def train(self, features: List[List[float]], labels: List[int]):
X = np.array(features)
N = X.shape[0]
pi = np.full((N), 1 / 2)
y = np.array(labels)
f = np.zeros((N))
hx = np.array((N))
for t in range(self.T):
num = ((y + 1) / 2) - pi
den = np.multiply(pi, 1 - pi)
z = num / den
w = np.multiply(pi, 1 - pi)
#step5
min = 9223372036854775807
for clf in self.clfs:
decstump = DecisionStump(clf.s, clf.b, clf.d)
hxpred = np.array(decstump.predict(features))
check = np.sum(
np.multiply(w, np.multiply(z - hxpred, z - hxpred)))
if check < min:
min_clf = clf
hx = hxpred
min = check
self.clfs_picked.append(min_clf)
self.betas.append(0.5)
f = f + (1 / 2) * hx
den = 1 + np.exp(-2 * f)
pi = 1 / den
def predict(self, features: List[List[float]]) -> List[int]:
x = np.array(features)
f = np.zeros(x.shape[0])
for t in range(self.T):
decstump = DecisionStump(self.clfs_picked[t].s,
self.clfs_picked[t].b,
self.clfs_picked[t].d)
f = f + (self.betas[t] * np.array(decstump.predict(features)))
predictions = np.ones(f.shape, np.int)
predictions[np.where(f < 0)[0]] = -1
return predictions.tolist()
def build_tree(self, X, Y, w, depth, curr_depth):
# See if we can do any splitting at all
tree = Node()
yw = Y*w
if len(X)<2 or len(unique(Y)) < 2 or curr_depth >= depth:
tree.stump = 1.0 if abs(sum(yw[yw>=0]))>abs(sum(yw[yw<0])) else -1.0
return tree
# TODO: check for inconsistent data
# Learn the decision stump
stump = DecisionStump().fit(X,Y,w)
side1 = stump.predict(X)>=0
side2 = stump.predict(X)<0
tree.stump = stump
tree.left = self.build_tree(X[side1], Y[side1], w[side1], depth, curr_depth+1)
tree.right = self.build_tree(X[side2], Y[side2], w[side2], depth, curr_depth+1)
return tree
def train(self, features: List[List[float]], labels: List[int]):
X = np.array(features)
N = X.shape[0]
w = np.full((N), 1 / N)
labels = np.array(labels)
hx = np.array((N))
for t in range(self.T):
#step3
min = 9223372036854775807
for clf in self.clfs:
decstump = DecisionStump(clf.s, clf.b, clf.d)
hxpred = np.array(decstump.predict(features))
indicator = np.zeros((N))
indicator[np.where(labels != hxpred)[0]] = 1
check = np.sum(np.multiply(w, indicator))
if check < min:
min_clf = clf
hx = hxpred
min = check
self.clfs_picked.append(min_clf)
error = 0
for i in range(N):
if labels[i] != hx[i]:
error = error + w[i]
beta = (1 / 2) * np.log((1 - error) / error)
self.betas.append(beta)
for i in range(N):
if labels[i] == hx[i]:
w[i] = w[i] * np.exp((-1) * self.betas[t])
else:
w[i] = w[i] * np.exp(self.betas[t])
w_sum = np.sum(w)
w = w / w_sum
def build_tree(self, X, Y, w, depth, curr_depth):
# See if we can do any splitting at all
tree = Node()
yw = Y * w
if len(X) < 2 or len(unique(Y)) < 2 or curr_depth >= depth:
tree.stump = 1.0 if abs(sum(yw[yw >= 0])) > abs(sum(
yw[yw < 0])) else -1.0
return tree
# TODO: check for inconsistent data
# Learn the decision stump
stump = DecisionStump().fit(X, Y, w)
side1 = stump.predict(X) >= 0
side2 = stump.predict(X) < 0
tree.stump = stump
tree.left = self.build_tree(X[side1], Y[side1], w[side1], depth,
curr_depth + 1)
tree.right = self.build_tree(X[side2], Y[side2], w[side2], depth,
curr_depth + 1)
return tree
choices=["1.1", "1.2", "1.3", "1.4", "1.5"])
io_args = parser.parse_args()
module = io_args.module
# Decision Stump using inequalities/threshold
if module == "1.1":
# 1. Load citiesSmall dataset
dataset = load_dataset("citiesSmall.pkl")
X = dataset["X"]
y = dataset["y"]
# 2. Evaluate decision stump
model = DecisionStump()
model.fit(X, y)
y_pred = model.predict(X)
error = np.mean(y_pred != y)
print("Decision Stump with inequality rule error: %.3f" % error)
# PLOT RESULT
utils.plotClassifier(model, X, y)
fname = os.path.join("..", "figs", "decision_stump_boundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
# Simple decision tree using decision stumps
elif module == "1.2":
# 1. Load citiesSmall dataset
dataset = load_dataset("citiesSmall.pkl")
