metric=opts.metric,
gamma=opts.gamma,
degree=opts.degree,
coef0=opts.coef0,
scale=opts.scale,
n_validate=opts.n_validate,
epsilon=opts.epsilon,
#score_func=f1_score,
verbose=1,
random_state=random_state,
n_jobs=-1)
clf.fit(X_train, y_train)
return clf
X_train, y_train, X_test, y_test, opts, args = parse_kmp()
X_tr, y_tr, X_te, y_te = X_train, y_train, X_test, y_test
clf_r = []
clf_b = []
clf_s = []
for X_tr, y_tr, X_te, y_te in split_data(X_train, y_train,
X_test, y_test,
opts.n_folds,
not opts.regression):
clf_r.append(fit_kmp(X_tr, y_tr, X_te, y_te, "random", opts,
random_state=0))
clf_b.append(fit_kmp(X_tr, y_tr, X_te, y_te, "balanced", opts,
random_state=0))
gamma=opts.gamma,
degree=opts.degree,
coef0=opts.coef0,
scale=opts.scale,
scale_y=opts.scale_y,
check_duplicates=opts.check_duplicates,
n_validate=opts.n_validate,
epsilon=opts.epsilon,
X_val=X_test, y_val=y_test,
verbose=1,
random_state=random_state,
n_jobs=-1)
clf.fit(X_train, y_train)
return clf
X_train, y_train, X_test, y_test, opts, args = parse_kmp(n_components=1.0,
check_duplicates=True)
X_tr, y_tr, X_te, y_te = X_train, y_train, X_test, y_test
if opts.n_nonzero_coefs < 1:
raise ValueError("n_nonzero_coefs must be a positive integer")
cv = list(split_data(X_train, y_train,
X_test, y_test,
opts.n_folds,
not opts.regression))
amounts = np.linspace(0.1, 1.0, 10)
#amounts = (0.25, 0.5, 0.75, 1.0)
#amounts = np.linspace(0.1, 0.5, 5)
acc_sup = np.zeros((len(amounts), len(cv)), dtype=np.float64)
acc_semi = np.zeros((len(amounts), len(cv)), dtype=np.float64)
n_refit=opts.n_refit,
estimator=Ridge(alpha=opts.alpha),
X_val=K_test, y_val=y_test,
metric="precomputed",
scale=True,
scale_y=opts.scale_y,
check_duplicates=opts.check_duplicates,
n_validate=opts.n_validate,
epsilon=opts.epsilon,
verbose=1,
random_state=random_state,
n_jobs=-1)
clf.fit(K_train, y_train)
return clf
dataset, opts, random_state = parse_kmp()
try:
X_train, y_train, X_test, y_test = load_dataset(dataset,
proportion_train=0.75,
random_state=random_state)
except KeyError:
raise ValueError("Wrong dataset name!")
print "X_train", X_train.shape
print "X_test", X_test.shape
# PCA view
print "Computing PCA..."
pca = RandomizedPCA(n_components=300)
X_train_pca = pca.fit_transform(X_train)
@memory.cache
def fit_kmp(X_train, y_train, X_test, y_test, components, opt_dict, regression,
random_state):
klass = KMPRegressor if regression else KMPClassifier
clf = klass(init_components=components,
estimator=Ridge(alpha=0.1),
X_val=X_test, y_val=y_test,
verbose=1,
random_state=random_state,
n_jobs=-1,
**opt_dict)
clf.fit(X_train, y_train)
return clf
X_train, y_train, X_test, y_test, opts, args = parse_kmp(check_duplicates=True)
opt_dict = options_to_dict(opts)
class_distrib = "random" if opts.regression else "balanced"
clf_s = []
clf_kg = []
clf_kb = []
clf_ks = []
j = 0
for X_tr, y_tr, X_te, y_te in split_data(X_train, y_train,
X_test, y_test,
opts.n_folds,
opts.cvtype,
opts.force_cv):
