elif question == "6":
# 1Load citiesSmall dataset
with open(os.path.join('..','data','citiesSmall.pkl'), 'rb') as f:
dataset = pickle.load(f)
X = dataset["X"]
y = dataset["y"]
# 2Evaluate majority predictor model
y_pred = np.zeros(y.size) + utils.mode(y)
error = np.mean(y_pred != y)
print("Mode predictor error: %.3f" % error)
# 3Evaluate decision stump
model = DecisionStumpEquality()
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", "q6_decisionBoundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
elif question == "6.2":
elif question == "2":
# 1. Load citiesSmall dataset
dataset = load_dataset("citiesSmall.pkl")
X = dataset["X"]
y = dataset["y"]
# 2. Evaluate majority predictor model
y_pred = np.zeros(y.size) + utils.mode(y)
error = np.mean(y_pred != y)
print("Mode predictor error: %.3f" % error)
# 3. Evaluate decision stump
model = DecisionStumpEquality()
model.fit(X, y)
y_pred = model.predict(X)
error = np.mean(y_pred != y)
print("Decision Stump with equality rule error: %.3f" % error)
# PLOT RESULT
utils.plotClassifier(model, X, y)
fname = os.path.join("..", "figs", "q2_decisionBoundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
elif question == "2.2":
# 1. Load citiesSmall dataset
elif question == "2":
# 1. Load citiesSmall dataset
dataset = load_dataset("citiesSmall.pkl")
X = dataset["X"]
y = dataset["y"]
# 2. Evaluate majority predictor model
y_pred = np.zeros(y.size) + utils.mode(y)
error = np.mean(y_pred != y)
print("Mode predictor error: %.3f" % error)
# 3. Evaluate decision stump
model = DecisionStumpEquality()
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", "q2_decisionBoundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
elif question == "2.2":
# 1. Load citiesSmall dataset
elif question == "2":
# 1. Load citiesSmall dataset
dataset = utils.load_dataset("citiesSmall")
X = dataset["X"]
y = dataset["y"]
# 2. Evaluate majority predictor model
y_pred = np.zeros(y.size) + utils.mode(y)
error = np.mean(y_pred != y)
print("Mode predictor error: %.3f" % error)
# 3. Evaluate decision stump
model = DecisionStumpEquality()
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", "q2_decisionBoundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
elif question == "2":
# 1. Load citiesSmall dataset
dataset = load_dataset("citiesSmall.pkl")
X = dataset["X"]
y = dataset["y"]
# 2. Evaluate majority predictor model
y_pred = np.zeros(y.size) + utils.mode(y)
error = np.mean(y_pred != y)
print("Mode predictor error: %.3f" % error)
# 3. Evaluate decision stump
model = DecisionStumpEquality()
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", "q2_decisionBoundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
elif question == "2.2":
# 1. Load citiesSmall dataset
elif question == "2":
# 1. Load citiesSmall dataset
dataset = utils.load_dataset("citiesSmall")
X = dataset["X"]
y = dataset["y"]
# 2. Evaluate majority predictor model
y_pred = np.zeros(y.size) + utils.mode(y)
error = np.mean(y_pred != y)
print("Mode predictor error: %.3f" % error)
# 3. Evaluate decision stump
model = DecisionStumpEquality()
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", "q2_decisionBoundary.pdf")
plt.savefig(fname)
print("\nFigure saved as '%s'" % fname)
elif question == "2.2":
# 1. Load citiesSmall dataset