I have the following example. How to rewrite the code - so I can use the model Predict for the given input? There is no example in the docs...
#include "yggdrasil_decision_forests/dataset/data_spec.h"
#include "yggdrasil_decision_forests/dataset/data_spec.pb.h"
#include "yggdrasil_decision_forests/dataset/data_spec_inference.h"
#include "yggdrasil_decision_forests/dataset/vertical_dataset_io.h"
#include "yggdrasil_decision_forests/learner/learner_library.h"
#include "yggdrasil_decision_forests/metric/metric.h"
#include "yggdrasil_decision_forests/metric/report.h"
#include "yggdrasil_decision_forests/model/model_library.h"
#include "yggdrasil_decision_forests/utils/filesystem.h"
#include "yggdrasil_decision_forests/utils/logging.h"
#include "yggdrasil_decision_forests/serving/decision_forest/decision_forest.h"
#include <chrono>
namespace ygg = yggdrasil_decision_forests;
int main(int argc, char** argv) {
// Enable the logging. Optional in most cases.
InitLogging(argv[0], &argc, &argv, true);
// Import the model.
LOG(INFO) << "Import the model";
const std::string model_path = "/tmp/my_saved_model/1/assets";
std::unique_ptr<ygg::model::AbstractModel> model;
QCHECK_OK(ygg::model::LoadModel(model_path, &model));
// Show information about the model.
// Like :show_model, but without the list of compatible engines.
std::string model_description;
model->AppendDescriptionAndStatistics(/*full_definition=*/false,
&model_description);
LOG(INFO) << "Model:\n" << model_description;
auto start = std::chrono::high_resolution_clock::now();
// Compile the model for fast inference.
const std::unique_ptr<ygg::serving::FastEngine> serving_engine =
model->BuildFastEngine().value();
const auto& features = serving_engine->features();
// Handle to two features.
const auto age_feature = features.GetNumericalFeatureId("age").value();
const auto sex_feature =
features.GetCategoricalFeatureId("sex").value();
// Allocate a batch of 1 examples.
std::unique_ptr<ygg::serving::AbstractExampleSet> examples =
serving_engine->AllocateExamples(1);
// Set all the values as missing. This is only necessary if you don't set all
// the feature values manually e.g. SetNumerical.
//examples->FillMissing(features);
// Set the value of "age" and "eduction" for the first example.
examples->SetNumerical(/*example_idx=*/0, age_feature, 50.f, features);
examples->SetCategorical(/*example_idx=*/0, sex_feature, "Male",
features);
// Run the predictions on the first two examples.
std::vector<float> batch_of_predictions;
serving_engine->Predict(*examples, 1, &batch_of_predictions);
auto stop = high_resolution_clock::now();
auto duration = std::chrono::duration_cast<milliseconds>(stop - start);
// To get the value of duration use the count()
// member function on the duration object
LOG(INFO) << duration.count();
LOG(INFO) << "Predictions:";
for (const float prediction : batch_of_predictions) {
LOG(INFO) << "\t" << prediction;
}
return 0;
}
[INFO beginner4.cc:31] Import the model
[INFO beginner4.cc:41] Model:
Type: "RANDOM_FOREST"
Task: CLASSIFICATION
Label: "__LABEL"
Input Features (2):
age
sex
No weights
Variable Importance: MEAN_MIN_DEPTH:
1. "__LABEL" 8.480250 ################
2. "sex" 1.313142 ##
3. "age" 0.000000
Variable Importance: NUM_AS_ROOT:
1. "age" 300.000000
Variable Importance: NUM_NODES:
1. "age" 34255.000000 ################
2. "sex" 1584.000000
Variable Importance: SUM_SCORE:
1. "age" 516361.208020 ################
2. "sex" 148174.885377
Winner take all: true
Out-of-bag evaluation: accuracy:0.756613 logloss:5.68258
Number of trees: 300
Total number of nodes: 71978
Number of nodes by tree:
Count: 300 Average: 239.927 StdDev: 4.94044
Min: 223 Max: 251 Ignored: 0
----------------------------------------------
[ 223, 224) 1 0.33% 0.33%
[ 224, 225) 0 0.00% 0.33%
[ 225, 227) 1 0.33% 0.67%
[ 227, 228) 3 1.00% 1.67% #
[ 228, 230) 3 1.00% 2.67% #
[ 230, 231) 0 0.00% 2.67%
[ 231, 233) 9 3.00% 5.67% ##
[ 233, 234) 17 5.67% 11.33% ###
[ 234, 236) 33 11.00% 22.33% ######
[ 236, 237) 0 0.00% 22.33%
[ 237, 238) 28 9.33% 31.67% #####
[ 238, 240) 51 17.00% 48.67% ##########
[ 240, 241) 0 0.00% 48.67%
[ 241, 243) 46 15.33% 64.00% #########
[ 243, 244) 47 15.67% 79.67% #########
[ 244, 246) 34 11.33% 91.00% #######
[ 246, 247) 0 0.00% 91.00%
[ 247, 249) 13 4.33% 95.33% ###
[ 249, 250) 10 3.33% 98.67% ##
[ 250, 251] 4 1.33% 100.00% #
Depth by leafs:
Count: 36139 Average: 8.48037 StdDev: 2.34049
Min: 3 Max: 15 Ignored: 0
----------------------------------------------
[ 3, 4) 70 0.19% 0.19%
[ 4, 5) 662 1.83% 2.03% #
[ 5, 6) 3633 10.05% 12.08% ######
[ 6, 7) 3980 11.01% 23.09% #######
[ 7, 8) 4520 12.51% 35.60% ########
[ 8, 9) 5143 14.23% 49.83% #########
[ 9, 10) 5817 16.10% 65.93% ##########
[ 10, 11) 5152 14.26% 80.18% #########
[ 11, 12) 3519 9.74% 89.92% ######
[ 12, 13) 2003 5.54% 95.46% ###
[ 13, 14) 975 2.70% 98.16% ##
[ 14, 15) 483 1.34% 99.50% #
[ 15, 15] 182 0.50% 100.00%
Number of training obs by leaf:
Count: 36139 Average: 190.182 StdDev: 179.903
Min: 5 Max: 2370 Ignored: 0
----------------------------------------------
[ 5, 123) 14849 41.09% 41.09% ##########
[ 123, 241) 10680 29.55% 70.64% #######
[ 241, 359) 3917 10.84% 81.48% ###
[ 359, 478) 6234 17.25% 98.73% ####
[ 478, 596) 137 0.38% 99.11%
[ 596, 714) 7 0.02% 99.13%
[ 714, 833) 17 0.05% 99.18%
[ 833, 951) 19 0.05% 99.23%
[ 951, 1069) 6 0.02% 99.24%
[ 1069, 1188) 135 0.37% 99.62%
[ 1188, 1306) 33 0.09% 99.71%
[ 1306, 1424) 0 0.00% 99.71%
[ 1424, 1542) 0 0.00% 99.71%
[ 1542, 1661) 8 0.02% 99.73%
[ 1661, 1779) 53 0.15% 99.88%
[ 1779, 1897) 6 0.02% 99.89%
[ 1897, 2016) 0 0.00% 99.89%
[ 2016, 2134) 2 0.01% 99.90%
[ 2134, 2252) 28 0.08% 99.98%
[ 2252, 2370] 8 0.02% 100.00%
Attribute in nodes:
34255 : age [NUMERICAL]
1584 : sex [CATEGORICAL]
Attribute in nodes with depth <= 0:
300 : age [NUMERICAL]
Attribute in nodes with depth <= 1:
600 : age [NUMERICAL]
300 : sex [CATEGORICAL]
Attribute in nodes with depth <= 2:
1721 : age [NUMERICAL]
379 : sex [CATEGORICAL]
Attribute in nodes with depth <= 3:
3328 : age [NUMERICAL]
1102 : sex [CATEGORICAL]
Attribute in nodes with depth <= 5:
11208 : age [NUMERICAL]
1583 : sex [CATEGORICAL]
Condition type in nodes:
34255 : HigherCondition
1584 : ContainsBitmapCondition
Condition type in nodes with depth <= 0:
300 : HigherCondition
Condition type in nodes with depth <= 1:
600 : HigherCondition
300 : ContainsBitmapCondition
Condition type in nodes with depth <= 2:
1721 : HigherCondition
379 : ContainsBitmapCondition
Condition type in nodes with depth <= 3:
3328 : HigherCondition
1102 : ContainsBitmapCondition
Condition type in nodes with depth <= 5:
11208 : HigherCondition
1583 : ContainsBitmapCondition
Node format: BLOB_SEQUENCE
Training OOB:
trees: 1, Out-of-bag evaluation: accuracy:0.750237 logloss:9.00239
trees: 13, Out-of-bag evaluation: accuracy:0.754722 logloss:7.09704
trees: 23, Out-of-bag evaluation: accuracy:0.753863 logloss:6.3117
trees: 33, Out-of-bag evaluation: accuracy:0.75395 logloss:6.19856
trees: 43, Out-of-bag evaluation: accuracy:0.754299 logloss:6.0429
trees: 53, Out-of-bag evaluation: accuracy:0.753165 logloss:5.9747
trees: 63, Out-of-bag evaluation: accuracy:0.754867 logloss:5.96594
trees: 73, Out-of-bag evaluation: accuracy:0.75443 logloss:5.92934
trees: 83, Out-of-bag evaluation: accuracy:0.754954 logloss:5.92356
trees: 93, Out-of-bag evaluation: accuracy:0.756307 logloss:5.89293
trees: 103, Out-of-bag evaluation: accuracy:0.756569 logloss:5.89233
trees: 113, Out-of-bag evaluation: accuracy:0.755696 logloss:5.81216
trees: 123, Out-of-bag evaluation: accuracy:0.755653 logloss:5.81
trees: 133, Out-of-bag evaluation: accuracy:0.755347 logloss:5.80588
trees: 143, Out-of-bag evaluation: accuracy:0.755914 logloss:5.77847
trees: 153, Out-of-bag evaluation: accuracy:0.755783 logloss:5.77828
trees: 163, Out-of-bag evaluation: accuracy:0.755522 logloss:5.74301
trees: 173, Out-of-bag evaluation: accuracy:0.756264 logloss:5.73794
trees: 183, Out-of-bag evaluation: accuracy:0.756176 logloss:5.7384
trees: 193, Out-of-bag evaluation: accuracy:0.756831 logloss:5.73852
trees: 203, Out-of-bag evaluation: accuracy:0.756613 logloss:5.73565
trees: 213, Out-of-bag evaluation: accuracy:0.757268 logloss:5.73545
trees: 223, Out-of-bag evaluation: accuracy:0.757486 logloss:5.7286
trees: 233, Out-of-bag evaluation: accuracy:0.757093 logloss:5.72556
trees: 243, Out-of-bag evaluation: accuracy:0.757093 logloss:5.7087
trees: 253, Out-of-bag evaluation: accuracy:0.757224 logloss:5.70704
trees: 263, Out-of-bag evaluation: accuracy:0.757006 logloss:5.69758
trees: 273, Out-of-bag evaluation: accuracy:0.756831 logloss:5.69816
trees: 283, Out-of-bag evaluation: accuracy:0.756526 logloss:5.69813
trees: 293, Out-of-bag evaluation: accuracy:0.756526 logloss:5.68217
trees: 300, Out-of-bag evaluation: accuracy:0.756613 logloss:5.68258
[INFO decision_forest.cc:639] Model loaded with 300 root(s), 71978 node(s), and 2 input feature(s).
[INFO abstract_model.cc:1158] Engine "RandomForestOptPred" built
[INFO beginner4.cc:79] 18
[INFO beginner4.cc:81] Predictions:
[INFO beginner4.cc:83] 0.649999
Lets say I want to continuously read the inference input data from stdin - I only need to load model and initialized serving_engine once?
every time stdin has new data to run a prediction I just need to run the following code? does anything change when I change to a http inference - anything to consider regarding thread safety - I cant share the model and serving_engine across multiple threads?
Is there anything I should change when I dont need batching? I still need to use std::unique_ptr<ygg::serving::AbstractExampleSet> examples = serving_engine->AllocateExamples(1)?
// Handle to two features.
const auto age_feature = features.GetNumericalFeatureId("age").value();
const auto sex_feature =
features.GetCategoricalFeatureId("sex").value();
// Allocate a batch of 1 examples.
std::unique_ptr<ygg::serving::AbstractExampleSet> examples =
serving_engine->AllocateExamples(1);
// Set all the values as missing. This is only necessary if you don't set all
// the feature values manually e.g. SetNumerical.
//examples->FillMissing(features);
// Set the value of "age" and "eduction" for the first example.
examples->SetNumerical(/*example_idx=*/0, age_feature, 50.f, features);
examples->SetCategorical(/*example_idx=*/0, sex_feature, "Male",
features);
// Run the predictions on the first two examples.
std::vector<float> batch_of_predictions;
serving_engine->Predict(*examples, 1, &batch_of_predictions);
auto stop = high_resolution_clock::now();
auto duration = std::chrono::duration_cast<milliseconds>(stop - start);
// To get the value of duration use the count()
// member function on the duration object
LOG(INFO) << duration.count();
LOG(INFO) << "Predictions:";
for (const float prediction : batch_of_predictions) {
LOG(INFO) << "\t" << prediction;
}
I also tried to use the c-api here, but always get as Result Tensor: 1.0? any idea why?
// gcc -I/usr/local/include -L/usr/local/lib main.c -ltensorflow -o main
#include <stdio.h>
#include <tensorflow/c/c_api.h>
void NoOpDeallocator(void* data, size_t a, void* b) {}
int main() {
TF_Graph *Graph = TF_NewGraph();
TF_Status *Status = TF_NewStatus();
TF_SessionOptions *SessionOpts = TF_NewSessionOptions();
TF_Buffer *RunOpts = NULL;
TF_Library *library;
library = TF_LoadLibrary("/usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/tensorflow/ops/inference/inference.so",
Status);
const char *saved_model_dir = "/tmp/my_saved_model/1/";
const char *tags = "serve";
int ntags = 1;
TF_Session *Session = TF_LoadSessionFromSavedModel(
SessionOpts, RunOpts, saved_model_dir, &tags, ntags, Graph, NULL, Status);
printf("status: %s\n", TF_Message(Status));
if(TF_GetCode(Status) == TF_OK) {
printf("loaded\n");
}else{
printf("not loaded\n");
}
/* Get Input Tensor */
int NumInputs = 2;
TF_Output* Input = malloc(sizeof(TF_Output) * NumInputs);
TF_Output t0 = {TF_GraphOperationByName(Graph, "serving_default_age"), 0};
if(t0.oper == NULL)
printf("ERROR: Failed TF_GraphOperationByName serving_default_input_1\n");
else
printf("TF_GraphOperationByName serving_default_input_1 is OK\n");
TF_Output t1 = {TF_GraphOperationByName(Graph, "serving_default_sex"), 0};
if(t1.oper == NULL)
printf("ERROR: Failed TF_GraphOperationByName serving_default_input_2\n");
else
printf("TF_GraphOperationByName serving_default_input_2 is OK\n");
Input[0] = t0;
Input[1] = t1;
// Get Output tensor
int NumOutputs = 1;
TF_Output* Output = malloc(sizeof(TF_Output) * NumOutputs);
TF_Output tout = {TF_GraphOperationByName(Graph, "StatefulPartitionedCall_1"), 0};
if(tout.oper == NULL)
printf("ERROR: Failed TF_GraphOperationByName StatefulPartitionedCall\n");
else
printf("TF_GraphOperationByName StatefulPartitionedCall is OK\n");
Output[0] = tout;
/* Allocate data for inputs and outputs */
TF_Tensor** InputValues = (TF_Tensor**)malloc(sizeof(TF_Tensor*)*NumInputs);
TF_Tensor** OutputValues = (TF_Tensor**)malloc(sizeof(TF_Tensor*)*NumOutputs);
int ndims = 1;
int64_t dims[] = {1};
int64_t data[] = {50};
int ndata = sizeof(int64_t);
TF_Tensor* int_tensor0 = TF_NewTensor(TF_INT64, dims, ndims, data, ndata, &NoOpDeallocator, 0);
if (int_tensor0 != NULL)
printf("TF_NewTensor is OK\n");
else
printf("ERROR: Failed TF_NewTensor\n");
const char test_string[] = "Male";
TF_TString tstr[1];
TF_TString_Init(&tstr[0]);
TF_TString_Copy(&tstr[0], test_string, sizeof(test_string)-1);
TF_Tensor* int_tensor1 = TF_NewTensor(TF_STRING, NULL, 0, &tstr[0], sizeof(tstr), &NoOpDeallocator, 0);
if (int_tensor1 != NULL)
printf("TF_NewTensor is OK\n");
else
printf("ERROR: Failed TF_NewTensor\n");
InputValues[0] = int_tensor0;
InputValues[1] = int_tensor1;
// Run the Session
TF_SessionRun(Session,
NULL, // Run options.
Input, InputValues, NumInputs, // Input tensors name, input tensor values, number of inputs.
Output, OutputValues, NumOutputs, // Output tensors name, output tensor values, number of outputs.
NULL, 0, // Target operations, number of targets.
NULL,
Status); // Output status.
if(TF_GetCode(Status) == TF_OK)
printf("Session is OK\n");
else
printf("%s",TF_Message(Status));
// Free memory
TF_DeleteGraph(Graph);
TF_DeleteSession(Session, Status);
TF_DeleteSessionOptions(SessionOpts);
TF_DeleteStatus(Status);
/* Get Output Result */
void* buff = TF_TensorData(OutputValues[0]);
float* offsets = (float*)buff;
printf("Result Tensor :\n");
printf("%f\n",offsets[0]);
return 0;
}
# gcc -I/usr/local/include -L/usr/local/lib main.c -ltensorflow -o main; ./main
2022-06-16 21:05:24.070748: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/my_saved_model/1/
2022-06-16 21:05:24.072148: I tensorflow/cc/saved_model/reader.cc:81] Reading meta graph with tags { serve }
2022-06-16 21:05:24.072208: I tensorflow/cc/saved_model/reader.cc:122] Reading SavedModel debug info (if present) from: /tmp/my_saved_model/1/
2022-06-16 21:05:24.072280: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-06-16 21:05:24.085806: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
2022-06-16 21:05:24.086985: I tensorflow/cc/saved_model/loader.cc:228] Restoring SavedModel bundle.
2022-06-16 21:05:24.116570: I tensorflow/cc/saved_model/loader.cc:212] Running initialization op on SavedModel bundle at path: /tmp/my_saved_model/1/
[INFO kernel.cc:1176] Loading model from path /tmp/my_saved_model/1/assets/ with prefix cf8326335a66430a
[INFO decision_forest.cc:639] Model loaded with 300 root(s), 71978 node(s), and 2 input feature(s).
[INFO abstract_model.cc:1246] Engine "RandomForestOptPred" built
[INFO kernel.cc:1022] Use fast generic engine
2022-06-16 21:05:24.373058: I tensorflow/cc/saved_model/loader.cc:301] SavedModel load for tags { serve }; Status: success: OK. Took 302321 microseconds.
status:
loaded
TF_GraphOperationByName serving_default_input_1 is OK
TF_GraphOperationByName serving_default_input_2 is OK
TF_GraphOperationByName StatefulPartitionedCall is OK
TF_NewTensor is OK
TF_NewTensor is OK
Session is OK
Result Tensor :
1.000000
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