def q_2_4():
print("******RUNNING TITANIC DATA SET*****")
data, test_data, feature_names, class_names = load_titanic_data()
data = preprocess_titanic(data, True)
perm = np.random.RandomState(seed=20).permutation((data.shape[0]))
data = data[perm]
data, valid = data[:800], data[800:]
idy = data.shape[1] - 1
type_map, categories_map = gen_maps(data)
classifier = DecisionTree(type_map, categories_map)
classifier.fit(data, 4, 10)
train_predictions = classifier.predict(data)
train_actual = extract_column(data, idy)
valid_predictions = classifier.predict(valid)
valid_actual = extract_column(valid, idy)
print("Decision Tree training Accuracies: ",
error_rate(train_predictions, train_actual))
print("Decision Tree Validation Accuracies: ",
error_rate(valid_predictions, valid_actual))
classifier = RandomForest(300, 300, 2, type_map, categories_map, 20)
classifier.fit(data, 10, 10)
train_predictions = classifier.predict(data)
train_actual = extract_column(data, idy)
valid_predictions = classifier.predict(valid)
valid_actual = extract_column(valid, idy)
print("Random Forest training Accuracies: ",
error_rate(train_predictions, train_actual))
print("Random Forest Validation Accuracies: ",
error_rate(valid_predictions, valid_actual))
def gini_purification(data, idx, categorical, thresholds):
"""
Input: data (last column is label column), feature to split on, whether or not the split is categorical,
list of thresholds or categories
Output: the change in gini-impurity, lower (negative) is good.
"""
idy = data.shape[1] - 1
y = extract_column(data, idy)
x = extract_column(data, idx)
gini_before = DecisionTree.gini_impurity(y)
gini_after = 0
# joined represents two columns, left is idx feature, right is the label
joined = np.hstack((x, y))
total = len(joined)
if categorical:
for category in thresholds:
split_data = joined[joined[:, 0] == category]
new_y = extract_column(split_data, 1)
gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)
else:
for thresh in thresholds:
split_data_below = joined[joined[:, 0] < thresh]
joined = joined[joined[:, 0] >= thresh]
new_y = extract_column(split_data_below, 1)
gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)
new_y = extract_column(joined, 1)
gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)
gini_after /= total
return gini_before - gini_after
def image_delete_and_containers(image):
""" WARNING: This will remove an image and all its dependant containers
"""
for container in utils.extract_column(utils.filter_column(utils.Command('docker ps').stdout, 1, eq=image), -1):
container_stop(container)
for container in utils.extract_column(utils.filter_column(utils.Command('docker ps -a').stdout, 1, eq=image), -1):
container_delete(container)
return image_delete(image)
def extract_embeddings_as_features(self, inputfile, embedding_model='word2vec-google-news-300'):
'''
Function that extracts features and gold labels using word embeddings
:param inputfile: path to inputfile
:param token_column: header name of column with tokens
:param embedding_model: name of a pretrained word embedding model
:type inputfile: string
:type token_column: string
:type embedding_model: string
:return features: list of vector representation of tokens
'''
### This code was partially inspired by code included in the HLT course, obtained from https://github.com/cltl/ma-hlt-labs/, accessed in May 2020.
features = []
embedding_model = api.load(embedding_model)
tokens = extract_column(inputfile, 'token')
for token in tokens:
if token in embedding_model:
vector = embedding_model[token]
else:
vector = [0]*300
features.append(vector)
return features
def wait_running_process(cmd, container, timeout=1):
count, step = timeout, 0.2
while count > 0:
if cmd in utils.extract_column(docker_exec('ps -A', container, user='******'), -1, 1):
return True
time.sleep(step)
count -= step
def run_evaluations(data_infofile):
'''
Carry out standard evaluation for one or more system outputs
:param goldfile: path to file with goldstandard
:param data_infofile: filepath to json providing information about system and data to files
:type goldfile: string
:type data_infofile: string
:returns the evaluations for all systems
'''
evaluations = {}
data = load_json(data_infofile)
gold_annotations = extract_column(data['gold']['file'], data['gold']['annotation_column'])
for key, value in data.items():
if key != 'training' and key != 'gold':
system_annotations = extract_column(value['file'], value['annotation_column'])
sys_evaluation = carry_out_evaluation(gold_annotations, system_annotations)
evaluations[key] = sys_evaluation
return evaluations
def get_networks(filter=None, driver=None):
docker_cmd = 'docker network ls'
if driver:
networks = utils.extract_column(utils.filter_column(utils.Command(docker_cmd).stdout, 2, 1, eq=driver), 1)
else:
networks = utils.Command(docker_cmd).stdout_column(1, 1)
if filter:
if isinstance(filter, basestring):
return [x for x in networks if filter in x]
else:
return [x for x in networks if x in filter]
return networks
def create_user(user, container, groups=(), home=None, shell=None):
""" Create a user with optional groups, home and shell
"""
cmd = 'useradd {}{}{}'.\
format(user,
' -d {}'.format(home) if home else '',
' -s {}'.format(shell) if shell else '')
docker_exec(cmd, container)
existing_groups = utils.extract_column(docker_exec('cat /etc/group', container), 0, sep=':')
for group in groups:
if group not in existing_groups:
docker_exec('addgroup {}'.format(group), container)
docker_exec('usermod -a -G {} {}'.format(group, user), container)
def kaggle():
data, test_data, feature_names, class_names = load_titanic_data()
data = preprocess_titanic(data, True)
test = preprocess_titanic(test_data, False)
type_map, categories_map = gen_maps(data)
classifier = DecisionTree(type_map, categories_map)
classifier.fit(data, 4, 10)
predictions = classifier.predict(test)
pred_train = classifier.predict(data)
actual = extract_column(data, 9)
print(error_rate(pred_train, actual))
results_to_csv(predictions.flatten())
"""
def gen_maps(data):
type_map = {}
categories_map = {}
for i in [0, 1, 7, 8]:
type_map[i] = 'categorical'
for i in [2, 3, 4, 6]:
type_map[i] = 'quantitative'
type_map[5] = 'clustered'
categories_map[0] = [1, 2, 3]
categories_map[1] = ['male', 'female']
categories_map[7] = list(set(extract_column(data, 7).flatten()))
categories_map[8] = ['S', 'C', 'Q']
return type_map, categories_map
def get_labels(self, inputfile, annotation_column):
"""
This function extracts all the labels from the inputfile in the form required by the model
:param inputfile: path to inputfile
:param annotation_column: indication of column with annotations
:type inputfile: string
:type annotation_column: string
:returns: labels in required form
"""
# extract the labels in the form required for CRF and return
if self.modelname == 'crf':
return self.extract_crf_labels(inputfile, annotation_column)
# extract and return the labels in the form required for all other models
return extract_column(inputfile, annotation_column)
def get_containers(filter=None, image=None, all=True):
""" Get containers names, with optional filter on name.
:param filter: if string, get containers names containing it, if python container (list, set, ...),
get containers in this set.
:param image: if string, get containers from this image (ignore filter).
:param all: if False, get only running containers, else get all containers.
:return: a list of containers names
"""
docker_cmd = 'docker ps -a' if all else 'docker ps'
if image:
return utils.extract_column(utils.filter_column(utils.Command(docker_cmd).stdout, 1, eq=image), -1)
else:
containers = utils.Command(docker_cmd).stdout_column(-1, 1)
if filter:
if isinstance(filter, basestring):
return [x for x in containers if filter in x]
else:
return [x for x in containers if x in filter]
return containers
def preprocess_titanic(data, include_labels):
imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
imp_mode = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
columns = [0 for _ in range(9)]
current_col = extract_column(data, 5).flatten()
current_col = [re.sub('[^0-9]', '', ticket) for ticket in current_col] #remove all letters
current_col = [float(ticket) if ticket != '' else float(np.nan) for ticket in current_col] #convert to float
current_col = np.array([current_col]).T
current_col = imp_mean.fit_transform(current_col)
columns[5] = current_col
for col in [0, 1, 8]:
current_col = extract_column(data, col)
current_col =imp_mode.fit_transform(current_col)
columns[col] = (current_col)
for col in [2, 3, 4, 6]:
current_col = extract_column(data, col)
current_col = imp_mean.fit_transform(current_col)
columns[col] = (current_col)
current_col = extract_column(data, 7).flatten()
current_col = [re.sub('[^A-z]', '', cabin)[0] if isinstance(cabin, str) else cabin for cabin in current_col]
class_col = extract_column(data, 0).flatten()
current_col = ['B' if not isinstance(current_col[i], str) and class_col[i] == 1 else current_col[i] for i in
range(len(current_col))]
current_col = ['D' if not isinstance(current_col[i], str) and class_col[i] == 2 else current_col[i] for i in
range(len(current_col))]
current_col = ['F' if not isinstance(current_col[i], str) and class_col[i] == 3 else current_col[i] for i in
range(len(current_col))]
current_col = np.array([current_col]).T
columns[7] = current_col
if include_labels:
columns.append(extract_column(data, 9))
return np.hstack(tuple(columns))
def gen_quantitative_thresholds(data, idx):
xs = extract_column(data, idx)
return [np.mean(xs)]
def get_processes(container, filter=None):
processes = utils.extract_column(docker_exec('ps -A', container, user='******'), -1, 1)
if filter is None:
return processes
return [proc for proc in processes if filter in proc]
def get_version(app, container):
output = docker_exec('apt-cache policy {}'.format(app), container, user='******')
try:
return utils.extract_column(utils.filter_column(output, 0, startswith='Install'), 1, sep=':')[0]
except IndexError:
pass
