def dataframe():
path = scores_file_path(package_path(baseline_model_package))
baseline_df = load_dataframe(path)
path = scores_file_path(package_path(finetuned_model_package))
finetuned_df = load_dataframe(path)
return pd.concat([
baseline_df[Metric.LOSS.value], finetuned_df[Metric.LOSS.value],
baseline_df[Metric.ACCURACY.value], finetuned_df[Metric.ACCURACY.value]
],
axis=1,
keys=list(itertools.chain(*COMPARE_TEST_SCORES_COLUMNS)))
def dataframe():
path = scores_file_path(package_path(baseline_model_package))
baseline_df = load_dataframe(path)
path = scores_file_path(package_path(finetuned_model_package))
finetuned_df = load_dataframe(path)
return pd.concat([
finetuned_df[Metric.LOSS.value] - baseline_df[Metric.LOSS.value],
finetuned_df[Metric.ACCURACY.value] -
baseline_df[Metric.ACCURACY.value]
],
axis=1,
keys=[Metric.LOSS.value,
Metric.ACCURACY.value]).transpose()
def dataframe(baseline_training_run, finetuned_training_run):
path = history_path(package_path(baseline_model_package),
baseline_training_run)
baseline_df = load_dataframe(path)
path = history_path(package_path(finetuned_model_package),
finetuned_training_run)
finetuned_df = load_dataframe(path)
return pd.concat([
baseline_df[Metric.ACCURACY.value],
finetuned_df[Metric.ACCURACY.value],
baseline_df[Metric.VAL_ACCURACY.value],
finetuned_df[Metric.VAL_ACCURACY.value]
],
axis=1,
keys=list(itertools.chain(*COMPARE_TRAINING_COLUMNS)))
def display_scores(base_path: Path):
"""Displays a scores file.
Arguments:
base_path: A Path object pointing to the base directory where the testing artifacts are located.
"""
path = scores_file_path(base_path)
df = load_dataframe(path)
print(df)
def plot_learning_curves():
for training_run in TRAINING_RUNS:
path = history_path(Path(), training_run)
df = load_dataframe(path)
loss_accuracy_plot(df)