def plot_longs():
for n in [500, 5000, 20000]:
long_metric = Metric('x', 'y')
for i in range(n):
long_metric.add_record(random.random(), random.random())
plot(long_metric, 'temp', name=f'longer_metric_{n}')
def test_some_empty_plots(tmpdir):
files_before = len(os.listdir(tmpdir))
m1 = Metric('empty')
m2 = Metric('empty2')
m2.add_record(1, 1)
plot_group([m1, m2], folder=tmpdir)
assert len(os.listdir(tmpdir)) > files_before
def test_discard_warmup():
m = Metric()
for i in range(10):
m.add_record(i, i % 2)
m.discard_warmup(0.5)
assert 4 < len(m.data) < 6
assert 0 not in m.data
assert 8 in m.data
def test_ezplot(tmpdir):
m = Metric('x', 'y')
m.add_ys(1, [1, 2, 3])
m.add_record(2, 4)
m.add_record(2, 4)
m.add_record(2, 4)
m.add_ys(4, [5, 6, 12])
files_before = len(os.listdir(tmpdir))
plot(m, folder=tmpdir, name="bla3")
files_after = len(os.listdir(tmpdir))
assert files_after > files_before
def reinforce(lr: float):
assert 0 < lr < 1
m = Metric(f"lr={lr:.5f}")
env = ShortCoridorEnv()
agent = CoridorAgent()
for episode in range(3_000):
history, total_reward = rollout(agent, env)
m.add_record(episode, total_reward)
G = sum(r for o, a, r in history)
for o, a, r in history:
apply_grad(G, a, agent, lr)
G -= r
def test_plot_group(tmpdir):
m = Metric('x', 'y')
m.add_ys(1, [1, 2, 3])
m.add_record(2, 4)
m.add_record(2, 4)
m.add_record(2, 4)
m.add_ys(4, [5, 6, 12])
m2 = Metric('x', 'y')
m2.add_ys(1, [4, 3, 5])
m2.add_record(2, 5)
m2.add_record(2, 9)
m2.add_record(2, 9)
m2.add_ys(4, [5, 6, -1])
files_before = len(os.listdir(tmpdir))
plot_group([m, m2], folder=tmpdir, name="bla3")
files_after = len(os.listdir(tmpdir))
assert files_after > files_before
from pennpaper import Metric, plot
import random
metrics = [Metric('x', 'y') for x in range(10)]
for m in metrics:
for i in range(50):
m.add_record(random.random(), random.random())
m._sort()
m11 = sum(metrics)
plot(metrics[0], 'temp', name="single_random")
plot(m11, 'temp', name="summ_10_random")
straight = Metric('x', 'y')
for i in range(50):
straight.add_record(i, i + 0.1 * random.random())
plot(straight, 'temp', name='straight')
return _
pow2 = noisy_mapping(lambda x: x ** 2)
"""
lets record the pairs (x, f(x)) in a metric and make a plot:
"""
from pennpaper import Metric, plot_group, plot
m1 = Metric("pow2")
for x in X:
m1.add_record(x, pow2(x))
plot(m1)
m2 = Metric("pow2_2")
for x in X:
m2.add_record(x, pow2(x))
plot_group([m1, m2])
# Actually, m1 and m2 are metrics of the same process.
# What if we create a new metric tracking the mean and stddev of this process?
m3 = m1 + m2
plot(m3)
from pennpaper import Metric, plot m1 = Metric() m1.add_record(1,1) m1.add_record(5,5) m2 = Metric() m2.add_record(2, 3) m2.add_record(4, 3) m3 = m1 + m2 print(m3.data, m3.samples) plot(m3) plot(m3, smoothen=False, name='true')