def compute_and_cache_metrics(dataset_id):
# Returns a dict (one entry for each technique) with lists of dataframes
dataframes = Parser.parse_dataset(dataset_id)
for technique, df_list in dataframes.items():
print(technique, end='')
# Compute aspect ratios and weight of cells
ar_cache_path = 'metric_results/' + technique + '-' + dataset_id + '-ar.csv'
ar_df = pd.DataFrame()
if not os.path.isfile(ar_cache_path):
for revision, df in enumerate(df_list):
weight = compute_relative_weight(df, revision)
ar = compute_aspect_ratio(df, revision)
ar_df = pd.merge(ar_df,
weight,
how='outer',
left_index=True,
right_index=True)
ar_df = pd.merge(ar_df,
ar,
how='outer',
left_index=True,
right_index=True)
ar_df.fillna(0, inplace=True)
ar_df.to_csv(ar_cache_path, index_label='id')
# Compute Corner Travel (real and baseline)
ct_cache_path = 'metric_results/' + technique + '-' + dataset_id + '-ct.csv'
ct_df = pd.DataFrame()
if not os.path.isfile(ct_cache_path):
for revision in range(len(df_list) - 1):
r0 = df_list[revision][['rx', 'ry', 'rw',
'rh']].dropna(axis=0, subset=['rx'])
r1 = df_list[revision + 1][['rx', 'ry', 'rw',
'rh']].dropna(axis=0,
subset=['rx'])
b1 = df_list[revision + 1][['bx', 'by', 'bw',
'bh']].dropna(axis=0,
subset=['bx'])
ct = corner_travel_values(r0, r1, b1, revision)
ct_df = pd.merge(ct_df,
ct,
how='outer',
left_index=True,
right_index=True)
ct_df.fillna(0, inplace=True)
ct_df.to_csv(ct_cache_path, index_label='id')
# Compute Relative Position Change metric
rpc_cache_path = 'metric_results/' + technique + '-' + dataset_id + '-rpc.csv'
rpc_df = pd.DataFrame()
if not os.path.isfile(rpc_cache_path):
for revision in range(len(df_list) - 1):
real = relative_position_change_wrapper(
df_list[revision][['rx', 'ry', 'rw', 'rh']],
df_list[revision + 1][['rx', 'ry', 'rw', 'rh']])
baseline = relative_position_change_wrapper(
df_list[revision][['rx', 'ry', 'rw', 'rh']],
df_list[revision + 1][['bx', 'by', 'bw', 'bh']])
df_temp = pd.DataFrame({
'r_' + str(revision): real,
'b_' + str(revision): baseline
})
df_temp.sort_index(axis=1, ascending=False, inplace=True)
rpc_df = pd.merge(rpc_df,
df_temp,
how='outer',
left_index=True,
right_index=True)
rpc_df.fillna(0, inplace=True)
rpc_df.to_csv(rpc_cache_path, index_label='id')
print(' done.')
return None
Boxplots.plot_instability(ct_values, dataset_id, 'ct')
print('---')
rpc_values = Parser.read_rpc_metric(dataset_id)
Boxplots.plot_instability(rpc_values, dataset_id, 'rpc')
print('---')
elif action == 'scatter':
dataset_ids = sys.argv[2:]
Scatter.plot(dataset_ids, True, False)
# Scatter.plot(dataset_ids, False, True)
# Scatter.plot(dataset_ids, True, True)
elif action == "matrix":
dataset_ids = sys.argv[2:]
Matrix.plot(dataset_ids)
elif action == "simple-trails":
dataset_id = sys.argv[2]
dataframes = Parser.parse_dataset(dataset_id)
SimpleTrails.plot(dataframes, dataset_id)
elif action == "directed-trails":
dataset_id = sys.argv[2]
dataframes = Parser.parse_dataset(dataset_id)
DirectedTrails.plot(dataframes, dataset_id)
elif action == "trail-heatmap":
dataset_id = sys.argv[2]
dataframes = Parser.parse_dataset(dataset_id)
TrailHeatmap.plot(dataframes, dataset_id)