print("# nonZeros: %d" % (lrl2.coef_ != 0).sum())
lrl1 = LogisticRegression(penalty='l1', fit_intercept=False)
lrl1.fit(XBin,yBin)
print("\nTraining error %.3f" % utils.classification_error(lrl1.predict(XBin),yBin))
print("Validation error %.3f" % utils.classification_error(lrl1.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (lrl1.coef_ != 0).sum())
elif question == "3":
data = utils.load_dataset("multiData")
XMulti, yMulti = data['X'], data['y']
XMultiValid, yMultiValid = data['Xvalid'], data['yvalid']
# Run Q3 given example - Fit One-vs-all Least Squares
model = linear_model.leastSquaresClassifier()
model.fit(XMulti, yMulti)
print("leastSquaresClassifier Training error %.3f" % utils.classification_error(model.predict(XMulti), yMulti))
print("leastSquaresClassifier Validation error %.3f" % utils.classification_error(model.predict(XMultiValid), yMultiValid))
print(np.unique(model.predict(XMulti)))
elif question == "3.2":
# Load Binary and Multi -class data
data = utils.load_dataset("multiData")
XMulti, yMulti = data['X'], data['y']
XMultiValid, yMultiValid = data['Xvalid'], data['yvalid']
# Fit One-vs-all Logistic Regression
model = linear_model.logLinearClassifier(maxEvals=500, verbose=0)
# -------------------------------- 1.2.3 LEAST SQUARES CLASSIFIER --------------------------------------- #
lammy_m = np.arange(-4, 1, dtype=float)
best_lammy_mm = 0
min_validation_err = 1
if best_lammy_mm is None:
for lammy_mm in lammy_m:
val_errors_per_fold = []
for train, validate in kf.split(X, y):
X_train = X[train]
y_train = y[train]
X_validate = X[validate]
y_validate = y[validate]
least_squares_classifier_model = leastSquaresClassifier(
lammy=10**lammy_mm)
least_squares_classifier_model.fit(X_train, y_train)
y_pred = least_squares_classifier_model.predict(
X_validate)
val_errors_per_fold.append(
np.mean(y_pred != y_validate))
avg_validation_error = np.average(
np.asarray(val_errors_per_fold))
print(
f"Least Squares Classifier Validation Error: {avg_validation_error} with lammy: {10**lammy_mm}"
)
if avg_validation_error < min_validation_err:
min_validation_err = avg_validation_error
best_lammy_mm = lammy_mm
