def test_kmean():
train_set, valid_set, test_set = DatasetManager.read_dataset(dataset_name="dataset_simulation_20.csv", shared=False)
kmean = KMean()
_clusters, _centers = kmean.run(
dataset=train_set[0],
n_clusters=5,
max_iters=100,
threshold=1.0
)
assert _clusters
assert _centers is not None
def run_experiment():
from datasets import DatasetManager
from preprocessing.scaling import get_gaussian_normalization
from preprocessing.dimensionality_reduction import get_pca
train_set, valid_set, test_set = DatasetManager.read_dataset()
dataset, result = train_set
# Reduce to a 2D dimensionality for plotting the data
dataset = get_gaussian_normalization(dataset)
# dataset = get_LLE(dataset, num_components=2, n_neighbors=80)
dataset, explained_variance_ratio_ = get_pca(dataset, num_components=2)
plot_embedding(dataset, result)
def theano_experiments():
dataset_name = 'cinvestav_labeled.csv'
seed = 5
rgn = numpy.random.RandomState(seed)
datasets = DatasetManager.read_dataset(
dataset_name=os.path.join(os.path.dirname(__file__), 'dataset', 'meters', dataset_name),
shared=True,
seed=seed,
expected_output=['result_x', 'result_y'],
skipped_columns=[],
label_encoding_columns_name=[],
sklearn_preprocessing=preprocessing.StandardScaler(with_mean=True, with_std=True),
sklearn_feature_selection=feature_selection.VarianceThreshold(),
train_ratio=.8,
test_ratio=0,
valid_ratio=.2
)
test_set = DatasetManager.get_prediction_set(
dataset_name=os.path.join(os.path.dirname(__file__), 'dataset', 'meters', 'cinvestav_labeled_test.csv'),
expected_output=['result_x', 'result_y'],
label_encoding_columns_name=[],
skipped_columns=[],
sklearn_preprocessing=datasets['sklearn_preprocessing'],
sklearn_feature_selection=datasets['sklearn_feature_selection'],
shared=True
)
dataset_unlabeled = DatasetManager.get_prediction_set(
dataset_name=os.path.join(os.path.dirname(__file__), "dataset", 'cinvestav_unlabeled.csv'),
skipped_columns=['result_x', 'result_y'],
label_encoding_columns_name=[],
sklearn_preprocessing=datasets['sklearn_preprocessing'],
sklearn_feature_selection=datasets['sklearn_feature_selection'],
shared=True
)
datasets['test_set'] = test_set
datasets['dataset_unlabeled'] = dataset_unlabeled
datasets['prediction_set'] = datasets['test_set'][0].get_value()
train_set_x, train_set_y = datasets['train_set']
n_in = train_set_x.get_value().shape[1]
n_out = train_set_y.get_value().shape[1]
dnn_tanh_models = get_neural_networks(
n_in,
n_out,
rgn,
activation_function=T.tanh # T.nnet.relu
)
dnn_relu_models = get_neural_networks(
n_in,
n_out,
rgn,
activation_function=T.nnet.relu
)
dnn_sigmoid_models = get_neural_networks(
n_in,
n_out,
rgn,
activation_function=T.nnet.sigmoid
)
dbn_models = get_dbn(
n_in,
n_out,
rgn,
gaussian=False
)
gdbn_models = get_dbn(
n_in,
n_out,
rgn,
gaussian=True
)
models = []
models.extend(dnn_relu_models)
models.extend(dnn_sigmoid_models)
models.extend(dnn_tanh_models)
models.extend(gdbn_models)
models.extend(dbn_models)
params = {
'learning_rate': .01,
'annealing_learning_rate': .99999,
'l1_learning_rate': 0.01,
'l2_learning_rate': 0.001,
'n_epochs': 2000,
'batch_size': 20,
'pre_training_epochs': 50,
'pre_train_lr': 0.01,
'k': 1,
'datasets': datasets,
'noise_rate': .1,
'dropout_rate': None
}
run_theano_experiments(
models=models,
seed=seed,
params=params,
experiment_name='all_models_with_noise_without_dropout',
task_type='regression'
)
def sklearn_experiments():
dataset_name = 'cinvestav_labeled.csv'
seed = 5
datasets = DatasetManager.read_dataset(
dataset_name=os.path.join(os.path.dirname(__file__), "dataset", dataset_name),
shared=False,
seed=seed,
expected_output=['result_x', 'result_y'],
skipped_columns=[],
label_encoding_columns_name=[],
sklearn_preprocessing=preprocessing.StandardScaler(with_mean=True, with_std=True),
sklearn_feature_selection=feature_selection.VarianceThreshold(),
train_ratio=1,
test_ratio=0,
valid_ratio=0
)
test_set = DatasetManager.get_prediction_set(
dataset_name=os.path.join(os.path.dirname(__file__), "dataset", 'cinvestav_labeled_test.csv'),
expected_output=['result_x', 'result_y'],
label_encoding_columns_name=[],
skipped_columns=[],
shared=False,
sklearn_preprocessing=datasets['sklearn_preprocessing'],
sklearn_feature_selection=datasets['sklearn_feature_selection'],
)
datasets['test_set'] = test_set
datasets['prediction_set'] = datasets['test_set'][0]
train_set_x, train_set_y = datasets['train_set']
n_in = train_set_x.shape[1]
n_out = train_set_y.shape[1]
# Create Radial Basis Networks
rbf = RBF(
input_length=n_in,
hidden_length=500,
out_lenght=n_out
)
# Create KNN
knn = SklearnNetwork(
sklearn_model=KNeighborsRegressor(n_neighbors=10),
num_output=n_out
)
# Create ada boosting
ada_boosting = SklearnNetwork(
sklearn_model=GradientBoostingRegressor(n_estimators=1000, learning_rate=.1, max_depth=5, loss='ls'),
num_output=n_out
)
models = [
('Ada Boosting', ada_boosting),
('Radar', knn),
('cRBF', rbf)
]
params = {
'datasets': datasets
}
run_experiments_sklearn(
models=models,
seed=seed,
params=params,
experiment_name='traditional_algorithms',
task_type='regression'
)
