SPPAM comparison plot

A classifier comparison plot including SPPAM

SPPAM: 0.794405 (0.161600)
EN: 0.776548 (0.139156)
LCV: 0.768548 (0.132131)
LR: 0.801643 (0.163906)
LDA: 0.813810 (0.147801)
KNN: 0.824393 (0.107928)
CART: 0.631905 (0.127420)
NB: 0.784643 (0.117068)
SVM: 0.824952 (0.145940)
SPPAM: 0.710000 (0.164012)
EN: nan (nan)
LCV: nan (nan)
LR: 0.710000 (0.192094)
LDA: 0.720000 (0.188680)
KNN: 0.760000 (0.135647)
CART: 0.580000 (0.177764)
NB: 0.740000 (0.128062)
SVM: 0.820000 (0.116619)

import warnings

import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LassoCV, ElasticNetCV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, KFold
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

from sppam import SPPAM

warnings.filterwarnings("ignore")

# Make a classification problem
X, y = make_classification(
    n_samples=100,
    n_features=20,
    n_informative=10,
    n_redundant=5,
    n_classes=2,
    random_state=11
)
scaler = StandardScaler()
X = scaler.fit_transform(X)

# Models to be compared
# ElasticNetCV and LassoCV are expected to fail using the accuracy metric
# Here we show the versatility of CalfCV under both accuracy and auc
models = [
    ('SPPAM', SPPAM()),
    ('EN', ElasticNetCV()),
    ('LCV', LassoCV()),
    ('LR', LogisticRegression()),
    ('LDA', LinearDiscriminantAnalysis()),
    ('KNN', KNeighborsClassifier()),
    ('CART', DecisionTreeClassifier()),
    ('NB', GaussianNB()),
    ('SVM', SVC())
]

names = [x for (x, _) in models]
kfold = KFold(n_splits=10)
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig.suptitle('Algorithm Comparison')

# evaluate each model in turn against metrics auc and accuracy
plot_info = [('roc_auc', ax1), ('accuracy', ax2)]
for score, ax in plot_info:
    results = []
    for name, model in models:
        cv_results = cross_val_score(model, X, y, cv=kfold, scoring=score)
        results.append(cv_results)
        msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
        print(msg)
        ax.boxplot(results)
        ax.set_ylabel(score)

ax2.set(xticks=range(1, len(models) + 1), xticklabels=names)
fig.set_size_inches(18.5, 10.5)
plt.show()