Add benchmark performance

[kinto-b]

It would be useful to also see the performance of each SSL model against the purely supervised backbone run on the labeled data.

For example, TSVM vs pure SVM:

import numpy as np
from LAMDA_SSL.Dataset.Tabular.BreastCancer import BreastCancer

dataset = BreastCancer(labeled_size=0.1, stratified=True, shuffle=True)
labeled_X = dataset.labeled_X
labeled_y = dataset.labeled_y
unlabeled_X = dataset.unlabeled_X
unlabeled_y = dataset.unlabeled_y

from sklearn import preprocessing

pre_transform = preprocessing.StandardScaler()
pre_transform.fit(np.vstack([labeled_X, unlabeled_X]))
labeled_X = pre_transform.transform(labeled_X)
unlabeled_X = pre_transform.transform(unlabeled_X)

from LAMDA_SSL.Algorithm.Classification.TSVM import TSVM

# I tried using a range of Cl and Cu, starting from 15 and 0.0001 and then gradually 
# upping Cu and decreasing Cl. It didn't seem to make a difference?
model = TSVM(Cl=1, Cu=1, kernel="linear")

model.fit(X=labeled_X, y=labeled_y, unlabeled_X=unlabeled_X)
pred_y = model.predict()

from LAMDA_SSL.Evaluation.Classifier.Accuracy import Accuracy

score = Accuracy().scoring(unlabeled_y, pred_y)
print(f"SSL TSVM score: {score}")
#> SSL TSVM score: 0.9609375

# Compare with pure SVM
from sklearn import svm
model_sl = svm.SVC()
model_sl.fit(labeled_X, labeled_y)
pred_sl = model_sl.predict(unlabeled_X)
score_sl = Accuracy().scoring(unlabeled_y, pred_sl)
print(f"SL SVM score: {score_sl}")
#> SL SVM score: 0.955078125