Skip to content

Update foraging eff #1624

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 6 commits into
base: develop
Choose a base branch
from
+14 −148
Open

Update foraging eff #1624

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
b066c48
calculates foraging eff with aind_dynamic_foraging_basic_analysis fun…
micahwoodard Oct 1, 2025
727ea11
removes webview files
micahwoodard Oct 1, 2025
4505ebd
updates pyproject
micahwoodard Oct 2, 2025
48a5ca9
updates pyproject for aind-dynamic-foraging-models
micahwoodard Oct 2, 2025
00e6b6a
removes unneeded calculations for foraging eff
micahwoodard Oct 2, 2025
541f5c6
readds slims
micahwoodard Oct 2, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion pyproject.toml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,8 @@ dependencies = [
"newscale@git+https://github.com/AllenNeuralDynamics/python-newscale@axes-on-target",
"aind-auto-train@git+https://github.com/AllenNeuralDynamics/aind-foraging-behavior-bonsai-automatic-training.git@main",
"aind-slims-api@git+https://github.com/AllenNeuralDynamics/aind-slims-api@main",
"aind-dynamic-foraging-models@git+https://github.com/AllenNeuralDynamics/aind-dynamic-foraging-models@main",
"aind-dynamic-foraging-models >= 0.12.2",
"aind-dynamic-foraging-basic-analysis >= 0.3.34",
"aind-behavior-services >=0.8, <0.9",
"pynwb >=2, <3",
"requests >=2, <3",
Expand Down
159 changes: 12 additions & 147 deletions src/foraging_gui/MyFunctions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
from serial.tools.list_ports import comports as list_comports

from foraging_gui.reward_schedules.uncoupled_block import UncoupledBlocks
from aind_dynamic_foraging_basic_analysis import compute_foraging_efficiency

if PLATFORM == "win32":
from newscale.usbxpress import USBXpressDevice, USBXpressLib
Expand Down Expand Up @@ -928,42 +929,7 @@ def _GetBasic(self):
# foraging efficiency
Len = np.shape(self.B_RewardedHistory)[1]
if Len > 0:
reward_rate = np.sum(self.B_RewardedHistory) / Len
p_Ls = self.B_RewardProHistory[0][:Len]
p_Rs = self.B_RewardProHistory[1][:Len]
random_number_L = np.concatenate(
self.B_CurrentRewardProbRandomNumber, axis=0
)[0::2][:Len]
random_number_R = np.concatenate(
self.B_CurrentRewardProbRandomNumber, axis=0
)[1::2][:Len]
if self.TP_Task in ["Coupled Baiting", "Uncoupled Baiting"]:
self.B_for_eff_optimal, self.B_for_eff_optimal_random_seed = (
self.foraging_eff(
reward_rate,
p_Ls,
p_Rs,
random_number_L,
random_number_R,
)
)
elif self.TP_Task in [
"Coupled Without Baiting",
"Uncoupled Without Baiting",
]:
self.B_for_eff_optimal, self.B_for_eff_optimal_random_seed = (
self.foraging_eff_no_baiting(
reward_rate,
p_Ls,
p_Rs,
random_number_L,
random_number_R,
)
)
else:
self.B_for_eff_optimal = np.nan
self.B_for_eff_optimal_random_seed = np.nan
"""Some complex calculations can be separated from _GenerateATrial using different threads"""
self.B_for_eff_optimal, self.B_for_eff_optimal_random_seed = self.foraging_eff()

def _process_values(
self, values, auto_water_trial, multiplier_values, rewarded_history
Expand All @@ -984,118 +950,17 @@ def _process_values(
logging.error(str(e))
return BS_AutoWater, BS_EarnedReward, BS_AutoWater_N, BS_EarnedReward_N

def foraging_eff_no_baiting(
self,
reward_rate,
p_Ls,
p_Rs,
random_number_L=None,
random_number_R=None,
): # Calculate foraging efficiency (only for 2lp)
"""Calculating the foraging efficiency of no baiting tasks (Code is from Han)"""
# --- Optimal-aver (use optimal expectation as 100% efficiency) ---
for_eff_optimal = float(
reward_rate / np.nanmean(np.max([p_Ls, p_Rs], axis=0))
)

if random_number_L is None:
return for_eff_optimal, np.nan

# --- Optimal-actual (uses the actual random numbers by simulation)
reward_refills = np.vstack(
[p_Ls >= random_number_L, p_Rs >= random_number_R]
def foraging_eff(self):
"""Calculating the foraging efficiency"""
return compute_foraging_efficiency(
baited=self.TP_Task in ["Coupled Baiting", "Uncoupled Baiting"],
choice_history=np.where(self.B_AnimalResponseHistory == 2, np.nan, self.B_AnimalResponseHistory),
reward_history=self.B_RewardedHistory[0] | self.B_RewardedHistory[1],
p_reward=self.B_RewardProHistory[:, :-1],
random_number=[[arr[0] for arr in self.B_CurrentRewardProbRandomNumber],
[arr[1] for arr in self.B_CurrentRewardProbRandomNumber]],
autowater_offered=(self.B_AutoWaterTrial[0, :] == 1) | (self.B_AutoWaterTrial[1, :] == 1),
)
optimal_choices = np.argmax(
[p_Ls, p_Rs], axis=0
) # Greedy choice, assuming the agent knows the groundtruth
optimal_rewards = (
reward_refills[0][optimal_choices == 0].sum()
+ reward_refills[1][optimal_choices == 1].sum()
)
for_eff_optimal_random_seed = float(
reward_rate / (optimal_rewards / len(optimal_choices))
)

return for_eff_optimal, for_eff_optimal_random_seed

def foraging_eff(
self,
reward_rate,
p_Ls,
p_Rs,
random_number_L=None,
random_number_R=None,
): # Calculate foraging efficiency (only for 2lp)
"""Calculating the foraging efficiency of baiting tasks (Code is from Han)"""
# --- Optimal-aver (use optimal expectation as 100% efficiency) ---
p_stars = np.zeros_like(p_Ls)
for i, (p_L, p_R) in enumerate(zip(p_Ls, p_Rs)): # Sum over all ps
p_max = np.max([p_L, p_R])
p_min = np.min([p_L, p_R])
if p_min == 0 or p_max >= 1:
p_stars[i] = p_max
else:
m_star = np.floor(np.log(1 - p_max) / np.log(1 - p_min))
p_stars[i] = p_max + (
1 - (1 - p_min) ** (m_star + 1) - p_max**2
) / (m_star + 1)

for_eff_optimal = float(reward_rate / np.nanmean(p_stars))

if random_number_L is None:
return for_eff_optimal, np.nan

# --- Optimal-actual (uses the actual random numbers by simulation)
block_trans = np.where(np.diff(np.hstack([np.inf, p_Ls, np.inf])))[
0
].tolist()
reward_refills = [p_Ls >= random_number_L, p_Rs >= random_number_R]
reward_optimal_random_seed = 0

# Generate optimal choice pattern
for b_start, b_end in zip(block_trans[:-1], block_trans[1:]):
p_max = np.max([p_Ls[b_start], p_Rs[b_start]])
p_min = np.min([p_Ls[b_start], p_Rs[b_start]])
side_max = np.argmax([p_Ls[b_start], p_Rs[b_start]])

# Get optimal choice pattern and expected optimal rate
if p_min == 0 or p_max >= 1:
this_choice = np.array(
[1] * (b_end - b_start)
) # Greedy is obviously optimal
else:
m_star = np.floor(np.log(1 - p_max) / np.log(1 - p_min))
this_choice = np.array(
(
([1] * int(m_star) + [0])
* (1 + int((b_end - b_start) / (m_star + 1)))
)[: b_end - b_start]
)

# Do simulation, using optimal choice pattern and actual random numbers
reward_refill = np.vstack(
[
reward_refills[1 - side_max][b_start:b_end],
reward_refills[side_max][b_start:b_end],
]
).astype(
int
) # Max = 1, Min = 0
reward_remain = [0, 0]
for t in range(b_end - b_start):
reward_available = reward_remain | reward_refill[:, t]
reward_optimal_random_seed += reward_available[this_choice[t]]
reward_remain = reward_available.copy()
reward_remain[this_choice[t]] = 0

if reward_optimal_random_seed:
for_eff_optimal_random_seed = float(
reward_rate / (reward_optimal_random_seed / len(p_Ls))
)
else:
for_eff_optimal_random_seed = np.nan

return for_eff_optimal, for_eff_optimal_random_seed

def _LickSta(self, Trials=None):
"""Perform lick stats for the input trials"""
Expand Down