geniusrun/backend/internal/classify/rule.go

// Package classify is smartrun's classification rule engine: it evaluates a
// user-editable AND/OR condition tree against a run's metrics to decide
// which "workout kind" (Easy, Tempo, Threshold, ...) it belongs to. Pure
// logic only -- no I/O, no database, no Garmin client -- so it's fully
// unit-testable against fixture data.
package classify

import (
	"fmt"
	"math"
)

// Node is one node of a workout kind's rule condition tree. A branch node
// sets Match ("all" or "any") and Conditions (children); a leaf node sets
// Metric, Op, and Value instead.
type Node struct {
	Match      string `json:"match,omitempty"`
	Conditions []Node `json:"conditions,omitempty"`

	Metric string `json:"metric,omitempty"`
	Op     string `json:"op,omitempty"`
	Value  any    `json:"value,omitempty"`
}

const (
	MatchAll = "all"
	MatchAny = "any"

	OpEq      = "=="
	OpNeq     = "!="
	OpGt      = ">"
	OpGte     = ">="
	OpLt      = "<"
	OpLte     = "<="
	OpBetween = "between"
)

// Validate reports whether a rule tree is well-formed, without needing a
// MetricContext to evaluate against. Intended for the API layer to give
// immediate feedback when a user edits a workout kind's rule.
func (n Node) Validate() error {
	if n.Match != "" {
		if n.Match != MatchAll && n.Match != MatchAny {
			return fmt.Errorf("invalid match %q, want %q or %q", n.Match, MatchAll, MatchAny)
		}
		if len(n.Conditions) == 0 {
			return fmt.Errorf("branch node %q has no conditions", n.Match)
		}
		for i, c := range n.Conditions {
			if err := c.Validate(); err != nil {
				return fmt.Errorf("condition %d: %w", i, err)
			}
		}
		return nil
	}

	if n.Metric == "" {
		return fmt.Errorf("leaf node missing metric")
	}
	switch n.Op {
	case OpEq, OpNeq, OpGt, OpGte, OpLt, OpLte:
		if n.Value == nil {
			return fmt.Errorf("metric %q: op %q requires a value", n.Metric, n.Op)
		}
	case OpBetween:
		arr, ok := n.Value.([]any)
		if !ok || len(arr) != 2 {
			return fmt.Errorf("metric %q: op %q requires a 2-element array value", n.Metric, n.Op)
		}
	default:
		return fmt.Errorf("metric %q: unsupported op %q", n.Metric, n.Op)
	}
	return nil
}

// MetricContext is the set of computed metrics for one activity that a rule
// tree is evaluated against. Boolean metrics (e.g. has_interval_pattern) are
// represented as 1.0/0.0.
type MetricContext map[string]float64

// Evaluate recursively evaluates the tree against ctx, returning whether it
// matched and a confidence score. For branch nodes, "all" aggregates scores
// via min and requires every child matched; "any" aggregates via max and
// requires at least one child matched.
func (n Node) Evaluate(ctx MetricContext) (matched bool, score float64) {
	if n.Match != "" {
		switch n.Match {
		case MatchAll:
			matched = true
			score = math.Inf(1)
			for _, c := range n.Conditions {
				m, s := c.Evaluate(ctx)
				if !m {
					matched = false
				}
				if s < score {
					score = s
				}
			}
		case MatchAny:
			matched = false
			score = math.Inf(-1)
			for _, c := range n.Conditions {
				m, s := c.Evaluate(ctx)
				if m {
					matched = true
				}
				if s > score {
					score = s
				}
			}
		default:
			return false, 0
		}
		return matched, score
	}
	return evaluateLeaf(n, ctx)
}

func evaluateLeaf(n Node, ctx MetricContext) (matched bool, score float64) {
	v, ok := ctx[n.Metric]
	if !ok {
		return false, 0
	}

	switch n.Op {
	case OpEq, OpNeq:
		want, ok := toFloat(n.Value)
		if !ok {
			return false, 0
		}
		eq := v == want
		if n.Op == OpNeq {
			eq = !eq
		}
		if eq {
			return true, 1
		}
		return false, 0

	case OpGt, OpGte, OpLt, OpLte:
		threshold, ok := toFloat(n.Value)
		if !ok {
			return false, 0
		}
		var margin float64
		switch n.Op {
		case OpGt:
			matched = v > threshold
			margin = v - threshold
		case OpGte:
			matched = v >= threshold
			margin = v - threshold
		case OpLt:
			matched = v < threshold
			margin = threshold - v
		case OpLte:
			matched = v <= threshold
			margin = threshold - v
		}
		return matched, squash(margin, scaleFor(threshold))

	case OpBetween:
		arr, ok := n.Value.([]any)
		if !ok || len(arr) != 2 {
			return false, 0
		}
		lo, ok1 := toFloat(arr[0])
		hi, ok2 := toFloat(arr[1])
		if !ok1 || !ok2 || lo > hi {
			return false, 0
		}
		matched = v >= lo && v <= hi
		mid := (lo + hi) / 2
		halfRange := (hi - lo) / 2
		if halfRange == 0 {
			halfRange = 1
		}
		distance := math.Abs(v - mid)
		score := 1 - distance/halfRange // 1.0 centered, 0 at boundary, negative outside
		return matched, score

	default:
		return false, 0
	}
}

func toFloat(v any) (float64, bool) {
	switch t := v.(type) {
	case float64:
		return t, true
	case bool:
		if t {
			return 1, true
		}
		return 0, true
	case int:
		return float64(t), true
	default:
		return 0, false
	}
}

// scaleFor picks a margin-to-score scaling factor proportional to the
// threshold's magnitude, so e.g. a 30-second margin on a ~300s threshold
// scores similarly to a 3-minute margin on a ~1800s threshold.
func scaleFor(threshold float64) float64 {
	abs := math.Abs(threshold)
	if abs < 1e-9 {
		return 1
	}
	return 5 / abs
}

// squash maps a signed margin to a 0..1 score via a logistic curve: 0 margin
// (exactly at the threshold) scores 0.5, comfortably-matched margins
// approach 1, comfortably-unmatched margins approach 0.
func squash(margin, scale float64) float64 {
	return 1 / (1 + math.Exp(-margin*scale))
}