def perform_rca(data):
new_training_features = None
new_test_features = None
try:
model = random_projection.GaussianRandomProjection(random_state=7)
best_name = comparison_generation.create_attribute_storage_name(
data, model)
best = io_manager.load_best(best_name)
comparison_generation.update_model(model, best)
if not io_manager.is_converged(best, best_name):
best[
'n_components'] = comparison_generation.compare_cluster_numbers(
model,
data,
'n_components',
intervals=5,
interval_size=1,
start_index=2)
comparison_generation.update_model(model, best)
io_manager.save_best(best, best_name)
io_manager.update_convergence(best, best_name)
comparison_generation.plot_reduction(model, data)
training_features = data['training_features']
test_features = data['test_features']
new_training_features = pd.DataFrame(
model.transform(training_features))
new_test_features = pd.DataFrame(model.transform(test_features))
except Exception as ex:
print(ex)
traceback.print_exc()
return new_training_features, new_test_features
def perform_ica(data):
new_training_features = None
new_test_features = None
try:
model = decomposition.FastICA()
best_name = comparison_generation.create_attribute_storage_name(
data, model)
best = io_manager.load_best(best_name)
comparison_generation.update_model(model, best)
if not io_manager.is_converged(best, best_name):
best[
'n_components'] = comparison_generation.compare_mixing_numbers(
model,
data,
'n_components',
intervals=5,
interval_size=1,
start_index=2)
comparison_generation.update_model(model, best)
values = ['parallel', 'deflation']
best['algorithm'] = comparison_generation.compare_mixing_values(
model, data, 'algorithm', values)
comparison_generation.update_model(model, best)
best['max_iter'] = comparison_generation.compare_mixing_numbers(
model,
data,
'max_iter',
intervals=10,
interval_size=20,
start_index=20)
comparison_generation.update_model(model, best)
io_manager.save_best(best, best_name)
io_manager.update_convergence(best, best_name)
comparison_generation.plot_reduction(model, data)
training_features = data['training_features']
test_features = data['test_features']
new_training_features = pd.DataFrame(
model.transform(training_features))
new_test_features = pd.DataFrame(model.transform(test_features))
print("For ICA, how kurtotic are the distributions?")
model_name = type(model).__name__
print(comparison_generation.create_title(data, model_name) \
+ str(scipy.stats.kurtosis(new_training_features)))
except Exception as ex:
print(ex)
traceback.print_exc()
return new_training_features, new_test_features
def perform_pca(data):
new_training_features = None
new_test_features = None
try:
model = decomposition.PCA(random_state=7)
best_name = comparison_generation.create_attribute_storage_name(
data, model)
best = io_manager.load_best(best_name)
comparison_generation.update_model(model, best)
if not io_manager.is_converged(best, best_name):
best[
'n_components'] = comparison_generation.compare_explained_variance_numbers(
model,
data,
'n_components',
intervals=6,
interval_size=1,
start_index=2)
comparison_generation.update_model(model, best)
values = [False, True]
best[
'whiten'] = comparison_generation.compare_explained_variance_values(
model, data, 'whiten', values)
comparison_generation.update_model(model, best)
values = ['auto', 'full', 'arpack', 'randomized']
best[
'svd_solver'] = comparison_generation.compare_explained_variance_values(
model, data, 'svd_solver', values)
comparison_generation.update_model(model, best)
io_manager.save_best(best, best_name)
io_manager.update_convergence(best, best_name)
comparison_generation.plot_reduction(model, data)
training_features = data['training_features']
test_features = data['test_features']
new_training_features = pd.DataFrame(
model.transform(training_features))
new_test_features = pd.DataFrame(model.transform(test_features))
model_name = comparison_generation.get_model_name(model)
title = comparison_generation.create_title(data, model_name)
print(title)
print(model.explained_variance_)
except Exception as ex:
print(ex)
traceback.print_exc()
return new_training_features, new_test_features
def perform_expectation_maximization(data):
new_training_features = None
new_test_features = None
try:
model = mixture.GaussianMixture(random_state=7, reg_covar=1e10)
best_name = comparison_generation.create_attribute_storage_name(
data, model)
best = io_manager.load_best(best_name)
comparison_generation.update_model(model, best)
if not io_manager.is_converged(best, best_name):
best['n_clusters'] = comparison_generation.compare_mean_numbers(
model,
data,
'n_components',
intervals=5,
interval_size=1,
start_index=2)
comparison_generation.update_model(model, best)
best['reg_covar'] = comparison_generation.compare_mean_numbers(
model,
data,
'reg_covar',
intervals=10,
interval_size=1e-7,
start_index=1e-7)
comparison_generation.update_model(model, best)
io_manager.update_convergence(best, best_name)
io_manager.save_best(best, best_name)
comparison_generation.plot_reduction(model, data)
training_features = data['training_features']
training_classes = data['training_classes']
test_features = data['test_features']
new_training_features = pd.DataFrame(
model.sample(len(training_features))[0])
new_test_features = pd.DataFrame(model.sample(len(test_features))[0])
except Exception as ex:
print(ex)
traceback.print_exc()
return new_training_features, new_test_features