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fix warnings
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@JohnLangford
JohnLangford committed Jun 5, 2019
1 parent aa9b733 commit 51663aa
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Showing 2 changed files with 24 additions and 24 deletions.
4 changes: 2 additions & 2 deletions test/RunTests
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Original file line number Diff line number Diff line change
Expand Up @@ -1732,11 +1732,11 @@ printf '3 |f a b c |e x y z\n2 |f a y c |e x\n' | {VW} --oaa 3 -q ef --audit
train-sets/ref/malformed.stderr
# Test 196: online contextual memory tree
{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf 1 --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --online 1 --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 --passes 1 --loss_function squared --holdout_off
{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --online --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 --passes 1 --loss_function squared --holdout_off
train-sets/ref/cmt_rcv1_smaller_online.stderr
# Test 197: offline contextual memory tree
{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf 1 --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --online 0 --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 -c --passes 2 --loss_function squared --holdout_off
{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 -c --passes 2 --loss_function squared --holdout_off
train-sets/ref/cmt_rcv1_smaller_offline.stderr
# Do not delete this line or the empty line above it
44 changes: 22 additions & 22 deletions vowpalwabbit/memory_tree.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -134,13 +134,13 @@ namespace memory_tree_ns
//construct node for tree.
struct node
{
uint32_t parent; //parent index
uint64_t parent; //parent index
int internal;
//bool internal; //an internal or leaf
uint32_t depth; //depth.
uint32_t base_router; //use to index router.
uint32_t left; //left child.
uint32_t right; //right child.
uint64_t base_router; //use to index router.
uint64_t left; //left child.
uint64_t right; //right child.

double nl; //number of examples routed to left.
double nr; //number of examples routed to right.
Expand Down Expand Up @@ -176,7 +176,7 @@ namespace memory_tree_ns
size_t max_routers;
size_t max_num_labels;
float alpha; //for cpt type of update.
size_t routers_used;
uint64_t routers_used;
int iter;
uint32_t dream_repeats; //number of dream operations per example.

Expand All @@ -189,7 +189,7 @@ namespace memory_tree_ns
float test_time; //recording the test time

uint32_t num_mistakes;
int learn_at_leaf; //indicator for turning on learning the scorer function at the leaf level
bool learn_at_leaf; //indicator for turning on learning the scorer function at the leaf level

bool test_mode;

Expand All @@ -200,7 +200,7 @@ namespace memory_tree_ns
bool oas; //indicator for multi-label classification (oas = 1)
int dream_at_update;

int online; //indicator for running CMT in online fashion
bool online; //indicator for running CMT in online fashion

float F1_score;
float hamming_loss;
Expand Down Expand Up @@ -378,7 +378,7 @@ namespace memory_tree_ns
float prediction = ec.pred.scalar;
//float imp_weight = 1.f; //no importance weight.

float weighted_value = (1.-b.alpha)*log(b.nodes[cn].nl/(b.nodes[cn].nr+1e-1))/log(2.)+b.alpha*prediction;
float weighted_value = (float)(1.-b.alpha)*log(b.nodes[cn].nl/(b.nodes[cn].nr+1e-1))/log(2.)+b.alpha*prediction;
float route_label = weighted_value < 0.f ? -1.f : 1.f;

//ec.l.simple = {route_label, imp_weight, 0.f};
Expand Down Expand Up @@ -523,7 +523,7 @@ namespace memory_tree_ns
//template<typename T>
inline uint32_t hamming_loss(v_array<uint32_t>& array_1, v_array<uint32_t>& array_2){
uint32_t overlap = over_lap(array_1, array_2);
return array_1.size() + array_2.size() - 2*overlap;
return (uint32_t)(array_1.size() + array_2.size() - 2*overlap);
}

void collect_labels_from_leaf(memory_tree& b, const uint32_t cn, v_array<uint32_t>& leaf_labs){
Expand Down Expand Up @@ -621,8 +621,8 @@ namespace memory_tree_ns
//we use F1 score as the reward signal
float F1_score_for_two_examples(example& ec1, example& ec2){
float num_overlaps = get_overlap_from_two_examples(ec1, ec2);
float v1 = num_overlaps/(1e-7+ec1.l.multilabels.label_v.size()*1.);
float v2 = num_overlaps/(1e-7+ec2.l.multilabels.label_v.size()*1.);
float v1 = (float) num_overlaps/(1e-7+ec1.l.multilabels.label_v.size()*1.);
float v2 = (float) num_overlaps/(1e-7+ec2.l.multilabels.label_v.size()*1.);
if (num_overlaps == 0.f)
return 0.f;
else
Expand Down Expand Up @@ -685,7 +685,7 @@ namespace memory_tree_ns
b.F1_score += reward;
}
v_array<uint32_t> selected_labs = v_init<uint32_t>();
ec.loss = compute_hamming_loss_via_oas(b, base, cn, ec, selected_labs);
ec.loss = (float)compute_hamming_loss_via_oas(b, base, cn, ec, selected_labs);
b.hamming_loss += ec.loss;
}
}
Expand Down Expand Up @@ -822,7 +822,7 @@ namespace memory_tree_ns

if (path_to_leaf.size() > 1){
//uint32_t random_pos = merand48(b.all->random_state)*(path_to_leaf.size()-1);
uint32_t random_pos = merand48(b.all->random_state)*(path_to_leaf.size()); //include leaf
uint32_t random_pos = (uint32_t)(merand48(b.all->random_state)*(path_to_leaf.size())); //include leaf
uint32_t cn = path_to_leaf[random_pos];

if (b.nodes[cn].internal != -1){ //if it's an internal node:'
Expand All @@ -832,11 +832,11 @@ namespace memory_tree_ns
float weight = path_to_leaf.size()*1.f/(path_to_leaf.size() - 1.f);
if (coin == -1.f){ //go left
float reward_left_subtree = return_reward_from_node(b,base, b.nodes[cn].left, ec, weight);
objective = (float)(1.-b.alpha)*log(b.nodes[cn].nl/b.nodes[cn].nr) + b.alpha*(-reward_left_subtree/(1.-prob_right))/2.;
objective = (float)((1.-b.alpha)*log(b.nodes[cn].nl/b.nodes[cn].nr) + b.alpha*(-reward_left_subtree/(1.-prob_right))/2.);
}
else{ //go right:
float reward_right_subtree= return_reward_from_node(b,base, b.nodes[cn].right, ec, weight);
objective = (1.-b.alpha)*log(b.nodes[cn].nl/b.nodes[cn].nr) + b.alpha*(reward_right_subtree/prob_right)/2.;
objective = (float)((1.-b.alpha)*log(b.nodes[cn].nl/b.nodes[cn].nr) + b.alpha*(reward_right_subtree/prob_right)/2.);
}

float ec_input_weight = ec.weight;
Expand Down Expand Up @@ -968,11 +968,11 @@ namespace memory_tree_ns
}
else{ //starting from the current pass, we just learn using reinforcement signal, no insertion needed:
size_t ec_id = (b.iter)%b.examples.size();
update_rew(b, base, ec_id, *b.examples[ec_id]); //no insertion will happen in this call
update_rew(b, base, (uint32_t)ec_id, *b.examples[ec_id]); //no insertion will happen in this call
for (uint32_t i = 0; i < b.dream_repeats; i++)
experience_replay(b, base);
}
b.construct_time += double(clock() - begin)/CLOCKS_PER_SEC;
b.construct_time += float(clock() - begin)/CLOCKS_PER_SEC;
}
else if (b.test_mode == true){
b.iter++;
Expand All @@ -984,7 +984,7 @@ namespace memory_tree_ns
}
clock_t begin = clock();
predict(b, base, ec);
b.test_time += double(clock() - begin)/CLOCKS_PER_SEC;
b.test_time += float(clock() - begin)/CLOCKS_PER_SEC;
}

}
Expand Down Expand Up @@ -1103,7 +1103,7 @@ namespace memory_tree_ns

if (read)
{
size_t ss = 0;
uint32_t ss = 0;
writeit(ss, "stride_shift");
b.all->weights.stride_shift(ss);
}
Expand Down Expand Up @@ -1161,10 +1161,10 @@ base_learner* memory_tree_setup(options_i& options, vw& all)
.add(make_option("alpha", tree->alpha).default_value(0.1f).help("Alpha"))
.add(make_option("dream_repeats", tree->dream_repeats).default_value(1).help("number of dream operations per example (default = 1)"))
.add(make_option("top_K",tree->top_K).default_value(1).help("top K prediction error (default 1)"))
.add(make_option("learn_at_leaf",tree->learn_at_leaf).default_value(0).help("whether or not learn at leaf (defualt = True)"))
.add(make_option("learn_at_leaf",tree->learn_at_leaf).help("whether or not learn at leaf (defualt = True)"))
.add(make_option("oas", tree->oas).help("use oas at the leaf"))
.add(make_option("dream_at_update", tree->dream_at_update).default_value(0).help("turn on dream operations at reward based update as well"))
.add(make_option("online", tree->online).default_value(0).help("turn on dream operations at reward based update as well"));
.add(make_option("online", tree->online).help("turn on dream operations at reward based update as well"));
options.add_and_parse(new_options);
if (!tree->max_nodes)
{
Expand All @@ -1175,7 +1175,7 @@ if (!tree->max_nodes)
tree->current_pass = 0;
tree->final_pass = all.numpasses;

tree->max_leaf_examples = tree->leaf_example_multiplier*(log(tree->max_nodes)/log(2));
tree->max_leaf_examples = (size_t)(tree->leaf_example_multiplier*(log(tree->max_nodes)/log(2)));


init_tree(*tree);
Expand Down

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