88 lines
9.2 KiB
Plaintext
88 lines
9.2 KiB
Plaintext
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„ Z e d krje ƒ d S )
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zQ
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Causal Engine
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Understands cause-and-effect relationships, not just correlations
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é )ÚDictÚListÚOptionalÚTuple)Ú dataclass)Údatetimec @ sT e Zd ZU dZeed< eed< eed< eed< ee ed< eed< edœd d
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„Z d S ) ÚCausalRelationshipz%A causal relationship between signalsÚcauseÚeffectÚstrengthÚ mechanismÚevidenceÚ
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confidence©Úreturnc C s. | j | jt| jdƒ| jt| jƒt| jdƒdœS )Né )r r
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r r Zevidence_countr )r r
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Úroundr r Úlenr
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r ©Úself© r úA/home/bernt/.openclaw/workspace/iom/intelligence/causal_engine.pyÚto_dict s
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úzCausalRelationship.to_dictN)
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__module__Ú __qualname__Ú__doc__ÚstrÚ__annotations__Úfloatr r r r r r r r s
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edœdd„ZdS )Ú CausalEnginez0Identifies causal relationships in urban systemsc C s | ¡ | _d S )N)Ú_build_causal_graphÚ causal_graphr r r r Ú__init__# s zCausalEngine.__init__r c C sÚ dddddœdddœgdœd d
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dddœdddœgdœddg dœdddddœgdœdœdddd dœgdœd d d d dœgdœd!œd"d#g dœd$d%d&d'dœgdœd(d)g dœd*œd+d,d-d.dœgdœd/d0g dœd1œd2œS )3z-Build knowledge graph of causal relationshipsg¸ …ëQ¸æ?zbBetter lighting increases visibility, deters criminal activity, and improves pedestrian confidencezChicago Alley Lightingz7% crime reduction)Ústudyr
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zNYC Street Lightingz/36% reduction in outdoor nighttime index crimes)r r r
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gÍÌÌÌÌÌä?zKImproved lighting extends usable hours for commercial and social activities)Ú safety_indexZnight_activityg=
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×£p=ê?zPTrees provide shade and evapotranspiration, reducing surface and air temperaturezUrban Heat Islandu 2-8°C cooling effectzBarcelona Tree Strategyu" 4°C reduction in pedestrian areasg�Âõ(\�â?zFShaded walkways encourage walking and extend comfortable walking hoursgÍÌÌÌÌÌÜ?zNTree-lined streets are associated with higher property values and desirabilityzPortland Treesz$$1.35B total property value increase)Z heat_stressÚ walkabilityÚproperty_valuegö(\�Âõè?z:More pedestrians = more potential customers = higher saleszHigh Street Retailz*1% footfall increase = 1.3% sales increaseg¤p=
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×£à?zOEyes on the street effect - more people watching increases natural surveillancez Jane Jacobsz"Natural surveillance reduces crime)Zretail_revenuer% g)\�Âõ(ä?zUWell-maintained areas signal investment and care, attracting residents and businessesgš™™™™™á?zYBroken windows theory - visible neglect signals low enforcement and invites more disorderzBroken Windowsz+Maintenance prevents escalation of disorderg¸ …ëQ¸Þ?z4Tourists avoid areas that appear neglected or unsafe)r' r% Ztourismgq=
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×£på?zHTransit access reduces commute costs and increases location desirabilityz TOD Valuez*10-20% property value premium near transitg)\�Âõ(Ü?zCTransit hubs create walkable destinations and mixed-use development)r' r& )ÚlightingÚ
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tree_coverageÚpedestrian_flowÚmaintenance_qualityÚtransit_accessr r r r r r! & s~ þý ý÷þý ýÿýòÿýÿýøýÿýýóÿýýø®z CausalEngine._build_causal_graph)r r
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r c C sR || j v rN|| j | v rN| j | | }|||d |d |d | |d ¡dœS dS )z‚
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Explain why A causes B
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Returns:
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Causal explanation or None if no relationship known
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r r r
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ÚinterpretationN)r" Ú_interpret_strength)r r r
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Ú relationshipr r r Úexplain_causalityŠ s ú z CausalEngine.explain_causality)r
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r c C sd g }| j ¡ D ]>\}}||v r|| }| ||d |d dd… d dœ¡ q|jdd„ d d
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� |S ) z"Find all known causes of an effectr r Néd ú...)r r r c S s | d S ©Nr r ©Úxr r r Ú<lambda> ó z*CausalEngine.find_causes.<locals>.<lambda>T©ÚkeyÚreverse©r" ÚitemsÚappendÚsort)r r
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Úcausesr Úeffectsr/ r r r Ú find_causesŸ s ý
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zCausalEngine.find_causes)r r c C sf || j vrg S g }| j | ¡ D ].\}}| ||d |d dd… d dœ¡ q |jdd„ d d
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� |S ) z!Find all known effects of a causer r Nr1 r2 )r
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r r c S s | d S r3 r r4 r r r r6 ¿ r7 z+CausalEngine.find_effects.<locals>.<lambda>Tr8 r; )r r r@ r
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r/ r r r Ú find_effects± s
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zCausalEngine.find_effects)r
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signal_valuesr c C sô | |¡}|s |dd|› �dœS g }tdd„ |D ƒƒ}|D ]j}|d }| |d¡}td| ƒd } |d | d | }
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d
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d ƒ|t| d
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d„ dd� || |d¡ttdd„ |D ƒƒd ƒ||rì|d d nddœS )u
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Calculate how much each cause contributes to an effect
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Example:
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"Safety Index = 45. What causes this?"
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→ Lighting: 31% of variation
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→ Maintenance: 22% of variation
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→ Pedestrian flow: 18% of variation
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ÚunknownzNo causal model for )r
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ÚstatusÚmessagec s s | ]}|d V qdS )r Nr )Ú.0Úcr r r Ú <genexpr>Ü r7 z5CausalEngine.calculate_attribution.<locals>.<genexpr>r é2 r é r1 r )r Úattribution_percentÚ
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current_valueÚpotential_improvementr c S s | d S )NrL r r4 r r r r6 ï r7 z4CausalEngine.calculate_attribution.<locals>.<lambda>Tr8 c s s | ]}|d V qdS )rL Nr )rG Úar r r rI ô r7 r N)r
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rM Ztotal_attributionÚ attributionsZ
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top_driver)rA ÚsumÚgetÚabsr= r r> ) r r
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rC r? rP Ztotal_strengthr Ú
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cause_namerM Z deviationÚ attributionr r r Úcalculate_attributionà s6
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ý û
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ûz"CausalEngine.calculate_attribution)ÚtargetrM Ú target_valuer c C sŒ | |¡}g }|D ]b}|d }|d }|| } | | }
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dƒt|dƒ|d |
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œ¡ q|jd d „ d
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d� |S )u3
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Recommend interventions to achieve a target
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Example:
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"Improve safety from 45 to 70"
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→ Install lighting (expected improvement: +15)
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