Python plot_decision_regions Exemples

Exemple #1

Fichier : ml_ch03_13.py Projet : ybbobasorde/ProjectX

    #
    #
    #

X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

    
    #
    # 5
    #

lr = LogisticRegression(C=100.0, random_state=1, solver='lbfgs', multi_class='auto')
lr.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined,
                      classifier=lr, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()

img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)
    
ml03_plot_url5 = b64encode(img.getvalue()).decode('ascii')

Exemple #2

Fichier : ml_ch03_4.py Projet : ybbobasorde/ProjectX

plt.close()
img.seek(0)

ml03_plot_url12 = b64encode(img.getvalue()).decode('ascii')

#
# 13
#

tree = DecisionTreeClassifier(criterion='gini', max_depth=4, random_state=1)
tree.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,
                      classifier=tree,
                      test_idx=range(105, 150))

plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.tight_layout()

img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)

ml03_plot_url13 = b64encode(img.getvalue()).decode('ascii')

#

Exemple #3

Fichier : ml_ch03_1.py Projet : ybbobasorde/ProjectX

img.seek(0)

ml03_plot_url3 = b64encode(img.getvalue()).decode('ascii')

#
# 4
#

X_train_01_subset = X_train[(y_train == 0) | (y_train == 1)]
y_train_01_subset = y_train[(y_train == 0) | (y_train == 1)]

lrgd = LogisticRegressionGD(eta=0.05, max_iter=1000, random_state=1)
lrgd.fit(X_train_01_subset, y_train_01_subset)

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

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')

plt.tight_layout()

img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)

ml03_plot_url4 = b64encode(img.getvalue()).decode('ascii')

Exemple #4

    
ml03_plot_url3 = b64encode(img.getvalue()).decode('ascii')

    #
    # 4
    #

X_train_01_subset = X_train[(y_train == 0) | (y_train == 1)]
y_train_01_subset = y_train[(y_train == 0) | (y_train == 1)]

lrgd = LogisticRegressionGD(eta=0.05, max_iter=1000, random_state=1)
lrgd.fit(X_train_01_subset,
         y_train_01_subset)

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

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')

plt.tight_layout()

img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)
    
ml03_plot_url4 = b64encode(img.getvalue()).decode('ascii')