def testPdToDict():
df = hw3.load_and_normalize_spambase()
cols = df.columns[0:3]
sub = utils.train_subset(df, cols, 5)
print sub
print hw3.pandas_to_data(sub)
def q7():
h_test, h_train = utils.load_and_normalize_housing_set()
housingData_test = hw3.pandas_to_data(h_test)
housingData_train = hw3.pandas_to_data(h_train)
y, X = hw4.split_truth_from_data(housingData_train)
y_test, X_test = hw4.split_truth_from_data(housingData_test)
#gb = GradientBoostingRegressor(learning_rate=.1, n_estimators=1, max_depth=1)
gb = gradb.GradientBoostRegressor(learning_rate=.1, n_estimators=100, max_depth=1, learner=lambda: DecisionTreeRegressor(max_depth=1))
gb.fit(X, y)
gb.print_stats()
yhat = gb.predict(X_test)
print y_test[:10]
print yhat[:10]
print 'MSE: {}'.format(hw4.compute_mse(y_test, yhat))
def q1():
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
k = 10
all_folds = hw3.partition_folds(spamData, k)
tprs = []
fprs = []
for i in [0]: #range(len(all_folds)):
kf_data, kf_test = dl.get_train_and_test(all_folds, i)
y, X = hw4.split_truth_from_data(kf_data)
y_test, X_test = hw4.split_truth_from_data(kf_test)
adaboost = run_adaboost(X, y, X_test, y_test, i)
predicted = adaboost.predict(X)
print(roc_auc_score(y, predicted))
for i in range(len(adaboost.snapshots)):
round_number = i + 1
ab = adaboost.snapshots[i]
yt_pred = ab.predict(X_test)
round_err = float(np.sum([1 if yt==yp else 0 for yt, yp in zip(yt_pred, y_test)]))/len(y_test)
adaboost.adaboost_error_test[round_number] = round_err
print predicted[:20]
print y[:20]
name = 'q1'
directory = '/Users/Admin/Dropbox/ML/MachineLearning_CS6140/CS6140_A_MacLeay/Homeworks'
path = os.path.join(directory, name + 'hw4errors.pdf')
tterrpath = os.path.join(directory, name + 'hw4_errors_test_train.pdf')
print path
plt.Errors([adaboost.local_errors]).plot_all_errors(path)
plt.Errors([adaboost.adaboost_error, adaboost.adaboost_error_test]).plot_all_errors(tterrpath)
roc = plt.ROC()
#roc.add_tpr_fpr_arrays(adaboost.tpr.values(), adaboost.fpr.values())
get_tpr_fpr(adaboost, roc, X_test, y_test, 30)
roc.plot_ROC(os.path.join(directory, name + 'hw4_roc.pdf'))
def q2_plots():
models = ['Bernoulli', 'Gaussian', '4-bins', '9-bins']
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
k = 10
num_points = 50
k_folds = hw3.partition_folds(spamData, k)
for model_type in range(4):
roc = ROC.ROC()
print '\nModel: {}'.format(models[model_type])
train_acc_sum = 0
nb_models = []
for ki in [0]:
alpha = .001 if model_type==0 else 0
nb_model = nb.NaiveBayes(model_type, alpha=alpha)
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(k_folds[ki])
nb_model.train(data_rows, truth_rows)
for ti in range(num_points + 2):
theta = ti * 1./(num_points + 1)
predict = nb_model.predict(data_rows, theta)
print predict
accuracy = hw3.get_accuracy(predict, truth_rows)
train_acc_sum += accuracy
roc.add_tp_tn(predict, truth_rows, theta)
#print_plot_output(ki, accuracy, theta)
roc.plot_ROC('/Users/Admin/Dropbox/ML/MachineLearning_CS6140/CS6140_A_MacLeay/Homeworks/roc_{}.pdf'.format(model_type))
roc.print_info()
def q2():
models = ['Bernoulli', 'Gaussian', '4-bins', '9-bins']
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
k = 10
k_folds = hw3.partition_folds(spamData, k)
for model_type in range(4):
print '\nModel: {}'.format(models[model_type])
train_acc_sum = 0
nb_models = []
for ki in range(k - 1):
alpha = .001 if model_type==0 else 0
nb_model = nb.NaiveBayes(model_type, alpha=alpha)
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(k_folds[ki])
nb_model.train(data_rows, truth_rows)
predict = nb_model.predict(data_rows)
print predict
accuracy = hw3.get_accuracy(predict, truth_rows)
train_acc_sum += accuracy
print_output(ki, accuracy)
nb_models.append(nb_model)
nb_combined = nb.NaiveBayes(model_type, alpha=.001)
if model_type < 2:
nb_combined.aggregate_model(nb_models)
else:
nb_combined.aggregate_model3(nb_models)
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(k_folds[k - 1])
test_predict = nb_combined.predict(data_rows)
test_accuracy = hw3.get_accuracy(test_predict, truth_rows)
print_test_output(test_accuracy, float(train_acc_sum)/(k-1))
#print len(k_folds[0])
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(spamData)
def q1():
"""GDA """
"""Run the Gaussian Discriminant Analysis on the spambase data. Use the k-folds from the previous problem (1 for testing, k-1 for training, for each fold)
Since you have 57 real value features, each of the 2gaussians (for + class and for - class) will have a mean vector with 57 components, and a they will have
either a common (shared) covariance matrix size 57x57. This covariance is estimated from all training data (both classes)
or two separate covariance 57x57 matrices (estimated separately for each class)
(you can use a Matlab or Python of Java built in function to estimated covariance matrices, but the estimator is easy to code up).
Looking at the training and testing performance, does it appear that the gaussian assumption (normal distributed data) holds for this particular dataset?
"""
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase()) # returns an array of arrays - this is by row
k = 10
train_acc_sum = 0
k_folds = hw3.partition_folds(spamData, k)
gdas = []
for ki in range(k - 1):
subset = []
gda = hw3.GDA()
X, truth = hw3.separate_X_and_y(k_folds[ki])
covariance_matrix = hw3.get_covar(X)
gda.p_y = float(sum(truth)) / len(truth)
gda.train(X, covariance_matrix, truth)
predictions = gda.predict(X)
#print predictions
accuracy = mystats.get_error(predictions, truth, True)
#gdas.append(gda)
print_output(ki, accuracy)
#print gda.prob
gdas.append(gda)
def testTransposeArray():
dfup = hw3.load_and_normalize_spambase()
cols = dfup.columns[0:3]
sub = utils.train_subset(dfup, cols, 5)
up = hw3.pandas_to_data(sub)
print up
trans = hw3.transpose_array(up)
print trans
def q6():
""" Bagging - sample with replacement """
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
y, X = hw4.split_truth_from_data(spamData)
bagged = bag.Bagging(max_rounds=100, sample_size=1000, learner=lambda: DecisionTreeClassifier(max_depth=3))
bagged.fit(X, y)
kf_fold = hw4.partition_folds(spamData, .4)
test_y, test_X = hw4.split_truth_from_data(kf_fold[0])
test_pred = bagged.predict(test_X)
test_y = bagged._check_y(test_y)
test_pred = bagged._check_y(test_pred)
test_error = float(sum([0 if py == ty else 1 for py, ty in zip(test_pred, test_y)]))/len(test_y)
print 'Final testing error: {}'.format(test_error)
def q3():
"""Run your code from PB1 on Spambase dataset to perform Active Learning.
Specifically:
- start with a training set of about 5% of the data (selected randomly)
- iterate M episodes: train the Adaboost for T rounds; from the datapoints
not in the training set, select the 2% ones that are closest to the
separation surface (boosting score F(x) closest to ) and add these to the
training set (with labels). Repeat until the size of the training set
reaches 50% of the data.
How is the performance improving with the training set increase? Compare the
performance of the Adaboost algorithm on the c% randomly selected training set
with c% actively-built training set for several values of c : 5, 10, 15, 20,
30, 50.
"""
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
percent = .05
all_folds = hw4.partition_folds_q4(spamData, percent)
kf_train = all_folds[0]
kf_test = all_folds[1]
left_over = all_folds[2]
while len(kf_train) < len(spamData)/2:
y, X = hw4.split_truth_from_data(kf_train)
y_test, X_test = hw4.split_truth_from_data(kf_test)
adaboost = run_adaboost(X, y, X_test, y_test, 'q2_crx')
yt_pred = adaboost.predict(X_test)
order = adaboost.rank(X_test)
yt_pred = adaboost._check_y(yt_pred)
y_test = adaboost._check_y(y_test)
round_err = float(np.sum([1 if yt!=yp else 0 for yt, yp in zip(yt_pred, y_test)]))/len(y_test)
print 'Error {}'.format(round_err)
shift_number = int(len(order) * .02) # number of items to switch into training set
mask = []
for i in xrange(shift_number):
mask.append(order[i])
kf_train.append(kf_test[order[i]])
new_test = [kf_test[i] for i in range(len(kf_test)) if i not in mask]
for i in xrange(len(mask)):
new_test.append(left_over[i])
left_over = left_over[len(mask):]
kf_test = new_test[:]
print 'test len {} train len {} leftover len {} shifting {}'.format(len(kf_test), len(kf_train), len(left_over), shift_number)
def spamData():
return hw3.pandas_to_data(hw3.load_and_normalize_spambase())