viz.plot_feature_importances(clf, X, y, ds._feature_names)
clf.fit(X, y)
export_graphviz(
clf.tree_,
out_file="tree.dot",
class_names=ds._class_names,
filled=True,
impurity=False,
proportion=True,
rounded=True,
)
subprocess.call(["dot", "-Tpng", "tree.dot", "-o", "tree.png"])
viz.plot_class_balances(clf, lambda x: ds.sample(class_balance=x))
viz.plot_learning_curve(clf, X, y)
for param in [
{"name": "max_depth", "range": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}, # ,20,40,100]}, #,160,280,560,1120]},
{"name": "max_features", "range": np.array(np.linspace(1, 41, 20), dtype=np.int_)},
{"name": "min_samples_leaf", "range": [2, 4, 8, 16, 32, 64, 128]},
{"name": "min_samples_split", "range": [2, 4, 8, 16, 32, 64, 128]},
]:
viz.plot_validation_curve(clf, X, y, param, name="DecisionTree")
X, y = ds.sample(class_balance=None)
viz.print_cv_score(clf, X, y)
from lib import dao, viz
#ds = dao.load_ads()
ds = dao.load_credit()
#ds.sanitize(strategy='impute_mean', scale=False)
ds.onehot()
X, y = ds.sample(class_balance=None)
base = DecisionTreeClassifier(max_depth=1, splitter='best', min_samples_split=math.log(y.shape[0]),
random_state=5557)
clf = AdaBoostClassifier(base_estimator=base, n_estimators=50, learning_rate=0.01,
algorithm='SAMME.R', random_state=5557)
viz.plot_feature_selection(clf, X, y)
viz.plot_feature_importances(clf, X, y, ds._feature_names)
viz.plot_class_balances(clf, lambda x: ds.sample(class_balance=x))
viz.plot_learning_curve(clf, X, y)
for param in [ {'name': 'n_estimators', 'range': np.array(np.linspace(10, 500, 20), dtype=np.int_)},
{'name': 'learning_rate', 'range': [0.001, 0.01, 0.1, 1, 10]}, ]:
viz.plot_validation_curve(clf, X, y, param, name='AdaBoost')
X, y = ds.sample(class_balance=None)
viz.print_cv_score(clf, X, y)
from lib import dao, viz
#ds = dao.load_ads()
ds = dao.load_credit()
#ds.sanitize(strategy='impute_mean', scale=True)
ds.onehot()
ds.scale_zmuv()
X, y = ds.sample(class_balance=None)
n_samples = ds.data.shape[0]
svm = {
'linear': SVC(kernel='linear', gamma='auto', max_iter=1000, probability=False, random_state=5557),
'poly': SVC(kernel='poly', gamma='auto', max_iter=1000, probability=False, random_state=5557),
'rbf': SVC(kernel='rbf', gamma='auto', max_iter=1000, probability=False, random_state=5557)
}
for param in [ {'name': 'max_iter', 'range': np.array(np.linspace(1, 1000, 5), dtype=np.int_)} ]:
X, y = ds.sample(class_balance=None, sparse=True)
viz.plot_validation_curve(svm['linear'], X, y, param, name='linear_SVC')
viz.plot_validation_curve(svm['poly'], X, y, param, name='polynomial_SVC')
viz.plot_validation_curve(svm['rbf'], X, y, param, name='rbf_SVC')
ds.scale_zmuv()
X, y = ds.sample(class_balance=None)
viz.print_cv_score(svm['linear'], X, y)
viz.print_cv_score(svm['poly'], X, y)
viz.print_cv_score(svm['rbf'], X, y)
import math
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from lib import dao, viz
#ds = dao.load_ads()
ds = dao.load_credit()
#ds.sanitize(strategy='impute_mean', scale=True)
ds.onehot()
ds.scale_zmuv()
X, y = ds.sample(class_balance=None)
clf = KNeighborsClassifier(n_neighbors=25, n_jobs=-1)
param = {'name': 'n_neighbors', 'range': [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] }
viz.plot_validation_curve(clf, X, y, param, name='KNeighbors')
ds.scale_zmuv()
X, y = ds.sample(class_balance=None)
viz.print_cv_score(clf, X, y)
