def q12():
clf = DecisionStump()
X, y = load_ada_boost_train()
clf.fit(X, y)
print 'Ein:', clf.score(X, y)
X, y = load_ada_boost_test()
print 'Eout:', clf.score(X, y)
def boostRound(self): weakLearners = [] self.weights /= self.weights.sum() for feature in self.featuretbl: rec1 = feature[:4] rec2 = feature[2:] stump = DecisionStump(rec1, rec2) stump.fit(self.iimages, self.labels, self.weights) weakLearners.append(stump) errors = np.array([learner.error for learner in weakLearners]) bestLearner = weakLearners[errors.argmin()] error = bestLearner.error beta = error/(1-error) alpha = np.log(1/beta) predictions = bestLearner.predict(self.iimages) self.featuretbl = np.delete(self.featuretbl, np.argmin(errors), 0) self.weights *= np.power(beta, 1 - np.equal(predictions, self.labels)) return alpha, bestLearner
required=True,
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
