Refactor and other improvements

This commit is contained in:
Andras Schmelczer 2026-02-08 18:25:58 +00:00
parent 04a78e7bfe
commit 6c90cf3c0f
47 changed files with 2705 additions and 1568 deletions

View file

@ -8,10 +8,14 @@ use axum::response::Json;
use serde::{Deserialize, Serialize};
use tracing::{info, warn};
use crate::consts::{H3_PRECOMPUTE_MAX, H3_REQUEST_MAX, H3_REQUEST_MIN};
use crate::parsing::{h3_cell_bounds, parse_filters, row_passes_filters};
use crate::parsing::{
cell_for_row, h3_cell_bounds, needs_parent, parse_field_set, parse_filters, row_passes_filters,
validate_h3_resolution,
};
use crate::state::AppState;
use super::stats;
#[derive(Serialize)]
pub struct HistogramStats {
pub min: f64,
@ -78,19 +82,8 @@ pub async fn get_hexagon_stats(
let cell_u64: u64 = cell.into();
let resolution = params.resolution;
if !(H3_REQUEST_MIN..=H3_REQUEST_MAX).contains(&resolution) {
warn!(
resolution,
"Resolution out of range [{}, {}]", H3_REQUEST_MIN, H3_REQUEST_MAX
);
return Err((
StatusCode::BAD_REQUEST,
format!(
"resolution must be between {} and {}",
H3_REQUEST_MIN, H3_REQUEST_MAX
),
));
}
validate_h3_resolution(resolution)?;
let h3_str = params.h3.clone();
let filters_str = params.filters.clone();
let (parsed_filters, parsed_enum_filters) = parse_filters(
@ -100,48 +93,25 @@ pub async fn get_hexagon_stats(
);
let num_filters = parsed_filters.len() + parsed_enum_filters.len();
let fields_specified = params.fields.is_some();
let field_set: std::collections::HashSet<String> = params
.fields
.as_ref()
.map(|fields_str| {
fields_str
.split(',')
.map(|field| field.trim().to_string())
.filter(|field| !field.is_empty())
.collect()
})
.unwrap_or_default();
let (fields_specified, field_set) = parse_field_set(params.fields.as_deref());
let response = tokio::task::spawn_blocking(move || {
let start_time = std::time::Instant::now();
let precomputed = &state.h3_cells;
let h3_res = h3o::Resolution::try_from(resolution)
.map_err(|err| format!("Invalid H3 resolution {}: {}", resolution, err))?;
let need_parent = resolution < H3_PRECOMPUTE_MAX;
let need_parent = needs_parent(resolution);
let num_features = state.data.num_features;
let feature_data = &state.data.feature_data;
let (min_lat, min_lon, max_lat, max_lon) = h3_cell_bounds(cell, 0.001);
let cell_for_row = |row: usize| -> u64 {
let max_cell = precomputed[row];
if !need_parent || max_cell == 0 {
return max_cell;
}
h3o::CellIndex::try_from(max_cell)
.ok()
.and_then(|ci| ci.parent(h3_res))
.map(u64::from)
.unwrap_or(0)
};
let mut matching_rows: Vec<usize> = Vec::new();
state
.grid
.for_each_in_bounds(min_lat, min_lon, max_lat, max_lon, |row_idx| {
let row = row_idx as usize;
if cell_for_row(row) == cell_u64
if cell_for_row(row, precomputed, h3_res, need_parent) == cell_u64
&& row_passes_filters(
row,
&parsed_filters,
@ -156,149 +126,23 @@ pub async fn get_hexagon_stats(
let total_count = matching_rows.len();
// Collect price history (year, price) pairs
let price_history = {
let year_idx = state
.feature_name_to_index
.get("Date of last transaction")
.copied();
let price_idx = state.feature_name_to_index.get("Last known price").copied();
match (year_idx, price_idx) {
(Some(yi), Some(pi)) => {
let mut points: Vec<PricePoint> = matching_rows
.iter()
.filter_map(|&row| {
let year = feature_data[row * num_features + yi];
let price = feature_data[row * num_features + pi];
if year.is_finite() && price.is_finite() {
Some(PricePoint { year, price })
} else {
None
}
})
.collect();
// Cap at 5000 points by evenly sampling
if points.len() > 5000 {
let step = points.len() as f64 / 5000.0;
points = (0..5000)
.map(|i| {
let idx = (i as f64 * step) as usize;
PricePoint {
year: points[idx].year,
price: points[idx].price,
}
})
.collect();
}
points
}
_ => Vec::new(),
}
};
let price_history = stats::extract_price_history(
&matching_rows,
feature_data,
num_features,
&state.feature_name_to_index,
);
let mut numeric_features = Vec::new();
let mut enum_features_out = Vec::new();
for (feature_index, feature_name) in state.data.feature_names.iter().enumerate() {
if fields_specified && !field_set.contains(feature_name.as_str()) {
continue;
}
// Check if this is an enum feature
if let Some(enum_values) = state.data.enum_values.get(&feature_index) {
// Enum feature: count occurrences of each value
let mut value_counts = vec![0u64; enum_values.len()];
for &row in &matching_rows {
let value = feature_data[row * num_features + feature_index];
if value.is_finite() {
let idx = value as usize;
if idx < value_counts.len() {
value_counts[idx] += 1;
}
}
}
let counts: HashMap<String, u64> = value_counts
.iter()
.enumerate()
.filter(|(_, &count)| count > 0)
.map(|(idx, &count)| (enum_values[idx].clone(), count))
.collect();
if !counts.is_empty() {
enum_features_out.push(EnumFeatureStats {
name: feature_name.clone(),
counts,
});
}
} else {
// Numeric feature: compute stats and histogram
let global_hist = &state.data.feature_stats[feature_index].histogram;
let p1 = global_hist.p1;
let p99 = global_hist.p99;
// Use same bin count as global histogram for consistency
let num_bins = global_hist.counts.len();
let mut count = 0usize;
let mut min_value = f32::INFINITY;
let mut max_value = f32::NEG_INFINITY;
let mut sum = 0.0f64;
let mut bins = vec![0u64; num_bins];
// Compute middle bin width (between p1 and p99)
let middle_bins = num_bins.saturating_sub(2);
let middle_width = if middle_bins > 0 && p99 > p1 {
(p99 - p1) / middle_bins as f32
} else {
0.0
};
for &row in &matching_rows {
let value = feature_data[row * num_features + feature_index];
if value.is_finite() {
count += 1;
if value < min_value {
min_value = value;
}
if value > max_value {
max_value = value;
}
sum += value as f64;
// Bin using p1/p99 outlier structure
let bin = if value < p1 {
0 // Low outlier bin
} else if value >= p99 {
num_bins - 1 // High outlier bin
} else if middle_width > 0.0 {
// Middle bins (1 to n-2)
let middle_bin = ((value - p1) / middle_width) as usize;
(1 + middle_bin).min(num_bins - 2)
} else {
num_bins / 2 // Fallback if p1 == p99
};
bins[bin] += 1;
}
}
if count > 0 {
numeric_features.push(NumericFeatureStats {
name: feature_name.clone(),
count,
min: min_value as f64,
max: max_value as f64,
mean: sum / count as f64,
histogram: HistogramStats {
min: global_hist.min as f64,
max: global_hist.max as f64,
p1: p1 as f64,
p99: p99 as f64,
counts: bins,
},
});
}
}
}
let (numeric_features, enum_features_out) = stats::compute_feature_stats(
&matching_rows,
feature_data,
&state.data.feature_names,
num_features,
&state.data.enum_values,
&state.data.feature_stats,
fields_specified,
&field_set,
);
let elapsed = start_time.elapsed();
info!(