Merge pull request 'chore/fix-some-stuff' (#2) from chore/fix-some-stuff into main

Reviewed-on: #2

.gitignore

@@ -3,3 +3,6 @@
 *.db-shm
 *.db-wal
 .env
+
+.idea/**
+.claude/worktrees/**

Cargo.toml

@@ -11,7 +11,7 @@ members = [
 [workspace.package]
 edition = "2021"
 version = "0.1.0"
-license = "MIT"
+license = "AGPL-3.0-or-later"
 
 [workspace.dependencies]
 tokio = { version = "1", features = ["full"] }

crates/core/src/affinity.rs

@@ -19,13 +19,14 @@ const LEARNING_RATE: f64 = 0.15;
 const DAILY_DECAY: f64 = 0.02;
 
 impl TopicAffinities {
+    /// Get score for given topic
     pub fn score(&self, topic: &str) -> f64 {
         self.scores.get(topic).copied().unwrap_or(0.0)
     }
 
     /// Mean affinity across an article's topics; 0.0 for an untagged
     /// article (neutral, defers entirely to the embedding/LLM stages).
-    pub fn score_topics(&self, topics: &[String]) -> f64 {
+    pub fn get_mean_affinity(&self, topics: &[String]) -> f64 {
         if topics.is_empty() {
             return 0.0;
         }
@@ -37,7 +38,7 @@ impl TopicAffinities {
     /// `[0.0, 1.0]` (see `scoring::engagement_score`). Positive surprise
     /// (the user engaged more than the pipeline predicted) nudges those
     /// topics up; negative surprise nudges them down. This is what lets
-    /// "the model thought this was irrelevant but I read the whole thing"
+    /// "the model thought this was irrelevant, but I read the whole thing"
     /// actually change future behavior.
     pub fn apply_feedback(&mut self, topics: &[String], surprise: f64) {
         for topic in topics {
@@ -52,11 +53,15 @@ impl TopicAffinities {
     /// neutral rather than staying permanently pinned from a few old
     /// signals.
     pub fn decay(&mut self) {
-        self.scores.retain(|_, v| v.abs() > 1e-4);
         for v in self.scores.values_mut() {
+            if v.abs() <= DAILY_DECAY {
+                *v = 0.0;
+            } else {
                 *v -= v.signum() * DAILY_DECAY;
             }
         }
+        self.scores.retain(|_, v| v.abs() > 1e-4);
+    }
 
     pub fn top_n(&self, n: usize) -> Vec<(&str, f64)> {
         let mut items: Vec<_> = self.scores.iter().map(|(k, v)| (k.as_str(), *v)).collect();
@@ -87,6 +92,7 @@ pub fn engagement_score(
     if dismissed && !opened {
         return 0.0;
     }
+
     let mut score = if !opened {
         0.0
     } else {
@@ -97,6 +103,7 @@ pub fn engagement_score(
             _ => 0.5,
         }
     };
+
     if starred {
         score = (score + 0.3).min(1.0);
     }
@@ -107,6 +114,10 @@ pub fn engagement_score(
 mod tests {
     use super::*;
 
+    // --- apply_feedback: new = clamp(current + LEARNING_RATE(0.15) * surprise, -1, 1) ---
+
+    /// Positive surprise (engaged more than predicted) should move the
+    /// score up, never down or unchanged.
     #[test]
     fn under_predicted_relevance_boosts_topic() {
         let mut aff = TopicAffinities::default();
@@ -116,6 +127,8 @@ mod tests {
         assert!(aff.score("rust") > 0.0);
     }
 
+    /// Negative surprise (engaged less than predicted) should move the
+    /// score down, the mirror image of the boost case above.
     #[test]
     fn over_predicted_relevance_lowers_topic() {
         let mut aff = TopicAffinities::default();
@@ -125,6 +138,60 @@ mod tests {
         assert!(aff.score("crypto") < 0.0);
     }
 
+    /// Scores are documented to live in [-1.0, 1.0]. Repeated max-surprise
+    /// feedback would overshoot 1.0 without the clamp, so this guards the
+    /// invariant directly rather than trusting a single update.
+    #[test]
+    fn apply_feedback_clamps_at_positive_one() {
+        let mut aff = TopicAffinities::default();
+        let topics = vec!["rust".to_string()];
+        for _ in 0..20 {
+            aff.apply_feedback(&topics, 1.0);
+        }
+        assert_eq!(aff.score("rust"), 1.0);
+    }
+
+    /// Verifies a decay from a large negative value doesn't overshoot and go beyond -1.0
+    #[test]
+    fn apply_feedback_clamps_at_negative_one() {
+        let mut aff = TopicAffinities::default();
+        let topics = vec!["crypto".to_string()];
+        for _ in 0..20 {
+            aff.apply_feedback(&topics, -1.0);
+        }
+        assert_eq!(aff.score("crypto"), -1.0);
+    }
+
+    /// apply_feedback loops over every topic on the article and applies
+    /// the same surprise to each independently; it must not skip topics
+    /// or bleed the update into topics the article wasn't tagged with.
+    #[test]
+    fn apply_feedback_updates_every_topic_on_the_article() {
+        let mut aff = TopicAffinities::default();
+        let topics = vec!["rust".to_string(), "async".to_string()];
+        aff.apply_feedback(&topics, 0.4);
+        assert_eq!(aff.score("rust"), 0.15 * 0.4);
+        assert_eq!(aff.score("async"), 0.15 * 0.4);
+        // Untouched topics are unaffected.
+        assert_eq!(aff.score("crypto"), 0.0);
+    }
+
+    /// surprise = 0.0 means engagement exactly matched the prediction, so
+    /// the score shouldn't move at all (current + 0.15 * 0.0 == current).
+    #[test]
+    fn apply_feedback_zero_surprise_is_a_noop() {
+        let mut aff = TopicAffinities::default();
+        let topics = vec!["rust".to_string()];
+        aff.apply_feedback(&topics, 0.5);
+        let before = aff.score("rust");
+        aff.apply_feedback(&topics, 0.0);
+        assert_eq!(aff.score("rust"), before);
+    }
+
+    // --- decay: v -= sign(v) * DAILY_DECAY(0.02), settling at 0 instead of overshooting ---
+
+    /// Core decay behavior: a positive score should shrink toward zero
+    /// after one nightly pass, without crossing it.
     #[test]
     fn decay_pulls_toward_zero() {
         let mut aff = TopicAffinities::default();
@@ -134,4 +201,177 @@ mod tests {
         assert!(aff.score("rust") < before);
         assert!(aff.score("rust") > 0.0);
     }
+
+    /// Pins the exact arithmetic (not just the direction) so a future
+    /// change to the decay formula is caught immediately.
+    /// surprise 1.0 -> 0.15, then one decay pass subtracts DAILY_DECAY (0.02).
+    #[test]
+    fn decay_gives_expected_value() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["java".to_string()], 1.0);
+        aff.decay();
+
+        assert_eq!(aff.score("java"), 0.13);
+    }
+
+    /// Regression test for a real bug: subtracting a fixed 0.02 from a
+    /// smaller score (e.g. 0.015) used to flip its sign to -0.005 instead
+    /// of landing on 0.0, which would make the score oscillate around
+    /// zero on every subsequent decay pass rather than settling.
+    #[test]
+    fn decay_settles_at_zero_instead_of_overshooting() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["rust".to_string()], 0.1); // score = 0.015
+        aff.decay();
+        assert_eq!(aff.score("rust"), 0.0);
+    }
+
+    /// Same fix as above, verified on the negative side, and also checks
+    /// that the post-decay prune (dropping |v| <= 1e-4) actually removes
+    /// the entry rather than leaving a stray 0.0 in the map.
+    #[test]
+    fn decay_prunes_negative_scores_that_settle_at_zero() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["crypto".to_string()], -0.1); // score = -0.015
+        aff.decay();
+        assert_eq!(aff.score("crypto"), 0.0);
+    }
+
+    /// decay_pulls_toward_zero's mirror image: negative scores should
+    /// shrink in magnitude too, not just positive ones.
+    #[test]
+    fn decay_is_symmetric_for_negative_scores() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["crypto".to_string()], -1.0);
+        let before = aff.score("crypto");
+        aff.decay();
+        assert!(aff.score("crypto") > before);
+        assert!(aff.score("crypto") < 0.0);
+    }
+
+    // --- score / get_mean_affinity ---
+
+    /// A topic with no feedback yet must read as neutral (0.0), not
+    /// panic or return some other sentinel.
+    #[test]
+    fn score_defaults_to_zero_for_unknown_topic() {
+        let aff = TopicAffinities::default();
+        assert_eq!(aff.score("never-seen"), 0.0);
+    }
+
+    /// Documented behavior for untagged articles: defer entirely to the
+    /// embedding/LLM stages by returning a neutral 0.0 rather than
+    /// dividing by zero.
+    #[test]
+    fn get_mean_affinity_given_empty_topics_returns_zero() {
+        let aff = TopicAffinities::default();
+        assert_eq!(aff.get_mean_affinity(&[]), 0.0);
+    }
+
+    /// Confirms it's a plain arithmetic mean: an equally strong positive
+    /// and negative topic on the same article should cancel out to 0.0.
+    #[test]
+    fn get_mean_affinity_averages_across_topics() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["rust".to_string()], 1.0); // 0.15
+        aff.apply_feedback(&["crypto".to_string()], -1.0); // -0.15
+        let topics = vec!["rust".to_string(), "crypto".to_string()];
+        assert_eq!(aff.get_mean_affinity(&topics), 0.0);
+    }
+
+    /// A topic mix of "known" and "never seen" shouldn't shrink the
+    /// denominator or get skipped — the unscored topic counts as 0.0 in
+    /// the average, per score()'s default.
+    #[test]
+    fn get_mean_affinity_treats_unscored_topics_as_zero() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["rust".to_string()], 1.0); // 0.15
+        let topics = vec!["rust".to_string(), "never-seen".to_string()];
+        assert_eq!(aff.get_mean_affinity(&topics), 0.075);
+    }
+
+    // --- top_n ---
+
+    /// top_n is used to surface a user's strongest interests, so it must
+    /// sort highest-first (not insertion order) and respect the limit.
+    #[test]
+    fn top_n_sorts_descending_and_truncates() {
+        let mut aff = TopicAffinities::default();
+        aff.apply_feedback(&["low".to_string()], 0.2);
+        aff.apply_feedback(&["high".to_string()], 1.0);
+        aff.apply_feedback(&["mid".to_string()], 0.5);
+
+        let top = aff.top_n(2);
+        assert_eq!(top.len(), 2);
+        assert_eq!(top[0].0, "high");
+        assert_eq!(top[1].0, "mid");
+    }
+
+    // --- engagement_score ---
+
+    /// No signal at all (not opened, not dismissed) is neutral, not
+    /// penalized.
+    #[test]
+    fn engagement_score_never_opened_is_zero() {
+        assert_eq!(engagement_score(false, None, None, false, false), 0.0);
+    }
+
+    /// Dismissing without opening is an explicit negative signal, but
+    /// engagement_score itself is floored at 0.0 (the doc comment notes
+    /// the negative direction is expressed later via `surprise`, not
+    /// here) — this pins that the dismissed+!opened branch returns 0.0,
+    /// not a negative number.
+    #[test]
+    fn engagement_score_dismissed_without_opening_is_zero() {
+        assert_eq!(engagement_score(false, None, None, false, true), 0.0);
+    }
+
+    /// Reading half the estimated time should score as half-engaged.
+    #[test]
+    fn engagement_score_opened_uses_dwell_over_estimate_ratio() {
+        assert_eq!(
+            engagement_score(true, Some(30), Some(60), false, false),
+            0.5
+        );
+    }
+
+    /// Dwelling far longer than the estimate (e.g. left the tab open)
+    /// must not push the score above the documented [0.0, 1.0] range.
+    #[test]
+    fn engagement_score_opened_caps_ratio_at_one() {
+        assert_eq!(
+            engagement_score(true, Some(600), Some(60), false, false),
+            1.0
+        );
+    }
+
+    /// When we simply don't have dwell/estimate data yet, the code
+    /// credits partial engagement (0.5) rather than assuming 0 (unfairly
+    /// penalizing) or 1 (unfairly rewarding).
+    #[test]
+    fn engagement_score_opened_without_dwell_or_estimate_defaults_to_half() {
+        assert_eq!(engagement_score(true, None, None, false, false), 0.5);
+    }
+
+    /// est == 0 would divide by zero, so the `est > 0` guard routes this
+    /// case to the same "unknown read time" default (0.5) instead of
+    /// panicking or producing NaN/infinity.
+    #[test]
+    fn engagement_score_opened_with_zero_estimate_defaults_to_half() {
+        assert_eq!(engagement_score(true, Some(10), Some(0), false, false), 0.5);
+    }
+
+    /// Starring is an explicit "yes" beyond dwell time: it should add
+    /// 0.3 on top of the dwell-ratio score.
+    #[test]
+    fn engagement_score_starred_adds_bonus() {
+        assert_eq!(engagement_score(true, Some(30), Some(60), true, false), 0.8);
+    }
+
+    /// The +0.3 star bonus must also respect the 1.0 ceiling, even when
+    /// the dwell ratio alone is already at the max.
+    #[test]
+    fn engagement_score_starred_bonus_caps_at_one() {
+        assert_eq!(engagement_score(true, Some(60), Some(60), true, false), 1.0);
+    }
 }

crates/core/src/scoring.rs

@@ -22,7 +22,7 @@ const W_EMBEDDING: f32 = 0.25;
 const W_AFFINITY: f32 = 0.15;
 
 pub fn score_article(inputs: RelevanceInputs) -> f32 {
-    let affinity = inputs.affinities.score_topics(inputs.topics) as f32; // [-1, 1]
+    let affinity = inputs.affinities.get_mean_affinity(inputs.topics) as f32; // [-1, 1]
     let affinity_component = (affinity + 1.0) / 2.0; // renormalize to [0, 1]
 
     match inputs.llm_score {

crates/web/src/app.rs

@@ -19,7 +19,7 @@ pub fn App() -> Element {
     let articles = use_server_future(list_ranked_articles)?;
 
     rsx! {
-        style { {include_str!("../assets/app.css")} }
+        Stylesheet { href: asset!("/assets/app.css") }
         main {
             h1 { "feedsignal" }
             match articles.read().as_ref() {