uv pip install "alquimia-fair-forge[humanity]"

from fair_forge.metrics.humanity import Humanity
from your_retriever import MyRetriever

# Run the metric (no LLM required)
metrics = Humanity.run(
    MyRetriever,
    verbose=True,
)

# Analyze results
for metric in metrics:
    print(f"QA ID: {metric.qa_id}")
    print(f"Emotional Entropy: {metric.humanity_assistant_emotional_entropy:.4f}")
    print(f"Spearman Correlation: {metric.humanity_ground_truth_spearman:.4f}")

class HumanityMetric(BaseMetric):
    session_id: str
    assistant_id: str
    qa_id: str
    humanity_assistant_emotional_entropy: float    # Shannon entropy
    humanity_ground_truth_spearman: float         # Correlation with ground truth

    # Per-emotion proportions (0-1)
    humanity_assistant_anger: float
    humanity_assistant_anticipation: float
    humanity_assistant_disgust: float
    humanity_assistant_fear: float
    humanity_assistant_joy: float
    humanity_assistant_sadness: float
    humanity_assistant_surprise: float
    humanity_assistant_trust: float

H = -Σ p(emotion) * log2(p(emotion))

from fair_forge.metrics.humanity import Humanity
from fair_forge.core.retriever import Retriever
from fair_forge.schemas.common import Dataset, Batch
import numpy as np

class HumanityRetriever(Retriever):
    def load_dataset(self) -> list[Dataset]:
        return [
            Dataset(
                session_id="humanity-eval-001",
                assistant_id="empathetic-bot",
                language="english",
                context="You are a supportive, empathetic assistant.",
                conversation=[
                    Batch(
                        qa_id="q1",
                        query="I just got promoted at work!",
                        assistant="That's wonderful news! Congratulations on your well-deserved promotion. Your hard work has clearly paid off, and I'm thrilled for your success!",
                        ground_truth_assistant="Express joy and congratulations enthusiastically.",
                    ),
                    Batch(
                        qa_id="q2",
                        query="I'm worried about my exam tomorrow.",
                        assistant="I understand your anxiety about the exam. It's natural to feel nervous. Remember to take deep breaths and trust in your preparation. You've got this!",
                        ground_truth_assistant="Acknowledge worry, provide reassurance and support.",
                    ),
                    Batch(
                        qa_id="q3",
                        query="Tell me about machine learning algorithms.",
                        assistant="Machine learning algorithms are mathematical methods that learn patterns from data.",
                        ground_truth_assistant="Provide factual, educational content.",
                    ),
                ]
            )
        ]

# Run evaluation
metrics = Humanity.run(
    HumanityRetriever,
    verbose=True,
)

# Analyze results
print("Humanity Evaluation Results")
print("=" * 60)

emotions = ["anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust"]

for metric in metrics:
    print(f"\nQA ID: {metric.qa_id}")
    print(f"Emotional Entropy: {metric.humanity_assistant_emotional_entropy:.4f}")
    print(f"Ground Truth Spearman: {metric.humanity_ground_truth_spearman:.4f}")
    print("Emotion Distribution:")
    for emotion in emotions:
        value = getattr(metric, f"humanity_assistant_{emotion}")
        if value > 0:
            bar = "█" * int(value * 20)
            print(f"  {emotion.capitalize():12}: {value:.3f} {bar}")

# Summary
print("\n" + "=" * 60)
print("Summary")
print("=" * 60)
avg_entropy = np.mean([m.humanity_assistant_emotional_entropy for m in metrics])
avg_spearman = np.mean([m.humanity_ground_truth_spearman for m in metrics])
print(f"Average Emotional Entropy: {avg_entropy:.4f}")
print(f"Average Spearman Correlation: {avg_spearman:.4f}")

import matplotlib.pyplot as plt
import numpy as np

emotions = ["anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust"]

# Calculate average emotion distributions
avg_emotions = {e: 0 for e in emotions}
for metric in metrics:
    for emotion in emotions:
        avg_emotions[emotion] += getattr(metric, f"humanity_assistant_{emotion}")

for emotion in emotions:
    avg_emotions[emotion] /= len(metrics)

# Plot
fig, ax = plt.subplots(figsize=(10, 6))
bars = ax.bar(emotions, [avg_emotions[e] for e in emotions], color='steelblue')
ax.set_xlabel('Emotion')
ax.set_ylabel('Average Distribution')
ax.set_title('Average Emotion Distribution Across Responses')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

entropies = [m.humanity_assistant_emotional_entropy for m in metrics]
qa_ids = [m.qa_id for m in metrics]

fig, ax = plt.subplots(figsize=(10, 5))
ax.bar(qa_ids, entropies, color='coral')
ax.set_xlabel('QA ID')
ax.set_ylabel('Emotional Entropy')
ax.set_title('Emotional Entropy per Response')
ax.axhline(y=np.mean(entropies), color='red', linestyle='--', label=f'Mean: {np.mean(entropies):.2f}')
ax.legend()
plt.tight_layout()
plt.show()