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Fix Balanced block mode order #77

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Fix Balanced block mode order #77

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60 changes: 34 additions & 26 deletions src/create_input.py
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Original file line number Diff line number Diff line change
Expand Up @@ -445,14 +445,21 @@ def create_input_for_dcrccr(cfg, df, output_path):
output_df = pd.DataFrame()
packing_strategy = cfg.get("clip_packing_strategy", "random").strip().lower()

if packing_strategy == "balanced_block":
add_clips_balanced_block_ccr(clips, refs, cfg["condition_pattern"], cfg.get("block_keys", cfg["condition_keys"])
, n_clips_per_session, output_df)
elif packing_strategy == "random":
add_clips_random_ccr(clips, refs, n_clips_per_session, output_df)

n_sessions = math.ceil(n_clips / n_clips_per_session)
print(f'{n_clips} clips and {n_sessions} sessions')
if packing_strategy == "balanced_block":
add_clips_balanced_block_ccr(
clips,
refs,
cfg["condition_pattern"],
cfg.get("block_keys", cfg["condition_keys"]),
n_clips_per_session,
output_df,
)
elif packing_strategy == "random":
add_clips_random_ccr(clips, refs, n_clips_per_session, output_df)

# number of sessions equals the number of rows in output_df
n_sessions = len(output_df)
print(f"{n_clips} clips and {n_sessions} sessions")

# create math
math_source = df['math'].dropna()
Expand Down Expand Up @@ -482,24 +489,25 @@ def create_input_for_dcrccr(cfg, df, output_path):
'CMP4_A': new_4[:, 6], 'CMP4_B': new_4[:, 7]})
# add math
output_df['math'] = math_output
# rating_clips
# repeat some clips to have a full design
n_questions = int(cfg['number_of_clips_per_session'])
needed_clips = n_sessions * n_questions

full_clips = np.tile(clips.to_numpy(), (needed_clips // n_clips) + 1)[:needed_clips]
full_refs = np.tile(refs.to_numpy(), (needed_clips // n_clips) + 1)[:needed_clips]

full = list(zip(full_clips, full_refs))
random.shuffle(full)
full_clips, full_refs = zip(*full)

clips_sessions = np.reshape(full_clips, (n_sessions, n_questions))
refs_sessions = np.reshape(full_refs, (n_sessions, n_questions))

for q in range(n_questions):
output_df[f'Q{q}_P'] = clips_sessions[:, q]
output_df[f'Q{q}_R'] = refs_sessions[:, q]
if packing_strategy == "random":
# rating_clips
# repeat some clips to have a full design
n_questions = int(cfg["number_of_clips_per_session"])
needed_clips = n_sessions * n_questions

full_clips = np.tile(clips.to_numpy(), (needed_clips // n_clips) + 1)[:needed_clips]
full_refs = np.tile(refs.to_numpy(), (needed_clips // n_clips) + 1)[:needed_clips]

full = list(zip(full_clips, full_refs))
random.shuffle(full)
full_clips, full_refs = zip(*full)

clips_sessions = np.reshape(full_clips, (n_sessions, n_questions))
refs_sessions = np.reshape(full_refs, (n_sessions, n_questions))

for q in range(n_questions):
output_df[f"Q{q}_P"] = clips_sessions[:, q]
output_df[f"Q{q}_R"] = refs_sessions[:, q]

# trappings
if int(cfg['number_of_trapping_per_session']) > 0:
Expand Down