feat(classifier): add DEFAULT_EXEMPLARS for embedding k-NN fallback
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@ -13,12 +13,14 @@ from typing import Any
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__all__ = [
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__all__ = [
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"LABELS",
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"LABELS",
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"LABEL_DESCRIPTIONS",
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"LABEL_DESCRIPTIONS",
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"DEFAULT_EXEMPLARS",
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"compute_metrics",
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"compute_metrics",
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"ClassifierAdapter",
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"ClassifierAdapter",
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"ZeroShotAdapter",
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"ZeroShotAdapter",
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"GLiClassAdapter",
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"GLiClassAdapter",
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"RerankerAdapter",
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"RerankerAdapter",
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"FineTunedAdapter",
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"FineTunedAdapter",
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"EmbeddingKNNAdapter",
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]
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]
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LABELS: list[str] = [
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LABELS: list[str] = [
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@ -126,6 +128,72 @@ def _cosine(a: list[float], b: list[float]) -> float:
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return dot / (norm_a * norm_b) if norm_a and norm_b else 0.0
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return dot / (norm_a * norm_b) if norm_a and norm_b else 0.0
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DEFAULT_EXEMPLARS: dict[str, list[str]] = {
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"interview_scheduled": [
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"Subject: Interview Invitation\n\nWe would like to invite you for a phone screen next week.",
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"Subject: Schedule a call\n\nCould you be available for a video interview on Tuesday?",
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"Subject: Next Steps\n\nWe'd like to move forward with a technical interview. Please select a time.",
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"Subject: Interview Details\n\nHere are the dial-in instructions for your interview tomorrow.",
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],
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"offer_received": [
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"Subject: Offer Letter Enclosed\n\nWe are pleased to extend you an offer of employment.",
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"Subject: Job Offer\n\nDear candidate, we are excited to offer you the position of Software Engineer.",
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"Subject: Employment Offer\n\nPlease find attached your formal offer letter and compensation details.",
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"Subject: Offer of Employment\n\nCongratulations! We would like to offer you a full-time position.",
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],
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"rejected": [
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"Subject: Your Application\n\nAfter careful consideration, we have decided to move forward with other candidates.",
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"Subject: Application Status\n\nWe regret to inform you that your application has not been selected.",
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"Subject: Thank you for applying\n\nWe appreciate your interest but have chosen not to proceed.",
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"Subject: Update on your candidacy\n\nWe will not be moving forward with your application at this time.",
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],
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"positive_response": [
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"Subject: Your profile\n\nI came across your LinkedIn and think you would be a great fit for our team.",
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"Subject: Exciting opportunity\n\nWe were impressed by your background and would love to connect.",
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"Subject: Following up\n\nThank you for your interest — we'd like to learn more about your experience.",
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"Subject: Great fit\n\nYour skills align well with what we are looking for. Let's set up a call.",
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],
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"survey_received": [
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"Subject: Candidate Experience Survey\n\nPlease complete this brief survey about your application experience.",
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"Subject: Culture Fit Assessment\n\nAs part of our process, we ask all candidates to complete a short assessment.",
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"Subject: Skills Assessment\n\nWe'd like you to complete our online coding assessment before proceeding.",
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"Subject: Personality Assessment\n\nPlease complete the following assessment as the next step in our process.",
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"Subject: Interview Feedback Survey\n\nThank you for interviewing — your feedback helps us improve our process.",
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],
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"neutral": [
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"Subject: Application Received\n\nWe have received your application and will be in touch.",
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"Subject: Thank you for applying\n\nYour application is under review. We will contact you if needed.",
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"Subject: Confirmation\n\nThis email confirms receipt of your application to our company.",
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"Subject: Application Confirmation\n\nThank you for your interest. We will review your materials and follow up.",
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],
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"event_rescheduled": [
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"Subject: Interview Rescheduled\n\nDue to a conflict, we need to move your interview to a new time.",
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"Subject: Change of interview time\n\nWe apologize — your interview has been rescheduled to Thursday.",
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"Subject: Updated interview details\n\nYour interview has been moved from Monday to Wednesday at 2pm.",
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"Subject: Reschedule request\n\nWould you be available to reschedule to a different time slot?",
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"Subject: New interview time\n\nYour phone screen has been moved from tomorrow to next week.",
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],
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"digest": [
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"Subject: 15 new jobs matching your search\n\nHere are the latest job postings that match your profile.",
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"Subject: Weekly Job Digest\n\nThis week's top opportunities for Software Engineers in your area.",
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"Subject: Jobs you might like\n\nBased on your profile, here are some positions we recommend.",
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"Subject: New jobs for you\n\nSee the latest openings from companies on your watchlist.",
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],
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"new_lead": [
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"Subject: Exciting opportunity at our company\n\nHi, I noticed your background and think you'd be a great fit.",
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"Subject: Are you open to new opportunities?\n\nI'm a recruiter reaching out about a role matching your experience.",
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"Subject: Quick question\n\nWould you be interested in hearing about a senior engineering role?",
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"Subject: Recruiting outreach\n\nI came across your profile and wanted to share an exciting opening.",
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],
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"hired": [
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"Subject: Welcome to the team!\n\nWe are thrilled to have you join us. Here are your onboarding details.",
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"Subject: Onboarding information\n\nCongratulations on accepting our offer. Your start date is confirmed.",
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"Subject: First day information\n\nWe look forward to your first day. Please arrive at 9am and ask for HR.",
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"Subject: Background check initiated\n\nAs part of your onboarding, we have initiated a background check.",
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"Subject: Equipment setup\n\nYour laptop and equipment will be ready for pickup on your first day.",
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],
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}
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class ClassifierAdapter(abc.ABC):
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class ClassifierAdapter(abc.ABC):
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"""Abstract base for all email classifier adapters."""
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"""Abstract base for all email classifier adapters."""
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@ -293,3 +293,21 @@ def test_cosine_known_value():
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def test_cosine_zero_vector_returns_zero():
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def test_cosine_zero_vector_returns_zero():
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from scripts.classifier_adapters import _cosine
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from scripts.classifier_adapters import _cosine
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assert _cosine([0.0, 0.0], [1.0, 0.0]) == pytest.approx(0.0)
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assert _cosine([0.0, 0.0], [1.0, 0.0]) == pytest.approx(0.0)
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# ---- DEFAULT_EXEMPLARS tests ----
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def test_default_exemplars_covers_all_labels():
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from scripts.classifier_adapters import DEFAULT_EXEMPLARS, LABELS
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for label in LABELS:
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assert label in DEFAULT_EXEMPLARS, f"DEFAULT_EXEMPLARS missing label: {label}"
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assert len(DEFAULT_EXEMPLARS[label]) >= 1, f"{label} has no exemplar texts"
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def test_default_exemplars_sparse_labels_have_at_least_four():
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from scripts.classifier_adapters import DEFAULT_EXEMPLARS
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# These labels have very few real examples; need >= 4 so k=3 vote is meaningful
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for label in ("hired", "survey_received", "event_rescheduled"):
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assert len(DEFAULT_EXEMPLARS[label]) >= 4, (
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f"{label} needs >= 4 exemplars for k=3 voting to work reliably"
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)
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