=1e-8, verbose=self._verbose)
best_params = experiments.perform_experiment(
self._details.ds, self._details.ds_name, self._details.ds_readable_name, learner, 'ANN', 'MLP',
params,
complexity_param=complexity_param,
seed=self._details.seed,
timing_params=timing_params,
iteration_pipe_params=timing_params, iteration_params=iteration_params,
threads=self._details.threads, verbose=self._verbose)
of_params = best_params.copy()
of_params['MLP__alpha'] = 0
learner = learners.ANNLearner()
experiments.perform_experiment(self._details.ds, self._details.ds_name, self._details.ds_readable_name, learner,
'ANN_OF', 'MLP', of_params, seed=self._details.seed, timing_params=timing_params,
iteration_pipe_params=timing_params, iteration_params=iteration_params,
threads=self._details.threads, verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/SVM.py
samples = self._details.ds.features.shape[0]
features = self._details.ds.features.shape[1]
gamma_fracs = np.arange(1 / features, 2.1, 0.2)
tols = np.arange(1e-8, 1e-1, 0.01)
C_values = np.arange(0.001, 2.5, 0.25)
iters = [-1, int((1e6 / samples) / 0.8) + 1]
best_params_linear = None
best_params_rbf = None
# # Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# # the various graphs
# #
# if self._details.ds_name == "spam":
# best_params_linear = {
# "C": 0.101,
# "class_weight": "balanced",
# "loss": "squared_hinge",
# "max_iter": 33,
# "tol": 1.00e-08,
# }
# best_params_rbf = {
# "C": 0.251,
# "class_weight": "balanced",
# "decision_function_shape": "ovo",
# "tol": 0.07000001,
# }
# elif self._details.ds_name == "poisonous_mushrooms":
# best_params_linear = {
# "C": 0.001,
# "class_weight": "balanced",
# "loss": "squared_hinge",
# "max_iter": 42,
# "tol": 1.00e-08,
# }
# best_params_rbf = {
# "C": 0.251,
# "class_weight": "balanced",
# "decision_function_shape": "ovo",
# "tol": 0.06000001,
# }
# Linear SVM
params = {
"SVM__max_iter": iters,
"SVM__tol": tols,
"SVM__class_weight": [{
1: 10
}],
"SVM__C": C_values,
}
complexity_param = {
"name": "SVM__C",
"display_name": "Penalty",
"values": np.arange(0.001, 2.5, 0.1),
}
iteration_details = {
"x_scale": "log",
"params": {
"SVM__max_iter": [2**x for x in range(12)]
},
}
# NOTE: If this is causing issues, try the RBFSVMLearner. Passing use_linear=True will use a linear kernel
# and passing use_linear=False will use the RBF kernel. This method is slower but if libsvm is not
# available it may be your only option
learner = learners.LinearSVMLearner(dual=False)
if best_params_linear is not None:
learner.set_params(**best_params_linear)
#
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"SVMLinear",
"SVM",
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params_linear,
threads=self._details.threads,
verbose=self._verbose,
)
of_params = best_params.copy()
learner = learners.LinearSVMLearner(dual=True)
if best_params_linear is not None:
learner.set_params(**best_params_linear)
experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"SVMLinear_OF",
"SVM",
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_linear,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True,
)
# RBF SVM
params = {
"SVM__max_iter": iters,
"SVM__tol": tols,
"SVM__class_weight": ["balanced"],
"SVM__C": C_values,
"SVM__decision_function_shape": ["ovo", "ovr"],
"SVM__gamma": gamma_fracs,
}
params = {
"SVM__C": C_values,
"SVM__class_weight": ["balanced"],
"SVM__tol": tols,
"SVM__decision_function_shape": ["ovo", "ovr"],
}
complexity_param = {
"name": "SVM__C",
"display_name": "Penalty",
"values": np.arange(0.001, 2.5, 0.25),
}
#
learner = learners.SVMLearner(kernel="rbf")
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"SVM_RBF",
"SVM",
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose,
)
of_params = best_params.copy()
learner = learners.SVMLearner(kernel="rbf")
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"SVM_RBF_OF",
"SVM",
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True,
)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# Search for good alphas
alphas = [10**-x for x in np.arange(-1, 9.01, 0.5)]
# TODO: Allow for better tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
hiddens = [(h, ) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
learning_rates = sorted([(2**x) / 1000 for x in range(8)] + [0.000001])
params = {
'MLP__activation': ['relu', 'logistic'],
'MLP__alpha': alphas,
'MLP__learning_rate_init': learning_rates,
'MLP__hidden_layer_sizes': hiddens
}
timing_params = {'MLP__early_stopping': False}
iteration_details = {
'x_scale': 'log',
'params': {
'MLP__max_iter': [2**x for x in range(12)] +
[2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000]
},
'pipe_params': timing_params
}
complexity_param = {
'name': 'MLP__alpha',
'display_name': 'Alpha',
'x_scale': 'log',
'values': alphas
}
best_params = None
if self._details.ds_best_params is not None and 'ANN' in self._details.ds_best_params:
best_params = self._details.ds_best_params['ANN']
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
if best_params is not None:
learner.set_params(**best_params)
cv_best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN',
'MLP',
params,
complexity_param=complexity_param,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
# TODO: This should turn OFF regularization
of_params = cv_best_params.copy()
of_params['MLP__alpha'] = 0
if best_params is not None:
learner.set_params(**best_params)
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN_OF',
'MLP',
of_params,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# Search for good alphas
alphas = [3, 1, 0.5, 0.25, 0.10, 0.005, 0.001]
# TODO: Allow for better tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
hiddens = [(2, 2), (4, 4), (8, 8), (16, 16), (32, 32), (64, 64),
(128, 128)]
learning_rates = [0.000001, 0.0001, 0.001, 0.01, 0.1, 0.5]
params = {
'MLP__activation': ['relu', 'logistic'],
'MLP__alpha': alphas,
'MLP__learning_rate_init': learning_rates,
'MLP__hidden_layer_sizes': hiddens
}
timing_params = {'MLP__early_stopping': False}
iteration_details = {
'x_scale': 'log',
'params': {
'MLP__max_iter': [1000, 2500, 5000, 10000, 30000]
},
'pipe_params': timing_params
}
complexity_param = {
'name': 'MLP__alpha',
'display_name': 'Alpha',
'x_scale': 'log',
'values': alphas
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'activation': 'relu', 'alpha': 1.0, 'hidden_layer_sizes': (36, 36),
# 'learning_rate_init': 0.016}
# Dataset 2:
# best_params = {'activation': 'relu', 'alpha': 1e-05, 'hidden_layer_sizes': (16, 16),
# 'learning_rate_init': 0.064}
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
if best_params is not None:
learner.set_params(**best_params)
cv_best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN',
'MLP',
params,
complexity_param=complexity_param,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
# TODO: This should turn OFF regularization
of_params = cv_best_params.copy()
of_params['MLP__alpha'] = 0
if best_params is not None:
learner.set_params(**best_params)
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN_OF',
'MLP',
of_params,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# TODO: Allow for better tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
timing_params = {"MLP__early_stopping": False}
iteration_details = {
"x_scale": "log",
"params": {
"MLP__max_iter": [2 ** x for x in range(12)]
+ [2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000]
},
"pipe_params": timing_params,
}
params = None
complexity_param = None
if self._details.ds_name == "spam":
# Search for good alphas
alphas = [10 ** -x for x in np.arange(0, 9.01, 0.5)]
hiddens = [(h,) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
learning_rates = [0.064]
# learning_rates = sorted([(2 ** x) / 1000 for x in range(8)] + [0.000001])
params = {
"MLP__activation": ["logistic"],
"MLP__alpha": alphas,
"MLP__learning_rate_init": learning_rates,
"MLP__hidden_layer_sizes": hiddens,
}
complexity_param = {
"name": "MLP__alpha",
"display_name": "Alpha",
"x_scale": "log",
"values": alphas,
}
elif self._details.ds_name == "poisonous_mushrooms":
# Search for good alphas
alphas = [10 ** -x for x in np.arange(0, 9.01, 0.5)]
# hiddens = [(16,)] # [(h,) * l for l in [1, 2] for h in [d, d // 2, d * 2]]
hiddens = [(h,) * l for l in [1, 2] for h in [d, d // 2, d * 2]]
learning_rates = [
0.064
] # sorted([(2**x)/1000 for x in range(8)]+[0.000001])
params = {
"MLP__activation": ["logistic"],
"MLP__alpha": alphas,
"MLP__learning_rate_init": learning_rates,
"MLP__hidden_layer_sizes": hiddens,
}
complexity_param = {
"name": "MLP__alpha",
"display_name": "Alpha",
"x_scale": "log",
"values": alphas,
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# if self._details.ds_name == 'spam':
# best_params = {'activation': 'logistic', 'alpha': 1.00E-06, 'hidden_layer_sizes': (16,), 'learning_rate_init': 0.064}
# elif self._details.ds_name == 'poisonous_mushrooms':
# best_params = {'activation': 'logistic', 'alpha': 0.003162278, 'hidden_layer_sizes': (16,),
# 'learning_rate_init': 0.064}
learner = learners.ANNLearner(
max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose,
)
if best_params is not None:
learner.set_params(**best_params)
cv_best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"ANN",
"MLP",
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_details=iteration_details,
timing_params=timing_params,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
)
# TODO: This should turn OFF regularization
of_params = cv_best_params.copy()
of_params["MLP__alpha"] = 0
if best_params is not None:
learner.set_params(**best_params)
learner = learners.ANNLearner(
max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose,
)
experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"ANN_OF",
"MLP",
of_params,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True,
)
def perform(self):
# TODO: Clean up the older alpha stuff?
max_depths = np.arange(1, 21, 1)
#alphas = [-1,-1e-3,-(1e-3)*10**-0.5, -1e-2, -(1e-2)*10**-0.5,-1e-1,-(1e-1)*10**-0.5, 0, (1e-1)*10**-0.5,1e-1,(1e-2)*10**-0.5,1e-2,(1e-3)*10**-0.5,1e-3]
alphas = [x / 1000 for x in range(-40, 40, 4)]
#params = {'DT__criterion': ['gini', 'entropy'],
# 'DT__max_depth': max_depths,
# 'alpha' : alphas,
# 'DT__class_weight': ['balanced', None]
#} # , 'DT__max_leaf_nodes': max_leaf_nodes}
params = {
'DT__criterion': ['gini', 'entropy'],
'DT__alpha': alphas,
'DT__class_weight': ['balanced'],
'DT__random_state': [self._details.seed]
}
complexity_param = {
'name': 'DT__alpha',
'display_name': 'alpha',
'values': alphas
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
params_wine = {
'DT__criterion': 'gini',
'DT__alpha': 0.008,
'DT__class_weight': 'balanced'
}
if self._details.ds_name == "wine-qual" and self._details.bparams:
for k in params.keys():
if k in params_wine.keys():
params[k] = [params_wine.get(k)]
#
# Dataset 2:
params_enhancer = {
'DT__criterion': 'gini',
'DT__alpha': 0.008,
'DT__class_weight': 'balanced'
}
if self._details.ds_name == "enhancer-b" and self._details.bparams:
for k in params.keys():
if k in params_enhancer.keys():
params[k] = [params_enhancer.get(k)]
learner = learners.DTLearner(random_state=self._details.seed)
if best_params is not None:
learner.set_params(**best_params)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'DT',
'DT',
params,
complexity_param=complexity_param,
seed=self._details.seed,
threads=self._details.threads,
best_params=best_params,
verbose=self._verbose,
apply_pruning=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# Search for good alphas
alphas = [10**-x for x in np.arange(-3, 9.01, 0.5)]
alphas = [0] + alphas
# TODO: Allow for better tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
hiddens = [(h, ) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
learning_rates = sorted([(2**x) / 1000 for x in range(8)] + [0.000001])
params = {
'MLP__activation': ['relu', 'logistic', 'tanh'],
'MLP__alpha': alphas,
'MLP__learning_rate_init': learning_rates,
'MLP__hidden_layer_sizes': hiddens,
'MLP__random_state': [self._details.seed],
'MLP__beta_1': [0.5, 0.9, 0.99, 0.999],
'MLP__beta_2': [0.5, 0.9, 0.99, 0.999]
}
timing_params = {'MLP__early_stopping': False}
iteration_details = {
'x_scale': 'log',
'params': {
'MLP__max_iter':
[2**x for x in range(12)] + [2100, 2400, 2700, 3000]
},
'pipe_params': timing_params
}
complexity_param = {
'name': 'MLP__alpha',
'display_name': 'Alpha',
'x_scale': 'log',
'values': alphas
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'activation': 'relu', 'alpha': 1.0, 'hidden_layer_sizes': (36, 36),
alpha = 0
params_wine = {
'MLP__activation': 'tanh',
'MLP__alpha': 0.1,
'MLP__learning_rate_init': 0.064,
'MLP__hidden_layer_sizes': (12, 12),
'MLP__beta_1': 0.99,
'MLP__beta_2': 0.99
}
if self._details.ds_name == "wine-qual" and self._details.bparams:
alpha = 0.1
for k in params.keys():
if k in params_wine.keys():
params[k] = [params_wine.get(k)]
params_enhancer = {
'MLP__activation': 'logistic',
'MLP__alpha': 0.001,
'MLP__learning_rate_init': 0.128,
'MLP__hidden_layer_sizes': (38, 38),
'MLP__beta_1': 0.5,
'MLP__beta_2': 0.999
}
if self._details.ds_name == "enhancer-b" and self._details.bparams:
alpha = 0.001
for k in params.keys():
if k in params_enhancer.keys():
params[k] = [params_enhancer.get(k)]
#if self._details.ds_name == "wine-qual":
# best_params = params_wine
# 'learning_rate_init': 0.016}
# Dataset 2:
# best_params = {'activation': 'relu', 'alpha': 1e-05, 'hidden_layer_sizes': (16, 16),
# 'learning_rate_init': 0.064}
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
if best_params is not None:
learner.set_params(**best_params)
cv_best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN',
'MLP',
params,
complexity_param=complexity_param,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
# TODO: This should turn OFF regularization
of_params = cv_best_params.copy()
of_params['MLP__alpha'] = alpha
if best_params is not None:
learner.set_params(**best_params)
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN_',
'MLP',
of_params,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# Search for good alphas
# alphas = [10 ** -x for x in np.arange(-1, 9.01, 0.5)]
# YS trying a larger intervals
# alphas = [10 ** -x for x in np.arange(-1, 5.01, 1)]
alphas = [10**-x for x in np.arange(1, 3.01, 1)]
# TODO: Allow for better tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
hiddens = [(h, ) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
# learning_rates = sorted([(2**x)/1000 for x in range(8)]+[0.000001])
# YS trying a larger intervals
learning_rates = sorted([(4**x) / 1000 for x in range(3)])
# YS trying a larger intervals
# params = {'MLP__activation': ['relu', 'logistic'], 'MLP__alpha': alphas,
params = {
'MLP__activation': ['relu', 'logistic'],
'MLP__alpha': alphas,
'MLP__learning_rate_init': learning_rates,
'MLP__hidden_layer_sizes': hiddens
}
# YS changing early stopping to True
# timing_params = {'MLP__early_stopping': False}
timing_params = {'MLP__early_stopping': True}
iteration_details = {
'x_scale': 'log',
# 'params': {'MLP__max_iter':
# [2 ** x for x in range(12)] + [2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900,
# 3000]},
# YS cutting the max_iter
'params': {
'MLP__max_iter': [2**x for x in range(11)]
},
'pipe_params': timing_params
}
complexity_param = {
'name': 'MLP__alpha',
'display_name': 'Alpha',
'x_scale': 'log',
'values': alphas
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'activation': 'relu', 'alpha': 1.0, 'hidden_layer_sizes': (36, 36),
# 'learning_rate_init': 0.016}
# Dataset 2:
# best_params = {'activation': 'relu', 'alpha': 1e-05, 'hidden_layer_sizes': (16, 16),
# 'learning_rate_init': 0.064}
# learner = learners.ANNLearner(max_iter=3000, early_stopping=True, random_state=self._details.seed,
# verbose=self._verbose)
# YS cutting the max_iter
learner = learners.ANNLearner(max_iter=2000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
if best_params is not None:
learner.set_params(**best_params)
cv_best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN',
'MLP',
params,
complexity_param=complexity_param,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
# TODO: This should turn OFF regularization
of_params = cv_best_params.copy()
of_params['MLP__alpha'] = 0
if best_params is not None:
learner.set_params(**best_params)
# learner = learners.ANNLearner(max_iter=3000, early_stopping=True, random_state=self._details.seed,
# verbose=self._verbose)
# YS cutting the max_iter
learner = learners.ANNLearner(max_iter=2000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN_OF',
'MLP',
of_params,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/SVM.py
samples = self._details.ds.features.shape[0]
features = self._details.ds.features.shape[1]
gamma_fracs = np.arange(1 / features, 2.1, 0.2)
tols = np.arange(1e-8, 1e-1, 0.01)
C_values = np.arange(0.001, 2.5, 0.25)
iters = [-1, int((1e6 / samples) / .8) + 1]
best_params_linear = None
best_params_rbf = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1 (credit default):
'''
best_params_linear = {
"C": 0.251,
"class_weight": "balanced",
"dual": False,
"fit_intercept": True,
"intercept_scaling": 1,
"loss": "squared_hinge",
"max_iter": 42,
"multi_class": "ovr",
"penalty": "l2",
"tol": 0.020000010000000002,
"verbose": False
}
best_params_rbf = {
"C": 0.751,
"cache_size": 200,
"class_weight": "balanced",
"coef0": 0,
"decision_function_shape": "ovo",
"degree": 3,
"gamma": 0.043478260869565216,
"kernel": "rbf",
"max_iter": -1,
"probability": False,
"shrinking": True,
"tol": 0.08000001,
"verbose": False
}
# Dataset 2:
best_params_linear = {
"C": 0.251,
"class_weight": "balanced",
"dual": False,
"fit_intercept": True,
"intercept_scaling": 1,
"loss": "squared_hinge",
"max_iter": 42,
"multi_class": "ovr",
"penalty": "l2",
"tol": 0.020000010000000002,
"verbose": False
}
best_params_rbf = {
"C": 1.501,
"cache_size": 200,
"class_weight": "balanced",
"coef0": 0,
"decision_function_shape": "ovo",
"degree": 3,
"gamma": 0.0056179775280898875,
"kernel": "rbf",
"max_iter": -1,
"probability": False,
"shrinking": True,
"tol": 0.09000000999999999,
"verbose": False
}
'''
# Linear SVM
params = {
'SVM__max_iter': iters,
'SVM__tol': tols,
'SVM__class_weight': ['balanced'],
'SVM__C': C_values
}
complexity_param = {
'name': 'SVM__C',
'display_name': 'Penalty',
'values': np.arange(0.001, 2.5, 0.1)
}
iteration_details = {
'x_scale': 'log',
'params': {
'SVM__max_iter': [2**x for x in range(12)]
},
}
# NOTE: If this is causing issues, try the RBFSVMLearner. Passing use_linear=True will use a linear kernel
# and passing use_linear=False will use the RBF kernel. This method is slower but if libsvm is not
# available it may be your only option
learner = learners.LinearSVMLearner(dual=False)
if best_params_linear is not None:
learner.set_params(**best_params_linear)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVMLinear',
'SVM',
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_linear,
threads=self._details.threads,
verbose=self._verbose)
of_params = best_params.copy()
learner = learners.LinearSVMLearner(dual=True)
if best_params_linear is not None:
learner.set_params(**best_params_linear)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVMLinear_OF',
'SVM',
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_linear,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
# RBF SVM
params = {
'SVM__max_iter': iters,
'SVM__tol': tols,
'SVM__class_weight': ['balanced'],
'SVM__C': C_values,
'SVM__decision_function_shape': ['ovo', 'ovr'],
'SVM__gamma': gamma_fracs
}
complexity_param = {
'name': 'SVM__C',
'display_name': 'Penalty',
'values': np.arange(0.001, 2.5, 0.1)
}
learner = learners.SVMLearner(kernel='rbf')
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF',
'SVM',
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose)
of_params = best_params.copy()
learner = learners.SVMLearner(kernel='rbf')
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF_OF',
'SVM',
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/Boosting.py
alphas = [x / 1000 for x in range(-10, 40, 4)]
crit = "entropy"
lr = [(2**x) / 100 for x in range(7)] + [1]
n_estimators = [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
n_estimators_iter = [
1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 140, 160,
200, 240, 300
]
# /output-ew2
if 'enhancer-b' == self._details.ds_name and self._details.bparams:
alphas = [0.05]
crit = "gini"
lr = [0.16] + [10**(x / 8) for x in range(-32, 16)]
lr = [0.32]
n_estimators = n_estimators_iter
n_estimators = [5]
if 'wine-qual' == self._details.ds_name and self._details.bparams:
alphas = [0.014]
crit = "gini"
lr = [0.16] # use old lr range here
n_estimators = [20]
# NOTE: Criterion may need to be adjusted here depending on the dataset
base = learners.DTLearner(criterion=crit,
class_weight='balanced',
random_state=self._details.seed)
of_base = learners.DTLearner(criterion=crit,
class_weight='balanced',
random_state=self._details.seed)
booster = learners.BoostingLearner(algorithm='SAMME.R',
learning_rate=1,
base_estimator=base,
random_state=self._details.seed)
of_booster = learners.BoostingLearner(algorithm='SAMME.R',
learning_rate=1,
base_estimator=of_base,
random_state=self._details.seed)
params = {
'Boost__n_estimators': n_estimators,
'Boost__learning_rate': lr,
'Boost__base_estimator__alpha': alphas,
'Boost__random_state': [self._details.seed],
'Boost__base_estimator__random_state': [self._details.seed]
}
iteration_details = {
'params': {
'Boost__n_estimators': n_estimators_iter
}
}
of_params = {'Boost__base_estimator__alpha': -1}
complexity_param = {
'name': 'Boost__learning_rate',
'display_name': 'Learning rate',
'x_scale': 'log',
'values': [10**(x / 8) for x in range(-32, 16)]
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'base_estimator__max_depth': 8, 'learning_rate': 0.32, 'n_estimators': 90}
#
# Dataset 2:
# best_params = {'base_estimator__max_depth': 6, 'learning_rate': 0.16, 'n_estimators': 20}
if best_params is not None:
booster.set_params(**best_params)
of_booster.set_params(**best_params)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
booster,
'Boost',
'Boost',
params,
complexity_param=complexity_param,
iteration_details=iteration_details,
best_params=best_params,
seed=self._details.seed,
threads=self._details.threads,
verbose=self._verbose)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/KNN.py
"""original"""
# params = {'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'], 'KNN__n_neighbors': np.arange(1, 51, 3),
# 'KNN__weights': ['uniform']}
"""new: changed metric and n_neighbors"""
"""
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
"distance"
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
KNN__metric is our distance functions: manhattan and euclidean
manhattan: sum(|x - y|)
euclidean: sqrt(sum((x - y)^2))
"weights"
uniform: uniform weights. All points in each neighborhood are weighted equally.
distance: weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a greater
influence than neighbors which are further away.
"""
params = {
'KNN__metric': ['manhattan', 'euclidean'],
'KNN__n_neighbors': np.arange(1, 51, 5),
'KNN__weights': ['uniform']
}
complexity_param = {
'name': 'KNN__n_neighbors',
'display_name': 'Neighbor count',
'values': np.arange(1, 51, 5)
}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'uniform'}
#
# Dataset 1:
# best_params = {'metric': 'euclidean', 'n_neighbors': 4, 'weights': 'uniform'}
learner = learners.KNNLearner(n_jobs=self._details.threads)
if best_params is not None:
learner.set_params(**best_params)
"""perform_experiment(ds, ds_name, ds_readable_name, clf, clf_name, clf_label, params, timing_params=None,
iteration_details=None, complexity_param=None, seed=0, threads=1,
iteration_lc_only=False, best_params=None, verbose=False)"""
"""pipe is built with
pipe = Pipeline([('Scale', StandardScaler()),
('KNN', learner)])
"""
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'KNN',
'KNN',
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/ANN.py
# Search for good alphas
"""
alphas:
- constrains/penalizes our max weights: high of 10 and diminishing drastically
- recall: the larger the weight for an attr, the more it dominates, can lead to OVERFITTING
"""
alphas = [10 ** -x for x in np.arange(-1, 9.01, 0.5)]
# TODO: Allow for better tuning of hidden layers based on dataset provided
d = self._details.ds.features.shape[1]
"""
hiddens
- based on the number of features (Xs, or attrs) in our data set
- we test 1-2-3 layers using a multiple or division of # Xs.
- ex: 23 attributes: test 11, 23, 46 hidden layer sizes
"""
hiddens = [(h,) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
"""
https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/
learning_rates:
- hyper-parameter that controls how much to change the model in response to the estimated error
each time the model weights are updated.
- a value too small may result in a long training process that could get stuck,
- a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process
- may be the most important hyper-parameter when configuring ANN.
- small positive value, often in the range between 0.0 and 1.0.
"""
learning_rates = sorted([(2**x)/1000 for x in range(8)]+[0.000001])
"""
logistic: the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)).
relu: the rectified linear unit function, returns f(x) = max(0, x)
"""
params = {'MLP__activation': ['relu', 'logistic'],
'MLP__alpha': alphas,
'MLP__learning_rate_init': learning_rates,
'MLP__hidden_layer_sizes': hiddens}
timing_params = {'MLP__early_stopping': False}
iteration_details = {'x_scale': 'log',
'params': {'MLP__max_iter': [2 ** x for x in range(12)] +
[2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000]},
'pipe_params': timing_params}
complexity_param = {'name': 'MLP__alpha',
'display_name': 'Alpha',
'x_scale': 'log',
'values': alphas}
best_params = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params = {'activation': 'relu', 'alpha': 1.0, 'hidden_layer_sizes': (36, 36),
# 'learning_rate_init': 0.016}
# Dataset 2:
# best_params = {'activation': 'relu', 'alpha': 1e-05, 'hidden_layer_sizes': (16, 16),
# 'learning_rate_init': 0.064}
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
if best_params is not None:
learner.set_params(**best_params)
cv_best_params = experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN',
'MLP',
params,
complexity_param=complexity_param,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose)
# TODO: This should turn OFF regularization
of_params = cv_best_params.copy()
of_params['MLP__alpha'] = 0
if best_params is not None:
learner.set_params(**best_params)
learner = learners.ANNLearner(max_iter=3000,
early_stopping=True,
random_state=self._details.seed,
verbose=self._verbose)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'ANN_OF',
'MLP',
of_params,
seed=self._details.seed,
timing_params=timing_params,
iteration_details=iteration_details,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/KNN.py
params = None
complexity_param = None
best_params = None
if self._details.ds_name == "spam":
params = {
"KNN__metric": ["minkowski", "chebyshev", "euclidean"],
"KNN__n_neighbors": np.arange(1, 21, 3),
# "KNN__n_neighbors": [3, 5, 7, 9],
"KNN__weights": ["uniform", "distance"],
}
complexity_param = {
"name": "KNN__n_neighbors",
"display_name": "Neighbor count",
"values": np.arange(1, 21, 3),
# "values": [3, 5, 7, 9],
# "values": [1, 2, 3, 4],
}
elif self._details.ds_name == "poisonous_mushrooms":
params = {
"KNN__metric": ["minkowski", "chebyshev", "euclidean"],
"KNN__n_neighbors": np.arange(1, 21, 3),
"KNN__weights": ["uniform", "distance"],
}
complexity_param = {
"name": "KNN__n_neighbors",
"display_name": "Neighbor count",
"values": np.arange(1, 21, 3),
}
# # Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# # the various graphs
# # #
# if self._details.ds_name == "spam":
# best_params = {
# "metric": "chebyshev",
# "n_neighbors": 7,
# "weights": "uniform",
# }
# elif self._details.ds_name == "poisonous_mushrooms":
# best_params = {
# "metric": "chebyshev",
# "n_neighbors": 7,
# "weights": "uniform",
# }
learner = learners.KNNLearner(n_jobs=self._details.threads)
if best_params is not None:
learner.set_params(**best_params)
experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
"KNN",
"KNN",
params,
complexity_param=complexity_param,
seed=self._details.seed,
best_params=best_params,
threads=self._details.threads,
verbose=self._verbose,
)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/Boosting.py
max_depths = np.arange(1, 15, 1)
# NOTE: Criterion may need to be adjusted here depending on the dataset
base = learners.DTLearner(
criterion="entropy",
class_weight="balanced",
max_depth=5,
random_state=self._details.seed,
)
of_base = learners.DTLearner(
criterion="entropy",
class_weight="balanced",
random_state=self._details.seed,
)
booster = learners.BoostingLearner(
algorithm="SAMME",
learning_rate=1,
base_estimator=base,
random_state=self._details.seed,
)
of_booster = learners.BoostingLearner(
algorithm="SAMME",
learning_rate=1,
base_estimator=of_base,
random_state=self._details.seed,
)
params = {
"Boost__n_estimators": [1, 2, 5, 10, 20, 30, 45, 60, 80, 90, 100],
"Boost__learning_rate": [(2**x) / 100 for x in range(7)] + [1],
"Boost__base_estimator__max_depth": max_depths,
}
iteration_details = {
"params": {
"Boost__n_estimators": [
1,
2,
5,
10,
20,
30,
40,
50,
60,
70,
80,
90,
100,
]
}
}
of_params = {"Boost__base_estimator__max_depth": None}
complexity_param = {
"name": "Boost__learning_rate",
"display_name": "Learning rate",
"x_scale": "log",
"values": [(2**x) / 100 for x in range(7)] + [1],
}
best_params = None
# # Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# # the various graphs
# #
# if self._details.ds_name == "spam":
# best_params = {
# "base_estimator__max_depth": 10,
# "learning_rate": 0.32,
# "n_estimators": 30,
# }
# elif self._details.ds_name == "poisonous_mushrooms":
# best_params = {
# "base_estimator__max_depth": 10,
# "learning_rate": 0.08,
# "n_estimators": 60,
# }
# # Dataset 1:
# # best_params = {'base_estimator__max_depth': 8, 'learning_rate': 0.32, 'n_estimators': 90}
# #
# # Dataset 2:
# # best_params = {'base_estimator__max_depth': 6, 'learning_rate': 0.16, 'n_estimators': 20}
# if best_params is not None:
# booster.set_params(**best_params)
# of_booster.set_params(**best_params)
experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
booster,
"Boost",
"Boost",
params,
complexity_param=complexity_param,
iteration_details=iteration_details,
best_params=best_params,
seed=self._details.seed,
threads=self._details.threads,
verbose=self._verbose,
)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/SVM.py
samples = self._details.ds.features.shape[0]
features = self._details.ds.features.shape[1]
# original
# gamma_fracs = np.arange(1/features, 2.1, 0.2)
# tols = np.arange(1e-8, 1e-1, 0.01)
# C_values = np.arange(0.001, 2.5, 0.25)
# iters = [-1, int((1e6/samples)/.8)+1]
# YS changed
gamma_fracs = np.arange(1 / features, 1.1, 0.25)
tols = np.arange(1e-3, 1e-1, 0.03)
C_values = np.arange(0.1, 2.5, 0.5)
iters = [-1, int((1e6 / samples) / .8) + 1]
best_params_linear = None
best_params_rbf = None
# Uncomment to select known best params from grid search. This will skip the grid search and just rebuild
# the various graphs
#
# Dataset 1:
# best_params_linear = {'C': 0.5, 'class_weight': 'balanced', 'loss': 'squared_hinge',
# 'max_iter': 1478, 'tol': 0.06000001}
# best_params_rbf = {'C': 2.0, 'class_weight': 'balanced', 'decision_function_shape': 'ovo',
# 'gamma': 0.05555555555555555, 'max_iter': -1, 'tol': 1e-08}
# Dataset 2:
# best_params_linear = {'C': 1.0, 'class_weight': 'balanced', 'loss': 'hinge', 'dual': True,
# 'max_iter': 70, 'tol': 0.08000001}
# best_params_rbf = {'C': 1.5, 'class_weight': 'balanced', 'decision_function_shape': 'ovo',
# 'gamma': 0.125, 'max_iter': -1, 'tol': 0.07000001}
# Linear SVM
params = {
'SVM__max_iter': iters,
'SVM__tol': tols,
'SVM__class_weight': ['balanced'],
'SVM__C': C_values
}
# original
# complexity_param = {'name': 'SVM__C', 'display_name': 'Penalty', 'values': np.arange(0.001, 2.5, 0.1)}
# YS changed
complexity_param = {
'name': 'SVM__C',
'display_name': 'Penalty',
'values': np.arange(0.1, 2.5, 0.5)
}
iteration_details = {
'x_scale': 'log',
# original
# 'params': {'SVM__max_iter': [2**x for x in range(12)]},
# YS changed from range(12)
'params': {
'SVM__max_iter': [4**x for x in range(6)]
},
}
# NOTE: If this is causing issues, try the RBFSVMLearner. Passing use_linear=True will use a linear kernel
# and passing use_linear=False will use the RBF kernel. This method is slower but if libsvm is not
# available it may be your only option
learner = learners.LinearSVMLearner(dual=False)
if best_params_linear is not None:
learner.set_params(**best_params_linear)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVMLinear',
'SVM',
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_linear,
threads=self._details.threads,
verbose=self._verbose)
of_params = best_params.copy()
learner = learners.LinearSVMLearner(dual=True)
if best_params_linear is not None:
learner.set_params(**best_params_linear)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVMLinear_OF',
'SVM',
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_linear,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
# RBF SVM
params = {
'SVM__max_iter': iters,
'SVM__tol': tols,
'SVM__class_weight': ['balanced'],
'SVM__C': C_values,
'SVM__decision_function_shape': ['ovo', 'ovr'],
'SVM__gamma': gamma_fracs
}
# original
# complexity_param = {'name': 'SVM__C', 'display_name': 'Penalty', 'values': np.arange(0.001, 2.5, 0.1)}
# YS changed
complexity_param = {
'name': 'SVM__C',
'display_name': 'Penalty',
'values': np.arange(0.1, 2.5, 0.5)
}
learner = learners.SVMLearner(kernel='rbf')
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF',
'SVM',
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose)
of_params = best_params.copy()
learner = learners.SVMLearner(kernel='rbf')
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF_OF',
'SVM',
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
def perform(self):
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-1/blob/master/SVM.py
samples = self._details.ds.features.shape[0]
features = self._details.ds.features.shape[1]
gamma_fracs = np.arange(1 / features, 2.1, 0.2)
tols = np.arange(1e-8, 1e-1, 0.01)
C_values = np.arange(0.001, 2.5, 0.25)
iters = [-1, int((1e6 / samples) / .8) + 1]
best_params_linear = None
if self._details.ds_best_params is not None and 'SVM_Linear' in self._details.ds_best_params:
best_params_linear = self._details.ds_best_params['SVM_Linear']
best_params_rbf = None
if self._details.ds_best_params is not None and 'SVM_RBF' in self._details.ds_best_params:
best_params_rbf = self._details.ds_best_params['SVM_RBF']
# Linear SVM
params = {
'SVM__max_iter': iters,
'SVM__tol': tols,
'SVM__class_weight': ['balanced'],
'SVM__C': C_values
}
complexity_param = {
'name': 'SVM__C',
'display_name': 'Penalty',
'values': np.arange(0.001, 2.5, 0.1)
}
iteration_details = {
'x_scale': 'log',
'params': {
'SVM__max_iter': [2**x for x in range(12)]
},
}
# RBF SVM
if len(np.unique(self._details.ds.classes)) > 2:
decision_functions = ['ovo']
else:
decision_functions = ['ovo', 'ovr']
params = {
'SVM__max_iter': iters,
'SVM__tol': tols,
'SVM__class_weight': ['balanced'],
'SVM__C': C_values,
'SVM__decision_function_shape': decision_functions,
'SVM__gamma': gamma_fracs
}
complexity_param = {
'name': 'SVM__C',
'display_name': 'Penalty',
'values': np.arange(0.001, 2.5, 0.1)
}
learner = learners.SVMLearner(kernel='rbf')
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
best_params = experiments.perform_experiment(
self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF',
'SVM',
params,
complexity_param=complexity_param,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose)
of_params = best_params.copy()
learner = learners.SVMLearner(kernel='rbf')
if best_params_rbf is not None:
learner.set_params(**best_params_rbf)
experiments.perform_experiment(self._details.ds,
self._details.ds_name,
self._details.ds_readable_name,
learner,
'SVM_RBF_OF',
'SVM',
of_params,
seed=self._details.seed,
iteration_details=iteration_details,
best_params=best_params_rbf,
threads=self._details.threads,
verbose=self._verbose,
iteration_lc_only=True)
