def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
if not os.path.exists(FLAGS.vars_file):
raise ValueError("No data found at %s" % FLAGS.vars_file)
if not os.path.exists(FLAGS.save_dir):
os.mkdir(FLAGS.save_dir)
ds = dataset_threetone.MaskingDataset()
for filename in os.listdir(FLAGS.data_path):
if filename.startswith("masker") and filename.endswith(".txt"):
ds.read_data(FLAGS.data_path, filename)
model_class = model.FullModel()
model_class.initialize_models(FLAGS.vars_file)
total_error = 0
total_points = 0
baseline_error = 0
for example in ds.get_all_data():
prediction = model_class.predict(example["masker_frequency"],
example["probe_level"],
example["masker_level"],
example["probe_frequency_bark"])
error = (example["target_bel_masking"] - prediction)**2
baseline_error += (example["target_bel_masking"] - 0)**2
total_error += error
total_points += 1
print("Baseline Mean squared error: {}".format(baseline_error / total_points))
print("Mean squared error: {}".format(total_error / total_points))
if FLAGS.plot_all_curves:
plot_all_learned_curves(ds,
[1370.0],
[30],
[80],
model_class,
FLAGS.save_dir)
if FLAGS.plot_ood_curves:
mask_frequencies = [1370.0]
probe_levels = [30]
mask_levels = [80]
plot_all_learned_curves(ds, mask_frequencies, probe_levels, mask_levels,
model_class, FLAGS.save_dir)
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
if not os.path.exists(FLAGS.vars_file):
raise ValueError("No data found at %s" % FLAGS.vars_file)
if not os.path.exists(FLAGS.infile_path):
raise ValueError("No data found at %s" % FLAGS.infile_path)
if not os.path.exists(FLAGS.save_dir):
os.mkdir(FLAGS.save_dir)
with open(FLAGS.infile_path, "r") as infile:
data = json.load(infile)
model_class = model.FullModel()
model_class.initialize_models(FLAGS.vars_file)
total_error = 0
total_points = 0
for example in data:
probe_frequency_khz = example["probe_frequency"] / 1000
prediction = model_class.predict(example["masker_frequency"],
example["probe_level"],
example["masker_level"],
probe_frequency_khz)
predicted_perceived_level = example["probe_level"] - (prediction * 10)
print("For masker f {}, masker level {}, probe f {}, probe level {}\n Predicted: {}".format(
example["masker_frequency"], example["masker_level"],
example["probe_frequency"], example["probe_level"],
predicted_perceived_level))
example["perceived_probe_levels"].append(int(predicted_perceived_level))
total_points += 1
with open(os.path.join(FLAGS.data_path, "predicted_extra_train_set.json"),
"w") as outfile:
json.dump(data, outfile, indent=4)
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
if not os.path.exists(FLAGS.vars_file):
raise ValueError("No data found at %s" % FLAGS.vars_file)
if not os.path.exists(FLAGS.save_dir):
os.mkdir(FLAGS.save_dir)
ds = dataset.MaskingDataset()
for filename in os.listdir(FLAGS.data_path):
if filename.startswith("masker") and filename.endswith(".txt"):
ds.read_data(FLAGS.data_path, filename)
ds_two = []
with open("predictions.txt", "r") as infile:
lines = infile.readlines()
for i, line in enumerate(lines):
if i == 0:
continue
splitted_line = line.split(";")
frequencies = splitted_line[0].split(",")
masker_frequency = float(frequencies[0])
probe_frequency = float(frequencies[1])
levels = splitted_line[1].split(",")
masker_level = float(levels[0])
probe_level = float(levels[1])
target_bel_masking = float(splitted_line[5]) / 10
ds_two.append({
"probe_frequency": probe_frequency,
"masker_frequency": masker_frequency,
"probe_level": probe_level,
"masker_level": masker_level,
"target_bel_masking": target_bel_masking
})
model_class = model.FullModel()
model_class.initialize_models(FLAGS.vars_file)
total_error = 0
total_points = 0
baseline_error = 0
for example in ds.get_all_data():
# bark_frequency = ds.frequency_to_cb(example["probe_frequency"])
actual_level = example["probe_level"]
target_bel_masking = example["target_bel_masking"]
prediction = model_class.predict(example["masker_frequency"],
example["probe_level"],
example["masker_level"],
example["probe_frequency_bark"])
error = (target_bel_masking - prediction)**2
baseline_error += (target_bel_masking - 0)**2
total_error += error
total_points += 1
print("Num predicted: ", total_points)
print("Baseline Mean squared error: {}".format(baseline_error / total_points))
print("Mean squared error: {}".format(total_error / total_points))
if FLAGS.plot_all_curves:
plot_all_learned_curves(ds,
[843.0, 843.0, 843.0, 843.0],
[20, 20, 40, 40],
[50, 70, 50, 70],
model_class,
FLAGS.save_dir)
if FLAGS.plot_ood_curves:
mask_frequencies = [1370.0]
probe_levels = [30]
mask_levels = [80]
plot_all_learned_curves(ds, mask_frequencies, probe_levels, mask_levels,
model_class, FLAGS.save_dir)