def test_solve_primal_l2_svc_with_line_search_optimizers():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(SVC(loss=squared_hinge, optimizer=SteepestGradientDescent))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=squared_hinge, optimizer=ConjugateGradient))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=squared_hinge, optimizer=Newton))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=squared_hinge, optimizer=BFGS))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
def test_solve_dual_l2_svc_with_AdaGrad():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(
SVC(loss=squared_hinge,
kernel=gaussian,
reg_intercept=True,
dual=True,
optimizer=AdaGrad,
learning_rate=1.))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
svc = OVR(
SVC(loss=squared_hinge,
kernel=gaussian,
reg_intercept=False,
dual=True,
optimizer=AdaGrad,
learning_rate=1.))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
def test_solve_dual_l1_svc_with_proximal_bundle():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(
SVC(loss=hinge,
kernel=gaussian,
reg_intercept=True,
dual=True,
optimizer=ProximalBundle,
max_iter=150))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
svc = OVR(
SVC(loss=hinge,
kernel=gaussian,
reg_intercept=False,
dual=True,
optimizer=ProximalBundle,
max_iter=150))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
def test_solve_dual_l1_svc_with_reg_intercept_with_bcqp_optimizers():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(
SVC(loss=hinge,
kernel=gaussian,
reg_intercept=True,
dual=True,
optimizer=ProjectedGradient))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
svc = OVR(
SVC(loss=hinge,
kernel=gaussian,
reg_intercept=True,
dual=True,
optimizer=ActiveSet))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
svc = OVR(
SVC(loss=hinge,
kernel=gaussian,
reg_intercept=True,
dual=True,
optimizer=InteriorPoint))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
svc = OVR(
SVC(loss=hinge,
kernel=gaussian,
reg_intercept=True,
dual=True,
optimizer=FrankWolfe))
svc = svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
def test_solve_dual_l1_svc_with_smo():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(SVC(loss=hinge, kernel=gaussian, dual=True, optimizer='smo'))
svc.fit(X_train, y_train)
assert svc.score(X_test, y_test) >= 0.97
def create_binary_multi_model():
DOOM = 42
#for small dataset drop these columns
def drop(df):
for col in list(df):
if 'src' in col or 'dst' in col or 'id' in col:
df.drop(col, axis=1, inplace=True)
path = 'DATA'
df = pd.read_csv('model_data/DATA/trainb.csv')
df = shuffle(df)
print('\nabsolute correlation :')
print(df.corr()[[
'label'
]].apply(lambda x: np.round(((x**2)**0.5), 4))['label'].sort_values(
ascending=False).head(100))
drop(df)
Y = df.label
df = df.drop('label', axis=1)
df = df.fillna(-1)
print(df.shape)
clf = lr(random_state=DOOM, solver='liblinear', penalty='l1', max_iter=50)
clf = clf.fit(df, Y)
filename = 'binary'
pickle.dump(clf, open(filename, 'wb'))
print('roc_auc : ')
print(cross_val_score(clf, df, Y, cv=3, verbose=3, scoring='roc_auc'))
print('Number of nonzero weights : ', sum((clf.coef_ != 0)[0]))
#multi (secondary)
path = 'DATA'
df = pd.read_csv('model_data/DATA/trainl.csv')
df = shuffle(df)
drop(df)
Y = pd.read_csv('model_data/DATA/labels.csv')
df = df.fillna(-1)
print(df.shape)
clf = OVR(
lr(random_state=DOOM, solver='lbfgs', penalty='l2',
max_iter=2000)).fit(df, Y)
filename = 'multi'
pickle.dump(clf, open(filename, 'wb'))
def test_solve_primal_l1_svc_with_stochastic_optimizers():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(SVC(loss=hinge, optimizer=StochasticGradientDescent))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=hinge, optimizer=Adam))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=hinge, optimizer=AMSGrad))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=hinge, optimizer=AdaMax))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=hinge, optimizer=AdaGrad))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=hinge, optimizer=AdaDelta, learning_rate=1.))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
svc = OVR(SVC(loss=hinge, optimizer=RMSProp))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
def test_solve_primal_l1_svc_with_proximal_bundle():
X, y = load_iris(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled,
y,
train_size=0.75,
random_state=123456)
svc = OVR(SVC(loss=hinge, optimizer=ProximalBundle))
svc = svc.fit(X_train, y_train)
assert (np.allclose(np.hstack((estimator.coef_, estimator.intercept_)),
estimator.loss.x_star())
for estimator in svc.estimators_)
assert svc.score(X_test, y_test) >= 0.57
def get_accuracy_report(self,X,y,multiclass=False,test_size=0.1):
if multiclass:
estimator = lambda : OVR(LR(), n_jobs=16)
y_trans = lambda y : y
else:
estimator = lambda : LR(n_jobs=16, max_iter=1000)
y_trans = lambda y : y.argmax(axis=1)
strat = y_trans(y) if not multiclass else None
lr = estimator()
Xtr, Xte, ytr, yte = train_test_split(
X, y_trans(y),
stratify=strat,
test_size=test_size,
random_state=1337
)
lr.fit(Xtr, ytr)
yprime = lr.predict(Xte)
return classification_report(yprime, yte, output_dict=True)
def main():
# load data
# training data
data = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data', 'usps',
'zip.train'),
header=None,
delimiter=' ').iloc[:, :-1]
y_train = data.pop(0).values
X_train = data.values
# test data
data = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data', 'usps',
'zip.test'),
header=None,
delimiter=' ')
y_test = data.pop(0).values
X_test = data.values
pca = PCA(n_components=.95)
pca.fit(X_train)
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)
svm_errs = []
with tqdm(desc="Problem 1", total=len(C_VALS)) as pbar:
for C in C_VALS:
svm = SVC(C=C, kernel='linear', decision_function_shape='ovo')
svm.fit(X_train, y_train)
pbar.update(1)
svm_errs.append(1 - svm.score(X_test, y_test))
lr = OVO(LR(solver='lbfgs', max_iter=5000))
lr.fit(X_train, y_train)
lr_score = lr.score(X_test, y_test)
err_plot([svm_errs], ["SVM"],
lr=1. - lr_score,
title="One vs. One Linear SVM",
out='hw7/ovo_linear_svm.pdf')
ovo_svm_errs = []
with tqdm(desc="Problem 2", total=len(C_VALS)) as pbar:
for C in C_VALS:
svm = OVO(SVC(C=C, kernel='poly', degree=3, gamma='auto'))
svm.fit(X_train, y_train)
pbar.update(1)
ovo_svm_errs.append(1 - svm.score(X_test, y_test))
err_plot([ovo_svm_errs], ["OvO SVM"],
lr=1. - lr_score,
title="One vs. One Cubic SVM",
out='hw7/ovo_cubic_svm.pdf')
ovr_svm_errs = []
with tqdm(desc="Problem 3", total=len(C_VALS)) as pbar:
for C in C_VALS:
svm = OVR(SVC(C=C, kernel='poly', degree=3, gamma='auto'))
svm.fit(X_train, y_train)
pbar.update(1)
ovr_svm_errs.append(1 - svm.score(X_test, y_test))
err_plot([ovo_svm_errs, ovr_svm_errs], ["OvO SVM", "OvR SVM"],
lr=1. - lr_score,
title="One vs. Rest Cubic SVM/OvO Cubic",
out='hw7/ovr_cubic_svm.pdf')
n = 5
# ensuring that we have at least n neighbors for all classes in the
# sample
while True:
index = np.random.choice(X_train.shape[0], 100, replace=False)
X_sample = X_train[index]
y_sample = y_train[index]
# can use a list comprehension to check
if all([
len(X_sample[y_sample == y_i]) >= n
for y_i in np.unique(y_sample)
]):
break
dists = []
for X_i, y_i in zip(X_sample, y_sample):
X_cls = X_sample[y_sample == y_i]
nbrs = NearestNeighbors(n_neighbors=n)
nbrs.fit(X_cls)
try:
distances, _ = nbrs.kneighbors(X_i.reshape(1, -1))
except ValueError as err:
raise err
# nee to use reshape b/c single sample
dists.append(distances[-1])
global SIGMA
SIGMA = np.mean(dists)
ovo_gauss_svm_errs = []
with tqdm(desc="Problem 4 (SVM)", total=len(C_VALS),
file=sys.stdout) as pbar:
for C in C_VALS:
svm = OVO(SVC(C=C, kernel='rbf', gamma=1. / (2. * SIGMA**2)))
# svm = SVC(C=C, kernel='rbf', gamma=1. / (2. * SIGMA ** 2),
# decision_function_shape='ovo')
svm.fit(X_train, y_train)
score = svm.score(X_test, y_test)
pbar.update(1)
ovo_gauss_svm_errs.append(1 - score)
knn_errs = []
with tqdm(desc="Problem 4 (kNN)",
total=len(np.arange(3, 11)),
file=sys.stdout) as pbar:
for k in np.arange(3, 11):
knn = KNeighborsClassifier(n_neighbors=k, weights=gaussian)
knn.fit(X_train, y_train)
pbar.update(1)
knn_errs.append((k, 1 - knn.score(X_test, y_test)))
err_plot([ovo_gauss_svm_errs], ["OvO SVM"],
knn=knn_errs,
title="One vs. One Gaussian SVM with kNN",
out='hw7/ovo_gaussian_svm_knn.pdf')
ovr_gauss_svm_errs = []
with tqdm(desc="Problem 5", total=len(C_VALS), file=sys.stdout) as pbar:
for C in C_VALS:
svm = OVR(SVC(C=C, kernel='rbf', gamma=1. / (2. * SIGMA**2)))
# svm = SVC(C=C, kernel='rbf', gamma=1. / (2. * SIGMA ** 2),
# decision_function_shape='ovr')
svm.fit(X_train, y_train)
score = svm.score(X_test, y_test)
pbar.update(1)
ovr_gauss_svm_errs.append(1 - score)
err_plot([ovr_gauss_svm_errs], ["OvR SVM"],
knn=knn_errs,
title="One vs. Rest Gaussian SVM with kNN",
out='hw7/ovr_gaussian_svm_knn.pdf')
err_plot([
svm_errs, ovo_svm_errs, ovr_svm_errs, ovo_gauss_svm_errs,
ovr_gauss_svm_errs
], [
"Linear SVM", "OvO Cubic SVM", "OvR Cubic SVM", "OvO Gaussian SVM",
"OvR Gaussian SVM"
],
lr=1. - lr_score,
knn=knn_errs,
title="Multiclass SVM Kernels",
out='hw7/all_svm_knn.pdf')
min_idx = np.argmin(svm_errs)
min_lin_err = svm_errs[min_idx]
min_lin_c = np.log2(C_VALS[min_idx])
print("Min Linear SVM Error = {0:.4f}".format(min_lin_err))
print("Min Linear SVM log2(C) = {0}".format(min_lin_c))
print("LR Error = {0:.4f}".format(1. - lr_score))
min_idx = np.argmin(ovo_svm_errs)
min_lin_err = ovo_svm_errs[min_idx]
min_lin_c = np.log2(C_VALS[min_idx])
print("Min OvO Cubic SVM Error = {0:.4f}".format(min_lin_err))
print("Min OvO Cubic SVM log2(C) = {0}".format(min_lin_c))
min_idx = np.argmin(ovr_svm_errs)
min_lin_err = ovr_svm_errs[min_idx]
min_lin_c = np.log2(C_VALS[min_idx])
print("Min OvR Cubic SVM Error = {0:.4f}".format(min_lin_err))
print("Min OvR Cubic SVM log2(C) = {0}".format(min_lin_c))
min_idx = np.argmin(knn_errs)
min_lin_k, min_lin_err = knn_errs[min_idx]
print("Min kNN Error = {0:.4f}".format(min_lin_err))
print("Min kNN log2(C) = {0}".format(min_lin_k))
min_idx = np.argmin(ovo_gauss_svm_errs)
min_lin_err = ovo_gauss_svm_errs[min_idx]
min_lin_c = np.log2(C_VALS[min_idx])
print("Min OvO Gaussian SVM Error = {0:.4f}".format(min_lin_err))
print("Min OvO Gaussian SVM log2(C) = {0}".format(min_lin_c))
min_idx = np.argmin(ovr_gauss_svm_errs)
min_lin_err = ovr_gauss_svm_errs[min_idx]
min_lin_c = np.log2(C_VALS[min_idx])
print("Min OvR Gaussian SVM Error = {0:.4f}".format(min_lin_err))
print("Min OvR Gaussian SVM log2(C) = {0}".format(min_lin_c))
print("sigma = {0:.4f}".format(SIGMA))
def compare_to_random(self, batch, w2v_params={}, multiclass=False, fast_walks=False):
if type(batch) != torch.Tensor and fast_walks:
batch = torch.tensor(batch)
# Generate policy guided walks
print("Generating policy guided walks")
if fast_walks:
walks = self.walker.fast_walks(batch, egreedy=True, weighted_rand=False)
else:
walks = None
pX, py = self.encode_nodes(batch=batch, w2v_params=w2v_params, walks=walks)
# Test against policy weighted walks
print("Generating policy weighted walks")
if fast_walks:
walks = self.walker.fast_walks(batch, egreedy=False, weighted_rand=True)
else:
walks = None
wX, wy = self.encode_nodes(batch=batch, weighted=True,
w2v_params=w2v_params, walks=walks)
# Test against random walk embeddings
print("Generating random walks")
if type(batch) != torch.Tensor:
batch = torch.tensor(batch)
walks = self.walker.fast_walks(batch, egreedy=False, weighted_rand=False)
rX, ry = self.encode_nodes(batch=batch, random=True, w2v_params=w2v_params, walks=walks)
if multiclass:
estimator = lambda : OVR(LR(), n_jobs=16)
y_trans = lambda y : y
else:
estimator = lambda : LR(n_jobs=16, max_iter=1000)
y_trans = lambda y : y.argmax(axis=1)
lr = estimator()
Xtr, Xte, ytr, yte = train_test_split(pX, y_trans(py))
lr.fit(Xtr, ytr)
yprime = lr.predict(Xte)
print(yprime)
print("Policy guided:")
print(classification_report(yprime, yte))
lr = estimator()
Xtr, Xte, ytr, yte = train_test_split(wX, y_trans(wy))
lr = OVR(LR(), n_jobs=16)
lr.fit(Xtr, ytr)
yprime = lr.predict(Xte)
print("Policy weighted:")
print(classification_report(yprime, yte))
lr = estimator()
Xtr, Xte, ytr, yte = train_test_split(rX, y_trans(ry))
lr = OVR(LR(), n_jobs=16)
lr.fit(Xtr, ytr)
yprime = lr.predict(Xte)
print("Random walk:")
print(classification_report(yprime, yte))
###### the main code starts here ################
# generate random features
wx = rp(250, [0.2, 2, 20], 1)
wy = rp(250, [0.2, 2, 20], 1)
wz = rp(250, [0.2, 2, 20], 1)
ww = rp(250, [0.2, 2, 20], 3)
# generate training data
print ':: generating triplet training data...'
(X, Y) = tripletset(5000)
# train the classifier and predict the test data
print ':: training the random forest classifier...'
reg = OVR(RFC(n_estimators=1000, random_state=0, n_jobs=24)).fit(X, Y)
(node_labels, final_scores, permute_idx, num_features,
desp) = infer_abalone(reg)
print ':: contructing a DAG...'
G = build_dag(node_labels, final_scores, permute_idx, 0.7)
# save dag in dot format
nx.write_dot(G, "abalone.dot")
# draw dag
#pos = nx.circular_layout(G,scale=3)
#nx.draw(G, pos, cmap=plt.get_cmap('jet'), node_size=2000, node_color='b', alpha=0.95)
#nx.draw_networkx_labels(G, pos, font_color='w')
#nx.draw_networkx_edges(G, pos, arrows=True)
test_data, test_labels = np.array(test_data,
dtype=float), np.array(test_labels,
dtype=int)
avgtrain = np.zeros(2500)
for i in range(2500):
avgtrain[i] = np.mean(train_data[:, i])
avgtest = np.zeros(2500)
for i in range(2500):
avgtest[i] = np.mean(test_data[:, i])
mstrain = []
for i in range(len(train_data)):
mstrain.append(train_data[i] - avgtrain)
mstest = []
for i in range(len(test_data)):
mstest.append(test_data[i] - avgtest)
U, s, V = np.linalg.svd(mstrain)
def eigenface_feature(train_data, test_data, V, r):
return np.dot(train_data, V[:r, :].T), np.dot(test_data, V[:r, :].T)
accuracy = []
for r in range(1, 201):
F, F_test = eigenface_feature(mstrain, mstest, V, r)
ovr = OVR(LR()).fit(F, train_labels)
accuracy.append(ovr.score(F_test, test_labels))
plt.plot(accuracy)
plt.savefig("1h.png")
