# Evaluators Documentation

This directory contains all evaluator implementations for the Therapist Conversation Evaluator tool. Each evaluator measures different aspects of therapeutic conversations.

## Overview

All evaluators follow a consistent interface and return standardized `EvaluationResult` objects. The result type depends on the granularity level:
- **Utterance-level**: Per-utterance scores (`granularity="utterance"`)
- **Segment-level**: Multi-utterance segment scores (`granularity="segment"`)
- **Conversation-level**: Overall conversation scores (`granularity="conversation"`)

## Evaluation Result Types

### Score Types

1. **Categorical Score**: Discrete labels (e.g., "High", "Medium", "Low")
   ```python
   {
       "type": "categorical",
       "label": "High",
       "confidence": 0.85  # Optional: 0-1 confidence score
   }
   ```

2. **Numerical Score**: Continuous values with max bounds (e.g., 1-5 scale)
   ```python
   {
       "type": "numerical",
       "value": 4.0,
       "max_value": 5.0,
       "label": "High"  # Optional: derived label
   }
   ```

### Granularity Levels

- **Utterance-level**: Scores for each individual utterance in the conversation
- **Segment-level**: Aggregate scores for multi-utterance segments
- **Conversation-level**: Overall scores for the entire conversation

---

## Evaluators

### 1. Empathy ER (Emotional Reaction) Evaluator

**File**: `empathy_er_evaluator.py`

**Description**: Measures the emotional reaction component of empathy in therapeutic responses. Evaluates how well the therapist responds to the patient's emotional state.

**Model**: `RyanDDD/empathy-mental-health-reddit-ER`

**Result Type**:
- **Granularity**: Utterance-level
- **Score Type**: Categorical (3 labels)
- **Labels**: `["Low", "Medium", "High"]`
- **Evaluates**: Therapist responses only

**Output Format**:
```python
{
    "granularity": "utterance",
    "per_utterance": [
        {
            "index": 0,
            "metrics": {
                "empathy_er": {
                    "type": "categorical",
                    "label": "High",
                    "confidence": 0.92
                }
            }
        }
    ]
}
```

---

### 2. Empathy IP (Interpretation) Evaluator

**File**: `empathy_ip_evaluator.py`

**Description**: Measures the interpretation component of empathy in therapeutic responses. Evaluates how well the therapist interprets and understands the patient's situation.

**Model**: `RyanDDD/empathy-mental-health-reddit-IP`

**Result Type**:
- **Granularity**: Utterance-level
- **Score Type**: Categorical (3 labels)
- **Labels**: `["Low", "Medium", "High"]`
- **Evaluates**: Therapist responses only

**Output Format**:
```python
{
    "granularity": "utterance",
    "per_utterance": [
        {
            "index": 0,
            "metrics": {
                "empathy_ip": {
                    "type": "categorical",
                    "label": "Medium",
                    "confidence": 0.78
                }
            }
        }
    ]
}
```

---

### 3. Empathy EX (Exploration) Evaluator

**File**: `empathy_ex_evaluator.py`

**Description**: Measures the exploration component of empathy in therapeutic responses. Evaluates how well the therapist explores and deepens understanding of the patient's concerns.

**Model**: `RyanDDD/empathy-mental-health-reddit-EX`

**Result Type**:
- **Granularity**: Utterance-level
- **Score Type**: Categorical (3 labels)
- **Labels**: `["Low", "Medium", "High"]`
- **Evaluates**: Therapist responses only

**Output Format**:
```python
{
    "granularity": "utterance",
    "per_utterance": [
        {
            "index": 0,
            "metrics": {
                "empathy_ex": {
                    "type": "categorical",
                    "label": "High",
                    "confidence": 0.89
                }
            }
        }
    ]
}
```

---

### 4. Talk Type Evaluator

**File**: `talk_type_evaluator.py`

**Description**: Classifies patient utterances into change talk, sustain talk, or neutral. Uses BERT model trained on motivational interviewing data.

**Model**: `RyanDDD/bert-motivational-interviewing`

**Result Type**:
- **Granularity**: Utterance-level
- **Score Type**: Categorical (3 labels)
- **Labels**: `["Change", "Neutral", "Sustain"]`
- **Evaluates**: Patient utterances only

**Output Format**:
```python
{
    "granularity": "utterance",
    "per_utterance": [
        {
            "index": 0,
            "metrics": {
                "talk_type": {
                    "type": "categorical",
                    "label": "Change",
                    "confidence": 0.91
                }
            }
        }
    ]
}
```

---

### 5. Mental Health Factuality Evaluator

**File**: `factuality_evaluator.py`

**Description**: LLM-as-Judge scoring of assistant responses for clinical accuracy, safety, scope appropriateness, evidence-based practice, and overall quality. Uses strict rubric to evaluate mental health chat responses.

**Model**: OpenAI GPT-4o (configurable)

**Result Type**:
- **Granularity**: Utterance-level
- **Score Type**: Numerical (5-point scale: 1-5)
- **Scale**: 1-5 for all dimensions
- **Evaluates**: Assistant/therapist responses only

**Dimensions**:
1. `overall_score` (1-5): Overall factuality and quality
2. `clinical_accuracy` (1-5): Clinical accuracy of information
3. `safety` (1-5): Safety of the response
4. `scope_appropriateness` (1-5): Appropriateness of scope
5. `evidence_based` (1-5): Evidence-based practice alignment
6. `explanation` (text): Reasoning for the scores

**Output Format**:
```python
{
    "granularity": "utterance",
    "per_utterance": [
        {
            "index": 0,
            "metrics": {
                "overall": {
                    "type": "numerical",
                    "value": 4,
                    "max_value": 5
                },
                "clinical_accuracy": {
                    "type": "numerical",
                    "value": 4,
                    "max_value": 5
                },
                "safety": {
                    "type": "numerical",
                    "value": 5,
                    "max_value": 5
                },
                "scope": {
                    "type": "numerical",
                    "value": 4,
                    "max_value": 5
                },
                "evidence": {
                    "type": "numerical",
                    "value": 3,
                    "max_value": 5
                },
                "explanation": {
                    "type": "text",
                    "value": "Response demonstrates good clinical accuracy..."
                }
            }
        }
    ]
}
```

**Scoring Rules**:
- Conservative scoring: Prefers 3 (neutral) for generic responses without clinical claims
- Safety gate: If safety ≤ 2, overall_score is capped at safety level
- Evidence requirement: Avoids 5 unless response references specific evidence-based techniques
- No clinical claims: If response lacks clinical terms, clinical_accuracy and evidence_based are capped at 3

---

### 6. Emotion Analysis Evaluator

**File**: `emotion_evaluator.py`

**Description**: Analyzes user emotions using emotion classification model. Calculates negative emotion sum, joy/neutral shift, and tracks emotion change trends across the conversation.

**Model**: `j-hartmann/emotion-english-distilroberta-base`

**Result Type**:
- **Granularity**: Utterance-level (with overall trend)
- **Score Type**: Numerical (with categorical labels)
- **Evaluates**: User/patient utterances only

**Metrics**:
1. `emotion_sum_negative` (0.0-1.0): Sum of negative emotions (anger, disgust, fear, sadness)
   - Labels: "Low" (< 0.2), "Medium" (0.2-0.5), "High" (> 0.5)
2. `emotion_joy_neutral_shift` (-1.0 to 1.0): Difference between joy and neutral emotions
   - Labels: "Positive" (> 0.2), "Neutral" (-0.2 to 0.2), "Negative" (< -0.2)
3. `emotion_trend_direction` (overall): Trend analysis across conversation
   - Labels: "improving", "declining", "stable", "neutral"

**Emotion Labels**: `["anger", "disgust", "fear", "joy", "neutral", "sadness", "surprise"]`

**Output Format**:
```python
{
    "granularity": "utterance",
    "per_utterance": [
        {
            "index": 0,
            "metrics": {
                "emotion_sum_negative": {
                    "type": "numerical",
                    "value": 0.35,
                    "max_value": 1.0,
                    "label": "Medium"
                },
                "emotion_joy_neutral_shift": {
                    "type": "numerical",
                    "value": -0.15,
                    "max_value": 1.0,
                    "label": "Neutral"
                }
            }
        }
    ],
    "overall": {
        "emotion_avg_sum_negative": {
            "type": "numerical",
            "value": 0.28,
            "max_value": 1.0,
            "label": "improving"
        },
        "emotion_avg_joy_neutral_shift": {
            "type": "numerical",
            "value": 0.12,
            "max_value": 1.0,
            "label": "improving"
        },
        "emotion_trend_direction": {
            "type": "categorical",
            "label": "improving"
        }
    }
}
```

**Trend Analysis**:
- Compares first half vs second half of conversation
- "improving": Negative emotions decrease AND joy/neutral shift increases
- "declining": Negative emotions increase AND joy/neutral shift decreases
- "stable": No significant change
- "neutral": Insufficient data

---

## Summary Table

| Evaluator | Granularity | Score Type | Labels/Scale | Evaluates | Model |
|-----------|-------------|-----------|--------------|-----------|-------|
| Empathy ER | Utterance | Categorical | 3 labels (Low/Medium/High) | Therapist | `RyanDDD/empathy-mental-health-reddit-ER` |
| Empathy IP | Utterance | Categorical | 3 labels (Low/Medium/High) | Therapist | `RyanDDD/empathy-mental-health-reddit-IP` |
| Empathy EX | Utterance | Categorical | 3 labels (Low/Medium/High) | Therapist | `RyanDDD/empathy-mental-health-reddit-EX` |
| Talk Type | Utterance | Categorical | 3 labels (Change/Neutral/Sustain) | Patient | `RyanDDD/bert-motivational-interviewing` |
| Factuality | Utterance | Numerical | 5-point scale (1-5) | Therapist | OpenAI GPT-4o |
| Emotion Analysis | Utterance + Overall | Numerical | 0.0-1.0 (with labels) | Patient | `j-hartmann/emotion-english-distilroberta-base` |

---

## Usage

All evaluators follow the same interface:

```python
from evaluators.impl.empathy_er_evaluator import EmpathyEREvaluator

# Initialize evaluator
evaluator = EmpathyEREvaluator()

# Evaluate conversation
result = evaluator.execute(conversation)

# Access results
for utterance_result in result["per_utterance"]:
    metrics = utterance_result["metrics"]
    if "empathy_er" in metrics:
        score = metrics["empathy_er"]
        print(f"Label: {score['label']}, Confidence: {score['confidence']}")
```

---

## Adding New Evaluators

To add a new evaluator:

1. Create a new file in `impl/` directory
2. Inherit from `Evaluator` base class
3. Register using `@register_evaluator` decorator
4. Implement `execute()` method that returns `EvaluationResult`
5. Use helper functions from `utils.evaluation_helpers`:
   - `create_categorical_score()` for categorical scores
   - `create_numerical_score()` for numerical scores
   - `create_utterance_result()` for utterance-level results
   - `create_conversation_result()` for conversation-level results
   - `create_segment_result()` for segment-level results

Example:
```python
from evaluators.base import Evaluator
from evaluators.registry import register_evaluator
from custom_types import Utterance, EvaluationResult
from utils.evaluation_helpers import create_categorical_score, create_utterance_result

@register_evaluator(
    "my_metric",
    label="My Metric",
    description="Description of what this metric measures",
    category="Category Name"
)
class MyEvaluator(Evaluator):
    METRIC_NAME = "my_metric"

    def execute(self, conversation: List[Utterance], **kwargs) -> EvaluationResult:
        # Implementation
        pass
```