anti
2254651270
Two-axis classification over the first_token_hash sequence: repetition_rate (drilling) vs backtrack_rate (jumping among prior tools). chaotic/targeted/methodical buckets. v0.1 thresholds; D.8 re-tunes.
53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
"""Registered feature functions.
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Each entry takes a ``SessionContext`` and yields zero or more
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``Observation`` instances. Adding a primitive = adding a function in a
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sibling module and appending it to ``FEATURES``.
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"""
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from __future__ import annotations
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from typing import Callable, Iterable
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from decnet_behave_core.spec.envelope import Observation
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from decnet.profiler.behave_shell._ctx import SessionContext
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from decnet.profiler.behave_shell._features.cognitive import (
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cognitive_load,
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command_branch_diversity,
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exploration_style,
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feedback_loop_engagement,
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inter_command_consistency,
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inter_command_latency_class,
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)
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from decnet.profiler.behave_shell._features.motor import (
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command_chunking,
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error_correction,
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input_modality,
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keystroke_cadence,
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motor_stability,
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paste_burst_rate,
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pipe_chaining_depth,
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shortcut_usage,
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tab_completion,
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)
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FeatureFn = Callable[[SessionContext], Iterable[Observation]]
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FEATURES: tuple[FeatureFn, ...] = (
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input_modality,
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paste_burst_rate,
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keystroke_cadence,
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motor_stability,
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error_correction,
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command_chunking,
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tab_completion,
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shortcut_usage,
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pipe_chaining_depth,
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inter_command_latency_class,
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command_branch_diversity,
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feedback_loop_engagement,
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inter_command_consistency,
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cognitive_load,
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exploration_style,
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)
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