def test_adaboost(self):
X, Y = ada.parse_spambase_data("tiny.spam.train")
Y2 = ada.new_label(self.y)
trees, weights = ada.adaboost(X, Y2, 2)
self.assertEqual(len(trees), 2)
self.assertEqual(len(weights), 2)
self.assertTrue(
isinstance(trees[0], sklearn.tree.tree.DecisionTreeClassifier))
x = np.array([[0, -1], [1, 0], [-1, 0]])
y = np.array([-1, 1, 1])
trees, weights = ada.adaboost(x, y, 1)
h = trees[0]
pred = h.predict(x)
for i in range(len(y)):
self.assertEqual(pred[i], y[i])
def test_adaboost_predict(self):
x = np.array([[0, -1], [1, 0], [-1, 0]])
y = np.array([-1, 1, 1])
trees, weights = ada.adaboost(x, y, 1)
pred = ada.adaboost_predict(x, trees, weights)
for i in range(len(y)):
self.assertEqual(pred[i], y[i])
def main():
filename = sys.argv[1]
hypothesisFile = sys.argv[2]
learning_type = sys.argv[3]
input_data = []
print("opening training file", filename)
file = open(filename, "r", encoding="utf8")
for line in file.readlines():
input_data.append(line.rstrip())
training_dataset = get_attributes(input_data)
attribute_list = [i for i in range(len(training_dataset[0]) - 1)]
if learning_type == "dt":
print("Calling decisionTree...")
root = decisiontree.decision_tree(training_dataset,
attribute_list,
depth=5)
print("Decision Tree Model ready..")
elif learning_type == "ada":
print("Calling Adaboost...")
root = adaboost.adaboost(training_dataset, attribute_list, K=8)
print("Adaboost Model ready..")
else:
print("Invalid option!")
sys.exit()
with open(hypothesisFile, "wb") as output_file:
pickle.dump(root, output_file)
def train(self):
"""
Adaboost, where 75 strong classifiers (decision trees with maxdepth = 10) are boosted,
will be used.
"""
self.boostedTrees, self.adaptiveParams = adaboost(self.trainDat, 200)
fp1 = open("proc_data/boostedTreesFinalChal.pkl", "wb")
fp2 = open("proc_data/adaptiveParamsFinalChal.pkl", "wb")
pkl.dump(self.boostedTrees, fp1)
pkl.dump(self.adaptiveParams, fp2)
fp1.close()
fp2.close()
def train(self):
'''
Adaboost, where 75 strong classifiers (decision trees with maxdepth = 10) are boosted,
will be used.
'''
self.boostedTrees, self.adaptiveParams = adaboost(self.trainDat, 200)
fp1 = open('proc_data/boostedTreesFinalChal.pkl', 'wb')
fp2 = open('proc_data/adaptiveParamsFinalChal.pkl', 'wb')
pkl.dump(self.boostedTrees, fp1)
pkl.dump(self.adaptiveParams, fp2)
fp1.close()
fp2.close()
N = 200
mat = sio.loadmat('../mnist.mat')
print mat['test_X'].shape[0]
chooseTrain = np.random.permutation(mat['train_X'].shape[0])
chooseTest = np.random.permutation(mat['test_X'].shape[0])
maxIter = 20
train_X = mat['train_X']
train_Y = mat['train_Y']
test_X = mat['test_X']
test_Y = mat['test_Y']
print train_X.shape
print train_Y.shape
print chooseTrain.shape
e_train, e_test, maxIter = adaboost(train_X[chooseTrain[0:10000], :],
train_Y[0, chooseTrain[0:10000]],
test_X[chooseTest[0:1000], :],
test_Y[0, chooseTest[0:1000]], maxIter)
print e_train
print e_test
# evenly sampled time at 200ms intervals
t = range(maxIter)
# red dashes, blue squares and green triangles
plt.plot(t, e_train, 'b:', t, e_test, 'k-', t, e_train[0] * np.ones(maxIter),
'b--', e_test[0] * np.ones(maxIter), 'b--')
plt.show()
#plot
'--test_users',
action=readFile,
help='Indexes in the trained model. Type = list, in json file')
parser.add_argument(
'-ai',
'--available_items',
default=slice(None),
action=readFile,
help='Indexes in the trained model. Type = list, in json file')
# parser.add_argument('-bs','--batch_size', type=int, default=5000, help='Size of user batch')
# parser.add_argument('-th','--thread', type=int, default=6, help='# of threads for multi processing')
args = parser.parse_args()
if args.command == "train":
ensemble = adaboost(args.opt_data,
args.data,
args.n_iter,
saveTime=args.save_time,
modelList=args.model) # retrain:ensemble=ensemble
with open("data/ensemble2", "wb") as f:
pickle.dump(ensemble, f)
elif args.command == "test":
recall = get_recall(args.model, args.data, args.opt_data,
args.recall_at, args.test_users,
args.available_items, args.in_out)
print(recall)
elif args.command == "rec":
topidx = getRecList(args.model, args.n_rec, args.opt_data,
args.test_users, args.available_items)
print(topidx.shape)
# =============================================================================
# ################ Main Function ################
# =============================================================================
maxDepth = 20 # Online\Avg Perceptron Loop number
fileName1 = "pa3_train_reduced_bo.csv" # Training File name
fileName2 = "pa3_valid_reduced.csv" # Validate File name
warnings.filterwarnings("error")
print("\n ------------ ImportDaTa ------------")
trainData = hp.importCsv(fileName1)
validateData = hp.importCsv(fileName2)
for l in [1, 5, 10, 20]:
# for l in [1]:
print("\n ------------ Adaboost-{0} ------------{1}".format(l, datetime.datetime.now()))
adaClass = ada.adaboost(ftrNum=trainData.shape[1] - 1, depth=1, lNum=l, dataNum=trainData.shape[0])
adaClass.runAdaboost(df=trainData)
print(adaClass.computeFinalAccNumRate(df=trainData))
# for d, m, n in [(9, 20, 1), (9, 20, 2), (9, 20, 5), (9, 20, 10), (9, 20, 25)]:
# for d, m, n in [(9, 50, 1), (9, 50, 2), (9, 50, 5), (9, 50, 10), (9, 50, 25)]:
# for d, m, n in [(9, 10, 1), (9, 10, 2), (9, 10, 5), (9, 10, 10), (9, 10, 25)]:
# print("\n ------------ Build Forest{0} ------------{1}".format(n, datetime.datetime.now()))
# ftClass = ft.randomForest(treeNum=n, ftrNum=m, depth=d, dataNum=trainData.shape[0])
# ftClass.buildRandomForest(df=trainData)
# ftClass.predicDataResult(df=trainData)
# print("\n ------------ Build DT ------------{0}".format(datetime.datetime.now()))
# dtClass = dt.decesionTree(maxDepth, trainData.shape[0])
# root1 = root2 = cur = Node((None, None, dtClass.getLabelFromLargeData(trainData)))
# cl, cr = dtClass.getResultInfo(trainData)
train_size = int(training_block.shape[0]) att_size = int(len(attributes)) forest_size = 100 [ensemble_error, ensemble_pred] = rf.ensemble(test_block, test_label_block, rf.raise_forest(training_block,training_label_block, forest_size, train_size, att_size)) error[0]+= (1.0/k) * ensemble_error #cross validation for decision tree print "Cross Validating Decision Tree..." dec_tree = id3.id3(train_examples, attributes) dec_tree_errors = 0 for i in xrange(len(test_block)): if id3.classify(dec_tree, test_block[i]) != test_label_block[i]: dec_tree_errors += 1 error[1] += (1.0/k) * (float(dec_tree_errors) / set_size) print "Cross Validating AdaBoost..." adaboost_classifier = adaboost.adaboost(train_examples, adaboost_rounds) adaboost_errors = 0 for i in xrange(len(test_block)): if adaboost.classify(adaboost_classifier, test_block[i]) != test_label_block[i]: adaboost_errors += 1 error[2] += (1.0/k) * (float(adaboost_errors) / set_size) print (1-error[0]), (1-error[1]) print 'Estimated accuracy of Random Forest:', (1-error[0]) print 'Estimated accuracy of Decision Tree:', (1-error[1]) print 'Estimated accuracy of AdaBoost:', (1-error[2])
This method is specific to the format of the classifiers
"""
output = {key: [] for key in classifiers}
N = len(X)
for cf in classifiers:
for i in range(N):
cf_classification = cf[2](X[i])
if cf_classification != Y[i]:
# output[cf].append(X[i])
output[cf].append(adaboost.key_from_value(ids_to_points, X[i]))
return output
digits_classifiers = digits_make_classifiers(X, y)
digits_ids_to_points = adaboost.make_point_identifiers(X)
digits_classifiers_to_misclassified = digits_make_classifiers_to_misclassified(
X, y, digits_classifiers, digits_ids_to_points)
digits_points = digits_ids_to_points.keys()
resulting_classifier = adaboost.adaboost(digits_points,
digits_classifiers_to_misclassified,
max_num_rounds=20)
print 'resulting_classifier', resulting_classifier
features_chosen = [i[0][0] for i in resulting_classifier]
print 'features_chosen', features_chosen
# Testing
# feature_test = lambda x,cutoff: x[0] > cutoff
# print test_feature(feature_test,X,y,0)
# print sum(y)
