perf: micro-optimize sample weighted method

- amortize allocations
- cache selected.len()
- also improve weighted method description

src/cmd/sample.rs

@@ -40,12 +40,13 @@ It supports seven sampling methods:
   https://en.wikipedia.org/wiki/Stratified_sampling
 
 - WEIGHTED: the sampling method when the --weighted option is specified.
-  Samples records with probability proportional to weights in the specified weight column.
-  If the weight column contains a value that is not a number for a record, the record will be
-  skipped. The weights are automatically scaled based on the maximum weight in the sample.
-  The number of records to sample is specified by the <sample-size> argument.
-  Useful when some records are more important than others.
-  Uses MEMORY PROPORTIONAL to the sample size (k) - O(k).
+  Samples records with probabilities proportional to values in a specified weight column.
+  Records with higher weights are more likely to be selected. For example, if you have
+  sales data and want to sample transactions weighted by revenue, high-value transactions
+  will have a higher chance of being included. Non-numeric weights are treated as zero.
+  The weights are automatically normalized using the maximum weight in the dataset.
+  Specify the desired sample size with <sample-size>. Uses MEMORY PROPORTIONAL to the
+  sample size (k) - O(k).
   "Weighted random sampling with a reservoir" https://doi.org/10.1016/j.ipl.2005.11.003
 
 - CLUSTER: the sampling method when the --cluster option is specified.
@@ -852,11 +853,12 @@ fn sample_weighted<R: io::Read, W: io::Write>(
 ) -> CliResult<()> {
     // First pass: find maximum weight
     let mut max_weight = 0.0f64;
+    let mut curr_record;
     for record in rdr.byte_records() {
-        let record = record?;
+        curr_record = record?;
 
         let weight: f64 = fast_float2::parse(
-            record
+            curr_record
                 .get(weight_column)
                 .ok_or_else(|| format!("Weight column index {weight_column} out of bounds"))?,
         )
@@ -931,17 +933,19 @@ fn do_weighted_sampling<T: Rng + ?Sized>(
     let mut selected = HashSet::with_capacity(sample_size);
     let mut attempts = 0;
     let max_attempts = sample_size * 100; // Prevent infinite loops
+    let mut curr_record;
+    let mut selected_len = 0;
 
-    while selected.len() < sample_size && attempts < max_attempts {
+    while selected_len < sample_size && attempts < max_attempts {
         for (i, record) in records.enumerate() {
-            if selected.len() >= sample_size {
+            if selected_len >= sample_size {
                 break;
             }
 
-            let record = record?;
+            curr_record = record?;
 
             let weight: f64 = fast_float2::parse(
-                record
+                curr_record
                     .get(weight_column)
                     .ok_or_else(|| format!("Weight column index {weight_column} out of bounds"))?,
             )
@@ -960,7 +964,8 @@ fn do_weighted_sampling<T: Rng + ?Sized>(
 
             if include_flag && !selected.contains(&i) {
                 selected.insert(i);
-                wtr.write_byte_record(&record)?;
+                selected_len += 1;
+                wtr.write_byte_record(&curr_record)?;
             }
 
             attempts += 1;
@@ -970,11 +975,9 @@ fn do_weighted_sampling<T: Rng + ?Sized>(
         }
     }
 
-    if selected.len() < sample_size {
-        log::warn!(
-            "Could only sample {} records out of requested {}",
-            selected.len(),
-            sample_size
+    if selected_len < sample_size {
+        wwarn!(
+            "Could only sample {selected_len} records out of requested {sample_size}"
         );
     }