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/*
Copyright (c) by respective owners including Yahoo!, Microsoft, and
individual contributors. All rights reserved. Released under a BSD (revised)
license as described in the file LICENSE.
*/
#include "search.h"
#include "v_hashmap.h"
#include "hash.h"
#include "rand48.h"
#include "cost_sensitive.h"
#include "multiclass.h"
#include "memory.h"
#include "constant.h"
#include "cb.h"
#include "gd.h" // for GD::foreach_feature
#include <math.h>
#include "search_sequencetask.h"
#include "search_multiclasstask.h"
#include "search_hooktask.h"
using namespace LEARNER;
using namespace std;
namespace CS = COST_SENSITIVE;
namespace MC = MULTICLASS;
namespace Search {
search_task* all_tasks[] = { &SequenceTask::task,
&SequenceSpanTask::task,
&ArgmaxTask::task,
&SequenceTask_DemoLDF::task,
&MulticlassTask::task,
&HookTask::task,
NULL }; // must NULL terminate!
const bool PRINT_UPDATE_EVERY_EXAMPLE =0;
const bool PRINT_UPDATE_EVERY_PASS =0;
const bool PRINT_CLOCK_TIME =0;
string neighbor_feature_space("neighbor");
string condition_feature_space("search_condition");
uint32_t AUTO_CONDITION_FEATURES = 1, AUTO_HAMMING_LOSS = 2, EXAMPLES_DONT_CHANGE = 4, IS_LDF = 8;
enum SearchState { INITIALIZE, INIT_TEST, INIT_TRAIN, LEARN, GET_TRUTH_STRING };
enum RollMethod { POLICY, ORACLE, MIX_PER_STATE, MIX_PER_ROLL, NO_ROLLOUT };
// a data structure to hold conditioning information
struct prediction {
ptag me; // the id of the current prediction (the one being memoized)
size_t cnt; // how many variables are we conditioning on?
ptag* tags; // which variables are they?
action* acts; // and which actions were taken at each?
uint32_t hash; // a hash of the above
};
// parameters for auto-conditioning
struct auto_condition_settings {
size_t max_bias_ngram_length; // add a "bias" feature for each ngram up to and including this length. eg., if it's 1, then you get a single feature for each conditional
size_t max_quad_ngram_length; // add bias *times* input features for each ngram up to and including this length
float feature_value; // how much weight should the conditional features get?
};
struct search_private {
vw* all;
bool auto_condition_features; // do you want us to automatically add conditioning features?
bool auto_hamming_loss; // if you're just optimizing hamming loss, we can do it for you!
bool examples_dont_change; // set to true if you don't do any internal example munging
bool is_ldf; // set to true if you'll generate LDF data
v_array<int32_t> neighbor_features; // ugly encoding of neighbor feature requirements
auto_condition_settings acset; // settings for auto-conditioning
size_t history_length; // value of --search_history_length, used by some tasks, default 1
size_t A; // total number of actions, [1..A]; 0 means ldf
size_t num_learners; // total number of learners;
bool cb_learner; // do contextual bandit learning on action (was "! rollout_all_actions" which was confusing)
SearchState state; // current state of learning
size_t learn_learner_id; // we allow user to use different learners for different states
int mix_per_roll_policy; // for MIX_PER_ROLL, we need to choose a policy to use; this is where it's stored (-2 means "not selected yet")
bool no_caching; // turn off caching
size_t rollout_num_steps; // how many calls of "loss" before we stop really predicting on rollouts and switch to oracle (0 means "infinite")
bool (*label_is_test)(void*); // tell me if the label data from an example is test
size_t t; // current search step
size_t T; // length of root trajectory
v_array<example> learn_ec_copy;// copy of example(s) at learn_t
example* learn_ec_ref; // reference to example at learn_t, when there's no example munging
size_t learn_ec_ref_cnt; // how many are there (for LDF mode only; otherwise 1)
v_array<ptag> learn_condition_on; // a copy of the tags used for conditioning at the training position
v_array<action> learn_condition_on_act;// the actions taken
v_array<char> learn_condition_on_names;// the names of the actions
v_array<action> learn_allowed_actions; // which actions were allowed at training time?
v_array<action> ptag_to_action;// tag to action mapping for conditioning
action learn_oracle_action; // store an oracle action for debugging purposes
void* allowed_actions_cache; // either a CS::label* or CB::label* depending on cb_learner
size_t loss_declared_cnt; // how many times did run declare any loss (implicitly or explicitly)?
v_array<action> train_trajectory; // the training trajectory
size_t learn_t; // what time step are we learning on?
size_t learn_a_idx; // what action index are we trying?
bool done_with_all_actions; // set to true when there are no more learn_a_idx to go
float test_loss; // loss incurred when run INIT_TEST
float learn_loss; // loss incurred when run LEARN
float train_loss; // loss incurred when run INIT_TRAIN
bool last_example_was_newline; // used so we know when a block of examples has passed
bool hit_new_pass; // have we hit a new pass?
// if we're printing to stderr we need to remember if we've printed the header yet
// (i.e., we do this if we're driving)
bool printed_output_header;
// various strings for different search states
bool should_produce_string;
stringstream *pred_string;
stringstream *truth_string;
stringstream *bad_string_stream;
// parameters controlling interpolation
float beta; // interpolation rate
float alpha; // parameter used to adapt beta for dagger (see above comment), should be in (0,1)
RollMethod rollout_method; // 0=policy, 1=oracle, 2=mix_per_state, 3=mix_per_roll
RollMethod rollin_method;
float subsample_timesteps; // train at every time step or just a (random) subset?
bool allow_current_policy; // should the current policy be used for training? true for dagger
bool adaptive_beta; // used to implement dagger-like algorithms. if true, beta = 1-(1-alpha)^n after n updates, and policy is mixed with oracle as \pi' = (1-beta)\pi^* + beta \pi
size_t passes_per_policy; // if we're not in dagger-mode, then we need to know how many passes to train a policy
uint32_t current_policy; // what policy are we training right now?
// various statistics for reporting
size_t num_features;
uint32_t total_number_of_policies;
size_t read_example_last_id;
size_t passes_since_new_policy;
size_t read_example_last_pass;
size_t total_examples_generated;
size_t total_predictions_made;
size_t total_cache_hits;
vector<example*> ec_seq; // the collected examples
v_hashmap<unsigned char*, action> cache_hash_map;
// for foreach_feature temporary storage for conditioning
uint32_t dat_new_feature_idx;
example* dat_new_feature_ec;
stringstream dat_new_feature_audit_ss;
size_t dat_new_feature_namespace;
string* dat_new_feature_feature_space;
float dat_new_feature_value;
// to reduce memory allocation
string rawOutputString;
stringstream* rawOutputStringStream;
CS::label ldf_test_label;
v_array<action> condition_on_actions;
v_array<size_t> timesteps;
v_array<float> learn_losses;
LEARNER::learner* base_learner;
clock_t start_clock_time;
example*empty_example;
search_task* task; // your task!
};
string audit_feature_space("conditional");
uint32_t conditional_constant = 8290743;
int random_policy(search_private& priv, bool allow_current, bool allow_optimal) {
if (priv.beta >= 1) {
if (allow_current) return (int)priv.current_policy;
if (priv.current_policy > 0) return (((int)priv.current_policy)-1);
if (allow_optimal) return -1;
std::cerr << "internal error (bug): no valid policies to choose from! defaulting to current" << std::endl;
return (int)priv.current_policy;
}
int num_valid_policies = (int)priv.current_policy + allow_optimal + allow_current;
int pid = -1;
if (num_valid_policies == 0) {
std::cerr << "internal error (bug): no valid policies to choose from! defaulting to current" << std::endl;
return (int)priv.current_policy;
} else if (num_valid_policies == 1)
pid = 0;
else if (num_valid_policies == 2)
pid = frand48() >= priv.beta;
else {
// SPEEDUP this up in the case that beta is small!
float r = frand48();
pid = 0;
if (r > priv.beta) {
r -= priv.beta;
while ((r > 0) && (pid < num_valid_policies-1)) {
pid ++;
r -= priv.beta * powf(1.f - priv.beta, (float)pid);
}
}
}
// figure out which policy pid refers to
if (allow_optimal && (pid == num_valid_policies-1))
return -1; // this is the optimal policy
pid = (int)priv.current_policy - pid;
if (!allow_current)
pid--;
return pid;
}
int select_learner(search_private& priv, int policy, size_t learner_id) {
if (policy<0) return policy; // optimal policy
else return policy*priv.num_learners+learner_id;
}
bool should_print_update(vw& all, bool hit_new_pass=false) {
//uncomment to print out final loss after all examples processed
//commented for now so that outputs matches make test
//if( parser_done(all.p)) return true;
if (PRINT_UPDATE_EVERY_EXAMPLE) return true;
if (PRINT_UPDATE_EVERY_PASS && hit_new_pass) return true;
return (all.sd->weighted_examples >= all.sd->dump_interval) && !all.quiet && !all.bfgs;
}
bool might_print_update(vw& all) {
// basically do should_print_update but check me and the next
// example because of off-by-ones
if (PRINT_UPDATE_EVERY_EXAMPLE) return true;
if (PRINT_UPDATE_EVERY_PASS) return true; // SPEEDUP: make this better
return (all.sd->weighted_examples + 1. >= all.sd->dump_interval) && !all.quiet && !all.bfgs;
}
bool must_run_test(vw&all, vector<example*>ec, bool is_test_ex) {
return
(all.final_prediction_sink.size() > 0) || // if we have to produce output, we need to run this
might_print_update(all) || // if we have to print and update to stderr
(all.raw_prediction > 0) || // we need raw predictions
// or:
// it's not quiet AND
// current_pass == 0
// OR holdout is off
// OR it's a test example
( // (! all.quiet) && // had to disable this because of library mode!
(! is_test_ex) &&
( all.holdout_set_off || // no holdout
ec[0]->test_only ||
(all.current_pass == 0) // we need error rates for progressive cost
) )
;
}
void clear_seq(vw&all, search_private& priv) {
if (priv.ec_seq.size() > 0)
for (size_t i=0; i < priv.ec_seq.size(); i++)
VW::finish_example(all, priv.ec_seq[i]);
priv.ec_seq.clear();
}
float safediv(float a,float b) { if (b == 0.f) return 0.f; else return (a/b); }
void to_short_string(string in, size_t max_len, char*out) {
for (size_t i=0; i<max_len; i++)
out[i] = ((i >= in.length()) || (in[i] == '\n') || (in[i] == '\t')) ? ' ' : in[i];
if (in.length() > max_len) {
out[max_len-2] = '.';
out[max_len-1] = '.';
}
out[max_len] = 0;
}
void number_to_natural(size_t big, char* c) {
if (big > 9999999999) sprintf(c, "%dg", (int)(big / 1000000000));
else if (big > 9999999) sprintf(c, "%dm", (int)(big / 1000000));
else if (big > 9999) sprintf(c, "%dk", (int)(big / 1000));
else sprintf(c, "%d", (int)(big));
}
void print_update(search_private& priv) {
vw& all = *priv.all;
if (!priv.printed_output_header && !all.quiet) {
const char * header_fmt = "%-10s %-10s %8s%24s %22s %5s %5s %7s %7s %7s %-8s\n";
fprintf(stderr, header_fmt, "average", "since", "instance", "current true", "current predicted", "cur", "cur", "predic", "cache", "examples", "");
fprintf(stderr, header_fmt, "loss", "last", "counter", "output prefix", "output prefix", "pass", "pol", "made", "hits", "gener", "beta");
std::cerr.precision(5);
priv.printed_output_header = true;
}
if (!should_print_update(all, priv.hit_new_pass))
return;
char true_label[21];
char pred_label[21];
to_short_string(priv.truth_string->str(), 20, true_label);
to_short_string(priv.pred_string->str() , 20, pred_label);
float avg_loss = 0.;
float avg_loss_since = 0.;
bool use_heldout_loss = (!all.holdout_set_off && all.current_pass >= 1) && (all.sd->weighted_holdout_examples > 0);
if (use_heldout_loss) {
avg_loss = safediv((float)all.sd->holdout_sum_loss, (float)all.sd->weighted_holdout_examples);
avg_loss_since = safediv((float)all.sd->holdout_sum_loss_since_last_dump, (float)all.sd->weighted_holdout_examples_since_last_dump);
all.sd->weighted_holdout_examples_since_last_dump = 0;
all.sd->holdout_sum_loss_since_last_dump = 0.0;
} else {
avg_loss = safediv((float)all.sd->sum_loss, (float)all.sd->weighted_examples);
avg_loss_since = safediv((float)all.sd->sum_loss_since_last_dump, (float) (all.sd->weighted_examples - all.sd->old_weighted_examples));
}
char inst_cntr[9]; number_to_natural(all.sd->example_number, inst_cntr);
char total_pred[8]; number_to_natural(priv.total_predictions_made, total_pred);
char total_cach[8]; number_to_natural(priv.total_cache_hits, total_cach);
char total_exge[8]; number_to_natural(priv.total_examples_generated, total_exge);
fprintf(stderr, "%-10.6f %-10.6f %8s [%s] [%s] %5d %5d %7s %7s %7s %-8f",
avg_loss,
avg_loss_since,
inst_cntr,
true_label,
pred_label,
(int)priv.read_example_last_pass,
(int)priv.current_policy,
total_pred,
total_cach,
total_exge,
priv.beta);
if (PRINT_CLOCK_TIME) {
size_t num_sec = (size_t)(((float)(clock() - priv.start_clock_time)) / CLOCKS_PER_SEC);
fprintf(stderr, " %15lusec", num_sec);
}
if (use_heldout_loss)
fprintf(stderr, " h");
fprintf(stderr, "\n");
all.sd->sum_loss_since_last_dump = 0.0;
all.sd->old_weighted_examples = all.sd->weighted_examples;
fflush(stderr);
VW::update_dump_interval(all);
}
void add_new_feature(search_private& priv, float val, uint32_t idx) {
size_t mask = priv.all->reg.weight_mask;
size_t ss = priv.all->reg.stride_shift;
size_t idx2 = ((idx & mask) >> ss) & mask;
feature f = { val * priv.dat_new_feature_value,
(uint32_t) (((priv.dat_new_feature_idx + idx2) << ss) ) };
priv.dat_new_feature_ec->atomics[priv.dat_new_feature_namespace].push_back(f);
priv.dat_new_feature_ec->sum_feat_sq[priv.dat_new_feature_namespace] += f.x * f.x;
if (priv.all->audit) {
audit_data a = { NULL, NULL, f.weight_index, f.x, true };
a.space = (char*)calloc_or_die(priv.dat_new_feature_feature_space->length()+1, sizeof(char));
a.feature = (char*)calloc_or_die(priv.dat_new_feature_audit_ss.str().length() + 32, sizeof(char));
strcpy(a.space, priv.dat_new_feature_feature_space->c_str());
int num = sprintf(a.feature, "fid=%lu_", (idx & mask) >> ss);
strcpy(a.feature+num, priv.dat_new_feature_audit_ss.str().c_str());
priv.dat_new_feature_ec->audit_features[priv.dat_new_feature_namespace].push_back(a);
}
}
void del_features_in_top_namespace(search_private& priv, example& ec, size_t ns) {
if ((ec.indices.size() == 0) || (ec.indices.last() != ns)) {
std::cerr << "internal error (bug): expecting top namespace to be '" << ns << "' but it was ";
if (ec.indices.size() == 0) std::cerr << "empty";
else std::cerr << (size_t)ec.indices.last();
std::cerr << endl;
throw exception();
}
ec.num_features -= ec.atomics[ns].size();
ec.total_sum_feat_sq -= ec.sum_feat_sq[ns];
ec.sum_feat_sq[ns] = 0;
ec.indices.decr();
ec.atomics[ns].erase();
if (priv.all->audit) {
for (size_t i=0; i<ec.audit_features[ns].size(); i++)
if (ec.audit_features[ns][i].alloced) {
free(ec.audit_features[ns][i].space);
free(ec.audit_features[ns][i].feature);
}
ec.audit_features[ns].erase();
}
}
void add_neighbor_features(search_private& priv) {
vw& all = *priv.all;
if (priv.neighbor_features.size() == 0) return;
for (int n=0; n<priv.ec_seq.size(); n++) { // iterate over every example in the sequence
example& me = *priv.ec_seq[n];
for (size_t n_id=0; n_id < priv.neighbor_features.size(); n_id++) {
int32_t offset = priv.neighbor_features[n_id] >> 24;
size_t ns = priv.neighbor_features[n_id] & 0xFF;
priv.dat_new_feature_ec = &me;
priv.dat_new_feature_value = 1.;
priv.dat_new_feature_idx = priv.neighbor_features[n_id] * 13748127;
priv.dat_new_feature_namespace = neighbor_namespace;
if (priv.all->audit) {
priv.dat_new_feature_feature_space = &neighbor_feature_space;
priv.dat_new_feature_audit_ss.str("");
priv.dat_new_feature_audit_ss << '@' << ((offset > 0) ? '+' : '-') << (char)(abs(offset) + '0');
if (ns != ' ') priv.dat_new_feature_audit_ss << (char)ns;
}
//cerr << "n=" << n << " offset=" << offset << endl;
if (n + offset < 0) // add <s> feature
add_new_feature(priv, 1., 925871901 << priv.all->reg.stride_shift);
else if (n + offset >= priv.ec_seq.size()) // add </s> feature
add_new_feature(priv, 1., 3824917 << priv.all->reg.stride_shift);
else { // this is actually a neighbor
example& other = *priv.ec_seq[n + offset];
GD::foreach_feature<search_private,add_new_feature>(all.reg.weight_vector, all.reg.weight_mask, other.atomics[ns].begin, other.atomics[ns].end, priv, me.ft_offset);
}
}
size_t sz = me.atomics[neighbor_namespace].size();
if ((sz > 0) && (me.sum_feat_sq[neighbor_namespace] > 0.)) {
me.indices.push_back(neighbor_namespace);
me.total_sum_feat_sq += me.sum_feat_sq[neighbor_namespace];
me.num_features += sz;
} else {
me.atomics[neighbor_namespace].erase();
if (priv.all->audit) me.audit_features[neighbor_namespace].erase();
}
}
}
void del_neighbor_features(search_private& priv) {
if (priv.neighbor_features.size() == 0) return;
for (int n=0; n<priv.ec_seq.size(); n++)
del_features_in_top_namespace(priv, *priv.ec_seq[n], neighbor_namespace);
}
void reset_search_structure(search_private& priv) {
// NOTE: make sure do NOT reset priv.learn_a_idx
priv.t = 0;
priv.loss_declared_cnt = 0;
priv.done_with_all_actions = false;
priv.test_loss = 0.;
priv.learn_loss = 0.;
priv.train_loss = 0.;
priv.num_features = 0;
priv.should_produce_string = false;
priv.mix_per_roll_policy = -2;
if (priv.adaptive_beta) {
float x = - log1pf(- priv.alpha) * (float)priv.total_examples_generated;
static const float log_of_2 = 0.6931471805599453;
priv.beta = (x <= log_of_2) ? -expm1f(-x) : (1-expf(-x)); // numerical stability
//float priv_beta = 1.f - powf(1.f - priv.alpha, (float)priv.total_examples_generated);
//assert( fabs(priv_beta - priv.beta) < 1e-2 );
if (priv.beta > 1) priv.beta = 1;
}
priv.ptag_to_action.erase();
if (! priv.cb_learner) { // was: if rollout_all_actions
uint32_t seed = (priv.read_example_last_id * 147483 + 4831921) * 2147483647;
msrand48(seed);
}
}
void search_declare_loss(search_private& priv, float loss) {
priv.loss_declared_cnt++;
switch (priv.state) {
case INIT_TEST: priv.test_loss += loss; break;
case INIT_TRAIN: priv.train_loss += loss; break;
case LEARN:
if ((priv.rollout_num_steps == 0) || (priv.loss_declared_cnt <= priv.rollout_num_steps))
priv.learn_loss += loss;
break;
default: break; // get rid of the warning about missing cases (danger!)
}
}
size_t random(size_t max) { return (size_t)(frand48() * (float)max); }
action choose_oracle_action(search_private& priv, size_t ec_cnt, const action* oracle_actions, size_t oracle_actions_cnt, const action* allowed_actions, size_t allowed_actions_cnt) {
cdbg << "choose_oracle_action from oracle_actions = ["; for (size_t i=0; i<oracle_actions_cnt; i++) cdbg << " " << oracle_actions[i]; cdbg << " ]" << endl;
return ( oracle_actions_cnt > 0) ? oracle_actions[random(oracle_actions_cnt )] :
(allowed_actions_cnt > 0) ? allowed_actions[random(allowed_actions_cnt)] :
random(ec_cnt);
}
void add_example_conditioning(search_private& priv, example& ec, const ptag* condition_on, size_t condition_on_cnt, const char* condition_on_names, const action* condition_on_actions) {
if (condition_on_cnt == 0) return;
uint32_t extra_offset=0;
if (priv.is_ldf) {
CS::label* lab = (CS::label*)ec.ld;
if (lab->costs.size() > 0)
extra_offset = 3849017 * lab->costs[0].class_index;
}
size_t I = condition_on_cnt;
size_t N = max(priv.acset.max_bias_ngram_length, priv.acset.max_quad_ngram_length);
for (size_t i=0; i<I; i++) { // position in conditioning
uint32_t fid = 71933 + 8491087 * extra_offset;
if (priv.all->audit) {
priv.dat_new_feature_audit_ss.str("");
priv.dat_new_feature_audit_ss.clear();
priv.dat_new_feature_feature_space = &condition_feature_space;
}
for (size_t n=0; n<N; n++) { // length of ngram
if (i + n >= I) break; // no more ngrams
// we're going to add features for the ngram condition_on_actions[i .. i+N]
char name = condition_on_names[i+n];
fid = fid * 328901 + 71933 * ((condition_on_actions[i+n] + 349101) * (name + 38490137));
priv.dat_new_feature_ec = &ec;
priv.dat_new_feature_idx = fid * quadratic_constant;
priv.dat_new_feature_namespace = conditioning_namespace;
priv.dat_new_feature_value = priv.acset.feature_value;
if (priv.all->audit) {
if (n > 0) priv.dat_new_feature_audit_ss << ',';
if ((33 <= name) && (name <= 126)) priv.dat_new_feature_audit_ss << name;
else priv.dat_new_feature_audit_ss << '#' << (int)name;
priv.dat_new_feature_audit_ss << '=' << condition_on_actions[i+n];
}
// add the single bias feature
if (n < priv.acset.max_bias_ngram_length)
add_new_feature(priv, 1., 4398201 << priv.all->reg.stride_shift);
// add the quadratic features
if (n < priv.acset.max_quad_ngram_length)
GD::foreach_feature<search_private,uint32_t,add_new_feature>(*priv.all, ec, priv);
}
}
size_t sz = ec.atomics[conditioning_namespace].size();
if ((sz > 0) && (ec.sum_feat_sq[conditioning_namespace] > 0.)) {
ec.indices.push_back(conditioning_namespace);
ec.total_sum_feat_sq += ec.sum_feat_sq[conditioning_namespace];
ec.num_features += sz;
} else {
ec.atomics[conditioning_namespace].erase();
if (priv.all->audit) ec.audit_features[conditioning_namespace].erase();
}
}
void del_example_conditioning(search_private& priv, example& ec) {
if ((ec.indices.size() > 0) && (ec.indices.last() == conditioning_namespace))
del_features_in_top_namespace(priv, ec, conditioning_namespace);
}
uint32_t cs_get_prediction(bool isCB, void* ld) {
return isCB ? ((CB::label*)ld)->prediction
: ((CS::label*)ld)->prediction;
}
size_t cs_get_costs_size(bool isCB, void* ld) {
return isCB ? ((CB::label*)ld)->costs.size()
: ((CS::label*)ld)->costs.size();
}
uint32_t cs_get_cost_index(bool isCB, void* ld, size_t k) {
return isCB ? ((CB::label*)ld)->costs[k].action
: ((CS::label*)ld)->costs[k].class_index;
}
float cs_get_cost_partial_prediction(bool isCB, void* ld, size_t k) {
return isCB ? ((CB::label*)ld)->costs[k].partial_prediction
: ((CS::label*)ld)->costs[k].partial_prediction;
}
void cs_costs_erase(bool isCB, void* ld) {
if (isCB) ((CB::label*)ld)->costs.erase();
else ((CS::label*)ld)->costs.erase();
}
void cs_costs_resize(bool isCB, void* ld, size_t new_size) {
if (isCB) ((CB::label*)ld)->costs.resize(new_size);
else ((CS::label*)ld)->costs.resize(new_size);
}
void cs_cost_push_back(bool isCB, void* ld, uint32_t index, float value) {
if (isCB) { CB::cb_class cost = { value, index, 0., 0. }; ((CB::label*)ld)->costs.push_back(cost); }
else { CS::wclass cost = { value, index, 0., 0. }; ((CS::label*)ld)->costs.push_back(cost); }
}
void* allowed_actions_to_ld(search_private& priv, size_t ec_cnt, const action* allowed_actions, size_t allowed_actions_cnt) {
bool isCB = priv.cb_learner;
void* ld = priv.allowed_actions_cache;
size_t num_costs = cs_get_costs_size(isCB, ld);
if (priv.is_ldf) { // LDF version easier
if (num_costs > ec_cnt)
cs_costs_resize(isCB, ld, ec_cnt);
else if (num_costs < ec_cnt)
for (action k = num_costs; k < ec_cnt; k++)
cs_cost_push_back(isCB, ld, k, FLT_MAX);
} else { // non-LDF version
if ((allowed_actions == NULL) || (allowed_actions_cnt == 0)) { // any action is allowed
if (num_costs != priv.A) { // if there are already A-many actions, they must be the right ones, unless the user did something stupid like putting duplicate allowed_actions...
cs_costs_erase(isCB, ld);
for (action k = 0; k < priv.A; k++)
cs_cost_push_back(isCB, ld, k+1, FLT_MAX); //+1 because MC is 1-based
}
} else { // we need to peek at allowed_actions
cs_costs_erase(isCB, ld);
for (size_t i = 0; i < allowed_actions_cnt; i++)
cs_cost_push_back(isCB, ld, allowed_actions[i], FLT_MAX);
}
}
return ld;
}
template<class T> bool array_contains(T target, const T*A, size_t n) {
if (A == NULL) return false;
for (size_t i=0; i<n; i++)
if (A[i] == target) return true;
return false;
}
void allowed_actions_to_losses(search_private& priv, size_t ec_cnt, const action* allowed_actions, size_t allowed_actions_cnt, const action* oracle_actions, size_t oracle_actions_cnt, v_array<float>& losses) {
if (priv.is_ldf) // LDF version easier
for (action k=0; k<ec_cnt; k++)
losses.push_back( array_contains<action>(k, oracle_actions, oracle_actions_cnt) ? 0. : 1. );
else { // non-LDF
if ((allowed_actions == NULL) || (allowed_actions_cnt == 0)) // any action is allowed
for (action k=1; k<=priv.A; k++)
losses.push_back( array_contains<action>(k, oracle_actions, oracle_actions_cnt) ? 0. : 1. );
else
for (size_t i=0; i<allowed_actions_cnt; i++) {
action k = allowed_actions[i];
losses.push_back( array_contains<action>(k, oracle_actions, oracle_actions_cnt) ? 0. : 1. );
}
}
}
action single_prediction_notLDF(search_private& priv, example& ec, int policy, const action* allowed_actions, size_t allowed_actions_cnt) {
vw& all = *priv.all;
void* old_label = ec.ld;
ec.ld = allowed_actions_to_ld(priv, 1, allowed_actions, allowed_actions_cnt);
priv.base_learner->predict(ec, policy);
uint32_t act = cs_get_prediction(priv.cb_learner, ec.ld);
// generate raw predictions if necessary
if ((priv.state == INIT_TEST) && (all.raw_prediction > 0)) {
priv.rawOutputStringStream->str("");
for (size_t k = 0; k < cs_get_costs_size(priv.cb_learner, ec.ld); k++) {
if (k > 0) (*priv.rawOutputStringStream) << ' ';
(*priv.rawOutputStringStream) << cs_get_cost_index(priv.cb_learner, ec.ld, k) << ':' << cs_get_cost_partial_prediction(priv.cb_learner, ec.ld, k);
}
all.print_text(all.raw_prediction, priv.rawOutputStringStream->str(), ec.tag);
}
ec.ld = old_label;
priv.total_predictions_made++;
priv.num_features += ec.num_features;
return act;
}
action single_prediction_LDF(search_private& priv, example* ecs, size_t ec_cnt, int policy) {
CS::cs_label.default_label(&priv.ldf_test_label);
// keep track of best (aka chosen) action
float best_prediction = 0.;
action best_action = 0;
for (action a=0; a<ec_cnt; a++) {
cdbg << "== single_prediction_LDF a=" << a << "==" << endl;
void* old_label = ecs[a].ld;
ecs[a].ld = &priv.ldf_test_label;
priv.base_learner->predict(ecs[a], policy);
ecs[a].ld = old_label;
priv.empty_example->in_use = true;
priv.base_learner->predict(*priv.empty_example);
if ((a == 0) || (ecs[a].partial_prediction < best_prediction)) {
best_prediction = ecs[a].partial_prediction;
best_action = a;
}
priv.num_features += ecs[a].num_features;
}
priv.total_predictions_made++;
return best_action;
}
int choose_policy(search_private& priv) {
RollMethod method = (priv.state == INIT_TEST ) ? POLICY :
(priv.state == LEARN ) ? priv.rollout_method :
(priv.state == INIT_TRAIN) ? priv.rollin_method :
NO_ROLLOUT; // this should never happen
switch (method) {
case POLICY:
return random_policy(priv, priv.allow_current_policy || (priv.state == INIT_TEST), false);
case ORACLE:
return -1;
case MIX_PER_STATE:
return random_policy(priv, priv.allow_current_policy, true);
case MIX_PER_ROLL:
if (priv.mix_per_roll_policy == -2) // then we have to choose one!
priv.mix_per_roll_policy = random_policy(priv, priv.allow_current_policy, true);
return priv.mix_per_roll_policy;
case NO_ROLLOUT:
std::cerr << "internal error (bug): trying to rollin or rollout with NO_ROLLOUT" << endl;
throw exception();
}
}
template<class T> void cdbg_print_array(string str, v_array<T>& A) { cdbg << str << " = ["; for (size_t i=0; i<A.size(); i++) cdbg << " " << A[i]; cdbg << " ]" << endl; }
template<class T> void cerr_print_array(string str, v_array<T>& A) { cerr << str << " = ["; for (size_t i=0; i<A.size(); i++) cerr << " " << A[i]; cerr << " ]" << endl; }
template<class T>
void ensure_size(v_array<T>& A, size_t sz) {
if (A.end_array - A.begin < sz) A.resize(sz*2+1, true);
A.end = A.begin + sz;
}
template<class T> void push_at(v_array<T>& v, T item, size_t pos) {
if (v.size() > pos)
v.begin[pos] = item;
else {
if (v.end_array > v.begin + pos) {
// there's enough memory, just not enough filler
v.begin[pos] = item;
v.end = v.begin + pos + 1;
} else {
// there's not enough memory
v.resize(2 * pos + 3, true);
v.begin[pos] = item;
v.end = v.begin + pos + 1;
}
}
}
void record_action(search_private& priv, ptag mytag, action a) {
if (mytag == 0) return;
push_at(priv.ptag_to_action, a, mytag);
}
bool cached_item_equivalent(unsigned char*& A, unsigned char*& B) {
size_t sz_A = *A;
size_t sz_B = *B;
if (sz_A != sz_B) return false;
return memcmp(A, B, sz_A) == 0;
}
void free_key(unsigned char* mem, action a) { free(mem); }
void clear_cache_hash_map(search_private& priv) {
priv.cache_hash_map.iter(free_key);
priv.cache_hash_map.clear();
}
// returns true if found and do_store is false. if do_store is true, always returns true.
bool cached_action_store_or_find(search_private& priv, ptag mytag, const ptag* condition_on, const char* condition_on_names, const action* condition_on_actions, size_t condition_on_cnt, int policy, action &a, bool do_store) {
if (priv.no_caching) return do_store;
if (mytag == 0) return do_store; // don't attempt to cache when tag is zero
size_t sz = sizeof(size_t) + sizeof(ptag) + sizeof(int) + sizeof(size_t) + condition_on_cnt * (sizeof(ptag) + sizeof(action) + sizeof(char));
if (sz % 4 != 0) sz = 4 * (sz / 4 + 1); // make sure sz aligns to 4 so that uniform_hash does the right thing
unsigned char* item = (unsigned char*)calloc(sz, 1);
unsigned char* here = item;
*here = sz; here += sizeof(size_t);
*here = mytag; here += sizeof(ptag);
*here = policy; here += sizeof(int);
*here = condition_on_cnt; here += sizeof(size_t);
for (size_t i=0; i<condition_on_cnt; i++) {
*here = condition_on[i]; here += sizeof(ptag);
*here = condition_on_actions[i]; here += sizeof(action);
*here = condition_on_names[i]; here += sizeof(char); // SPEEDUP: should we align this at 4?
}
uint32_t hash = uniform_hash(item, sz, 3419);
if (do_store) {
priv.cache_hash_map.put(item, hash, a);
return true;
} else { // its a find
a = priv.cache_hash_map.get(item, hash);
free(item);
return a != (action)-1;
}
}
void generate_training_example(search_private& priv, v_array<float>& losses, bool add_conditioning=true) {
// should we really subtract out min-loss?
float min_loss = FLT_MAX, max_loss = -FLT_MAX;
size_t num_min = 0;
for (size_t i=0; i<losses.size(); i++) {
if (losses[i] < min_loss) { min_loss = losses[i]; num_min = 1; }
else if (losses[i] == min_loss) num_min++;
if (losses[i] > max_loss) { max_loss = losses[i]; }
}
int learner = select_learner(priv, priv.current_policy, priv.learn_learner_id);
if (!priv.is_ldf) { // not LDF
// since we're not LDF, it should be the case that ec_ref_cnt == 1
// and learn_ec_ref[0] is a pointer to a single example
assert(priv.learn_ec_ref_cnt == 1);
assert(priv.learn_ec_ref != NULL);
void* labels = allowed_actions_to_ld(priv, priv.learn_ec_ref_cnt, priv.learn_allowed_actions.begin, priv.learn_allowed_actions.size());
cdbg_print_array("learn_allowed_actions", priv.learn_allowed_actions);
//bool any_gt_1 = false;
for (size_t i=0; i<losses.size(); i++) {
losses[i] = losses[i] - min_loss;
if (priv.cb_learner) ((CB::label*)labels)->costs[i].cost = losses[i];
else ((CS::label*)labels)->costs[i].x = losses[i];
}
example& ec = priv.learn_ec_ref[0];
void* old_label = ec.ld;
ec.ld = labels;
ec.in_use = true;
if (add_conditioning) add_example_conditioning(priv, ec, priv.learn_condition_on.begin, priv.learn_condition_on.size(), priv.learn_condition_on_names.begin, priv.learn_condition_on_act.begin);
priv.base_learner->learn(ec, learner);
if (add_conditioning) del_example_conditioning(priv, ec);
ec.ld = old_label;
priv.total_examples_generated++;
} else { // is LDF
assert(losses.size() == priv.learn_ec_ref_cnt);
for (action a=0; a<priv.learn_ec_ref_cnt; a++) {
example& ec = priv.learn_ec_ref[a];
CS::label* lab = (CS::label*)ec.ld;
lab->costs[0].x = losses[a] - min_loss;
ec.in_use = true;
if (add_conditioning) add_example_conditioning(priv, ec, priv.learn_condition_on.begin, priv.learn_condition_on.size(), priv.learn_condition_on_names.begin, priv.learn_condition_on_act.begin);
priv.base_learner->learn(ec, learner);
priv.total_examples_generated++;
}
priv.base_learner->learn(*priv.empty_example, learner);
if (add_conditioning)
for (action a=0; a<priv.learn_ec_ref_cnt; a++) {
example& ec = priv.learn_ec_ref[a];
del_example_conditioning(priv, ec);
}
}
}
// note: ec_cnt should be 1 if we are not LDF
action search_predict(search_private& priv, example* ecs, size_t ec_cnt, ptag mytag, const action* oracle_actions, size_t oracle_actions_cnt, const ptag* condition_on, const char* condition_on_names, const action* allowed_actions, size_t allowed_actions_cnt, size_t learner_id) {
size_t condition_on_cnt = condition_on_names ? strlen(condition_on_names) : 0;
size_t t = priv.t;
priv.t++;
// make sure parameters come in pairs correctly
assert((oracle_actions == NULL) == (oracle_actions_cnt == 0));
assert((condition_on == NULL) == (condition_on_names == NULL));
assert((allowed_actions == NULL) == (allowed_actions_cnt == 0));
// if we're just after the string, choose an oracle action
if (priv.state == GET_TRUTH_STRING)
return choose_oracle_action(priv, ec_cnt, oracle_actions, oracle_actions_cnt, allowed_actions, allowed_actions_cnt);
// if we're in LEARN mode and before learn_t, return the train action
if ((priv.state == LEARN) && (t < priv.learn_t)) {
assert(t < priv.train_trajectory.size());
return priv.train_trajectory[t];
}
// for LDF, # of valid actions is ec_cnt; otherwise it's either allowed_actions_cnt or A
size_t valid_action_cnt = priv.is_ldf ? ec_cnt :
(allowed_actions_cnt > 0) ? allowed_actions_cnt : priv.A;
// if we're in LEARN mode and _at_ learn_t, then:
// - choose the next action
// - decide if we're done
// - if we are, then copy/mark the example ref
if ((priv.state == LEARN) && (t == priv.learn_t)) {
action a = priv.learn_a_idx;
priv.loss_declared_cnt = 0;
priv.learn_a_idx++;
priv.learn_loss = 0.; // don't include "past cost"
// check to see if we're done with available actions
if (priv.learn_a_idx >= valid_action_cnt) {
priv.done_with_all_actions = true;
// set reference or copy example(s)
priv.learn_oracle_action = oracle_actions[0];
priv.learn_ec_ref_cnt = ec_cnt;
if (priv.examples_dont_change)
priv.learn_ec_ref = ecs;
else {
size_t label_size = priv.is_ldf ? sizeof(CS::label) : sizeof(MC::multiclass);
void (*label_copy_fn)(void*&,void*) = priv.is_ldf ? CS::cs_label.copy_label : NULL;
ensure_size(priv.learn_ec_copy, ec_cnt);
for (size_t i=0; i<ec_cnt; i++) {
if (!priv.learn_ec_copy[i].ld)
priv.learn_ec_copy[i].ld = calloc_or_die(1, label_size);
VW::copy_example_data(priv.all->audit, priv.learn_ec_copy.begin+i, ecs+i, label_size, label_copy_fn);
}
priv.learn_ec_ref = priv.learn_ec_copy.begin;
}
// copy conditioning stuff and allowed actions
if (priv.auto_condition_features) {
ensure_size(priv.learn_condition_on, condition_on_cnt);
ensure_size(priv.learn_condition_on_act, condition_on_cnt);
priv.learn_condition_on.end = priv.learn_condition_on.begin + condition_on_cnt; // allow .size() to be used in lieu of _cnt
memcpy(priv.learn_condition_on.begin, condition_on, condition_on_cnt * sizeof(ptag));
for (size_t i=0; i<condition_on_cnt; i++)
push_at(priv.learn_condition_on_act, ((1 <= condition_on[i]) && (condition_on[i] < priv.ptag_to_action.size())) ? priv.ptag_to_action[condition_on[i]] : 0, i);
if (condition_on_names == NULL) {
ensure_size(priv.learn_condition_on_names, 1);
priv.learn_condition_on_names[0] = 0;
} else {
ensure_size(priv.learn_condition_on_names, strlen(condition_on_names)+1);
strcpy(priv.learn_condition_on_names.begin, condition_on_names);
}
}
ensure_size(priv.learn_allowed_actions, allowed_actions_cnt);
memcpy(priv.learn_allowed_actions.begin, allowed_actions, allowed_actions_cnt*sizeof(action));
cdbg_print_array("in LEARN, learn_allowed_actions", priv.learn_allowed_actions);
}
assert((allowed_actions_cnt == 0) || (a < allowed_actions_cnt));
return (allowed_actions_cnt > 0) ? allowed_actions[a] : priv.is_ldf ? a : (a+1);
}
if ((priv.state == LEARN) && (t > priv.learn_t) && (priv.rollout_num_steps > 0) && (priv.loss_declared_cnt >= priv.rollout_num_steps)) {
cdbg << "... skipping" << endl;
if (priv.is_ldf) return 0;
else if (allowed_actions_cnt > 0) return allowed_actions[0];
else return 1;
}
if ((priv.state == INIT_TRAIN) ||
(priv.state == INIT_TEST) ||
((priv.state == LEARN) && (t > priv.learn_t))) {
// we actually need to run the policy
int policy = choose_policy(priv);
action a;
cdbg << "executing policy " << policy << endl;
bool gte_here = (priv.state == INIT_TRAIN) && (priv.rollout_method == NO_ROLLOUT) && (oracle_actions_cnt > 0);
if (policy == -1)
a = choose_oracle_action(priv, ec_cnt, oracle_actions, oracle_actions_cnt, allowed_actions, allowed_actions_cnt);
if ((policy >= 0) || gte_here) {
ensure_size(priv.condition_on_actions, condition_on_cnt);
for (size_t i=0; i<condition_on_cnt; i++)
priv.condition_on_actions[i] = ((1 <= condition_on[i]) && (condition_on[i] < priv.ptag_to_action.size())) ? priv.ptag_to_action[condition_on[i]] : 0;
if (cached_action_store_or_find(priv, mytag, condition_on, condition_on_names, priv.condition_on_actions.begin, condition_on_cnt, policy, a, false))
// if this succeeded, 'a' has the right action
priv.total_cache_hits++;
else { // we need to predict, and then cache
if (priv.auto_condition_features)
for (size_t n=0; n<ec_cnt; n++)
add_example_conditioning(priv, ecs[n], condition_on, condition_on_cnt, condition_on_names, priv.condition_on_actions.begin);
if (policy >= 0) // only make a prediction if we're going to use the output
a = priv.is_ldf ? single_prediction_LDF(priv, ecs, ec_cnt, policy)
: single_prediction_notLDF(priv, *ecs, policy, allowed_actions, allowed_actions_cnt);
if (gte_here) {
cdbg << "INIT_TRAIN, NO_ROLLOUT, at least one oracle_actions" << endl;
// we can generate a training example _NOW_ because we're not doing rollouts
v_array<float> losses; // SPEEDUP: move this to data structure
allowed_actions_to_losses(priv, ec_cnt, allowed_actions, allowed_actions_cnt, oracle_actions, oracle_actions_cnt, losses);
cdbg_print_array("losses", losses);
priv.learn_ec_ref = ecs;
priv.learn_ec_ref_cnt = ec_cnt;
ensure_size(priv.learn_allowed_actions, allowed_actions_cnt);
memcpy(priv.learn_allowed_actions.begin, allowed_actions, allowed_actions_cnt * sizeof(action));
generate_training_example(priv, losses, false);
losses.delete_v();
}
if (priv.auto_condition_features)
for (size_t n=0; n<ec_cnt; n++)
del_example_conditioning(priv, ecs[n]);
cached_action_store_or_find(priv, mytag, condition_on, condition_on_names, priv.condition_on_actions.begin, condition_on_cnt, policy, a, true);
}
}
if (priv.state == INIT_TRAIN)
priv.train_trajectory.push_back(a); // note the action for future reference
return a;
}
std::cerr << "error: predict called in unknown state" << endl;
throw exception();
}
inline bool cmp_size_t(const size_t a, const size_t b) { return a < b; }
void get_training_timesteps(search_private& priv, v_array<size_t>& timesteps) {
timesteps.erase();
// if there's no subsampling to do, just return [0,T)
if (priv.subsample_timesteps <= 0)
for (size_t t=0; t<priv.T; t++)
timesteps.push_back(t);
// if subsample in (0,1) then pick steps with that probability, but ensuring there's at least one!
else if (priv.subsample_timesteps < 1) {
for (size_t t=0; t<priv.T; t++)
if (frand48() <= priv.subsample_timesteps)
timesteps.push_back(t);
if (timesteps.size() == 0) // ensure at least one
timesteps.push_back((size_t)(frand48() * priv.T));
}
// finally, if subsample >= 1, then pick (int) that many uniformly at random without replacement; could use an LFSR but why? :P
else {
while ((timesteps.size() < (size_t)priv.subsample_timesteps) &&
(timesteps.size() < priv.T)) {
size_t t = (size_t)(frand48() * (float)priv.T);
if (! v_array_contains(timesteps, t))
timesteps.push_back(t);
}
std::sort(timesteps.begin, timesteps.end, cmp_size_t);
}
}
template <bool is_learn>
void train_single_example(search& sch, bool is_test_ex) {
search_private& priv = *sch.priv;
vw&all = *priv.all;
bool ran_test = false; // we must keep track so that even if we skip test, we still update # of examples seen
// do an initial test pass to compute output (and loss)
clear_cache_hash_map(priv);
if (must_run_test(all, priv.ec_seq, is_test_ex)) {
cdbg << "======================================== INIT TEST (" << priv.current_policy << "," << priv.read_example_last_pass << ") ========================================" << endl;
ran_test = true;
reset_search_structure(priv);
priv.state = INIT_TEST;
priv.should_produce_string = might_print_update(all) || (all.final_prediction_sink.size() > 0) || (all.raw_prediction > 0);
priv.pred_string->str("");
// do the prediction
priv.task->run(sch, priv.ec_seq);
// accumulate loss
if (! is_test_ex) { // we cannot accumulate loss on test examples!
if (priv.ec_seq[0]->test_only) {
all.sd->weighted_holdout_examples += 1.f;//test weight seen
all.sd->weighted_holdout_examples_since_last_dump += 1.f;
all.sd->weighted_holdout_examples_since_last_pass += 1.f;
all.sd->holdout_sum_loss += priv.test_loss;
all.sd->holdout_sum_loss_since_last_dump += priv.test_loss;
all.sd->holdout_sum_loss_since_last_pass += priv.test_loss;//since last pass
} else {
all.sd->weighted_examples += 1.f;
all.sd->total_features += priv.num_features;
all.sd->sum_loss += priv.test_loss;
all.sd->sum_loss_since_last_dump += priv.test_loss;
all.sd->example_number++;
}
}
// generate output
for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; ++sink)
all.print_text((int)*sink, priv.pred_string->str(), priv.ec_seq[0]->tag);
if (all.raw_prediction > 0)
all.print_text(all.raw_prediction, "", priv.ec_seq[0]->tag);
}
// if we're not training, then we're done!
if ((!is_learn) || is_test_ex || priv.ec_seq[0]->test_only || (!priv.all->training))
return;
// SPEEDUP: if the oracle was never called, we can skip this!
// do a pass over the data allowing oracle
cdbg << "======================================== INIT TRAIN (" << priv.current_policy << "," << priv.read_example_last_pass << ") ========================================" << endl;
reset_search_structure(priv);
priv.state = INIT_TRAIN;
priv.train_trajectory.erase(); // this is where we'll store the training sequence
priv.task->run(sch, priv.ec_seq);
if (!ran_test) { // was && !priv.ec_seq[0]->test_only) { but we know it's not test_only
all.sd->weighted_examples += 1.f;
all.sd->total_features += priv.num_features;
all.sd->sum_loss += priv.test_loss;
all.sd->sum_loss_since_last_dump += priv.test_loss;
all.sd->example_number++;
}
// if there's nothing to train on, we're done!
if ((priv.loss_declared_cnt == 0) || (priv.t == 0) || (priv.rollout_method == NO_ROLLOUT))
return;
// otherwise, we have some learn'in to do!
// RollMethod old_rollout = priv.rollout_method;
// for (size_t num_learn_passes=0; num_learn_passes<=(all.current_pass>0); num_learn_passes++){ // all.current_pass
// //if ((frand48() < 0.5) /* &(all.current_pass > 0) */) priv.rollout_method = ORACLE;
// if (num_learn_passes == 0) priv.rollout_method = ORACLE;
// else priv.rollout_method = old_rollout;
cdbg << "======================================== LEARN (" << priv.current_policy << "," << priv.read_example_last_pass << ") ========================================" << endl;
priv.T = priv.t;
get_training_timesteps(priv, priv.timesteps);
priv.learn_losses.erase();
cdbg_print_array("timesteps", priv.timesteps);
for (size_t tid=0; tid<priv.timesteps.size(); tid++) {
priv.learn_a_idx = 0;
priv.done_with_all_actions = false;
// for each action, roll out to get a loss
while (! priv.done_with_all_actions) {
reset_search_structure(priv);
priv.state = LEARN;
priv.learn_t = priv.timesteps[tid];
cdbg << "learn_t = " << priv.learn_t << ", learn_a_idx = " << priv.learn_a_idx << endl;
priv.task->run(sch, priv.ec_seq);
priv.learn_losses.push_back( priv.learn_loss ); // SPEEDUP: should we just put this in a CS structure from the get-go?
cdbg_print_array("learn_losses", priv.learn_losses);
}
// now we can make a training example
generate_training_example(priv, priv.learn_losses);
if (! priv.examples_dont_change)
for (size_t n=0; n<priv.learn_ec_copy.size(); n++) {
if (sch.priv->is_ldf) CS::cs_label.delete_label(priv.learn_ec_copy[n].ld);
else MC::mc_label.delete_label(priv.learn_ec_copy[n].ld);
}
priv.learn_losses.erase();
}
// }
// priv.rollout_method = old_rollout;
}
template <bool is_learn>
void do_actual_learning(vw&all, search& sch) {
search_private& priv = *sch.priv;
if (priv.ec_seq.size() == 0)
return; // nothing to do :)
bool is_test_ex = false;
for (size_t i=0; i<priv.ec_seq.size(); i++)
if (priv.label_is_test(priv.ec_seq[i]->ld)) { is_test_ex = true; break; }
if (priv.task->run_setup) priv.task->run_setup(sch, priv.ec_seq);
// if we're going to have to print to the screen, generate the "truth" string
cdbg << "======================================== GET TRUTH STRING (" << priv.current_policy << "," << priv.read_example_last_pass << ") ========================================" << endl;
if (might_print_update(all)) {
if (is_test_ex)
priv.truth_string->str("**test**");
else {
reset_search_structure(*sch.priv);
priv.state = GET_TRUTH_STRING;
priv.should_produce_string = true;
priv.truth_string->str("");
priv.task->run(sch, priv.ec_seq);
}
}
add_neighbor_features(priv);
train_single_example<is_learn>(sch, is_test_ex);
del_neighbor_features(priv);
if (priv.task->run_takedown) priv.task->run_takedown(sch, priv.ec_seq);
}
template <bool is_learn>
void search_predict_or_learn(search& sch, learner& base, example& ec) {
search_private& priv = *sch.priv;
vw* all = priv.all;
priv.base_learner = &base;
bool is_real_example = true;
if (example_is_newline(ec) || priv.ec_seq.size() >= all->p->ring_size - 2) {
if (priv.ec_seq.size() >= all->p->ring_size - 2) // -2 to give some wiggle room
std::cerr << "warning: length of sequence at " << ec.example_counter << " exceeds ring size; breaking apart" << std::endl;
do_actual_learning<is_learn>(*all, sch);
priv.hit_new_pass = false;
priv.last_example_was_newline = true;
is_real_example = false;
} else {
if (priv.last_example_was_newline)
priv.ec_seq.clear();
priv.ec_seq.push_back(&ec);
priv.last_example_was_newline = false;
}
if (is_real_example)
priv.read_example_last_id = ec.example_counter;
}
void end_pass(search& sch) {
search_private& priv = *sch.priv;
vw* all = priv.all;
priv.hit_new_pass = true;
priv.read_example_last_pass++;
priv.passes_since_new_policy++;
if (priv.passes_since_new_policy >= priv.passes_per_policy) {
priv.passes_since_new_policy = 0;
if(all->training)
priv.current_policy++;
if (priv.current_policy > priv.total_number_of_policies) {
std::cerr << "internal error (bug): too many policies; not advancing" << std::endl;
priv.current_policy = priv.total_number_of_policies;
}
//reset search_trained_nb_policies in options_from_file so it is saved to regressor file later
std::stringstream ss;
ss << priv.current_policy;
VW::cmd_string_replace_value(all->file_options,"--search_trained_nb_policies", ss.str());
}
}
void finish_example(vw& all, search& sch, example& ec) {
if (ec.end_pass || example_is_newline(ec) || sch.priv->ec_seq.size() >= all.p->ring_size - 2) {
print_update(*sch.priv);
VW::finish_example(all, &ec);
clear_seq(all, *sch.priv);
}
}
void end_examples(search& sch) {
search_private& priv = *sch.priv;
vw* all = priv.all;
do_actual_learning<true>(*all, sch);
if( all->training ) {
std::stringstream ss1;
std::stringstream ss2;
ss1 << ((priv.passes_since_new_policy == 0) ? priv.current_policy : (priv.current_policy+1));
//use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --search_trained_nb_policies
VW::cmd_string_replace_value(all->file_options,"--search_trained_nb_policies", ss1.str());
ss2 << priv.total_number_of_policies;
//use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --search_total_nb_policies
VW::cmd_string_replace_value(all->file_options,"--search_total_nb_policies", ss2.str());
}
}
bool mc_label_is_test(void* lab) { return MC::label_is_test((MC::multiclass*)lab); }
void search_initialize(vw* all, search& sch) {
search_private& priv = *sch.priv;
priv.all = all;
priv.auto_condition_features = false;
priv.auto_hamming_loss = false;
priv.examples_dont_change = false;
priv.is_ldf = false;
priv.label_is_test = mc_label_is_test;
priv.A = 1;
priv.num_learners = 1;
priv.cb_learner = false;
priv.state = INITIALIZE;
priv.learn_learner_id = 0;
priv.mix_per_roll_policy = -2;
priv.t = 0;
priv.T = 0;
priv.learn_ec_ref = NULL;
priv.learn_ec_ref_cnt = 0;
priv.allowed_actions_cache = NULL;
priv.loss_declared_cnt = 0;
priv.learn_t = 0;
priv.learn_a_idx = 0;
priv.done_with_all_actions = false;
priv.test_loss = 0.;
priv.learn_loss = 0.;
priv.train_loss = 0.;
priv.last_example_was_newline = false;
priv.hit_new_pass = false;
priv.printed_output_header = false;
priv.should_produce_string = false;
priv.pred_string = new stringstream();
priv.truth_string = new stringstream();
priv.bad_string_stream = new stringstream();
priv.bad_string_stream->clear(priv.bad_string_stream->badbit);
priv.beta = 0.5;
priv.alpha = 1e-10f;
priv.rollout_method = MIX_PER_ROLL;
priv.rollin_method = MIX_PER_ROLL;
priv.subsample_timesteps = 0.;
priv.allow_current_policy = true;
priv.adaptive_beta = true;
priv.passes_per_policy = 1; //this should be set to the same value as --passes for dagger
priv.current_policy = 0;
priv.num_features = 0;
priv.total_number_of_policies = 1;
priv.read_example_last_id = 0;
priv.passes_per_policy = 0;
priv.read_example_last_pass = 0;
priv.total_examples_generated = 0;
priv.total_predictions_made = 0;
priv.total_cache_hits = 0;
priv.history_length = 1;
priv.acset.max_bias_ngram_length = 1;
priv.acset.max_quad_ngram_length = 0;
priv.acset.feature_value = 1.;
priv.cache_hash_map.set_default_value((action)-1);
priv.cache_hash_map.set_equivalent(cached_item_equivalent);
priv.task = NULL;
sch.task_data = NULL;
priv.empty_example = alloc_examples(sizeof(CS::label), 1);
CS::cs_label.default_label(priv.empty_example->ld);
priv.empty_example->in_use = true;
priv.rawOutputStringStream = new stringstream(priv.rawOutputString);
}
void search_finish(search& sch) {
search_private& priv = *sch.priv;
cdbg << "search_finish" << endl;
clear_cache_hash_map(priv);
delete priv.truth_string;
delete priv.pred_string;
delete priv.bad_string_stream;
priv.neighbor_features.delete_v();
priv.timesteps.delete_v();
priv.learn_losses.delete_v();
priv.condition_on_actions.delete_v();
priv.learn_allowed_actions.delete_v();
if (priv.cb_learner) {
((CB::label*)priv.allowed_actions_cache)->costs.delete_v();
delete (CB::label*)priv.allowed_actions_cache;
} else {
((CS::label*)priv.allowed_actions_cache)->costs.delete_v();
delete (CS::label*)priv.allowed_actions_cache;
}
priv.train_trajectory.delete_v();
priv.ptag_to_action.delete_v();
dealloc_example(CS::cs_label.delete_label, *(priv.empty_example));
free(priv.empty_example);
priv.ec_seq.clear();
// destroy copied examples if we needed them
if (! priv.examples_dont_change) {
void (*delete_label)(void*) = priv.is_ldf ? CS::cs_label.delete_label : MC::mc_label.delete_label;
for(example*ec = priv.learn_ec_copy.begin; ec!=priv.learn_ec_copy.end; ++ec)
dealloc_example(delete_label, *ec);
priv.learn_ec_copy.delete_v();
}
priv.learn_condition_on_names.delete_v();
priv.learn_condition_on.delete_v();
priv.learn_condition_on_act.delete_v();
if (priv.task->finish != NULL) {
priv.task->finish(sch);
}
delete priv.rawOutputStringStream;
delete sch.priv;
}
void ensure_param(float &v, float lo, float hi, float def, const char* string) {
if ((v < lo) || (v > hi)) {
std::cerr << string << endl;
v = def;
}
}
bool string_equal(string a, string b) { return a.compare(b) == 0; }
bool float_equal(float a, float b) { return fabs(a-b) < 1e-6; }
bool uint32_equal(uint32_t a, uint32_t b) { return a==b; }
bool size_equal(size_t a, size_t b) { return a==b; }
template<class T> void check_option(T& ret, vw&all, po::variables_map& vm, const char* opt_name, bool default_to_cmdline, bool(*equal)(T,T), const char* mismatch_error_string, const char* required_error_string) {
if (vm.count(opt_name)) {
ret = vm[opt_name].as<T>();
stringstream ss;
ss << " --" << opt_name << " " << ret;
all.file_options.append(ss.str());
} else if (strlen(required_error_string)>0) {
std::cerr << required_error_string << endl;
if (! vm.count("help"))
throw exception();
}
}
void check_option(bool& ret, vw&all, po::variables_map& vm, const char* opt_name, bool default_to_cmdline, const char* mismatch_error_string) {
if (vm.count(opt_name)) {
ret = true;
stringstream ss;
ss << " --" << opt_name;
all.file_options.append(ss.str());
} else
ret = false;
}
void handle_condition_options(vw& vw, auto_condition_settings& acset, po::variables_map& vm) {
po::options_description condition_options("Search Auto-conditioning Options");
condition_options.add_options()
("search_max_bias_ngram_length", po::value<size_t>(), "add a \"bias\" feature for each ngram up to and including this length. eg., if it's 1 (default), then you get a single feature for each conditional")
("search_max_quad_ngram_length", po::value<size_t>(), "add bias *times* input features for each ngram up to and including this length (def: 0)")
("search_condition_feature_value", po::value<float> (), "how much weight should the conditional features get? (def: 1.)");
vm = add_options(vw, condition_options);
check_option<size_t>(acset.max_bias_ngram_length, vw, vm, "search_max_bias_ngram_length", false, size_equal,
"warning: you specified a different value for --search_max_bias_ngram_length than the one loaded from regressor. proceeding with loaded value: ", "");
check_option<size_t>(acset.max_quad_ngram_length, vw, vm, "search_max_quad_ngram_length", false, size_equal,
"warning: you specified a different value for --search_max_quad_ngram_length than the one loaded from regressor. proceeding with loaded value: ", "");
check_option<float> (acset.feature_value, vw, vm, "search_condition_feature_value", false, float_equal,
"warning: you specified a different value for --search_condition_feature_value than the one loaded from regressor. proceeding with loaded value: ", "");
}
v_array<CS::label> read_allowed_transitions(action A, const char* filename) {
FILE *f = fopen(filename, "r");
if (f == NULL) {
std::cerr << "error: could not read file " << filename << " (" << strerror(errno) << "); assuming all transitions are valid" << endl;
throw exception();
}
bool* bg = (bool*)malloc((A+1)*(A+1) * sizeof(bool));
int rd,from,to,count=0;
while ((rd = fscanf(f, "%d:%d", &from, &to)) > 0) {
if ((from < 0) || (from > (int)A)) { std::cerr << "warning: ignoring transition from " << from << " because it's out of the range [0," << A << "]" << endl; }
if ((to < 0) || (to > (int)A)) { std::cerr << "warning: ignoring transition to " << to << " because it's out of the range [0," << A << "]" << endl; }
bg[from * (A+1) + to] = true;
count++;
}
fclose(f);
v_array<CS::label> allowed;
for (size_t from=0; from<A; from++) {
v_array<CS::wclass> costs;
for (size_t to=0; to<A; to++)
if (bg[from * (A+1) + to]) {
CS::wclass c = { FLT_MAX, (action)to, 0., 0. };
costs.push_back(c);
}
CS::label ld = { costs, 0 };
allowed.push_back(ld);
}
free(bg);
std::cerr << "read " << count << " allowed transitions from " << filename << endl;
return allowed;
}
void parse_neighbor_features(string& nf_string, search&sch) {
search_private& priv = *sch.priv;
priv.neighbor_features.erase();
size_t len = nf_string.length();
if (len == 0) return;
char * cstr = new char [len+1];
strcpy(cstr, nf_string.c_str());
char * p = strtok(cstr, ",");
v_array<substring> cmd;
while (p != 0) {
cmd.erase();
substring me = { p, p+strlen(p) };
tokenize(':', me, cmd, true);
int32_t posn = 0;
char ns = ' ';
if (cmd.size() == 1) {
posn = int_of_substring(cmd[0]);
ns = ' ';
} else if (cmd.size() == 2) {
posn = int_of_substring(cmd[0]);
ns = (cmd[1].end > cmd[1].begin) ? cmd[1].begin[0] : ' ';
} else {
std::cerr << "warning: ignoring malformed neighbor specification: '" << p << "'" << endl;
}
int32_t enc = (posn << 24) | (ns & 0xFF);
priv.neighbor_features.push_back(enc);
p = strtok(NULL, ",");
}
cmd.delete_v();
delete[] cstr;
}
learner* setup(vw&all, po::variables_map& vm) {
search* sch = (search*)calloc_or_die(1,sizeof(search));
sch->priv = new search_private();
search_initialize(&all, *sch);
search_private& priv = *sch->priv;
po::options_description search_opts("Search Options");
search_opts.add_options()
("search_task", po::value<string>(), "the search task (use \"--search_task list\" to get a list of available tasks)")
("search_interpolation", po::value<string>(), "at what level should interpolation happen? [*data|policy]")
("search_rollout", po::value<string>(), "how should rollouts be executed? [policy|oracle|*mix_per_state|mix_per_roll|none]")
("search_rollin", po::value<string>(), "how should past trajectories be generated? [policy|oracle|*mix_per_state|mix_per_roll]")
("search_passes_per_policy", po::value<size_t>(), "number of passes per policy (only valid for search_interpolation=policy) [def=1]")
("search_exp_perturbation", po::value<float>(), "interpolation rate for policies in rollout (only valid for search_rollout=mix_per_state|mix_per_roll) [def=0.5]")
("search_beta", po::value<float>(), "interpolation rate for policies (only valid for search_interpolation=policy) [def=0.5]")
("search_alpha", po::value<float>(), "annealed beta = 1-(1-alpha)^t (only valid for search_interpolation=data) [def=1e-10]")
("search_total_nb_policies", po::value<size_t>(), "if we are going to train the policies through multiple separate calls to vw, we need to specify this parameter and tell vw how many policies are eventually going to be trained")
("search_trained_nb_policies", po::value<size_t>(), "the number of trained policies in a file")
("search_allowed_transitions",po::value<string>(),"read file of allowed transitions [def: all transitions are allowed]")
("search_subsample_time", po::value<float>(), "instead of training at all timesteps, use a subset. if value in (0,1), train on a random v%. if v>=1, train on precisely v steps per example")
("search_neighbor_features", po::value<string>(), "copy features from neighboring lines. argument looks like: '-1:a,+2' meaning copy previous line namespace a and next next line from namespace _unnamed_, where ',' separates them")
("search_rollout_num_steps", po::value<size_t>(), "how many calls of \"loss\" before we stop really predicting on rollouts and switch to oracle (def: 0 means \"infinite\")")
("search_history_length", po::value<size_t>(), "some tasks allow you to specify how much history their depend on; specify that here [def: 1]")
("search_no_caching", "turn off the built-in caching ability (makes things slower, but technically more safe)")
;
vm = add_options(all, search_opts);
std::string task_string;
std::string interpolation_string = "data";
std::string rollout_string = "mix_per_state";
std::string rollin_string = "mix_per_state";
check_option<string>(task_string, all, vm, "search_task", false, string_equal,
"warning: specified --search_task different than the one loaded from regressor. using loaded value of: ",
"error: you must specify a task using --search_task");
check_option<string>(interpolation_string, all, vm, "search_interpolation", false, string_equal,
"warning: specified --search_interpolation different than the one loaded from regressor. using loaded value of: ", "");
if (vm.count("search_passes_per_policy")) priv.passes_per_policy = vm["search_passes_per_policy"].as<size_t>();
if (vm.count("search_beta")) priv.beta = vm["search_beta" ].as<float>();
if (vm.count("search_alpha")) priv.alpha = vm["search_alpha" ].as<float>();
if (vm.count("search_beta")) priv.beta = vm["search_beta" ].as<float>();
if (vm.count("search_subsample_time")) priv.subsample_timesteps = vm["search_subsample_time"].as<float>();
if (vm.count("search_no_caching")) priv.no_caching = true;
if (vm.count("search_rollout_num_steps")) priv.rollout_num_steps = vm["search_rollout_num_steps"].as<size_t>();
priv.A = vm["search"].as<size_t>();
string neighbor_features_string;
check_option<string>(neighbor_features_string, all, vm, "search_neighbor_features", false, string_equal,
"warning: you specified a different feature structure with --search_neighbor_features than the one loaded from predictor. using loaded value of: ", "");
parse_neighbor_features(neighbor_features_string, *sch);
if (interpolation_string.compare("data") == 0) { // run as dagger
priv.adaptive_beta = true;
priv.allow_current_policy = true;
priv.passes_per_policy = all.numpasses;
if (priv.current_policy > 1) priv.current_policy = 1;
} else if (interpolation_string.compare("policy") == 0) {
} else {
std::cerr << "error: --search_interpolation must be 'data' or 'policy'" << endl;
throw exception();
}
if (vm.count("search_rollout")) rollout_string = vm["search_rollout"].as<string>();
if (vm.count("search_rollin" )) rollin_string = vm["search_rollin" ].as<string>();
if (rollout_string.compare("policy") == 0) priv.rollout_method = POLICY;
else if (rollout_string.compare("oracle") == 0) priv.rollout_method = ORACLE;
else if (rollout_string.compare("mix_per_state") == 0) priv.rollout_method = MIX_PER_STATE;
else if (rollout_string.compare("mix_per_roll") == 0) priv.rollout_method = MIX_PER_ROLL;
else if (rollout_string.compare("none") == 0) { priv.rollout_method = NO_ROLLOUT; priv.no_caching = true; cerr << "no rollout!" << endl; }
else {
std::cerr << "error: --search_rollout must be 'policy', 'oracle', 'mix_per_state', 'mix_per_roll' or 'none'" << endl;
throw exception();
}
if (rollin_string.compare("policy") == 0) priv.rollin_method = POLICY;
else if (rollin_string.compare("oracle") == 0) priv.rollin_method = ORACLE;
else if (rollin_string.compare("mix_per_state") == 0) priv.rollin_method = MIX_PER_STATE;
else if (rollin_string.compare("mix_per_roll") == 0) priv.rollin_method = MIX_PER_ROLL;
else {
std::cerr << "error: --search_rollin must be 'policy', 'oracle', 'mix_per_state' or 'mix_per_roll'" << endl;
throw exception();
}
check_option<size_t>(priv.A, all, vm, "search", false, size_equal,
"warning: you specified a different number of actions through --search than the one loaded from predictor. using loaded value of: ", "");
check_option<size_t>(priv.history_length, all, vm, "search_history_length", false, size_equal,
"warning: you specified a different history length through --search_history_length than the one loaded from predictor. using loaded value of: ", "");
//check if the base learner is contextual bandit, in which case, we dont rollout all actions.
if (vm.count("cb")) {
priv.cb_learner = true;
priv.allowed_actions_cache = new CB::label();
} else {
priv.cb_learner = false;
priv.allowed_actions_cache = new CS::label();
}
//if we loaded a regressor with -i option, --search_trained_nb_policies contains the number of trained policies in the file
// and --search_total_nb_policies contains the total number of policies in the file
if (vm.count("search_total_nb_policies"))
priv.total_number_of_policies = (uint32_t)vm["search_total_nb_policies"].as<size_t>();
ensure_param(priv.beta , 0.0, 1.0, 0.5, "warning: search_beta must be in (0,1); resetting to 0.5");
ensure_param(priv.alpha, 0.0, 1.0, 1e-10f, "warning: search_alpha must be in (0,1); resetting to 1e-10");
//compute total number of policies we will have at end of training
// we add current_policy for cases where we start from an initial set of policies loaded through -i option
uint32_t tmp_number_of_policies = priv.current_policy;
if( all.training )
tmp_number_of_policies += (int)ceil(((float)all.numpasses) / ((float)priv.passes_per_policy));
//the user might have specified the number of policies that will eventually be trained through multiple vw calls,
//so only set total_number_of_policies to computed value if it is larger
cdbg << "current_policy=" << priv.current_policy << " tmp_number_of_policies=" << tmp_number_of_policies << " total_number_of_policies=" << priv.total_number_of_policies << endl;
if( tmp_number_of_policies > priv.total_number_of_policies ) {
priv.total_number_of_policies = tmp_number_of_policies;
if( priv.current_policy > 0 ) //we loaded a file but total number of policies didn't match what is needed for training
std::cerr << "warning: you're attempting to train more classifiers than was allocated initially. Likely to cause bad performance." << endl;
}
//current policy currently points to a new policy we would train
//if we are not training and loaded a bunch of policies for testing, we need to subtract 1 from current policy
//so that we only use those loaded when testing (as run_prediction is called with allow_current to true)
if( !all.training && priv.current_policy > 0 )
priv.current_policy--;
std::stringstream ss1, ss2;
ss1 << priv.current_policy; VW::cmd_string_replace_value(all.file_options,"--search_trained_nb_policies", ss1.str());
ss2 << priv.total_number_of_policies; VW::cmd_string_replace_value(all.file_options,"--search_total_nb_policies", ss2.str());
cdbg << "search current_policy = " << priv.current_policy << " total_number_of_policies = " << priv.total_number_of_policies << endl;
if (task_string.compare("list") == 0) {
cerr << endl << "available search tasks:" << endl;
for (search_task** mytask = all_tasks; *mytask != NULL; mytask++)
cerr << " " << (*mytask)->task_name << endl;
cerr << endl;
exit(0);
}
for (search_task** mytask = all_tasks; *mytask != NULL; mytask++)
if (task_string.compare((*mytask)->task_name) == 0) {
priv.task = *mytask;
sch->task_name = (*mytask)->task_name;
break;
}
if (priv.task == NULL) {
if (! vm.count("help")) {
std::cerr << "fail: unknown task for --search_task '" << task_string << "'; use --search_task list to get a list" << endl;
throw exception();
}
}
all.p->emptylines_separate_examples = true;
// default to OAA labels unless the task wants to override this (which they can do in initialize)
all.p->lp = MC::mc_label;
if (priv.task)
priv.task->initialize(*sch, priv.A, vm);
if (vm.count("search_allowed_transitions")) read_allowed_transitions((action)priv.A, vm["search_allowed_transitions"].as<string>().c_str());
// set up auto-history if they want it
if (priv.auto_condition_features) {
handle_condition_options(all, priv.acset, vm);
// turn off auto-condition if it's irrelevant
if (((priv.acset.max_bias_ngram_length == 0) && (priv.acset.max_quad_ngram_length == 0)) ||
(priv.acset.feature_value == 0.f)) {
std::cerr << "warning: turning off AUTO_CONDITION_FEATURES because settings make it useless" << endl;
priv.auto_condition_features = false;
}
}
if (!priv.allow_current_policy) // if we're not dagger
all.check_holdout_every_n_passes = priv.passes_per_policy;
all.searchstr = sch;
priv.start_clock_time = clock();
learner* l = new learner(sch, all.l, priv.total_number_of_policies);
l->set_learn<search, search_predict_or_learn<true> >();
l->set_predict<search, search_predict_or_learn<false> >();
l->set_finish_example<search,finish_example>();
l->set_end_examples<search,end_examples>();
l->set_finish<search,search_finish>();
l->set_end_pass<search,end_pass>();
return l;
}
float action_hamming_loss(action a, const action* A, size_t sz) {
if (sz == 0) return 0.; // latent variables have zero loss
for (size_t i=0; i<sz; i++)
if (a == A[i]) return 0.;
return 1.;
}
// the interface:
action search::predict(example& ec, ptag mytag, const action* oracle_actions, size_t oracle_actions_cnt, const ptag* condition_on, const char* condition_on_names, const action* allowed_actions, size_t allowed_actions_cnt, size_t learner_id) {
action a = search_predict(*this->priv, &ec, 1, mytag, oracle_actions, oracle_actions_cnt, condition_on, condition_on_names, allowed_actions, allowed_actions_cnt, learner_id);
record_action(*this->priv, mytag, a);
if (this->priv->auto_hamming_loss)
loss(action_hamming_loss(a, oracle_actions, oracle_actions_cnt));
cdbg << "predict returning " << a << endl;
return a;
}
action search::predictLDF(example* ecs, size_t ec_cnt, ptag mytag, const action* oracle_actions, size_t oracle_actions_cnt, const ptag* condition_on, const char* condition_on_names, size_t learner_id) {
action a = search_predict(*this->priv, ecs, ec_cnt, mytag, oracle_actions, oracle_actions_cnt, condition_on, condition_on_names, NULL, 0, learner_id);
action a_name = ((CS::label*)ecs[a].ld)->costs[0].class_index;
record_action(*this->priv, mytag, a_name);
if (this->priv->auto_hamming_loss)
loss(action_hamming_loss(a, oracle_actions, oracle_actions_cnt));
cdbg << "predict returning " << a << endl;
return a;
}
void search::loss(float loss) { search_declare_loss(*this->priv, loss); }
stringstream& search::output() {
if (!this->priv->should_produce_string ) return *(this->priv->bad_string_stream);
else if ( this->priv->state == GET_TRUTH_STRING) return *(this->priv->truth_string);
else return *(this->priv->pred_string);
}
void search::set_options(uint32_t opts) {
if (this->priv->state != INITIALIZE) {
std::cerr << "error: task cannot set options except in initialize function!" << endl;
throw exception();
}
if ((opts & AUTO_CONDITION_FEATURES) != 0) this->priv->auto_condition_features = true;
if ((opts & AUTO_HAMMING_LOSS) != 0) this->priv->auto_hamming_loss = true;
if ((opts & EXAMPLES_DONT_CHANGE) != 0) this->priv->examples_dont_change = true;
if ((opts & IS_LDF) != 0) this->priv->is_ldf = true;
}
void search::set_label_parser(label_parser&lp, bool (*is_test)(void*)) {
if (this->priv->state != INITIALIZE) {
std::cerr << "error: task cannot set label parser except in initialize function!" << endl;
throw exception();
}
this->priv->all->p->lp = lp;
this->priv->label_is_test = is_test;
}
void search::set_num_learners(size_t num_learners) { this->priv->num_learners = num_learners; }
void search::add_program_options(po::variables_map& vm, po::options_description& opts) { vm = add_options( *this->priv->all, opts ); }
uint32_t search::get_history_length() { return this->priv->history_length; }
// predictor implementation
predictor::predictor(search& sch, ptag my_tag) : is_ldf(false), my_tag(my_tag), ec(NULL), ec_cnt(0), oracle_is_pointer(false), allowed_is_pointer(false), learner_id(0), sch(sch) { }
predictor::~predictor() {
if (! oracle_is_pointer) oracle_actions.delete_v();
if (! allowed_is_pointer) allowed_actions.delete_v();
condition_on_tags.delete_v();
condition_on_names.delete_v();
}
predictor& predictor::set_input(example&input_example) {
is_ldf = false;
ec = &input_example;
ec_cnt = 1;
return *this;
}
predictor& predictor::set_input(example*input_example, size_t input_length) {
is_ldf = true;
ec = input_example;
ec_cnt = input_length;
return *this;
}
void predictor::make_new_pointer(v_array<action>& A, size_t new_size) {
size_t old_size = A.size();
action* old_pointer = A.begin;
A.begin = (action*)calloc_or_die(new_size, sizeof(action));
A.end = A.begin + new_size;
A.end_array = A.end;
memcpy(A.begin, old_pointer, old_size * sizeof(action));
}
predictor& predictor::add_to(v_array<action>& A, bool& A_is_ptr, action a, bool clear_first) {
if (A_is_ptr) { // we need to make our own memory
if (clear_first)
A.end = A.begin;
size_t new_size = clear_first ? 1 : (A.size() + 1);
make_new_pointer(A, new_size);
A_is_ptr = false;
A[new_size-1] = a;
} else { // we've already allocated our own memory
if (clear_first) A.erase();
A.push_back(a);
}
return *this;
}
predictor& predictor::add_to(v_array<action>&A, bool& A_is_ptr, action*a, size_t action_count, bool clear_first) {
size_t old_size = A.size();
if (old_size > 0) {
if (A_is_ptr) { // we need to make our own memory
if (clear_first) {
A.end = A.begin;
old_size = 0;
}
size_t new_size = old_size + action_count;
make_new_pointer(A, new_size);
A_is_ptr = false;
memcpy(A.begin + old_size, a, action_count * sizeof(action));
} else { // we already have our own memory
if (clear_first) A.erase();
push_many<action>(A, a, action_count);
}
} else { // old_size == 0, clear_first is irrelevant
if (! A_is_ptr)
A.delete_v(); // avoid memory leak
A.begin = a;
A.end = a + action_count;
A.end_array = A.end;
A_is_ptr = true;
}
return *this;
}
predictor& predictor::add_oracle(action a) { return add_to(oracle_actions, oracle_is_pointer, a, false); }
predictor& predictor::add_oracle(action*a, size_t action_count) { return add_to(oracle_actions, oracle_is_pointer, a, action_count, false); }
predictor& predictor::add_oracle(v_array<action>& a) { return add_to(oracle_actions, oracle_is_pointer, a.begin, a.size(), false); }
predictor& predictor::set_oracle(action a) { return add_to(oracle_actions, oracle_is_pointer, a, true); }
predictor& predictor::set_oracle(action*a, size_t action_count) { return add_to(oracle_actions, oracle_is_pointer, a, action_count, true); }
predictor& predictor::set_oracle(v_array<action>& a) { return add_to(oracle_actions, oracle_is_pointer, a.begin, a.size(), true); }
predictor& predictor::add_allowed(action a) { return add_to(allowed_actions, allowed_is_pointer, a, false); }
predictor& predictor::add_allowed(action*a, size_t action_count) { return add_to(allowed_actions, allowed_is_pointer, a, action_count, false); }
predictor& predictor::add_allowed(v_array<action>& a) { return add_to(allowed_actions, allowed_is_pointer, a.begin, a.size(), false); }
predictor& predictor::set_allowed(action a) { return add_to(allowed_actions, allowed_is_pointer, a, true); }
predictor& predictor::set_allowed(action*a, size_t action_count) { return add_to(allowed_actions, allowed_is_pointer, a, action_count, true); }
predictor& predictor::set_allowed(v_array<action>& a) { return add_to(allowed_actions, allowed_is_pointer, a.begin, a.size(), true); }
predictor& predictor::add_condition(ptag tag, char name) { condition_on_tags.push_back(tag); condition_on_names.push_back(name); return *this; }
predictor& predictor::set_condition(ptag tag, char name) { condition_on_tags.erase(); condition_on_names.erase(); return add_condition(tag, name); }
predictor& predictor::add_condition_range(ptag hi, ptag count, char name0) {
if (count == 0) return *this;
for (ptag i=0; i<count; i++) {
if (i > hi) break;
char name = name0 + i;
condition_on_tags.push_back(hi-i);
condition_on_names.push_back(name);
}
return *this;
}
predictor& predictor::set_condition_range(ptag hi, ptag count, char name0) { condition_on_tags.erase(); condition_on_names.erase(); return add_condition_range(hi, count, name0); }
predictor& predictor::set_learner_id(size_t id) { learner_id = id; return *this; }
predictor& predictor::set_tag(ptag tag) { my_tag = tag; return *this; }
action predictor::predict() {
const action* orA = oracle_actions.size() == 0 ? NULL : oracle_actions.begin;
const ptag* cOn = condition_on_names.size() == 0 ? NULL : condition_on_tags.begin;
const char* cNa = NULL;
if (condition_on_names.size() > 0) {
condition_on_names.push_back((char)0); // null terminate
cNa = condition_on_names.begin;
}
const action* alA = (allowed_actions.size() == 0) ? NULL : allowed_actions.begin;
action p = is_ldf ? sch.predictLDF(ec, ec_cnt, my_tag, orA, oracle_actions.size(), cOn, cNa, learner_id)
: sch.predict(*ec, my_tag, orA, oracle_actions.size(), cOn, cNa, alA, allowed_actions.size(), learner_id);
if (condition_on_names.size() > 0)
condition_on_names.pop(); // un-null-terminate
return p;
}
}
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