def test_open(self):
with open('LICENSE.txt') as f:
data = f.readlines()
self.assertListEqual(data, seq.open('LICENSE.txt').to_list())
text = ''.join(data).split(',')
self.assertListEqual(text, seq.open('LICENSE.txt', delimiter=',').to_list())
with self.assertRaises(ValueError):
seq.open('LICENSE.txt', mode='w').to_list()
def plot(filename):
data = seq.open(filename).map(parse_line)
bfs = data.filter(_.algorithm == 'bfs')
dfs = data.filter(_.algorithm == 'dfs')
x = np.array(bfs.map(lambda x: x.vertexes * x.edges * x.edges).list())
y = np.array(bfs.map(_.runtime).list())
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print(slope, intercept, r_value, p_value, std_err)
plt.title('Numerical Performance of Edmonds-Karp')
plt.xlabel('Input Size in VE^2')
plt.ylabel('Running Time in Seconds')
plt.scatter(x, y)
plt.show()
plt.clf()
ff_data = dfs.map(lambda x: (x.flow, x.flow * x.edges, x.runtime)).group_by(_[0]).cache()
plt.title('Numerical Performance of Ford-Fulkerson')
plt.xlabel('Input Size in Ef')
plt.ylabel('Running Time in Seconds')
max_flow = ff_data.max_by(lambda kv: kv[0])[0]
all_x = list()
all_y = list()
for k, v in ff_data:
x = list(map(_[1], v))
all_x.extend(x)
y = list(map(_[2], v))
all_y.extend(y)
ratio = 1 - k / max_flow
if ratio > .8:
ratio = .8
plt.scatter(x, y, color=str(ratio))
x = np.array(all_x)
y = np.array(all_y)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print(slope, intercept, r_value, p_value, std_err)
plt.show()
def test_seq_open(self):
path = _make_tmp_file('''
red
green
blue
''')
res = seq.open(path)
assert res == ['red\n', 'green\n', 'blue\n', '\n']
def load_meta(meta_file):
def parse_line(line):
tokens = line.split()
question = int(tokens[0])
sentence = int(tokens[1])
token = int(tokens[2])
guess = ' '.join(tokens[3:])
return Meta(question, sentence, token, guess)
return seq.open(meta_file).map(parse_line)
def load_predictions(pred_file):
def parse_line(line):
try:
tokens = line.split()
score = float(tokens[0])
if len(tokens) < 2:
question, sentence, token = None, None, None
else:
question, sentence, token = [int(x) for x in tokens[1].split('_')]
return Prediction(score, question, sentence, token)
except Exception:
print("Error parsing line: {0}".format(line))
raise
return seq.open(pred_file).map(parse_line)
def load_predictions(pred_file: str) -> Sequence:
def parse_line(line: str) -> Prediction:
try:
tokens = line.split()
score = float(tokens[0])
if len(tokens) < 2:
question, sentence, token = None, None, None
else:
question, sentence, token = [
int(x) for x in tokens[1].split('_')
]
return Prediction(score, question, sentence, token)
except Exception:
log.info("Error parsing line: {0}".format(line))
raise
return seq.open(pred_file).map(parse_line)
def plot(filename):
data = seq.open(filename).map(parse_line)
bfs = data.filter(_.algorithm == 'bfs')
dfs = data.filter(_.algorithm == 'dfs')
x = np.array(bfs.map(lambda x: x.vertexes * x.edges * x.edges).list())
y = np.array(bfs.map(_.runtime).list())
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print(slope, intercept, r_value, p_value, std_err)
plt.title('Numerical Performance of Edmonds-Karp')
plt.xlabel('Input Size in VE^2')
plt.ylabel('Running Time in Seconds')
plt.scatter(x, y)
plt.show()
plt.clf()
ff_data = dfs.map(lambda x:
(x.flow, x.flow * x.edges, x.runtime)).group_by(
_[0]).cache()
plt.title('Numerical Performance of Ford-Fulkerson')
plt.xlabel('Input Size in Ef')
plt.ylabel('Running Time in Seconds')
max_flow = ff_data.max_by(lambda kv: kv[0])[0]
all_x = list()
all_y = list()
for k, v in ff_data:
x = list(map(_[1], v))
all_x.extend(x)
y = list(map(_[2], v))
all_y.extend(y)
ratio = 1 - k / max_flow
if ratio > .8:
ratio = .8
plt.scatter(x, y, color=str(ratio))
x = np.array(all_x)
y = np.array(all_y)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print(slope, intercept, r_value, p_value, std_err)
plt.show()
def logfile2blocks(self, path):
# type: (Path) -> Iterable[str]
return seq.open(path.as_posix(), encoding='utf8')
def _read_file(path):
content = seq.open(path, delimiter='\n') \
.map(lambda x: x.strip()) \
.filter(lambda x: x != '') \
.make_string('\n')
return content
def test_open_gzip(self):
with open("functional/test/data/test.csv", "rb") as f:
data = f.readlines()
self.assertListEqual(data, seq.open('functional/test/data/test.csv.gz').to_list())