def plot_all_matched_vals(df):
param_keys = set(df.keys()) - set(['test', 'time', 'train',
'test_sample', 'train_sample'])
for key in param_keys:
if len(df[key].unique()) > 1:
dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals(
df, key)
if len(dfs[0]) > 0:
plt.figure()
plot_per_sub_unique_vals(df, key, matched=True)
plt.title(key, fontsize=12)
def plot_all_matched_vals(df):
param_keys = set(df.keys()) - set(
['test', 'time', 'train', 'test_sample', 'train_sample'])
for key in param_keys:
if len(df[key].unique()) > 1:
dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals(
df, key)
if len(dfs[0]) > 0:
plt.figure()
plot_per_sub_unique_vals(df, key, matched=True)
plt.title(key, fontsize=12)
def plot_per_sub_unique_vals(df, col_name, values_fn=lambda df: df.test,
matched=False):
assert len(df) > 0
if matched:
dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals(df,
col_name)
else:
unique_vals = df[col_name].unique()
dfs = [df[df[col_name] == val] for val in unique_vals]
plot_per_sub_dfs(dfs, values_fn=values_fn)
plt.xticks(range(len(dfs) * 3), np.tile(unique_vals,3), rotation=30, ha='right')
def plot_per_sub_unique_vals(df,
col_name,
values_fn=lambda df: df.test,
matched=False):
assert len(df) > 0
if matched:
dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals(
df, col_name)
else:
unique_vals = df[col_name].unique()
dfs = [df[df[col_name] == val] for val in unique_vals]
plot_per_sub_dfs(dfs, values_fn=values_fn)
plt.xticks(range(len(dfs) * 3),
np.tile(unique_vals, 3),
rotation=30,
ha='right')
