Add analyze subset command

[gayanW]

Add new command analyze subset which can be used to demonstrate PTS effectiveness.

Usage:

$ smart-tests analyze subset --help
Usage: smart-tests analyze subset [OPTIONS] <SUBSET_ID>

Arguments:
  SUBSET_ID
      Subset ID to analyze

Options:
  --baseline-subset-id INT
      Baseline subset ID to compare against (auto-detected if not provided) [optional]

Example:

$ smart-tests analyze subset 2019138
→ Fetching subset data...
✓ Found 67 tests in subset

──────────────────────────────────────────────────────────────────────
Select Expected Tests
──────────────────────────────────────────────────────────────────────

Code files modified:
  - smart_tests/commands/inspect/model.py

Which tests do you expect PTS to prioritize?

? Select expected tests? Yes

Use Tab/Space to toggle selection, Enter when done

? Search and select expected tests: ['file=tests/commands/inspect/test_model.py']

✓ Selected 1 expected test(s):
  • file=tests/commands/inspect/test_model.py

──────────────────────────────────────────────────────────────────────
Step 2: Comparison Results
──────────────────────────────────────────────────────────────────────

Δ Rank      Subset Rank  Test Name                                  Reason                                                 Density
--------  -------------  -----------------------------------------  ---------------------------------------------------  ---------
↑42                   1  file=tests/commands/inspect/test_model.py  Changed file: smart_tests/commands/inspect/model.py   0.840091


✅ Tests Promoted:
- test_model.py promoted by 42 positions to rank #1

✅ Tests Prioritized:
- Expected test prioritized at rank #1 out of 67 (top 1.5%)

✅ Test Selection:
- PTS successfully picked expected tests with high confidence
- test_model.py: density 0.84 (very strong confidence)

[gayanW]

Light review should be fine, since I tested all the execution paths locally.