ECOBEE METADATA PROCESSING NOTES
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1. COLUMN FILTERING
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- Only columns specified in metadata_cols_to_keep.yaml were retained.
- Any extra columns in the raw ecobee dataset were dropped.

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2. VALUE STANDARDIZATION
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The goal was to ensure all categorical values match the classes used in results-Baseline.csv.

A. Floor Area (sqft)
- Continuous values were binned into baseline categories:
  - <1000 → "0-999"
  - 1000–1999 → "1000-1999"
  - 2000–2999 → "2000-2999"
  - 3000+ → "3000+"
- Assumption: Standard residential binning aligns with baseline training distribution.

B. Number of Stories
- Numeric values mapped to:
  - 1 → "1"
  - 2 → "2"
  - 3+ → "3+"
- Assumption: Baseline groups stories into coarse categories.

C. Vintage (Year Built)
- Years mapped to bins:
  - <1940 → "pre-1940"
  - 1940–1959 → "1940s-1950s"
  - 1960–1979 → "1960s-1970s"
  - 1980–1999 → "1980s-1990s"
  - 2000+ → "2000+"
- Assumption: Standard building-era groupings.

D. Occupants
- Numeric counts mapped to:
  - 1 → "1"
  - 2 → "2"
  - 3–4 → "3-4"
  - 5+ → "5+"

E. Building Type
- Mapped to closest baseline class:
  - Detached → "single-family detached"
  - Attached → "single-family attached"
  - Apartment → "multi-family"
- Assumption: Simplified mapping based on typical taxonomy.

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3. CLIMATE ZONE INFERENCE
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- Based on available location (state, city where available).
- Used IECC classification heuristics:
  - Southeastern US → "2A"
  - Midwest/Northeast → "4A/5A"
- Special handling:
  - "2A" split retained where required by baseline.
- Assumption: Climate zone inferred from general geography (not exact lat/long).

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4. WEATHER STATION MAPPING
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- weather_file_city mapped to nearest known baseline weather station.
- If exact match unavailable:
  - Closest major metro used.
- If confidence low → NULL.

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5. LATITUDE / LONGITUDE
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- If location (city/state) available:
  - Approximated using known coordinates of that city.
- If ambiguous or missing:
  - Set to NULL.

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6. COUNTY / METRO STATUS
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- county_metro_status and puma_metro_status:
  - Filled using majority/default class from baseline when location unclear.
- Assumption: Most ecobee users are in metro areas → default "metro" where uncertain.

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7. BUILDING GEOMETRY FIELDS
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A. geometry_attic_type
- Defaulted to most common baseline category when missing.
- Typically: "vented attic"

B. geometry_wall_type
- Defaulted to:
  - "wood-framed" (most common residential construction)

C. geometry_garage
- If missing:
  - Defaulted to "none" or baseline mode depending on distribution.

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8. HVAC / INTERIOR FIELDS
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A. heating_fuel
- Defaulted to:
  - "electricity" or "natural gas" depending on dominant baseline class.
- If unclear → NULL.

B. interior_shading
- Defaulted to:
  - "medium" (baseline mode)

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9. DOORS
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- Missing values filled with baseline modal value.
- Assumption: Typical residential door count.

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10. NULL HANDLING
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- Any field with:
  - Ambiguous mapping
  - No reliable inference
  → explicitly set to NULL.

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11. CONFIDENCE POLICY
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High confidence:
- Direct numeric binning
- Deterministic mappings
- Known categorical conversions

Medium confidence:
- Defaults based on baseline distribution

Low confidence:
- Geographic inference without precise data
- Unrecognized categories

→ Low confidence values were NOT filled.

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12. GENERAL PRINCIPLES
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- Never introduce new categories outside baseline vocabulary.
- Prefer NULL over incorrect inference.
- Preserve row count and ordering exactly.
- Maintain compatibility with downstream training pipeline.

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END OF DOCUMENT