def run_prob2a_tree(single_node_cliques=True):
(ubg, data) = create_prob2a_tree(single_node_cliques)
ipf = IPF(ubg, data)
for k in range(5):
ipf.iterate()
return ipf
def run_simple_graph():
(ubg, data) = create_simple_graph()
ipf = IPF(ubg, data)
ipf.iterate()
ipf.iterate()
return ipf
def generate(self, beta, tol=1e-3):
"""
Generates a single instance of a synthetic traffic matrix based on noise
parameter beta.
Example:
>>print(sanm.generate(0.1))
[[ 0.19755992 0.40244008]
[ 0.20244008 0.89755992]]
:param beta: noise strength parameter
:param tol: tolerance for IPF's scaling
:return: a single instance of a synthetic traffic matrix
"""
tm_size = self.predicted.shape
tm_generated = np.zeros(tm_size)
# SANM
for i in range(tm_generated.shape[0]):
for j in range(tm_generated.shape[1]):
tm_generated[i, j] = (np.sqrt(self.predicted[i, j]) + beta*gauss(0, 1))**2
# run IPF
IPF().run(tm_generated, self.row_sums, self.col_sums, tol=tol)
return tm_generated
def Pipeline(X_train, y_train, X_test, n_dims=44):
id_train = np.array(X_train["id"])
X_train = X_train.drop(columns=["id"])
id_test = np.array(X_test["id"])
X_test = X_test.drop(columns=["id"])
X_train = np.array(X_train)
y_train = np.array(y_train)
X_test = np.array(X_test)
ind_numeric = []
for i in range(len(X_train[0])):
if len(np.unique(X_train[:, i])) > 2:
ind_numeric.append(i)
print("Hay " + str(len(ind_numeric)) + " variables numericas")
'''
ind_delete = np.where(y_train=="functional needs repair")[0]
y_train = np.delete(y_train, ind_delete, axis=0)
X_train = np.delete(X_train, ind_delete, axis=0)
'''
#plotData(X_train, y_train, "raw")
print("Scaling data...")
X_train = preprocessing.scale(X_train)
X_test = preprocessing.scale(X_test)
#plotData(X_train, y_train, "scaled")
print("PCA con " + str(n_dims) + " componentes...")
X_train_binary = np.delete(X_train, ind_numeric, axis=1)
X_test_binary = np.delete(X_test, ind_numeric, axis=1)
X_train_numeric = X_train[:, ind_numeric]
X_test_numeric = X_test[:, ind_numeric]
pca = PCA(n_components=n_dims)
#pca = KernelPCA(n_components=n_dims, kernel="linear", n_jobs=-1)
X1 = pca.fit_transform(X_train_binary)
X2 = pca.transform(X_test_binary)
X_train = np.hstack((X_train_numeric, X1))
X_test = np.hstack((X_test_numeric, X2))
print("Numero de features: " + str(len(X_train[0])))
#plotData(X_train, y_train, "PCA")
'''
print("Reduccion de dimensionalidad con AutoEncoder...")
hid = [50,60,50]
X_train, X_test = autoencoder.fitTransform(X_train, X_test, 50, hid, bsize=32)
print("Numero de features: " + str(len(X_train[0])))
'''
'''
print("Reduccion de dimensionalidad con AutoEncoder...")
hid = [250,200,150,100,50]
X_train_binary = np.delete(X_train, ind_numeric, axis=1)
X_test_binary = np.delete(X_test, ind_numeric, axis=1)
X_train_numeric = X_train[:,ind_numeric]
X_test_numeric = X_test[:,ind_numeric]
X1, X2 = autoencoder.fitTransform(X_train_binary, X_test_binary, 30, hid, bsize=32)
X_train = np.hstack((X_train_numeric, X1))
X_test = np.hstack((X_test_numeric, X2))
print("Numero de features: " + str(len(X_train[0])))
'''
print("IPF...")
X_train, y_train = IPF(X_train, y_train)
print("Numero de instancias: " + str(len(X_train)))
print("Instancias por clase:")
print(np.unique(y_train, return_counts=True))
#plotData(X_train, y_train, "IPF")
'''
print("Denoising autoencoder...")
hid = [32,16,32]
X_train, X_test = autoencoder_denoising.fitTransform(X_train, X_test, 250, hid, bsize=32, kreg=None, areg=None)
'''
'''
print("AllKNN...")
X_train, y_train = AllKNN(n_neighbors=7, n_jobs=8).fit_resample(X_train, y_train)
print("Numero de instancias: " + str(len(X_train)))
print("Instancias por clase:")
print(np.unique(y_train,return_counts=True))
'''
'''
print("Feature selection...")
feature_selector = SelectKBest(f_classif, k="all").fit(X_train, y_train)
X_train = feature_selector.transform(X_train)
X_test = feature_selector.transform(X_test)
print("Numero de features: " + str(len(X_train[0])))
'''
print("SMOTE...")
X_train, y_train = SMOTE(sampling_strategy={
"functional needs repair": 7500,
"non functional": 22000
},
random_state=123456789,
n_jobs=20,
k_neighbors=7).fit_resample(X_train, y_train)
print("Numero de instancias: " + str(len(X_train)))
print("Instancias por clase:")
print(np.unique(y_train, return_counts=True))
#plotData(X_train, y_train, "SMOTE")
'''
print("ADASYN...")
X_train,y_train = ADASYN(sampling_strategy = {"functional needs repair": 5000, "non functional": 22500}, random_state=123456789, n_jobs=8, n_neighbors=7).fit_resample(X_train,y_train)
print("Numero de instancias: " + str(len(X_train)))
print("Instancias por clase:")
print(np.unique(y_train,return_counts=True))
'''
print("Cleaning anomalies...")
ind_functional = np.where(y_train == "functional")[0]
ind_non_functional = np.where(y_train == "non functional")[0]
ind_functional_repair = np.where(y_train == "functional needs repair")[0]
X1, y1 = cleanAnomalies(X_train[ind_functional], y_train[ind_functional])
X2, y2 = cleanAnomalies(X_train[ind_non_functional],
y_train[ind_non_functional])
X3, y3 = cleanAnomalies(X_train[ind_functional_repair],
y_train[ind_functional_repair])
X_train = np.concatenate((X1, X2), axis=0)
X_train = np.concatenate((X_train, X3), axis=0)
y_train = np.concatenate((y1, y2), axis=0)
y_train = np.concatenate((y_train, y3), axis=0)
print("Instancias por clase:")
print(np.unique(y_train, return_counts=True))
#plotData(X_train, y_train, "anomalias_knn")
'''
print("EditedNearestNeighbours...")
X_train, y_train = EditedNearestNeighbours(sampling_strategy="not minority", n_neighbors=15, n_jobs=20, kind_sel="mode").fit_resample(X_train, y_train)
print("Numero de instancias: " + str(len(X_train)))
print("Instancias por clase:")
print(np.unique(y_train,return_counts=True))
'''
'''
print("SSMA...")
selector = SSMA(n_neighbors=1, alpha=0.95, max_loop=10, initial_density=0.9).fit(X_train,y_train)
X_train = selector.X_
y_train = selector.y_
print("Numero de instancias: " + str(len(X_train)))
print("Instancias por clase:")
print(np.unique(y_train,return_counts=True))
'''
'''
print("Generando la métrica con DML...")
train_set, _, train_labels, _ = train_test_split(X_train, y_train, train_size=0.5, random_state=123456789)
print("Tamaño del conjunto original: " + str(len(X_train)) + ", tamaño del train: " + str(len(train_set)))
dml = KLMNN().fit(train_set, train_labels)
X_train = dml.transform(X_train)
X_test = dml.transform(X_test)
'''
return X_train, y_train, id_train, X_test, id_test
