def exercise1():
breast_cancer = pd.read_csv('breast_cancer.csv')
X, y = split_dataset_transformed(breast_cancer, 'Class', ['?'])
# 1.a
clf = DecisionTreeClassifier()
cross_val_stats = cross_val(clf, X, y)
print_dict(cross_val_stats)
# 1.b
min_samples = np.arange(2, 11)
for samples in min_samples:
print('Experimenting with {} samples'.format(samples))
clf = DecisionTreeClassifier(min_samples_split=samples)
cross_val_stats = cross_val(clf, X, y)
print_dict(cross_val_stats)
# 1.c
clf = DecisionTreeClassifier()
clf.fit(X, y)
export_graphviz(clf, out_file='unpruned.dot')
clf = DecisionTreeClassifier(max_depth=4)
clf.fit(X, y)
export_graphviz(clf, out_file='pruned.dot')
def exercise2():
breast_cancer = pd.read_csv('breast_cancer.csv')
X, y = split_dataset_transformed(breast_cancer, 'Class', ['?'])
X_train, X_test, y_train, y_test = split_train_test(X, y)
# 2.a
clf = RandomForestClassifier()
clf_stats = classifier_statistics(clf, X_train, X_test, y_train, y_test)
print_dict(clf_stats, ['predicted'])
# 2.b
numb_trees = np.arange(10, 201, step=10)
for trees in numb_trees:
print('Experimenting with {} number of trees'.format(trees))
clf = RandomForestClassifier(n_estimators=trees)
clf_stats = classifier_statistics(clf, X_train, X_test, y_train,
y_test)
print_dict(clf_stats, ['predicted'])
# 2.c
depths = np.arange(5, 20)
for dep in depths:
print('Experimenting with {} depth'.format(dep))
clf = RandomForestClassifier(max_depth=dep)
clf_stats = classifier_statistics(clf, X_train, X_test, y_train,
y_test)
print_dict(clf_stats, ['predicted'])
def exercise3():
credit = pd.read_csv('credit.csv')
X, y = split_dataset_transformed(credit, 'class')
X_train, X_test, y_train, y_test = split_train_test(X, y)
min_samples = np.arange(2, 11)
for samples in min_samples:
print('Experimenting with {} number of instances to split'.format(
samples))
print('Train data')
clf = DecisionTreeClassifier(min_samples_split=samples)
clf_stats = classifier_statistics(clf, X_train, X_train, y_train,
y_train)
print_dict(clf_stats, ['predicted'])
print('Test data')
clf = DecisionTreeClassifier(min_samples_split=samples)
clf_stats = classifier_statistics(clf, X_train, X_test, y_train,
y_test)
print_dict(clf_stats, ['predicted'])
print()
print()
numb_trees = np.arange(10, 201, step=10)
for trees in numb_trees:
print('Experimenting with {} number of trees'.format(trees))
print('Train data')
clf = RandomForestClassifier(n_estimators=trees)
clf_stats = classifier_statistics(clf, X_train, X_train, y_train,
y_train)
print_dict(clf_stats, ['predicted'])
print('Test data')
clf = RandomForestClassifier(n_estimators=trees)
clf_stats = classifier_statistics(clf, X_train, X_test, y_train,
y_test)
print_dict(clf_stats, ['predicted'])
print()
print()
from sklearn.neighbors import KNeighborsClassifier
X, X_test, y, y_test = get_data(False)
#X_train, X_test, y_train, y_test = split_train_test(X, y)
"""
Balancing data only with training instances
so we can check if it generalizes well on the
unbalanced set with X_test and y_test
"""
sm = SMOTE(random_state=12, ratio = 1.0)
X_train_res, y_train_res = sm.fit_sample(X, y)
print('Balanced data: {}'.format(collections.Counter(y_train_res)))
"""
clf = RandomForestClassifier(n_estimators=25, random_state=42)
results = classifier_statistics(clf, X_train_res, X_test, y_train_res, y_test)
print_dict(results, excluded_keys=['predicted'])
cost1 = 10
cost2 = 500
conf_matrix = results['confusion_matrix']
fp = conf_matrix[0][1]
fn = conf_matrix[1][0]
total_cost = cost1*fp + cost2*fn
print('Total cost aachived: {}'.format(total_cost))
"""
print_dict(clf_stats, ['predicted'])
print()
print()
diabetes = pd.read_csv('diabetes.csv')
X, y = split_dataset_transformed(diabetes, 'class')
X_train, X_test, y_train, y_test = split_train_test(X, y)
# 3.a
enc = KBinsDiscretizer(n_bins=10, encode='onehot')
X_binned = enc.fit_transform(X)
X_binned = X_binned.toarray()
X_binned_train, X_binned_test, y_binned_train, y_binned_test = split_train_test(
X_binned, y)
print('With discretization')
clf1 = RandomForestClassifier()
clf_stats = classifier_statistics(clf1, X_binned_train, X_binned_test,
y_binned_train, y_binned_test)
print_dict(clf_stats, ['predicted'])
print('Without discretization')
clf2 = RandomForestClassifier()
clf_stats = classifier_statistics(clf2, X_train, X_test, y_train, y_test)
print_dict(clf_stats, ['predicted'])