def test_heatmap_archive(_grid_archive, _cvt_archive, _sliding_archive):
plt.figure(figsize=(8, 6))
grid_archive_heatmap(_grid_archive)
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_cvt_archive)
plt.figure(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_sliding_archive)
def test_heatmap_long_square(_long_grid_archive, _long_cvt_archive,
_long_sliding_archive):
plt.figure(figsize=(8, 6))
grid_archive_heatmap(_long_grid_archive, square=True)
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_long_cvt_archive, square=True)
plt.figure(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_long_sliding_archive, square=True)
def test_cvt_archive_heatmap_with_custom_axis(_grid_archive, _cvt_archive,
_sliding_archive):
_, ax = plt.subplots(figsize=(8, 6))
grid_archive_heatmap(_grid_archive, ax=ax)
_, ax = plt.subplots(figsize=(8, 6))
cvt_archive_heatmap(_cvt_archive, ax=ax)
_, ax = plt.subplots(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_sliding_archive, ax=ax)
def test_heatmap_listed_cmap(_grid_archive, _cvt_archive, _sliding_archive):
cmap = [[1, 0, 0], [0, 1, 0], [0, 0, 1]] # Primary red, green, blue.
plt.figure(figsize=(8, 6))
grid_archive_heatmap(_grid_archive, cmap=cmap)
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_cvt_archive, cmap=cmap)
plt.figure(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_sliding_archive, cmap=cmap)
def test_heatmap_long_transpose(_long_grid_archive, _long_cvt_archive,
_long_sliding_archive):
plt.figure(figsize=(8, 6))
grid_archive_heatmap(_long_grid_archive, transpose_bcs=True)
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_long_cvt_archive, transpose_bcs=True)
plt.figure(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_long_sliding_archive,
transpose_bcs=True)
def test_heatmap_coolwarm_cmap(_grid_archive, _cvt_archive, _sliding_archive):
cmap = matplotlib.cm.get_cmap("coolwarm")
plt.figure(figsize=(8, 6))
grid_archive_heatmap(_grid_archive, cmap=cmap)
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_cvt_archive, cmap=cmap)
plt.figure(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_sliding_archive, cmap=cmap)
def test_heatmap_with_limits(_grid_archive, _cvt_archive, _sliding_archive):
# Negative sphere function should have range (-2, 0). These limits should
# give a more uniform-looking archive.
kwargs = {"vmin": -1.0, "vmax": -0.5}
plt.figure(figsize=(8, 6))
grid_archive_heatmap(_grid_archive, **kwargs)
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_cvt_archive, **kwargs)
plt.figure(figsize=(8, 6))
sliding_boundaries_archive_heatmap(_sliding_archive, **kwargs)
def save_heatmap(archive, heatmap_path):
"""Saves a heatmap of the archive to the given path.
Args:
archive (GridArchive or CVTArchive): The archive to save.
heatmap_path: Image path for the heatmap.
"""
if isinstance(archive, GridArchive):
plt.figure(figsize=(8, 6))
grid_archive_heatmap(archive, vmin=0, vmax=100)
plt.tight_layout()
plt.savefig(heatmap_path)
elif isinstance(archive, CVTArchive):
plt.figure(figsize=(16, 12))
cvt_archive_heatmap(archive, vmin=0, vmax=100)
plt.tight_layout()
plt.savefig(heatmap_path)
plt.clf()
def test_heatmap_with_custom_axis__cvt(cvt_archive):
_, ax = plt.subplots(figsize=(8, 6))
cvt_archive_heatmap(cvt_archive, ax=ax)
def test_heatmap_coolwarm_cmap__cvt(cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(cvt_archive, cmap="coolwarm")
def test_heatmap_listed_cmap__cvt(cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(cvt_archive, cmap=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
def test_heatmap_with_limits__cvt(cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(cvt_archive, vmin=-1.0, vmax=-0.5)
def test_heatmap_long_transpose__cvt(long_cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(long_cvt_archive, transpose_bcs=True)
def test_heatmap_long_square__cvt(long_cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(long_cvt_archive, square=True)
def test_heatmap_long__cvt(long_cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(long_cvt_archive)
def sphere_main(algorithm,
dim=20,
itrs=4500,
outdir="sphere_output",
log_freq=250,
seed=None):
"""Demo on the Sphere function.
Args:
algorithm (str): Name of the algorithm.
dim (int): Dimensionality of solutions.
itrs (int): Iterations to run.
outdir (str): Directory to save output.
log_freq (int): Number of iterations to wait before recording metrics
and saving heatmap.
seed (int): Seed for the algorithm. By default, there is no seed.
"""
outdir = Path(outdir)
if not outdir.is_dir():
outdir.mkdir()
optimizer = create_optimizer(algorithm, dim, seed)
archive = optimizer.archive
metrics = {
"QD Score": {
"x": [0],
"y": [0.0],
},
"Archive Coverage": {
"x": [0],
"y": [0.0],
},
}
non_logging_time = 0.0
with alive_bar(itrs) as progress:
for itr in range(1, itrs + 1):
itr_start = time.time()
sols = optimizer.ask()
objs, bcs = sphere(sols)
optimizer.tell(objs, bcs)
non_logging_time += time.time() - itr_start
progress()
# Logging and output.
final_itr = itr == itrs
if itr % log_freq == 0 or final_itr:
name = f"{algorithm}_{dim}"
data = archive.as_pandas(include_solutions=final_itr)
if final_itr:
data.to_csv(str(outdir / f"{name}_archive.csv"))
# Record and display metrics.
total_cells = 10_000 if isinstance(archive,
CVTArchive) else 500 * 500
metrics["QD Score"]["x"].append(itr)
metrics["QD Score"]["y"].append(data['objective'].sum())
metrics["Archive Coverage"]["x"].append(itr)
metrics["Archive Coverage"]["y"].append(
len(data) / total_cells * 100)
print(f"Iteration {itr} | Archive Coverage: "
f"{metrics['Archive Coverage']['y'][-1]:.3f}% "
f"QD Score: {metrics['QD Score']['y'][-1]:.3f}")
# Generate heatmap.
heatmap_path = str(outdir / f"{name}_heatmap_{itr:05d}.png")
if isinstance(archive, GridArchive):
heatmap_data = np.full(archive.dims, np.nan)
for row in data.itertuples():
heatmap_data[row.index_0, row.index_1] = row.objective
sns.heatmap(heatmap_data, cmap="magma", vmin=0, vmax=100)
plt.savefig(heatmap_path)
elif isinstance(archive, CVTArchive):
plt.figure(figsize=(16, 12))
cvt_archive_heatmap(archive, vmin=0, vmax=100)
plt.savefig(heatmap_path)
plt.clf()
# Plot metrics.
print(f"Algorithm Time (Excludes Logging and Setup): {non_logging_time}s")
for metric in metrics:
plt.plot(metrics[metric]["x"], metrics[metric]["y"])
plt.title(metric)
plt.xlabel("Iteration")
plt.savefig(
str(outdir / f"{name}_{metric.lower().replace(' ', '_')}.png"))
plt.clf()
with (outdir / f"{name}_metrics.json").open("w") as file:
json.dump(metrics, file, indent=2)
def test_cvt_archive_heatmap_with_samples(_cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(_cvt_archive, plot_samples=True)
def test_heatmap_archive__cvt(cvt_archive):
plt.figure(figsize=(8, 6))
cvt_archive_heatmap(cvt_archive)
