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):
# 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):
# 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/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/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)
