geniusrun/backend/internal/classify/engine_test.go

package classify

import "testing"

func easyRule() Node {
	return Node{Match: MatchAll, Conditions: []Node{
		{Metric: "avg_pace_sec_per_km", Op: OpBetween, Value: []any{330.0, 420.0}},
		{Metric: "avg_hr_pct_max", Op: OpLte, Value: 0.75},
	}}
}

func tempoRule() Node {
	return Node{Match: MatchAll, Conditions: []Node{
		{Metric: "avg_pace_sec_per_km", Op: OpBetween, Value: []any{270.0, 330.0}},
		{Metric: "avg_hr_pct_max", Op: OpGte, Value: 0.80},
	}}
}

func testKinds() []RuleKind {
	return []RuleKind{
		{WorkoutKindID: 1, Name: "Easy", Rule: easyRule()},
		{WorkoutKindID: 2, Name: "Tempo", Rule: tempoRule()},
	}
}

func TestClassify_CleanSingleMatch(t *testing.T) {
	ctx := MetricContext{"avg_pace_sec_per_km": 375, "avg_hr_pct_max": 0.65}
	result := Classify(ctx, testKinds(), DefaultMinConfidence)

	if result.Status != StatusAssigned {
		t.Fatalf("Status = %q, want %q", result.Status, StatusAssigned)
	}
	if result.WorkoutKindID == nil || *result.WorkoutKindID != 1 {
		t.Fatalf("WorkoutKindID = %v, want 1 (Easy)", result.WorkoutKindID)
	}
	if result.Confidence == nil || *result.Confidence < DefaultMinConfidence {
		t.Fatalf("Confidence = %v, want >= %v", result.Confidence, DefaultMinConfidence)
	}
}

func TestClassify_AmbiguousMultiMatch(t *testing.T) {
	// Overlapping rule zone: a kind covering the same pace range as both
	// Easy and Tempo, so a run in the overlap matches two kinds at once.
	overlap := RuleKind{WorkoutKindID: 3, Name: "Overlap", Rule: Node{
		Match: MatchAll, Conditions: []Node{
			{Metric: "avg_pace_sec_per_km", Op: OpBetween, Value: []any{300.0, 400.0}},
		},
	}}
	kinds := append(testKinds(), overlap)

	ctx := MetricContext{"avg_pace_sec_per_km": 375, "avg_hr_pct_max": 0.65}
	result := Classify(ctx, kinds, DefaultMinConfidence)

	if result.Status != StatusNeedsReview {
		t.Fatalf("Status = %q, want %q", result.Status, StatusNeedsReview)
	}
	if len(result.Candidates) < 2 {
		t.Fatalf("expected >= 2 candidates for ambiguous match, got %d: %+v", len(result.Candidates), result.Candidates)
	}
	if result.WorkoutKindID != nil {
		t.Fatalf("WorkoutKindID should be nil when needs_review, got %v", *result.WorkoutKindID)
	}
}

func TestClassify_NoMatch(t *testing.T) {
	// A very slow, low-HR run that fits neither Easy nor Tempo's pace range.
	ctx := MetricContext{"avg_pace_sec_per_km": 600, "avg_hr_pct_max": 0.55}
	result := Classify(ctx, testKinds(), DefaultMinConfidence)

	if result.Status != StatusNeedsReview {
		t.Fatalf("Status = %q, want %q", result.Status, StatusNeedsReview)
	}
	if len(result.Candidates) != 0 {
		t.Fatalf("expected 0 candidates for no match, got %d: %+v", len(result.Candidates), result.Candidates)
	}
}

func TestClassify_LowConfidenceSingleMatchNeedsReview(t *testing.T) {
	// Right at the edge of Easy's pace window and right at the HR ceiling --
	// technically matches but only barely, so confidence should be low.
	ctx := MetricContext{"avg_pace_sec_per_km": 419, "avg_hr_pct_max": 0.75}
	result := Classify(ctx, testKinds(), 0.9) // deliberately strict threshold

	if result.Status != StatusNeedsReview {
		t.Fatalf("Status = %q, want %q (low confidence should force review)", result.Status, StatusNeedsReview)
	}
	if len(result.Candidates) != 1 {
		t.Fatalf("expected exactly 1 (low-confidence) candidate, got %d", len(result.Candidates))
	}
}

func TestClassify_MissingMetricDoesNotMatch(t *testing.T) {
	ctx := MetricContext{"avg_pace_sec_per_km": 375} // avg_hr_pct_max absent
	result := Classify(ctx, testKinds(), DefaultMinConfidence)

	if result.Status != StatusNeedsReview {
		t.Fatalf("Status = %q, want %q", result.Status, StatusNeedsReview)
	}
}

func TestDetectIntervalPattern(t *testing.T) {
	cases := []struct {
		name string
		laps []LapInfo
		want bool
	}{
		{
			name: "clear interval workout: alternating active/rest",
			laps: []LapInfo{
				{IntensityType: "ACTIVE"}, {IntensityType: "REST"},
				{IntensityType: "ACTIVE"}, {IntensityType: "REST"},
			},
			want: true,
		},
		{
			name: "long run with a single hill lap should not look like intervals",
			laps: []LapInfo{
				{IntensityType: "ACTIVE"}, {IntensityType: "ACTIVE"}, {IntensityType: "ACTIVE"},
			},
			want: false,
		},
		{
			name: "warmup + single effort + cooldown is not repeated intervals",
			laps: []LapInfo{
				{IntensityType: "WARMUP"}, {IntensityType: "ACTIVE"}, {IntensityType: "COOLDOWN"},
			},
			want: false,
		},
	}
	for _, c := range cases {
		t.Run(c.name, func(t *testing.T) {
			got := DetectIntervalPattern(c.laps)
			if got != c.want {
				t.Errorf("DetectIntervalPattern() = %v, want %v", got, c.want)
			}
		})
	}
}

func hr(v float64) *float64 { return &v }

func TestHRDrift_RisingHeartRateDetected(t *testing.T) {
	var samples []SampleInfo
	for i := 0; i < 20; i++ {
		samples = append(samples, SampleInfo{ElapsedSeconds: float64(i * 10), HeartRate: hr(140 + float64(i))})
	}
	drift, ok := HRDrift(samples)
	if !ok {
		t.Fatal("expected ok=true with enough samples")
	}
	if drift <= 0 {
		t.Errorf("drift = %v, want positive (rising HR)", drift)
	}
}

func TestHRDrift_TooFewSamplesIsNotOk(t *testing.T) {
	samples := []SampleInfo{{ElapsedSeconds: 0, HeartRate: hr(140)}, {ElapsedSeconds: 10, HeartRate: hr(142)}}
	_, ok := HRDrift(samples)
	if ok {
		t.Error("expected ok=false with too few samples")
	}
}

func TestHRRecovery_FallingHeartRateIsPositiveRecoveryRate(t *testing.T) {
	var samples []SampleInfo
	for i := 0; i < 20; i++ {
		samples = append(samples, SampleInfo{ElapsedSeconds: float64(i * 5), HeartRate: hr(170 - float64(i)*2)})
	}
	recovery, ok := HRRecovery(samples)
	if !ok {
		t.Fatal("expected ok=true with enough samples")
	}
	if recovery <= 0 {
		t.Errorf("recovery = %v, want positive (HR dropping)", recovery)
	}
}

func TestHRRecovery_StillRisingIsNegative(t *testing.T) {
	var samples []SampleInfo
	for i := 0; i < 20; i++ {
		samples = append(samples, SampleInfo{ElapsedSeconds: float64(i * 5), HeartRate: hr(120 + float64(i))})
	}
	recovery, ok := HRRecovery(samples)
	if !ok {
		t.Fatal("expected ok=true with enough samples")
	}
	if recovery >= 0 {
		t.Errorf("recovery = %v, want negative (HR still rising during recovery lap)", recovery)
	}
}