def run_vmaf_cv(train_dataset_filepath,
test_dataset_filepath,
param_filepath,
output_model_filepath=None,
**kwargs):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
train_dataset = import_python_file(train_dataset_filepath)
test_dataset = import_python_file(
test_dataset_filepath) if test_dataset_filepath is not None else None
param = import_python_file(param_filepath)
# === plot scatter ===
nrows = 1
ncols = 2
fig, axs = plt.subplots(figsize=(5 * ncols, 5 * nrows),
nrows=nrows,
ncols=ncols)
train_test_vmaf_on_dataset(train_dataset,
test_dataset,
param,
param,
axs[0],
axs[1],
result_store,
parallelize=True,
logger=None,
output_model_filepath=output_model_filepath,
**kwargs)
if 'xlim' in kwargs:
axs[0].set_xlim(kwargs['xlim'])
axs[1].set_xlim(kwargs['xlim'])
if 'ylim' in kwargs:
axs[0].set_ylim(kwargs['ylim'])
axs[1].set_ylim(kwargs['ylim'])
bbox = {'facecolor': 'white', 'alpha': 1, 'pad': 20}
axs[0].annotate('Training Set',
xy=(0.1, 0.85),
xycoords='axes fraction',
bbox=bbox)
axs[1].annotate('Testing Set',
xy=(0.1, 0.85),
xycoords='axes fraction',
bbox=bbox)
plt.tight_layout()
# === clean up ===
close_logger(logger)
def run_vmaf_kfold_cv(dataset_filepath, contentid_groups, param_filepath, aggregate_method):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
dataset = import_python_file(dataset_filepath)
param = import_python_file(param_filepath)
fig, ax = plt.subplots(figsize=(5, 5), nrows=1, ncols=1)
cv_on_dataset(dataset, param, param, ax, result_store, contentid_groups, logger, aggregate_method)
ax.set_xlim([0, 120])
ax.set_ylim([0, 120])
plt.tight_layout()
# === clean up ===
close_logger(logger)
def run_vmaf_cv(train_dataset_filepath, test_dataset_filepath, param_filepath, output_model_filepath=None, **kwargs):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
train_dataset = import_python_file(train_dataset_filepath)
test_dataset = import_python_file(test_dataset_filepath) if test_dataset_filepath is not None else None
param = import_python_file(param_filepath)
# === plot scatter ===
nrows = 1
ncols = 2
fig, axs = plt.subplots(figsize=(5 * ncols, 5 * nrows), nrows=nrows, ncols=ncols)
train_test_vmaf_on_dataset(
train_dataset,
test_dataset,
param,
param,
axs[0],
axs[1],
result_store,
parallelize=True,
logger=None,
output_model_filepath=output_model_filepath,
**kwargs
)
if "xlim" in kwargs:
axs[0].set_xlim(kwargs["xlim"])
axs[1].set_xlim(kwargs["xlim"])
if "ylim" in kwargs:
axs[0].set_ylim(kwargs["ylim"])
axs[1].set_ylim(kwargs["ylim"])
bbox = {"facecolor": "white", "alpha": 1, "pad": 20}
axs[0].annotate("Training Set", xy=(0.1, 0.85), xycoords="axes fraction", bbox=bbox)
axs[1].annotate("Testing Set", xy=(0.1, 0.85), xycoords="axes fraction", bbox=bbox)
plt.tight_layout()
# === clean up ===
close_logger(logger)
def run_vmaf_kfold_cv(dataset_filepath,
contentid_groups,
param_filepath,
aggregate_method,
):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
dataset = import_python_file(dataset_filepath)
param = import_python_file(param_filepath)
fig, ax = plt.subplots(figsize=(5, 5), nrows=1, ncols=1)
cv_on_dataset(dataset, param, param, ax, result_store, contentid_groups,
logger, aggregate_method)
ax.set_xlim([0, 120])
ax.set_ylim([0, 120])
plt.tight_layout()
# === clean up ===
close_logger(logger)
def run_vmaf_cv(train_dataset_filepath,
test_dataset_filepath,
param_filepath,
output_model_filepath=None):
logger = get_stdout_logger()
result_store = FileSystemResultStore()
train_dataset = import_python_file(train_dataset_filepath)
test_dataset = import_python_file(test_dataset_filepath)
param = import_python_file(param_filepath)
# === plot scatter ===
nrows = 1
ncols = 2
fig, axs = plt.subplots(figsize=(5*ncols, 5*nrows), nrows=nrows, ncols=ncols)
train_test_on_dataset(train_dataset, test_dataset, param, param, axs[0], axs[1],
result_store, parallelize=False, logger=None,
output_model_filepath=output_model_filepath)
# axs[0].set_xlim([0, 120])
# axs[0].set_ylim([0, 120])
# axs[1].set_xlim([0, 120])
# axs[1].set_ylim([0, 120])
bbox = {'facecolor':'white', 'alpha':1, 'pad':20}
axs[0].annotate('Training Set', xy=(0.1, 0.85), xycoords='axes fraction', bbox=bbox)
axs[1].annotate('Testing Set', xy=(0.1, 0.85), xycoords='axes fraction', bbox=bbox)
plt.tight_layout()
# === clean up ===
close_logger(logger)
