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%load_ext watermark
%watermark -a 'Sebastian Raschka' -d -v -p matplotlib
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import sys
sys.path = ['/Users/sebastian/github/mlxtend'] + sys.path
import mlxtend
mlxtend.__version__
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%matplotlib inline
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from mlxtend.evaluate import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0,2]]
sc = StandardScaler()
X = sc.fit_transform(X)
y = iris.target
# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X, y)
##########################################################
# Plotting decision regions
plot_decision_regions(X, y, clf=svm, res=0.02, legend=2)
##########################################################
# Adding axes annotations
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.title('SVM on Iris')
plt.show()
Via the X_highlight, a second dataset can be provided to highlight particular points in the dataset via a circle.
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from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)
# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X_train, y_train)
##########################################################
# Plotting decision regions
plot_decision_regions(X, y, clf=svm,
X_highlight=X_test,
res=0.02, legend=2)
##########################################################
# Adding axes annotations
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.title('SVM on Iris')
plt.show()
If you are plotting 1D data, please make sure that the NumPy array has 2 dimensions.
E.g.,
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import numpy as np
# Not okay!
X = np.array([1, 2, 3])
X
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# Okay!
X = X[:, None]
X
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from mlxtend.evaluate import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, 2]
X = X[:, None]
y = iris.target
# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X,y)
# Plotting decision regions
plot_decision_regions(X, y, clf=svm, res=0.02, legend=2)
# Adding axes annotations
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')
plt.show()
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0,2]]
y = iris.target
# Fit classifiers
gnb = GaussianNB().fit(X, y)
knn = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_neighbors=3, p=2, weights='uniform').fit(X, y)
lin_svm = SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False).fit(X, y)
tree = DecisionTreeClassifier(criterion='entropy',
max_depth=2, max_features=None, max_leaf_nodes=None,
min_samples_leaf=1, min_samples_split=2,
random_state=None, splitter='best').fit(X, y)
tree2 = DecisionTreeClassifier(criterion='gini',
max_depth=4, max_features=None, max_leaf_nodes=None,
min_samples_leaf=1, min_samples_split=2,
random_state=None, splitter='best').fit(X, y)
### Plotting
fig = plt.figure(figsize=(10,10))
titles = ['SVM - linear kernel',
'Gaussian Naive Bayes',
'K-Nearest Neighbor',
'Decision Tree',
'Decision Tree']
for i, clf in enumerate((lin_svm, gnb, knn, tree, tree2)):
plt.subplot(3, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
# Plotting decision regions
plot_decision_regions(X, y, clf=clf, res=0.02, legend=2)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title(titles[i])
plt.tight_layout()
plt.show()
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