def Log_regression_our_method(X_train, X_test, y_train, y_test):
for k in xrange(1):
# Reduce dimensions in the data
from distanceHDR import dim_reduction, dim_reduction_test
Level, Train = dim_reduction(X_train,
i_dim=X_train.shape[1],
o_dim=2,
g_size=2)
Test = dim_reduction_test(X_test,
Level,
i_dim=X_train.shape[1],
o_dim=2,
g_size=2)
y_train = y_train.reshape(-1)
y_test = y_test.reshape(-1)
# Classification
inputs = Train.shape[1]
classes = int(max(y_train))
y = tflearn.data_utils.to_categorical(y_train - 1, classes)
yT = tflearn.data_utils.to_categorical(y_test - 1, classes)
# classification
model = classification(Train,
y,
Test,
yT,
iterate=400,
classes=classes)
return C
from scipy import stats
# Gather the normal and the test samples from the data
N = extract_samples(train_dataset, train_labels, 1);
T = extract_samples(train_dataset, train_labels, 4);
temp_scalar = preprocessing.StandardScaler(with_mean = True, with_std = True).fit(N)
N = temp_scalar.transform(N)
T = temp_scalar.transform(T)
import time
start = time.time()
# # 1 -- Dimension reduction
# Ref, Tree = initialize_calculation(T = None, Data = Xtr_s, gsize = 2,\
# par_train = 0, output_dimension = 4)
# # Test, Tree = initialize_calculation(T = Tree, Data = Xte_s, gsize = 2,\
# # par_train = 1, output_dimension = 4)
# 2 - Second type of dimension reduction
from distanceHDR import dim_reduction, dim_reduction_test
start = time.time()
Level, Ref = dim_reduction(N, i_dim=N.shape[1], o_dim=5, g_size=2)
# print(stats.describe(Ref))
Test = dim_reduction_test(T, Level, i_dim=T.shape[1], o_dim=5, g_size=2)
print("The time elapsed is", time.time()-start)
print("\nRef", Ref.shape, "Test", Test.shape)
print("\nref-ref", traditional_MTS(Ref, Ref, 0).mean())
print("\nref-test", traditional_MTS(Ref, Test, 0).mean())
from sklearn import preprocessing
train_dataset = preprocessing.scale(train_dataset)
valid_dataset = preprocessing.scale(valid_dataset)
test_dataset = preprocessing.scale(test_dataset)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
# 1 - Nonlinear Dimension reduction
from distanceHDR import dim_reduction, dim_reduction_test
Train = train_dataset
Test = test_dataset
print("New dimension reduction")
start = time.time()
Level, Train = dim_reduction(train_dataset,
i_dim=train_dataset.shape[1],
o_dim=20,
g_size=2)
print("Time elapsed", start - time.time())
print("Dimension reduced shape", Train.shape)
## Linear Discriminant Analysis
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
lda = LinearDiscriminantAnalysis(solver="svd", store_covariance=True)
y_pred = lda.fit(Train, train_labels).predict(Train)
print("1 -- LDA")
from sklearn.metrics import accuracy_score
print(accuracy_score(train_labels, y_pred, normalize=True, sample_weight=None))
qda = QuadraticDiscriminantAnalysis(store_covariances=True)
y_pred = qda.fit(Train, train_labels).predict(Train)
dataset = "sensorless"
No, y_train, T, y_test = Paper_res_v1.import_pickled_data(dataset)
# Transform the train data-set
scaler = preprocessing.StandardScaler(with_mean = True,\
with_std = True).fit(No)
X_train = scaler.transform(No)
X_test = scaler.transform(T)
N = 1
sco = np.zeros((1, 9))
print("DR is NDR")
n_comp = 4
g_size = 2
for i in tqdm(xrange(N)):
#from distanceHDR import dim_reduction, dim_reduction_test
Level, Train = dim_reduction(X_train,
i_dim=X_train.shape[1],
o_dim=n_comp,
g_size=g_size)
Test = dim_reduction_test(X_test,
Level,
i_dim=X_train.shape[1],
o_dim=n_comp,
g_size=g_size)
sco[i, :] = comparison_class(Train, y_train, Test, y_test)
print("p-values are")
print(sco)
def dim_reduction_comparison(dataset, n_comp, g_size):
N, y_train, T, y_test = import_pickled_data(dataset)
name_1 = ["PCA", "ISOMAP", "LLE", "FA", "KPCA"]
dims =[PCA(n_components=n_comp, \
copy=False, whiten=True, \
svd_solver='auto', tol=0.00001, iterated_power='auto', random_state=None),
Isomap(n_neighbors=n_comp, n_components=10, eigen_solver='auto',\
tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=1),
LocallyLinearEmbedding(n_neighbors=5, \
n_components=n_comp, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, \
method='standard', hessian_tol=0.0001, modified_tol=1e-12, \
neighbors_algorithm='auto', random_state=None, n_jobs=1),
FactorAnalysis(n_components= n_comp, tol=0.01, \
copy=True, max_iter=1000, noise_variance_init=None,\
svd_method='randomized', iterated_power=3, random_state=0),
KernelPCA(n_components= n_comp, kernel='linear', gamma=None, degree=3, \
coef0=1, kernel_params=None, alpha=1.0, \
fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None,\
remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=1),
]
# Transform the train data-set
scaler = preprocessing.StandardScaler(with_mean = True,\
with_std = True).fit(N)
X_train = scaler.transform(N)
X_test = scaler.transform(T)
N = 1
for n, clf in zip(name_1, dims):
scores = np.zeros((N, 9))
print("DR is", n)
for i in tqdm(xrange(N)):
Train = clf.fit_transform(X_train)
Test = clf.transform(X_test)
names, scores[i, :] = comparison_class(Train, y_train, Test,
y_test)
np.savetxt(str(n) + str(dataset) + ".csv", scores)
print("score is", scores)
scores = np.zeros((N, 9))
print("DR is NDR")
for i in tqdm(xrange(N)):
#from distanceHDR import dim_reduction, dim_reduction_test
Level, Train = dim_reduction(X_train,
i_dim=X_train.shape[1],
o_dim=n_comp,
g_size=g_size)
Test = dim_reduction_test(X_test,
Level,
i_dim=X_train.shape[1],
o_dim=n_comp,
g_size=g_size)
names, scores[i, :] = comparison_class(Train, y_train, Test, y_test)
print(scores)
np.savetxt("NDR" + str(dataset) + ".csv", scores)
def comparson_dataset():
## Lets define define an array that can define dimensions
D = [800, 1000, 1600, 3200, 6400, 10000]
name_1 = ["PCA", "ISOMAP", "LLE", "FA", "KPCA"]
dims =[PCA(n_components=9, \
copy=False, whiten=True, \
svd_solver='auto', tol=0.00001, iterated_power='auto', random_state=None),
Isomap(n_neighbors=9, n_components=10, eigen_solver='auto',\
tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=1),
LocallyLinearEmbedding(n_neighbors=5, \
n_components=9, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, \
method='standard', hessian_tol=0.0001, modified_tol=1e-12, \
neighbors_algorithm='auto', random_state=None, n_jobs=1),
FactorAnalysis(n_components= 9, tol=0.01, \
copy=True, max_iter=1000, noise_variance_init=None,\
svd_method='randomized', iterated_power=3, random_state=0),
KernelPCA(n_components= 9, kernel='linear', gamma=None, degree=3, \
coef0=1, kernel_params=None, alpha=1.0, \
fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None,\
remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=1),
]
for element in D:
X_train, X_test, y_train, y_test = generate_new_data(
(1000 + (element * 2)), element, n_inf=4)
start = time.time()
# Transform the train data-set
scaler = preprocessing.StandardScaler(with_mean = True,\
with_std = True).fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
N = 2
p = 0
Time = np.zeros((N, len(dims) + 1))
for n, clf in zip(name_1, dims):
scores = np.zeros((N, 9))
print("DR is", n)
for i in tqdm(xrange(N)):
start = time.time()
Train = clf.fit_transform(X_train)
Test = clf.transform(X_test)
names, scores[i, :] = comparison_class(Train, y_train, Test,
y_test)
Time[i, p] = time.time() - start
p = p + 1
np.savetxt(str(n) + str(element) + "acc.csv", scores)
print("The value of p after the first set", p)
names.append("NDR")
scores = np.zeros((N, 9))
for i in tqdm(xrange(N)):
#from distanceHDR import dim_reduction, dim_reduction_test
start = time.time()
Level, Train = dim_reduction(X_train,
i_dim=X_train.shape[1],
o_dim=4,
g_size=2)
Test = dim_reduction_test(X_test,
Level,
i_dim=X_train.shape[1],
o_dim=4,
g_size=2)
print("Train shape", Train.shape, "Test shape", Test.shape)
names, scores[i, :] = comparison_class(Train, y_train, Test,
y_test)
Time[i, p] = time.time() - start
np.savetxt(str(element) + "Time.csv", Time)
