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learn_rules.py
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learn_rules.py
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import argparse
import multiprocessing as mp
import itertools
import pickle
import torch
import wandb
import numpy as np
import dataset
class Node:
def __init__(self, left, right, object_bindings, action_bindings, relation_bindings, counts, gating) -> None:
self.left = left
self.right = right
self.object_bindings = object_bindings
self.action_bindings = action_bindings
self.relation_bindings = relation_bindings
self.counts = counts
self.gating = gating
def __repr__(self) -> str:
return f"Node({self.object_bindings}, {self.action_bindings}, {self.relation_bindings}, {self.gating.sum()})"
def create_effect_classes(loader, given_effect_to_class=None):
if given_effect_to_class is None:
effect_to_class = {}
class_idx = 0
effects = []
changed_indices = []
for i, (obj_pre, rel_pre, _, obj_post, rel_post, mask) in enumerate(loader):
obj_pre = obj_pre[0, mask[0]]
obj_post = obj_post[0, mask[0]]
obj_diff_idx = torch.where(obj_pre != obj_post)[0]
obj_diff_idx = torch.unique(obj_diff_idx)
obj_effects = []
obj_indices = []
for idx in obj_diff_idx:
obj_effects.append(tuple(obj_post[idx].int().tolist()))
obj_indices.append(idx.item())
# sort obj_effects together with obj_indices
if len(obj_effects) > 0:
obj_effects, obj_indices = zip(*sorted(zip(obj_effects, obj_indices)))
else:
obj_effects = ()
obj_indices = ()
mm = (mask.T.float() @ mask.float()).bool()
c = mask.sum()
rel_pre = rel_pre[0, :, mm].reshape(4, c, c)
rel_post = rel_post[0, :, mm].reshape(4, c, c)
rel_diffs = torch.where(rel_pre != rel_post)
rel_effects = [[], [], [], []] # for four relations
rel_indices = [[], [], [], []]
for rel, obj1, obj2 in zip(*rel_diffs):
rel_value = rel_post[rel, obj1, obj2]
tup = (rel_value.int().item(),)
rel_effects[rel.item()].append(tup)
rel_indices[rel.item()].append((obj1.item(), obj2.item()))
# sort rel_effects together with rel_indices
for i in range(4):
if len(rel_effects[i]) == 0:
rel_effects[i] = ()
rel_indices[i] = ()
continue
rel_effects[i], rel_indices[i] = zip(*sorted(zip(rel_effects[i], rel_indices[i])))
rel_indices = tuple(rel_indices)
key = (obj_effects,) + tuple(rel_effects)
if given_effect_to_class is not None:
if key not in given_effect_to_class:
effects.append(-1)
else:
effects.append(given_effect_to_class[key])
elif key not in effect_to_class:
effect_to_class[key] = class_idx
class_idx += 1
effects.append(effect_to_class[key])
else:
effects.append(effect_to_class[key])
changed_indices.append((obj_indices, rel_indices))
if given_effect_to_class is not None:
return effects, changed_indices
return effects, changed_indices, effect_to_class
def get_top_classes(sorted_effect_counts, perc, dataset_size):
total_count = 0
selected_keys = []
for key in sorted_effect_counts:
count = sorted_effect_counts[key]
total_count += count
selected_keys.append(key)
if total_count/dataset_size >= perc:
break
return selected_keys
def get_effect_counts(effects, gating):
effect_counts = {}
for i, e in enumerate(effects):
if gating[i]:
if e not in effect_counts:
effect_counts[e] = 0
effect_counts[e] += 1
effect_counts = dict(sorted(effect_counts.items(), key=lambda x: x[1], reverse=True))
return effect_counts
def filter_effect_classes(effects, selected_classes):
filtered_effects = []
for e in effects:
if e in selected_classes:
filtered_effects.append(e)
else:
filtered_effects.append(-1)
return filtered_effects
def matrix_to_tuple(matrix):
return tuple([tuple(row) for row in matrix])
def preprocess_data(o_i, r_i, a, o_f, r_f, m):
o_i = o_i[0, m[0]].int()
o_f = o_f[0, m[0]].int()
c = m.sum()
mm = (m.T.float() @ m.float()).bool()
r_i = r_i[0, :, mm].reshape(4, c, c).int()
r_f = r_f[0, :, mm].reshape(4, c, c).int()
a = a[0, m[0]].int()
return o_i, r_i, a, o_f, r_f
def is_satisfied(sample, object_bindings, action_bindings, relation_bindings):
o_i, r_i, a, _, _ = preprocess_data(*sample)
# get possible object indices
obj_exists = True
obj_possible_indices = {}
for name in object_bindings:
indices = torch.where((o_i == object_bindings[name]).all(dim=1))[0]
if len(indices) > 0:
obj_possible_indices[name] = indices
else:
obj_exists = False
break
# get possible action indices
act_exists = True
act_possible_indices = {}
for name in action_bindings:
indices = torch.where((a == action_bindings[name]).all(dim=1))[0]
if len(indices) > 0:
act_possible_indices[name] = indices
else:
act_exists = False
break
# constraints
obj_act_binded = True
all_names = list(set(list(object_bindings.keys()) + list(action_bindings.keys())))
filtered_possible_indices = {}
for name in all_names:
if name in obj_possible_indices:
obj_indices = obj_possible_indices[name]
else:
obj_indices = None
if name in act_possible_indices:
act_indices = act_possible_indices[name]
else:
act_indices = None
if obj_indices is None and act_indices is None:
obj_act_binded = False
break
elif obj_indices is None:
filtered_possible_indices[name] = act_indices
elif act_indices is None:
filtered_possible_indices[name] = obj_indices
else:
filtered_possible_indices[name] = torch.tensor(np.intersect1d(obj_indices.numpy(),
act_indices.numpy()),
dtype=torch.long)
if len(filtered_possible_indices[name]) == 0:
obj_act_binded = False
break
possible_bindings = []
if obj_act_binded:
tensors = []
for name in all_names:
tensors.append(filtered_possible_indices[name])
bindings = torch.cartesian_prod(*tensors)
if bindings.ndim == 1:
bindings = bindings.unsqueeze(1)
num_vars = len(all_names)
for binding in bindings:
if torch.unique(binding).shape[0] == num_vars:
possible_bindings.append({all_names[i]: binding[i] for i in range(num_vars)})
if len(possible_bindings) == 0:
obj_act_binded = False
rel_filtered_bindings = []
for binding in possible_bindings:
binding_valid = True
for (rel_idx, name1, name2) in relation_bindings:
val = relation_bindings[(rel_idx, name1, name2)]
name1_idx = binding[name1]
name2_idx = binding[name2]
if r_i[rel_idx, name1_idx, name2_idx] != val:
binding_valid = False
break
if binding_valid:
rel_filtered_bindings.append(binding)
rel_exists = len(rel_filtered_bindings) > 0
satisfied = obj_exists and act_exists and obj_act_binded and rel_exists
return satisfied, rel_filtered_bindings
def check_rule(object_bindings, action_bindings, relation_bindings,
loader, effects, effect_indices, gating):
left_counts = {}
right_counts = {}
left_gating = np.zeros(len(gating), dtype=bool)
right_gating = np.zeros(len(gating), dtype=bool)
effect_bindings = []
total_binded = 0
for i, sample in enumerate(loader):
if gating[i]:
satisfied, bindings = is_satisfied(sample, object_bindings, action_bindings, relation_bindings)
flattened_indices = flatten_tuple(effect_indices[i])
unique_indices = list(set(flattened_indices))
current_candidates = {}
for binding in bindings:
variables = list(binding.keys())
# TODO: this should work for non-equalities as well
# think how to do that later
if sorted(tuple(v.item() for v in binding.values())) != sorted(tuple(unique_indices)):
continue
for perm in itertools.permutations(unique_indices):
current_mapping = {p: v for p, v in zip(perm, variables)}
computed_indices = tuple([binding[current_mapping[idx]].item() for idx in flattened_indices])
similarity = np.sum(np.array(computed_indices) == np.array(flattened_indices)) / len(flattened_indices)
if (tuple(perm), tuple(variables)) not in current_candidates:
current_candidates[(tuple(perm), tuple(variables))] = []
current_candidates[(tuple(perm), tuple(variables))].append(similarity)
# select the best binding
if len(current_candidates) > 0:
best_binding = None
best_avg_sim = -1
for binding in current_candidates:
avg_sim = np.mean(current_candidates[binding])
if avg_sim > best_avg_sim:
best_binding = binding
best_avg_sim = avg_sim
best_mapping = {p: v for p, v in zip(best_binding[0], best_binding[1])}
binding = transform_tuple(effect_indices[i], best_mapping)
effect_bindings.append(binding)
total_binded += 1
else:
effect_bindings.append(effect_indices[i])
if satisfied:
if effects[i] not in left_counts:
left_counts[effects[i]] = 0
left_counts[effects[i]] += 1
left_gating[i] = True
else:
if effects[i] not in right_counts:
right_counts[effects[i]] = 0
right_counts[effects[i]] += 1
right_gating[i] = True
return left_counts, left_gating, right_counts, right_gating, total_binded
def calculate_entropy(counts):
probs = {k: counts[k]/np.sum(list(counts.values())) for k in counts}
entropy = -np.sum([probs[k]*np.log(probs[k]) for k in probs])
return entropy
def calculate_best_split(node, loader, effects, effect_indices, unique_object_values,
unique_action_values, min_samples_split, num_procs=1):
"""
Calculate the best split for the given node.
Args:
node (Node): The node to expand.
loader (torch.utils.data.DataLoader): The data loader.
effects (List[int]): The effects.
effect_indices (List[Tuple[Tuple[int], Tuple[Tuple[int]]]]): The indices of the effects.
unique_object_values (torch.Tensor): The unique object values.
unique_action_values (torch.Tensor): The unique action values.
min_samples_split (int): The minimum number of samples required to split a node.
num_procs (int): The number of processes to use.
Returns:
Tuple[float, Node]: The entropy and the best node.
"""
left_node = None
right_node = None
best_impurity = 1e10
if node.gating.sum() < min_samples_split:
return best_impurity, left_node, right_node
obj_var_list = list(node.object_bindings.keys())
act_var_list = list(node.action_bindings.keys())
# if a new object variable is needed
max_obj_idx = max([int(obj_var[3:]) for obj_var in obj_var_list]) if len(obj_var_list) > 0 else -1
max_act_idx = max([int(act_var[3:]) for act_var in act_var_list]) if len(act_var_list) > 0 else -1
new_obj_idx = max(max_obj_idx, max_act_idx) + 1
new_obj_name = "obj" + str(new_obj_idx)
# process argument list
proc_args = []
# bind a variable in action list to a new object value
for act_var in act_var_list:
# continue if the variable already is bound to an object value
if act_var in node.object_bindings:
continue
# bind the variable to each object value
for obj_val in unique_object_values:
object_bindings = node.object_bindings.copy()
object_bindings[act_var] = obj_val
proc_args.append((object_bindings, node.action_bindings, node.relation_bindings,
loader, effects, effect_indices, node.gating))
# bind a new variable to a new object value
for obj_val in unique_object_values:
object_bindings = node.object_bindings.copy()
object_bindings[new_obj_name] = obj_val
proc_args.append((object_bindings, node.action_bindings, node.relation_bindings,
loader, effects, effect_indices, node.gating))
# bind a variable in object list to a new action value
for obj_var in obj_var_list:
# continue if the variable already is bound to an action value
if obj_var in node.action_bindings:
continue
# bind the variable to each action value
for act_val in unique_action_values:
action_bindings = node.action_bindings.copy()
action_bindings[obj_var] = act_val
proc_args.append((node.object_bindings, action_bindings, node.relation_bindings,
loader, effects, effect_indices, node.gating))
# bind a new variable to a new action value
for act_val in unique_action_values:
action_bindings = node.action_bindings.copy()
action_bindings[new_obj_name] = act_val
proc_args.append((node.object_bindings, action_bindings, node.relation_bindings,
loader, effects, effect_indices, node.gating))
# bind two variables in either object list or action list to a new relation value
all_vars = list(set(obj_var_list + act_var_list))
for v1 in all_vars:
for v2 in all_vars:
for rel in [0, 1, 2, 3]: # TODO: this is hard-coded to four for now
for val in [0, 1]:
key = (rel, v1, v2)
# continue if the relation is already bound
if key in node.relation_bindings:
continue
# bind the relation to each value
relation_bindings = node.relation_bindings.copy()
relation_bindings[key] = val
proc_args.append((node.object_bindings, node.action_bindings, relation_bindings,
loader, effects, effect_indices, node.gating))
with mp.get_context("spawn").Pool(num_procs) as pool:
results = pool.starmap(check_rule, proc_args)
best_binded = 0
for (left_counts, left_gating, right_counts, right_gating, total_binded), (args) in zip(results, proc_args):
left_entropy = calculate_entropy(left_counts)
right_entropy = calculate_entropy(right_counts)
impurity = (left_entropy * np.sum(left_gating) + right_entropy * np.sum(right_gating)) / node.gating.sum()
if (1e-8 < impurity < best_impurity) and \
(np.sum(left_gating) >= min_samples_split) and \
(np.sum(right_gating) >= min_samples_split):
left_node = Node(left=None, right=None,
object_bindings=args[0].copy(),
action_bindings=args[1].copy(),
relation_bindings=args[2].copy(),
counts=left_counts,
gating=left_gating)
right_node = Node(left=None, right=None,
object_bindings={},
action_bindings={},
relation_bindings={},
counts=right_counts,
gating=right_gating)
best_impurity = impurity
best_binded = total_binded
print(best_binded)
return best_impurity, left_node, right_node
def learn_tree(loader, effects, effect_indices, unique_object_values,
unique_action_values, min_samples_split=100, num_procs=1):
"""Learn a decision tree from the given dataset.
Args:
loader (DataLoader): the dataset loader
effects (np.ndarray): the effects of the dataset
effect_indices List[Tuple[Tuple[int], Tuple[Tuple[int]]]]: the indices of the effects
unique_object_values (torch.Tensor): the unique object values in the dataset
unique_action_values (torch.Tensor): the unique action values in the dataset
min_samples_split (int): the minimum number of samples required to split a node
Returns:
Node: the root node of the decision tree
"""
# initialize the root node
gating = np.ones(len(loader), dtype=bool)
root_node = Node(left=None, right=None,
object_bindings={},
action_bindings={},
relation_bindings={},
counts=get_effect_counts(effects, gating),
gating=gating)
# learn the tree
queue = [root_node]
num_nodes = 0
while len(queue) > 0:
node = queue.pop(0)
num_nodes += 1
_, left_node, right_node = calculate_best_split(node, loader, effects, effect_indices,
unique_object_values, unique_action_values,
min_samples_split, num_procs)
if left_node is not None:
print(f"Left node:\n"
f" object bindings={left_node.object_bindings},\n"
f" action bindings={left_node.action_bindings},\n"
f" relation bindings={left_node.relation_bindings},\n"
f" entropy={calculate_entropy(left_node.counts)},\n"
f" count={left_node.gating.sum()},\n"
f"Right node:\n"
f" object bindings={right_node.object_bindings},\n"
f" action bindings={right_node.action_bindings},\n"
f" relation bindings={right_node.relation_bindings},\n"
f" entropy={calculate_entropy(right_node.counts)},\n"
f" count={right_node.gating.sum()},\n"
f"Num nodes: {num_nodes}")
node.left = left_node
node.right = right_node
queue.append(node.left)
queue.append(node.right)
if num_nodes == 1:
# keep the root node pointer
root_node = node
else:
print(f"Terminal node: \n"
f" object bindings={node.object_bindings},\n"
f" action bindings={node.action_bindings},\n"
f" relation bindings={node.relation_bindings},\n"
f" counts={node.counts},\n"
f" entropy={calculate_entropy(node.counts)},\n"
f"Num nodes: {num_nodes}")
return root_node
def print_tree(node, negatives):
if node.left is None and node.right is None:
print("Rule:")
if len(negatives) > 0:
print("\t(negations:")
for neg in negatives:
print("\t\t(")
if len(neg[0]) > 0:
print("\t\t\t(objects: ", end="")
print(" AND ".join([f"{obj}!={tuple(vals.tolist())}" for obj, vals in neg[0].items()]), end="")
print(")")
if len(neg[1]) > 0:
print("\t\t\t(actions: ", end="")
print(" AND ".join([f"{act}!={tuple(vals.tolist())}" for act, vals in neg[1].items()]), end="")
print(")")
if len(neg[2]) > 0:
print("\t\t\t(relations: ", end="")
print(" AND ".join([f"rel({rel[0]}, {rel[1]}, {rel[2]})!={vals}" for rel, vals in neg[2].items()]), end="")
print(")")
print("\t\t)")
print("\t)")
if len(node.object_bindings) > 0:
# e.g., obj0=(0, 1, 1, 1)
print("\t(objects: ", end="")
print(" AND ".join([f"{obj}={tuple(vals.tolist())}" for obj, vals in node.object_bindings.items()]), end="")
print(")")
if len(node.action_bindings) > 0:
print("\t(actions: ", end="")
print(" AND ".join([f"{act}={tuple(vals.tolist())}" for act, vals in node.action_bindings.items()]), end="")
print(")")
if len(node.relation_bindings) > 0:
print("\t(relations: ", end="")
print(" AND ".join([f"rel({rel[0]}, {rel[1]}, {rel[2]})={vals}" for rel, vals in node.relation_bindings.items()]), end="")
print(")")
print("\tTHEN")
print(f"\t{node.counts}")
else:
print_tree(node.left, negatives)
# TODO: this is wrong. a right node does not mean all negating the left node's features
if len(node.object_bindings) > 0 or len(node.action_bindings) > 0 or len(node.relation_bindings) > 0:
print_tree(node.right, negatives + [(node.object_bindings, node.action_bindings, node.relation_bindings)])
else:
print_tree(node.right, negatives)
def flatten_tuple(nested_tuple):
flattened = []
for item in nested_tuple:
if isinstance(item, tuple):
flattened.extend(flatten_tuple(item))
else:
flattened.append(item)
return tuple(flattened)
def transform_tuple(nested_tuple, mapping):
transformed = []
for item in nested_tuple:
if isinstance(item, tuple):
transformed.append(transform_tuple(item, mapping))
else:
transformed.append(mapping[item])
return tuple(transformed)
if __name__ == "__main__":
args = argparse.ArgumentParser()
args.add_argument("-i", type=str, required=True, help="Run id")
args.add_argument("-p", type=int, default=1, help="Number of processes")
args = args.parse_args()
run = wandb.init(entity="colorslab", project="active_exploration", resume="must", id=args.i)
# load the dataset
trainset = dataset.load_symbol_dataset("train", run, "cpu")
valset = dataset.load_symbol_dataset("val", run, "cpu")
trainloader = torch.utils.data.DataLoader(trainset, batch_size=1)
train_effects, train_changed_indices, train_effect_classes = create_effect_classes(trainloader)
train_class_to_effect = {v: k for k, v in train_effect_classes.items()}
effect_counts = get_effect_counts(train_effects, np.ones(len(trainset), dtype=bool))
selected_classes = get_top_classes(effect_counts, 0.95, len(trainset))
filtered_effects = filter_effect_classes(train_effects, selected_classes)
filtered_counts = get_effect_counts(filtered_effects, np.ones(len(trainset), dtype=bool))
unique_object_values = trainset.tensors[0].int().flatten(0, 1).unique(dim=0)
unique_action_values = trainset.tensors[2].int().flatten(0, 1).unique(dim=0)
root = learn_tree(trainloader, filtered_effects, train_changed_indices,
unique_object_values, unique_action_values,
min_samples_split=50, num_procs=args.p)
pickle.dump(root, open("tree.pkl", "wb"))