ref(navi/dr_transform): fix clippy & formatting issues

This commit is contained in:
Pouriya Jahanbakhsh 2023-04-01 02:21:44 +03:30
parent ec83d01dca
commit 2dbdfe173c
3 changed files with 133 additions and 169 deletions

View File

@ -44,6 +44,5 @@ pub struct RenamedFeatures {
}
pub fn parse(json_str: &str) -> Result<AllConfig, Error> {
let all_config: AllConfig = serde_json::from_str(json_str)?;
return std::result::Result::Ok(all_config);
serde_json::from_str(json_str)
}

View File

@ -16,8 +16,7 @@ use segdense::util;
use thrift::protocol::{TBinaryInputProtocol, TSerializable};
use thrift::transport::TBufferChannel;
use crate::{all_config};
use crate::all_config::AllConfig;
use crate::{all_config, all_config::AllConfig};
pub fn log_feature_match(
dr: &DataRecord,
@ -27,26 +26,22 @@ pub fn log_feature_match(
// Note the following algorithm matches features from config using linear search.
// Also the record source is MinDataRecord. This includes only binary and continous features for now.
for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap().into_iter() {
for (feature_id, feature_value) in dr.continuous_features.as_ref().unwrap() {
debug!(
"{} - Continous Datarecord => Feature ID: {}, Feature value: {}",
dr_type, feature_id, feature_value
"{dr_type} - Continuous Datarecord => Feature ID: {feature_id}, Feature value: {feature_value}"
);
for input_feature in &seg_dense_config.cont.input_features {
if input_feature.feature_id == *feature_id {
debug!("Matching input feature: {:?}", input_feature)
debug!("Matching input feature: {input_feature:?}")
}
}
}
for feature_id in dr.binary_features.as_ref().unwrap().into_iter() {
debug!(
"{} - Binary Datarecord => Feature ID: {}",
dr_type, feature_id
);
for feature_id in dr.binary_features.as_ref().unwrap() {
debug!("{dr_type} - Binary Datarecord => Feature ID: {feature_id}");
for input_feature in &seg_dense_config.binary.input_features {
if input_feature.feature_id == *feature_id {
debug!("Found input feature: {:?}", input_feature)
debug!("Found input feature: {input_feature:?}")
}
}
}
@ -96,15 +91,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
reporting_feature_ids: Vec<(i64, &str)>,
register_metric_fn: Option<impl Fn(&HistogramVec)>,
) -> BatchPredictionRequestToTorchTensorConverter {
let all_config_path = format!("{}/{}/all_config.json", model_dir, model_version);
let seg_dense_config_path = format!(
"{}/{}/segdense_transform_spec_home_recap_2022.json",
model_dir, model_version
);
let all_config_path = format!("{model_dir}/{model_version}/all_config.json");
let seg_dense_config_path =
format!("{model_dir}/{model_version}/segdense_transform_spec_home_recap_2022.json");
let seg_dense_config = util::load_config(&seg_dense_config_path);
let all_config = all_config::parse(
&fs::read_to_string(&all_config_path)
.unwrap_or_else(|error| panic!("error loading all_config.json - {}", error)),
.unwrap_or_else(|error| panic!("error loading all_config.json - {error}")),
)
.unwrap();
@ -138,11 +131,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
let (discrete_feature_metrics, continuous_feature_metrics) = METRICS.get_or_init(|| {
let discrete = HistogramVec::new(
HistogramOpts::new(":navi:feature_id:discrete", "Discrete Feature ID values")
.buckets(Vec::from(&[
0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
.buckets(Vec::from([
0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0,
300.0, 500.0, 1000.0, 10000.0, 100000.0,
] as &'static [f64])),
])),
&["feature_id"],
)
.expect("metric cannot be created");
@ -151,18 +144,18 @@ impl BatchPredictionRequestToTorchTensorConverter {
":navi:feature_id:continuous",
"continuous Feature ID values",
)
.buckets(Vec::from(&[
0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0,
130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0, 500.0,
1000.0, 10000.0, 100000.0,
] as &'static [f64])),
.buckets(Vec::from([
0.0f64, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0,
120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 250.0, 300.0,
500.0, 1000.0, 10000.0, 100000.0,
])),
&["feature_id"],
)
.expect("metric cannot be created");
register_metric_fn.map(|r| {
if let Some(r) = register_metric_fn {
r(&discrete);
r(&continuous);
});
}
(discrete, continuous)
});
@ -171,16 +164,13 @@ impl BatchPredictionRequestToTorchTensorConverter {
for (feature_id, feature_type) in reporting_feature_ids.iter() {
match *feature_type {
"discrete" => discrete_features_to_report.insert(feature_id.clone()),
"continuous" => continuous_features_to_report.insert(feature_id.clone()),
_ => panic!(
"Invalid feature type {} for reporting metrics!",
feature_type
),
"discrete" => discrete_features_to_report.insert(*feature_id),
"continuous" => continuous_features_to_report.insert(*feature_id),
_ => panic!("Invalid feature type {feature_type} for reporting metrics!"),
};
}
return BatchPredictionRequestToTorchTensorConverter {
BatchPredictionRequestToTorchTensorConverter {
all_config,
seg_dense_config,
all_config_path,
@ -193,7 +183,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
continuous_features_to_report,
discrete_feature_metrics,
continuous_feature_metrics,
};
}
}
fn get_feature_id(feature_name: &str, seg_dense_config: &Root) -> i64 {
@ -203,7 +193,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
return feature.feature_id;
}
}
return -1;
-1
}
fn parse_batch_prediction_request(bytes: Vec<u8>) -> BatchPredictionRequest {
@ -211,7 +201,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
let mut bc = TBufferChannel::with_capacity(bytes.len(), 0);
bc.set_readable_bytes(&bytes);
let mut protocol = TBinaryInputProtocol::new(bc, true);
return BatchPredictionRequest::read_from_in_protocol(&mut protocol).unwrap();
BatchPredictionRequest::read_from_in_protocol(&mut protocol).unwrap()
}
fn get_embedding_tensors(
@ -228,9 +218,9 @@ impl BatchPredictionRequestToTorchTensorConverter {
let mut working_set = vec![0 as f32; total_size];
let mut bpr_start = 0;
for (bpr, &bpr_end) in bprs.iter().zip(batch_size) {
if bpr.common_features.is_some() {
if bpr.common_features.as_ref().unwrap().tensors.is_some() {
if bpr
if bpr.common_features.is_some()
&& bpr.common_features.as_ref().unwrap().tensors.is_some()
&& bpr
.common_features
.as_ref()
.unwrap()
@ -268,8 +258,6 @@ impl BatchPredictionRequestToTorchTensorConverter {
}
}
}
}
}
// find the feature in individual feature list and add to corresponding batch.
for (index, datarecord) in bpr.individual_features_list.iter().enumerate() {
if datarecord.tensors.is_some()
@ -300,7 +288,7 @@ impl BatchPredictionRequestToTorchTensorConverter {
}
bpr_start = bpr_end;
}
return Array2::<f32>::from_shape_vec([rows, cols], working_set).unwrap();
Array2::<f32>::from_shape_vec([rows, cols], working_set).unwrap()
}
// Todo : Refactor, create a generic version with different type and field accessors
@ -310,9 +298,9 @@ impl BatchPredictionRequestToTorchTensorConverter {
// (INT64 --> INT64, DataRecord.discrete_feature)
fn get_continuous(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
// These need to be part of model schema
let rows: usize = batch_ends[batch_ends.len() - 1];
let cols: usize = 5293;
let full_size: usize = (rows * cols).try_into().unwrap();
let rows = batch_ends[batch_ends.len() - 1];
let cols = 5293;
let full_size = rows * cols;
let default_val = f32::NAN;
let mut tensor = vec![default_val; full_size];
@ -337,55 +325,48 @@ impl BatchPredictionRequestToTorchTensorConverter {
.unwrap();
for feature in common_features {
match self.feature_mapper.get(feature.0) {
Some(f_info) => {
if let Some(f_info) = self.feature_mapper.get(feature.0) {
let idx = f_info.index_within_tensor as usize;
if idx < cols {
// Set value in each row
for r in bpr_start..bpr_end {
let flat_index: usize = (r * cols + idx).try_into().unwrap();
let flat_index = r * cols + idx;
tensor[flat_index] = feature.1.into_inner() as f32;
}
}
}
None => (),
}
if self.continuous_features_to_report.contains(feature.0) {
self.continuous_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()])
.observe(feature.1.into_inner() as f64)
.observe(feature.1.into_inner())
} else if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()])
.observe(feature.1.into_inner() as f64)
.observe(feature.1.into_inner())
}
}
}
// Process the batch of datarecords
for r in bpr_start..bpr_end {
let dr: &DataRecord =
&bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
if dr.continuous_features.is_some() {
for feature in dr.continuous_features.as_ref().unwrap() {
match self.feature_mapper.get(&feature.0) {
Some(f_info) => {
if let Some(f_info) = self.feature_mapper.get(feature.0) {
let idx = f_info.index_within_tensor as usize;
let flat_index: usize = (r * cols + idx).try_into().unwrap();
let flat_index = r * cols + idx;
if flat_index < tensor.len() && idx < cols {
tensor[flat_index] = feature.1.into_inner() as f32;
}
}
None => (),
}
if self.continuous_features_to_report.contains(feature.0) {
self.continuous_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()])
.observe(feature.1.into_inner() as f64)
.observe(feature.1.into_inner())
} else if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()])
.observe(feature.1.into_inner() as f64)
.observe(feature.1.into_inner())
}
}
}
@ -393,22 +374,19 @@ impl BatchPredictionRequestToTorchTensorConverter {
bpr_start = bpr_end;
}
return InputTensor::FloatTensor(
Array2::<f32>::from_shape_vec(
[rows.try_into().unwrap(), cols.try_into().unwrap()],
tensor,
)
InputTensor::FloatTensor(
Array2::<f32>::from_shape_vec([rows, cols], tensor)
.unwrap()
.into_dyn(),
);
)
}
fn get_binary(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
// These need to be part of model schema
let rows: usize = batch_ends[batch_ends.len() - 1];
let cols: usize = 149;
let full_size: usize = (rows * cols).try_into().unwrap();
let default_val: i64 = 0;
let rows = batch_ends[batch_ends.len() - 1];
let cols = 149;
let full_size = rows * cols;
let default_val = 0;
let mut v = vec![default_val; full_size];
@ -432,55 +410,48 @@ impl BatchPredictionRequestToTorchTensorConverter {
.unwrap();
for feature in common_features {
match self.feature_mapper.get(feature) {
Some(f_info) => {
if let Some(f_info) = self.feature_mapper.get(feature) {
let idx = f_info.index_within_tensor as usize;
if idx < cols {
// Set value in each row
for r in bpr_start..bpr_end {
let flat_index: usize = (r * cols + idx).try_into().unwrap();
let flat_index = r * cols + idx;
v[flat_index] = 1;
}
}
}
None => (),
}
}
}
// Process the batch of datarecords
for r in bpr_start..bpr_end {
let dr: &DataRecord =
&bpr.individual_features_list[usize::try_from(r - bpr_start).unwrap()];
let dr: &DataRecord = &bpr.individual_features_list[r - bpr_start];
if dr.binary_features.is_some() {
for feature in dr.binary_features.as_ref().unwrap() {
match self.feature_mapper.get(&feature) {
Some(f_info) => {
if let Some(f_info) = self.feature_mapper.get(feature) {
let idx = f_info.index_within_tensor as usize;
let flat_index: usize = (r * cols + idx).try_into().unwrap();
let flat_index = r * cols + idx;
v[flat_index] = 1;
}
None => (),
}
}
}
}
bpr_start = bpr_end;
}
return InputTensor::Int64Tensor(
Array2::<i64>::from_shape_vec([rows.try_into().unwrap(), cols.try_into().unwrap()], v)
InputTensor::Int64Tensor(
Array2::<i64>::from_shape_vec([rows, cols], v)
.unwrap()
.into_dyn(),
);
)
}
#[allow(dead_code)]
fn get_discrete(&self, bprs: &[BatchPredictionRequest], batch_ends: &[usize]) -> InputTensor {
// These need to be part of model schema
let rows: usize = batch_ends[batch_ends.len() - 1];
let cols: usize = 320;
let full_size: usize = (rows * cols).try_into().unwrap();
let default_val: i64 = 0;
let rows = batch_ends[batch_ends.len() - 1];
let cols = 320;
let full_size = rows * cols;
let default_val = 0;
let mut v = vec![default_val; full_size];
@ -504,19 +475,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
.unwrap();
for feature in common_features {
match self.feature_mapper.get(feature.0) {
Some(f_info) => {
if let Some(f_info) = self.feature_mapper.get(feature.0) {
let idx = f_info.index_within_tensor as usize;
if idx < cols {
// Set value in each row
for r in bpr_start..bpr_end {
let flat_index: usize = (r * cols + idx).try_into().unwrap();
let flat_index = r * cols + idx;
v[flat_index] = *feature.1;
}
}
}
None => (),
}
if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()])
@ -527,19 +495,16 @@ impl BatchPredictionRequestToTorchTensorConverter {
// Process the batch of datarecords
for r in bpr_start..bpr_end {
let dr: &DataRecord = &bpr.individual_features_list[usize::try_from(r).unwrap()];
let dr: &DataRecord = &bpr.individual_features_list[r];
if dr.discrete_features.is_some() {
for feature in dr.discrete_features.as_ref().unwrap() {
match self.feature_mapper.get(&feature.0) {
Some(f_info) => {
if let Some(f_info) = self.feature_mapper.get(feature.0) {
let idx = f_info.index_within_tensor as usize;
let flat_index: usize = (r * cols + idx).try_into().unwrap();
let flat_index = r * cols + idx;
if flat_index < v.len() && idx < cols {
v[flat_index] = *feature.1;
}
}
None => (),
}
if self.discrete_features_to_report.contains(feature.0) {
self.discrete_feature_metrics
.with_label_values(&[feature.0.to_string().as_str()])
@ -550,11 +515,11 @@ impl BatchPredictionRequestToTorchTensorConverter {
}
bpr_start = bpr_end;
}
return InputTensor::Int64Tensor(
Array2::<i64>::from_shape_vec([rows.try_into().unwrap(), cols.try_into().unwrap()], v)
InputTensor::Int64Tensor(
Array2::<i64>::from_shape_vec([rows, cols], v)
.unwrap()
.into_dyn(),
);
)
}
fn get_user_embedding(
@ -604,7 +569,7 @@ impl Converter for BatchPredictionRequestToTorchTensorConverter {
.map(|bpr| bpr.individual_features_list.len())
.scan(0usize, |acc, e| {
//running total
*acc = *acc + e;
*acc += e;
Some(*acc)
})
.collect::<Vec<_>>();

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@ -12,11 +12,11 @@ pub fn load_batch_prediction_request_base64(file_name: &str) -> Vec<Vec<u8>> {
for line in io::BufReader::new(file).lines() {
match base64::decode(line.unwrap().trim()) {
Ok(payload) => result.push(payload),
Err(err) => println!("error decoding line {}", err),
Err(err) => println!("error decoding line {err}"),
}
}
println!("reslt len: {}", result.len());
return result;
println!("result len: {}", result.len());
return result
}
pub fn save_to_npy<T: npyz::Serialize + AutoSerialize>(data: &[T], save_to: String) {
let mut writer = WriteOptions::new()