def run():
X, y = get_wine_data()
X1, y1 = get_abalone_data()
classifier = DecisionTreeClassifier(max_depth=2, min_samples_leaf=3)
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
title = "Wine - Validation Curve For Decision Tree"
plot_learning_curve(classifier,
title,
X,
y,
ylim=(0.4, 0.6),
cv=cv,
n_jobs=4).show()
classifier = DecisionTreeClassifier(max_depth=2, min_samples_leaf=3)
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
title = "Abalone - Validation Curve For Decision Tree"
plot_learning_curve(classifier,
title,
X1,
y1,
ylim=(0.2, 0.4),
cv=cv,
n_jobs=4).show()
def run():
X, y = get_wine_data()
X1, y1 = get_abalone_data()
dt = DecisionTreeClassifier(max_depth=2,
min_samples_leaf=3,
splitter='random')
classifier = AdaBoostClassifier(base_estimator=dt,
random_state=0,
n_estimators=3)
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
title = "Wine - Validation Curve For AdaBoostClassifier"
plot_learning_curve(classifier,
title,
X,
y,
ylim=(0.4, 0.6),
cv=cv,
n_jobs=4).show()
dt = DecisionTreeClassifier(max_depth=1,
min_samples_leaf=3,
splitter='random')
classifier = AdaBoostClassifier(base_estimator=dt,
random_state=0,
n_estimators=15)
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
title = "Abalone - Validation Curve For AdaBoostClassifier"
plot_learning_curve(classifier,
title,
X1,
y1,
ylim=(0.1, 0.3),
cv=cv,
n_jobs=4).show()
def run():
X, y = get_wine_data()
X1, y1 = get_abalone_data()
classifier = MLPClassifier(alpha=0.1, hidden_layer_sizes=13, max_iter=5, random_state=0, solver='lbfgs')
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
title = "Wine - Validation Curve For Neural Network"
plot_learning_curve(classifier, title, X, y, ylim=(0.2, 0.6), cv=cv, n_jobs=4).show()
classifier = MLPClassifier(alpha=0.1, hidden_layer_sizes=10, max_iter=9, random_state=0, solver='lbfgs')
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
title = "Abalone - Validation Curve For Neural Network"
plot_learning_curve(classifier, title, X1, y1, ylim=(0.1, 0.3), cv=cv, n_jobs=4).show()
def run():
X, y = get_wine_data()
X1, y1 = get_abalone_data()
classifier = SVC(C=10, kernel='linear')
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
title = "Wine - Validation Curve For Support Vector Machine With Linear Kernel"
plot_learning_curve(classifier, title, X, y, ylim=(0.0, 1.0), cv=cv, n_jobs=4).show()
classifier = SVC(C=10, kernel='linear')
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
title = "Abalone - Validation Curve For Support Vector Machine With Linear Kernel"
plot_learning_curve(classifier, title, X1, y1, ylim=(0.0, 1.), cv=cv, n_jobs=4).show()
classifier = SVC(C=10, kernel='rbf', gamma=0.001)
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
title = "Wine - Validation Curve For Support Vector Machine with RBF Kernel"
plot_learning_curve(classifier, title, X, y, ylim=(0.0, 1.0), cv=cv, n_jobs=4).show()
classifier = SVC(C=10, kernel='rbf', gamma=0.001)
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
title = "Abalone - Validation Curve For Support Vector Machine With Linear Kernel"
plot_learning_curve(classifier, title, X1, y1, ylim=(0.0, 1.), cv=cv, n_jobs=4).show()
models3 = {'MLPClassifier': MLPClassifier()}
params3 = {
'MLPClassifier': {
'solver': ['lbfgs'],
'max_iter': [1, 3, 5, 7, 9],
'alpha': 10.0**-np.arange(1, 10),
'hidden_layer_sizes': np.arange(10, 15),
'random_state': [0, 1]
}
}
if __name__ == "__main__":
X, y = get_wine_data()
X1, y1 = get_abalone_data()
# helper1 = EstimatorSelectionHelper(models1, params1)
# helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)
# results = (helper1.score_summary(sort_by='max_score'))
# results.to_csv("out/wine_params_1.csv")
# helper1 = EstimatorSelectionHelper(models1, params1)
# helper1.fit(X1, y1, scoring='accuracy', n_jobs=4, cv=5)
# results = (helper1.score_summary(sort_by='max_score'))
# results.to_csv("out/abalone_params_1.csv")
helper1 = EstimatorSelectionHelper(models2, params2)
helper1.fit(X, y, scoring='accuracy', n_jobs=4, cv=5)
results = (helper1.score_summary(sort_by='max_score'))
results.to_csv("out/wine_params_2.csv")