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extract plot function
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@mario-s
mario-s committed Oct 13, 2023
1 parent 0fbd0e4 commit 5d9cbe7
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140 changes: 36 additions & 104 deletions ch03/ch03.py
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Expand Up @@ -7,10 +7,7 @@
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import matplotlib
from distutils.version import LooseVersion
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.linear_model import SGDClassifier
Expand All @@ -21,10 +18,12 @@
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier

from plotter import plot_decision_regions

# *Python Machine Learning 3rd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2019
#
#
# Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
#
#
# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-2nd-edition/blob/master/LICENSE.txt)

# # Python Machine Learning - Code Examples
Expand All @@ -37,14 +36,14 @@



# *The use of `watermark` is optional. You can install this Jupyter extension via*
#
# conda install watermark -c conda-forge
#
# or
#
# pip install watermark
#
# *The use of `watermark` is optional. You can install this Jupyter extension via*
#
# conda install watermark -c conda-forge
#
# or
#
# pip install watermark
#
# *For more information, please see: https://github.com/rasbt/watermark.*

# ### Overview
Expand Down Expand Up @@ -103,8 +102,6 @@
X, y, test_size=0.3, random_state=1, stratify=y)




print('Labels count in y:', np.bincount(y))
print('Labels count in y_train:', np.bincount(y_train))
print('Labels count in y_test:', np.bincount(y_test))
Expand Down Expand Up @@ -132,7 +129,7 @@


# **Note**
#
#
# - You can replace `Perceptron(n_iter, ...)` by `Perceptron(max_iter, ...)` in scikit-learn >= 0.19. The `n_iter` parameter is used here deliberately, because some people still use scikit-learn 0.18.


Expand All @@ -155,62 +152,6 @@



# To check recent matplotlib compatibility


def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):

# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])

# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())

for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
color=colors[idx],
marker=markers[idx],
label=cl,
edgecolor='black')

# highlight test examples
if test_idx:
# plot all examples
X_test, y_test = X[test_idx, :], y[test_idx]


if LooseVersion(matplotlib.__version__) < LooseVersion('0.3.4'):
plt.scatter(X_test[:, 0],
X_test[:, 1],
c='',
edgecolor='black',
alpha=1.0,
linewidth=1,
marker='o',
s=100,
label='test set')
else:
plt.scatter(X_test[:, 0],
X_test[:, 1],
c='none',
edgecolor='black',
alpha=1.0,
linewidth=1,
marker='o',
s=100,
label='test set')


# Training a perceptron model using the standardized training data:
Expand Down Expand Up @@ -354,13 +295,13 @@ def fit(self, X, y):
errors = (y - output)
self.w_[1:] += self.eta * X.T.dot(errors)
self.w_[0] += self.eta * errors.sum()

# note that we compute the logistic `cost` now
# instead of the sum of squared errors cost
cost = -y.dot(np.log(output)) - ((1 - y).dot(np.log(1 - output)))
self.cost_.append(cost)
return self

def net_input(self, X):
"""Calculate net input"""
return np.dot(X, self.w_[1:]) + self.w_[0]
Expand All @@ -386,7 +327,7 @@ def predict(self, X):
lrgd.fit(X_train_01_subset,
y_train_01_subset)

plot_decision_regions(X=X_train_01_subset,
plot_decision_regions(X=X_train_01_subset,
y=y_train_01_subset,
classifier=lrgd)

Expand Down Expand Up @@ -496,9 +437,9 @@ def predict(self, X):
svm = SVC(kernel='linear', C=1.0, random_state=1)
svm.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std,
plot_decision_regions(X_combined_std,
y_combined,
classifier=svm,
classifier=svm,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
Expand Down Expand Up @@ -589,7 +530,7 @@ def predict(self, X):
svm = SVC(kernel='rbf', random_state=1, gamma=100.0, C=1.0)
svm.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined,
plot_decision_regions(X_combined_std, y_combined,
classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
Expand Down Expand Up @@ -636,8 +577,8 @@ def error(p):

fig = plt.figure()
ax = plt.subplot(111)
for i, lab, ls, c, in zip([ent, sc_ent, gini(x), err],
['Entropy', 'Entropy (scaled)',
for i, lab, ls, c, in zip([ent, sc_ent, gini(x), err],
['Entropy', 'Entropy (scaled)',
'Gini impurity', 'Misclassification error'],
['-', '-', '--', '-.'],
['black', 'lightgray', 'red', 'green', 'cyan']):
Expand All @@ -661,14 +602,14 @@ def error(p):



tree_model = DecisionTreeClassifier(criterion='gini',
max_depth=4,
tree_model = DecisionTreeClassifier(criterion='gini',
max_depth=4,
random_state=1)
tree_model.fit(X_train, y_train)

X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X_combined, y_combined,
plot_decision_regions(X_combined, y_combined,
classifier=tree_model,
test_idx=range(105, 150))

Expand All @@ -693,16 +634,16 @@ def error(p):


dot_data = export_graphviz(tree_model,
filled=True,
filled=True,
rounded=True,
class_names=['Setosa',
class_names=['Setosa',
'Versicolor',
'Virginica'],
feature_names=['petal length',
feature_names=['petal length',
'petal width'],
out_file=None)
graph = graph_from_dot_data(dot_data)
graph.write_png('tree.png')
out_file=None)
graph = graph_from_dot_data(dot_data)
graph.write_png('tree.png')



Expand All @@ -716,12 +657,12 @@ def error(p):


forest = RandomForestClassifier(criterion='gini',
n_estimators=25,
n_estimators=25,
random_state=1,
n_jobs=2)
forest.fit(X_train, y_train)

plot_decision_regions(X_combined, y_combined,
plot_decision_regions(X_combined, y_combined,
classifier=forest, test_idx=range(105, 150))

plt.xlabel('petal length [cm]')
Expand All @@ -742,12 +683,12 @@ def error(p):



knn = KNeighborsClassifier(n_neighbors=5,
p=2,
knn = KNeighborsClassifier(n_neighbors=5,
p=2,
metric='minkowski')
knn.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined,
plot_decision_regions(X_combined_std, y_combined,
classifier=knn, test_idx=range(105, 150))

plt.xlabel('petal length [standardized]')
Expand All @@ -764,14 +705,5 @@ def error(p):
# ...

# ---
#
#
# Readers may ignore the next cell.









62 changes: 62 additions & 0 deletions ch03/plotter.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,62 @@
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from distutils.version import LooseVersion

# To check recent matplotlib compatibility


def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):

# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])

# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())

for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
color=colors[idx],
marker=markers[idx],
label=cl,
edgecolor='black')

# highlight test examples
if test_idx:
# plot all examples
X_test, y_test = X[test_idx, :], y[test_idx]


if LooseVersion(matplotlib.__version__) < LooseVersion('0.3.4'):
plt.scatter(X_test[:, 0],
X_test[:, 1],
c='',
edgecolor='black',
alpha=1.0,
linewidth=1,
marker='o',
s=100,
label='test set')
else:
plt.scatter(X_test[:, 0],
X_test[:, 1],
c='none',
edgecolor='black',
alpha=1.0,
linewidth=1,
marker='o',
s=100,
label='test set')
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