def test_selects_all():
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.data import wine_data
X, y = wine_data()
knn = KNeighborsClassifier(n_neighbors=4)
sffs = SFFS(knn, k_features=13, scoring='accuracy', cv=3, print_progress=False)
sffs.fit(X, y)
assert(len(sffs.indices_) == 13)
def test_selects_all():
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.data import wine_data
X, y = wine_data()
knn = KNeighborsClassifier(n_neighbors=4)
sfbs = SFBS(knn, k_features=13, scoring='accuracy', cv=3, print_progress=False)
sfbs.fit(X, y)
assert(len(sfbs.indices_) == 13)
def winePipeline(desired_lbl):
"""
divide the data to train, test and validation sets
input:
1. a list of the desired wine lables: 0
1
2
output:
1. nd.array of data concatenated with binarized labels
"""
assert len(desired_lbl) == 2, "should get only 2 labels"
data, lbl = wine_data()
data = normalizeByMax(data)
sliced_lbl = np.logical_or(lbl == desired_lbl[0], lbl == desired_lbl[1])
sliced_data = data[sliced_lbl, :]
sliced_lbl = lbl[sliced_lbl]
binary_lbl = binarizeLabels(sliced_lbl)
return np.append(sliced_data,
binary_lbl.reshape(binary_lbl.shape[0], 1),
axis=1)
def test_import_wine_data():
X, y = wine_data()
assert (X.shape[0] == 178)
assert (X.shape[1] == 13)
assert (y.shape[0] == 178)
from mlxtend.data import wine_data
from mlxtend.plotting import plot_decision_regions
from mlxtend.classifier import Perceptron
import matplotlib.pyplot as plt
# Loading Data
X, y = wine_data()
X = X[:, [3, 12]] # hue, ash
X = X[0:100] # class 0 and class 1
y = y[0:100] # class 0 and class 1
# standardize
X[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()
X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()
# Rosenblatt Perceptron
ppn = Perceptron(epochs=500,
eta=0.05,
random_seed=1,
print_progress=3)
ppn.fit(X, y)
plot_decision_regions(X, y, clf=ppn)
plt.title('Wine Data - Hue and Ash 500 Epochs')
plt.show()
print('Bias & Weights: %s' % ppn.w_)
def test_import_wine_data():
X, y = wine_data()
assert(X.shape[0] == 178)
assert(X.shape[1] == 13)
print(y.shape)
assert(y.shape[0] == 178)