diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/.gitignore b/graph-analysis/J-Lawrence10/analogy-phase-audit/.gitignore new file mode 100644 index 0000000..34afa08 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/.gitignore @@ -0,0 +1,6 @@ +.env +.venv/ +.pytest_cache/ +__pycache__/ +*.py[cod] +~$*.pptx diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/METHODS.md b/graph-analysis/J-Lawrence10/analogy-phase-audit/METHODS.md new file mode 100644 index 0000000..1e2289f --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/METHODS.md @@ -0,0 +1,156 @@ +# How the analysis works + +This document explains what the scripts do in plain language. It separates the +steps performed by the code from the conclusions we might draw from them. + +## Part 1: Count features across graphs + +Each attribution graph can contain the same feature at several token positions. +Those are several uses of one feature inside one graph. They are not several +independent graphs. + +The code identifies a feature by two values: + +- the model layer; +- the feature number in that layer. + +For each feature, the code keeps a set of graph names in which the feature +appears. Its cross-graph count is the size of that set. + +```text +for each graph: + for each feature occurrence in that graph: + feature = (layer, feature number) + add the graph name to that feature's set + +cross-graph count = number of graph names in the set +``` + +A feature occurring five times in the Berlin graph and nowhere else therefore +has a cross-graph count of one, not five. + +The code also records the total number of occurrences, but does not use that +number as evidence that the feature appears in more graphs. + +When averaging influence or activation, the code first averages all occurrences +inside each graph. It then averages those graph-level values. This gives each +prompt equal weight even when a feature appears at more token positions in one +prompt than another. + +## Part 2: Summarize every layer + +The model has 26 layers, numbered 0 through 25. For every prompt and layer, the +code calculates four measurements: + +1. the number of different features in the layer; +2. the middle influence value across those features; +3. the middle activation value across those features; +4. the average number of token positions at which each feature appears. + +Before calculating the influence and activation summaries, repeated occurrences +of the same feature in one layer are averaged together. + +The feature count and activation measurements can contain a few values much +larger than the rest. The code uses `log(1 + value)` for those two measurements +so that a small number of large values do not control the result. + +## Part 3: Put the measurements on the same scale + +The four measurements use different units. Feature count might be in the +hundreds, while influence may be below one. Comparing the raw numbers would give +the largest-numbered measurement too much weight. + +For each prompt and each measurement, the code: + +1. subtracts that prompt's average across layers; +2. divides by that prompt's spread across layers. + +After this step, zero means typical for that prompt. Positive values are above +its average, and negative values are below its average. The code then averages +the five prompt profiles. + +## Part 4: Try possible layer splits + +The code tests models with one through five sections. A section is a continuous +run of layers, and every section must contain at least three layers. + +For any proposed section, the code finds its average four-measurement profile. +It then measures how far each layer is from that section average. Smaller total +error means the layers grouped into a section are more alike. + +The code checks all valid split locations using dynamic programming. In this +case, that means it stores the best earlier result and reuses it instead of +recalculating every complete combination. It still returns the split locations +with the lowest total error; it is not a greedy search that accepts the first +improvement. + +## Part 5: Choose how many sections to keep + +Adding more sections almost always lowers the fitting error. That alone would +favor as many sections as the code allows. The Bayesian Information Criterion, +or BIC, adds a penalty for every extra section and boundary. + +The exact score used by the code is: + +```text +BIC = N × log(error / N) + K × log(N) +``` + +Here: + +- `N` is the number of layer measurements being fitted; +- `error` is the total squared distance from the section averages; +- `K` is the number of fitted section values plus the number of boundaries. + +Lower BIC is better. Among the tested section-based models, the lowest BIC came +from three sections beginning at layers 7 and 15. This gives L0–L6, L7–L14, and +L15–L25. + +This result means that three sections are the best balance of fit and simplicity +among the tested section-based models. It does not prove that the model contains +three sharply separated mechanisms. + +## Part 6: Check whether a result carries to an omitted prompt + +The code also leaves out one prompt at a time: + +1. find the splits using the other four prompts; +2. use those section averages to predict the omitted prompt; +3. measure the prediction error; +4. repeat until every prompt has been omitted once. + +The reported value is the average error across the five rounds. This checks +whether a model describes prompts that were not used to choose its boundaries. + +## Part 7: Compare sections with a gradual change + +The code also fits simple smooth curves across layer depth. This checks whether +the measurements require clear breaks or can be described as a gradual change. + +The current data give lower BIC and lower omitted-prompt error to a smooth cubic +curve, which can change direction up to twice. The three-section result should +therefore be read as the best simple section summary, not as proof of sharp +boundaries. + +## Part 8: Check how stable the result is + +The code repeats the analysis 1,000 times. On each repeat, it draws five prompts +from the original five, allowing the same prompt to be drawn more than once. + +It records two things: + +- how often BIC chooses each number of sections; +- when three sections are held fixed, where the two boundaries appear. + +The first boundary usually appears at L7 or L8. The second is less precise and +appears from L14 through L18, most often at L15. + +## What the algorithm does not determine + +The algorithm finds patterns in graph measurements. It does not determine what +the sections mean. Names such as representation, transformation, or output +consolidation are working descriptions that require separate evidence. + +Claims about function require blinded feature annotation. Claims that one stage +causes another require controlled interventions, downstream measurements, and +tests showing that restoring the downstream activity restores performance. diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/README.md b/graph-analysis/J-Lawrence10/analogy-phase-audit/README.md new file mode 100644 index 0000000..b576864 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/README.md @@ -0,0 +1,102 @@ +# Analogy Feature-Overlap and Phase Audit + +This folder is an isolated, reproducible audit package. It does not replace or +move any files in the original analysis directories. + +## Main findings + +- All five attribution graphs were regenerated with the published prompts, + model, and pruning parameters. Their node and edge counts match the published + graph summaries. +- Recurrence is counted by **distinct graph membership**, so repeated + token-position activations within one graph do not count as independent + cross-prompt appearances. +- The corrected all-five feature core contains 119 features rather than 180, a + reduction of 33.9%. +- The five named manuscript features remain present in all five graphs. +- Among one- through five-segment models, BIC selects three segments beginning + at L7 and L15: L0-6, L7-14, and L15-25. +- These are objective numeric regimes. Their functional labels remain + provisional pending blinded semantic annotation and causal validation. + +## Folder layout + +```text +METHODS.md +analysis/ + DATA_MANIFEST.md + rerun_corrected_analysis.py + detect_numeric_phase_boundaries.py + plot_corrected_audit_figures.py + tests/ + audit_outputs/corrected_20260712/ + graphs/ # five raw regenerated graphs + figures/ # meeting-ready figures + summary.json + corrected_overlap.json + numeric_phase_detection.json + phase_model_metrics.csv + core_5_of_5_annotation_template.csv +presentation/ + analogy_phase_audit_meeting.pptx + meeting_script.md +requirements.txt +requirements-lock.txt +``` + +## Reproduce from the included graphs + +From this folder: + +```powershell +python -m pip install -r requirements.txt +python analysis/rerun_corrected_analysis.py +python analysis/detect_numeric_phase_boundaries.py +python analysis/plot_corrected_audit_figures.py +python -m pytest analysis/tests +``` + +The first command reuses the included graph JSON files and does not require an +API key. + +The package was verified with Python 3.13.3. Use `requirements-lock.txt` when +an exact recreation of the tested environment is preferred. + +## Regenerate graphs from Neuronpedia + +Set the key in the environment and request fresh graphs: + +```powershell +$env:NEURONPEDIA_API_KEY = "" +python analysis/rerun_corrected_analysis.py --regenerate --fetch-labels +``` + +No API credential is stored in this package. + +## Statistical procedure + +A step-by-step explanation without assumed statistical background is available +in [How the analysis works](METHODS.md). + +For each prompt and layer, the phase analysis calculates: + +1. log unique-feature count; +2. median feature influence; +3. log median feature activation; and +4. mean token contexts per feature. + +The metrics are standardized within prompt. Exact dynamic programming finds +the minimum-error segmentation for one through five phases, with a minimum +segment size of three layers. BIC penalizes additional boundaries and segment +parameters. Boundary stability is evaluated with 1,000 prompt-bootstrap +reruns. + +Lower BIC is better. The three-phase model has BIC -74.378, compared with +-70.377 for four phases and -68.310 for five phases. + +## Interpretation limits + +The three phases are the best discrete summary among the tested segmented +models. BIC does not prove that the boundaries are mechanistically sharp. +Semantic phase names require blinded annotation, and a causal hierarchy +requires matched interventions, mediation measurements, and rescue tests. diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/DATA_MANIFEST.md b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/DATA_MANIFEST.md new file mode 100644 index 0000000..a1b1261 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/DATA_MANIFEST.md @@ -0,0 +1,26 @@ +# Data manifest + +The five graph JSON files were regenerated from Neuronpedia using the prompts, +model, and pruning parameters encoded in `rerun_corrected_analysis.py`. + +| Graph | Prompt | Nodes | Edges | +|---|---|---:|---:| +| analog_berlin | Paris is to France as Berlin is to | 930 | 25,915 | +| analog_rome | Paris is to France as Rome is to | 963 | 27,608 | +| analog_tokyo | Paris is to France as Tokyo is to | 905 | 22,414 | +| analog_teacher | Doctor is to hospital as teacher is to | 1,040 | 35,481 | +| analog_bird | Fish is to water as bird is to | 1,071 | 38,741 | + +## Derived files + +- `summary.json`: graph sizes and original-versus-corrected overlap counts. +- `corrected_overlap.json`: feature-level graph membership, occurrences, and + per-graph summaries. +- `core_5_of_5_annotation_template.csv`: corrected all-five feature set and + Neuronpedia labels, with empty blinded-annotation fields. +- `numeric_phase_detection.json`: segmented models, smooth controls, selected + boundaries, and bootstrap output. +- `phase_model_metrics.csv`: compact one- through five-phase comparison. +- `figures/`: presentation-ready plots generated from the JSON outputs. + +The raw graphs and derived files contain no API credential. diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/core_5_of_5_annotation_template.csv b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/core_5_of_5_annotation_template.csv new file mode 100644 index 0000000..e6c224a --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/core_5_of_5_annotation_template.csv @@ -0,0 +1,120 @@ +layer,sae_index,graph_count,occurrence_count,avg_influence,avg_activation,neuronpedia_label,label_fetch_error,semantic_category,annotator +6,558,5,5,0.775456,7.258579,phrases indicating a comparison and mathematical notations.,,, +9,7011,5,5,0.774518,8.489513, common prepositional phrases,,, +0,1548,5,5,0.762838,2.864397," uses of the word ""than"" and the prepositional words ""for"", ""as"", and ""from"".",,, +3,1931,5,5,0.753792,6.151778, statements of fact linked to importance or findings,,, +5,10903,5,5,0.748657,5.62652," legal wording related to accusations, correspondence, or information provided to others",,, +13,2249,5,8,0.746069,10.736337," noun phrases ending in ""-ists"", and also seems to have some other unrelated triggers.",,, +5,15819,5,8,0.742346,7.308961, technical writing about technology standards and mathematical theorems,,, +3,12478,5,5,0.741593,3.313118," words or phrases suggesting a lack of jurisdiction or failure (""without"")",,, +7,11973,5,5,0.73901,6.990788," mentions of tech companies and products, especially in the mobile device market",,, +6,2539,5,5,0.735638,7.957063, words and phrases associated with political opinions or debates.,,, +4,1489,5,5,0.735415,5.494072," a wide variety of words that sometimes seem related, but don't have a clear common context",,, +6,5451,5,8,0.733007,7.207643,opinionated political discussions,,, +4,2221,5,5,0.732914,5.706113," words and phrases that describe qualities or attributes, often with an emotional or evaluative tone",,, +6,4742,5,5,0.729486,5.985522, words that define the meaning of concepts in a mathematical or general context,,, +4,4849,5,6,0.729403,4.685522," hyphenated words that end with the term ""host"", start with the term ""to"", or relate to analog to digital computing",,, +1,3135,5,5,0.726952,3.712162,terms used across a wide range of technical documentation,,, +6,2267,5,20,0.722497,14.778773,"words that appear in programming code, legal jargon, or scientific texts",,, +14,8179,5,5,0.719027,12.1155,"phrases with lots of function words (articles, prepositions, etc) strung together.",,, +4,1312,5,5,0.716567,8.880349, sentences that describe something,,, +0,15038,5,6,0.715756,3.53923,locations and organizations,,, +22,14727,5,6,0.714862,110.840662,snippets from different kinds of documents,,, +12,2416,5,5,0.714695,16.97381,"words related to making choices to improve oneself, work performance, shopping, definitions, spending/budgets",,, +3,3976,5,6,0.711274,4.16813, a variety of seemingly unrelated words,,, +3,8907,5,5,0.704291,4.297965," uses of the word ""since.""",,, +4,14729,5,8,0.700944,7.51828," scientific discourse relating to biology, disease and chemistry",,, +10,6033,5,9,0.698328,15.271079," references to popular culture, food preparation, disease, immunity, and politics",,, +5,7191,5,9,0.6926,11.455099, words and phrases related to legal proceedings or agreements,,, +1,1321,5,9,0.688712,9.449826," the word ""is""",,, +4,14857,5,22,0.688346,13.98597,code snippets and license agreements,,, +5,6473,5,10,0.68799,8.162798," a mix of computer programming terms, words describing bad health outcomes, and words quantifying degree",,, +6,10428,5,6,0.68766,9.510391, references to animals or young humans,,, +0,12200,5,11,0.686597,3.980263, a variety of specific nouns,,, +14,11455,5,7,0.683946,28.934161, phrases performing comparisons.,,, +11,15947,5,10,0.680067,14.537845, references to historical or social change.,,, +13,10969,5,11,0.675028,15.247173,comparisons between disciplines and relationships between concepts.,,, +1,39,5,5,0.674141,4.256696," conjunctions and discourse markers occurring at the beginning of clauses in German and English, including both contrasting and additive words",,, +3,3205,5,20,0.674058,13.511326," code snippets and documentation references, possibly related to web development",,, +5,13874,5,5,0.673886,5.152406,"phrases using ""like"", ""similar to"", ""case of"", or comparisons",,, +6,12605,5,10,0.673739,9.838267," a variety of seemingly unrelated words, including acronyms, medical terms, software, and minerals.",,, +0,2608,5,5,0.673692,3.94212," the character sequence ""ub""",,, +2,9088,5,5,0.673431,3.031545," things which are believed, demonstrated, or thought to be true",,, +2,8364,5,5,0.673409,3.208867," instances of passive voice such as ""is thought to be""",,, +0,12747,5,11,0.673315,3.59437,"occurrences of the words 'they', 'them', 'that', 'these', or something that can be replaced with 'they' or 'them'",,, +9,13344,5,14,0.671062,13.850056, phrases suggesting uncertainty or comparison between two things,,, +9,2909,5,8,0.668425,13.730421," formulas, ratios, and mathematical notation",,, +5,6109,5,5,0.666651,7.170269, terms related to scientific or academic concepts,,, +2,1154,5,8,0.666159,5.050591," paragraphs that include the word ""that"" or derivatives of the word ""form"", and sometimes words relating to legal or scientific claims.",,, +3,12615,5,5,0.665779,5.104949," the term ""ischemic"" and related terms describing vascular incidents",,, +5,13039,5,6,0.664876,9.631059, words and phrases that suggest a comparison or an analogy is being made or a summary of something is being presented.,,, +8,8823,5,5,0.664371,11.223368, the word 'from' or conjunctive phrases using commas and 'and',,, +4,1802,5,5,0.663038,9.51645," verbs inflected for the past tense, or words with the basic form changing to show number",,, +6,6732,5,8,0.660041,9.85196, phrases related to time running out or a race against time,,, +10,5559,5,5,0.657825,28.141153,"the letter ""L"" at the beginning of a token",,, +2,15420,5,8,0.657525,8.661437," the word ""is"" and words near it",,, +12,7938,5,5,0.656926,12.721792, language related to financial planning and retirement investment,,, +5,2141,5,12,0.652765,7.327073, comparisons of people or figures using well-known public figures,,, +1,6265,5,5,0.649505,2.445772," the word ""properly"" and words and phrases associated with needing permission to do something",,, +1,2493,5,8,0.646872,5.322861," the word ""to""",,, +0,4823,5,14,0.645135,6.531396," the word ""part"" followed by prepositions or words related to sections or components.",,, +13,2904,5,5,0.644863,13.477888, mathematical equations and financial terms,,, +2,7856,5,6,0.642653,3.299199," the archaic word ""unto"" and words immediately surrounding it",,, +2,1531,5,5,0.642513,3.584251,"a variety of seemingly unrelated words that end in particular letter combinations, such as ""rin,"" ""gg,"" ""zer,"" ""mar,"" ""RG,"" and also some positive and negative descriptors.",,, +0,9977,5,5,0.642157,4.951589," the word ""as"" used in various contexts",,, +3,12006,5,10,0.640009,8.487745,"words that express surprise, court rulings, directions or requirements",,, +2,11475,5,10,0.637919,5.775604," the word ""refers"" and words related to it",,, +7,6756,5,5,0.633471,16.997517," somewhat uncommon, short (4-5 letter) words",,, +0,11651,5,10,0.633396,5.503576,"the word ""to""",,, +0,6274,5,5,0.62907,3.448511," references to legal proceedings and pronouncements, especially related to court decisions and legal actions",,, +9,2750,5,5,0.628765,12.325571, phrases where someone is suggesting skepticism or doubt,,, +7,749,5,7,0.62731,12.478118,analogies and comparisons,,, +0,8047,5,5,0.626809,4.116435, research documents,,, +4,10752,5,10,0.625842,11.605871,"uses of the verb ""to be"" preceded by ""to"" or ""should"" or followed by a past tense verb",,, +0,1448,5,5,0.62569,4.574785," the verb ""is"" and ""are"".",,, +4,10469,5,5,0.622922,10.753911," occurrences of modal verbs, belief, putting things, or talking",,, +0,1847,5,13,0.622597,6.078559,scientific terms and experimental details related to biological and chemical research,,, +0,2115,5,11,0.621764,6.969368," data reported as a percentage inside brackets, especially in a laboratory or medical context, and also recognizes countries",,, +0,9773,5,8,0.618082,4.950436, references to dates and times,,, +0,9033,5,5,0.617334,5.442299,"the word ""as.""",,, +5,6257,5,9,0.613913,9.729148," pronouns or possessive pronouns, relative pronouns, and words associated with groups or individuals",,, +5,10251,5,5,0.611919,8.557521, words related to relationships between entities,,, +1,11356,5,10,0.608848,5.931782," the word ""to"" followed by a verb.",,, +3,3289,5,5,0.607907,5.703409, ampersands and programming-related terms,,, +6,3178,5,6,0.606991,14.44345, mentions of a company dominating the Android market,,, +4,410,5,10,0.60284,12.748059,"the word ""due"" and nearby words.",,, +3,10923,5,5,0.598663,6.902741,"the word ""as"" and surrounding words",,, +6,11805,5,5,0.595407,14.361543,"uses of ""and"" and words indicating opposition or alternatives like ""versus"" and ""or""",,, +4,2485,5,5,0.595056,11.508718," verbs relating to business transactions with a high activation on ""to""",,, +5,5793,5,11,0.587576,16.626808, analogies,,, +0,4739,5,5,0.585891,4.385902,"the word ""is""",,, +0,3756,5,5,0.585306,6.363197,"the word ""as""",,, +3,169,5,9,0.582246,10.339726," the word ""to"" followed by a verb.",,, +3,8196,5,5,0.576243,8.469805,LaTeX or other mathematical notation,,, +0,15414,5,7,0.573852,4.073164," words and phrases related to obligation, judgement, or destiny",,, +5,9672,5,12,0.571789,21.301216," the phrase ""there/they are"" or the phrase ""it is to.""",,, +8,14883,5,5,0.566828,12.704211," instances of comparison using ""like"" or ""as""",,, +0,11834,5,7,0.565233,4.451462, words related to expectations and forward-looking statements as well as words related to finding employees,,, +1,10752,5,9,0.562702,8.684966,"the word ""to""",,, +0,1903,5,5,0.561977,4.817806,"instances of sentences starting with ""The""",,, +2,8165,5,9,0.560247,7.01108, lines of code with a vertical bar and the letter 'c',,, +4,12254,5,10,0.559228,16.19839,"the word ""to"" and words directly preceding or following it",,, +0,1998,5,6,0.557129,6.412475," occurrences of the word ""seem"" or ""is"" or ""are.""",,, +25,13416,5,5,0.553959,31.450512, snippets of code in various programming languages along with related technical terms.,,, +4,4021,5,5,0.552433,9.806666," code snippets or configurations with the word ""as"".",,, +2,9848,5,10,0.536424,8.940865, parts of words,,, +15,15954,5,5,0.532804,26.034247,"instances of the word ""in"" followed or preceeded by certain words",,, +8,13766,5,21,0.528799,29.333616,analogies or comparisons,,, +24,13541,5,5,0.520455,66.696007,"a mix of numbers, measurements, specialized vocabulary, and statistics.",,, +4,8051,5,5,0.518655,6.495972, expressions indicating similarity or comparison,,, +0,7370,5,5,0.517338,9.830527," the word ""as"" in a variety of contexts",,, +5,3992,5,5,0.50218,18.981945,the start of documentation blocks in code,,, +1,10469,5,5,0.497324,11.046489,"the word ""as""",,, +0,6028,5,9,0.431805,11.436872,"the word ""to""",,, +3,10018,5,10,0.407065,20.72322,"""to be"" indicating something in the process of being determined or considered",,, +0,11375,5,10,0.387376,33.148059," the word ""is""",,, +4,9110,5,5,0.355971,19.467506," words and short phrases using ""way"" or ""what"" relative to direction or manner",,, +25,4717,5,5,0.322946,207.073071," common short words such as ""to"", ""from"", ""the"", and ""of"" occurring in close proximity to numbers or other identified keywords.",,, +24,13277,5,5,0.318051,118.980104, words related to questions and requests,,, +0,1053,5,5,0.316608,28.262611," the word ""as.""",,, +0,8444,5,10,0.292667,38.653331,"the word ""to""",,, diff --git 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Their node and edge counts exactly match the published graph summaries. + +## Corrected counts + +| Threshold | Original occurrence-based result | Correct distinct-graph result | False inclusions | +|---|---:|---:|---:| +| At least 3/5 | 510 | 490 | 20 | +| At least 4/5 | 277 | 210 | 67 | +| All 5/5 | 180 | 119 | 61 | + +The claimed 180-feature all-five core falls to **119 features**, a reduction of **33.9%**. + +Among the 61 false core inclusions, 3 appeared in only two graphs, 42 in three graphs, and 16 in four graphs. + +## What survives the correction + +The five named comparison-related features used in the manuscript—L5/5793, L5/2141, L8/13766, L9/13344, and L13/10969—are genuinely present in all five graphs. Their cross-prompt membership survives the correction, although that fact alone does not establish the proposed phase boundaries or causal hierarchy. + +## Next phase-analysis requirement + +Neuronpedia labels were retrieved for all corrected 5/5 features in `core_5_of_5_annotation_template.csv`. The semantic category and annotator columns must be completed under a blinded, preregistered annotation protocol before those labels can be used for confirmatory change-point analysis. diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/figures/01-corrected-feature-overlap.png b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/figures/01-corrected-feature-overlap.png new file mode 100644 index 0000000..2bdc56c Binary files /dev/null and b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/figures/01-corrected-feature-overlap.png differ diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/figures/02-phase-model-comparison.png b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/figures/02-phase-model-comparison.png new file mode 100644 index 0000000..11e9451 Binary files 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a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/numeric_phase_detection.json b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/numeric_phase_detection.json new file mode 100644 index 0000000..15c02c9 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/numeric_phase_detection.json @@ -0,0 +1,291 @@ +{ + "scope": "objective graph metrics only; no semantic phase labels", + "prompt_names": [ + "analog_berlin", + "analog_bird", + "analog_rome", + "analog_teacher", + "analog_tokyo" + ], + "metric_names": [ + "log_unique_feature_count", + "median_influence", + "log_median_activation", + "mean_contexts_per_feature" + ], + "normalization": "within-prompt z-score for each metric", + "minimum_segment_size": 3, + "candidate_piecewise_models": [ + { + "n_boundaries": 0, + "n_phases": 1, + "boundaries": [], + "segments": [ + [ + 0, + 25 + ] + ], + "rss": 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"boundaries": [ + 8, + 18 + ], + "count": 9 + } + ] + } +} \ No newline at end of file diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/numeric_phase_detection.md b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/numeric_phase_detection.md new file mode 100644 index 0000000..ca35ffe --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/numeric_phase_detection.md @@ -0,0 +1,29 @@ +# Objective Layer Segmentation Audit + +This analysis uses graph statistics only. It does not establish semantic phase roles. + +## Candidate piecewise models + +| Phases | Boundaries | BIC | Leave-one-prompt-out loss | +|---:|---|---:|---:| +| 1 | [] | -1.932 | 1.000000 | +| 2 | [10] | -53.799 | 0.582066 | +| 3 | [7, 15] | -74.378 | 0.476662 | +| 4 | [3, 7, 15] | -70.377 | 0.451973 | +| 5 | [7, 14, 18, 22] | -68.310 | 0.421420 | + +BIC selected **3 phase(s)** with boundaries [7, 15]. + +Leave-one-prompt-out prediction selected **5 phase(s)** with full-data boundaries [7, 14, 18, 22]. + +## Smooth controls + +| Polynomial degree | BIC | Leave-one-prompt-out loss | +|---:|---:|---:| +| 1 | -61.223 | 0.571702 | +| 2 | -95.981 | 0.421724 | +| 3 | -97.897 | 0.385894 | + +Across both model families, BIC selected **degree-3** (smooth_polynomial). + +A publishable semantic phase claim additionally requires blinded feature annotations, held-out prompts, stable boundaries, and independent causal validation. diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/phase_model_metrics.csv b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/phase_model_metrics.csv new file mode 100644 index 0000000..1392700 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/phase_model_metrics.csv @@ -0,0 +1,6 @@ +phases,boundaries,bic,leave_one_prompt_out_loss +1,,-1.932,1.000000 +2,L10,-53.799,0.582066 +3,L7;L15,-74.378,0.476662 +4,L3;L7;L15,-70.377,0.451973 +5,L7;L14;L18;L22,-68.310,0.421420 diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/summary.json b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/summary.json new file mode 100644 index 0000000..e0bfb7d --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/audit_outputs/corrected_20260712/summary.json @@ -0,0 +1,44 @@ +{ + "graph_summaries": { + "analog_berlin": { + "nodes": 930, + "links": 25915, + "prompt": "Paris is to France as Berlin is to" + }, + "analog_rome": { + "nodes": 963, + "links": 27608, + "prompt": "Paris is to France as Rome is to" + }, + "analog_tokyo": { + "nodes": 905, + "links": 22414, + "prompt": "Paris is to France as Tokyo is to" + }, + "analog_teacher": { + "nodes": 1040, + "links": 35481, + "prompt": "Doctor is to hospital as teacher is to" + }, + "analog_bird": { + "nodes": 1071, + "links": 38741, + "prompt": "Fish is to water as bird is to" + } + }, + "original_occurrence_threshold_counts_on_fresh_graphs": { + "3": 510, + "4": 277, + "5": 180 + }, + "corrected_distinct_graph_counts_on_fresh_graphs": { + "3": 490, + "4": 210, + "5": 119 + }, + "inflation": { + "3": 20, + "4": 67, + "5": 61 + } +} \ No newline at end of file diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/detect_numeric_phase_boundaries.py b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/detect_numeric_phase_boundaries.py new file mode 100644 index 0000000..0d9876f --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/detect_numeric_phase_boundaries.py @@ -0,0 +1,394 @@ +#!/usr/bin/env python3 +"""Exploratory change-point analysis of objective layer-wise graph metrics. + +This does not assign semantic phase names. It tests whether the ordered layer +metrics are better described by discrete piecewise-constant regimes. Semantic +phase claims require the separately generated blinded annotation dataset. +""" + +from __future__ import annotations + +import argparse +import json +import math +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +import numpy as np + + +METRIC_NAMES = ( + "log_unique_feature_count", + "median_influence", + "log_median_activation", + "mean_contexts_per_feature", +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--graph-dir", + type=Path, + default=Path(__file__).parent + / "audit_outputs" + / "corrected_20260712" + / "graphs", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).parent + / "audit_outputs" + / "corrected_20260712", + ) + parser.add_argument("--min-segment-size", type=int, default=3) + parser.add_argument("--max-boundaries", type=int, default=4) + parser.add_argument("--bootstrap-samples", type=int, default=1000) + parser.add_argument("--seed", type=int, default=20260712) + return parser.parse_args() + + +def feature_identity(node: dict[str, Any]) -> tuple[int, int] | None: + if node.get("feature_type") not in ("cross layer transcoder", "transcoder"): + return None + try: + return int(node["layer"]), int(str(node["node_id"]).split("_")[1]) + except (KeyError, TypeError, ValueError, IndexError): + return None + + +def graph_layer_metrics(graph: dict[str, Any], n_layers: int = 26) -> np.ndarray: + by_layer_feature: dict[int, dict[int, list[dict[str, Any]]]] = defaultdict( + lambda: defaultdict(list) + ) + for node in graph.get("nodes", []): + identity = feature_identity(node) + if identity is not None: + layer, sae_index = identity + by_layer_feature[layer][sae_index].append(node) + + result = np.zeros((n_layers, len(METRIC_NAMES)), dtype=float) + for layer in range(n_layers): + features = by_layer_feature.get(layer, {}) + if not features: + continue + feature_influences = [] + feature_activations = [] + context_counts = [] + for occurrences in features.values(): + feature_influences.append( + np.mean([float(node.get("influence") or 0.0) for node in occurrences]) + ) + feature_activations.append( + np.mean([float(node.get("activation") or 0.0) for node in occurrences]) + ) + context_counts.append(len(occurrences)) + result[layer] = ( + math.log1p(len(features)), + float(np.median(feature_influences)), + math.log1p(max(0.0, float(np.median(feature_activations)))), + float(np.mean(context_counts)), + ) + return result + + +def standardize_within_prompt(values: np.ndarray) -> np.ndarray: + means = values.mean(axis=0, keepdims=True) + standard_deviations = values.std(axis=0, keepdims=True) + standard_deviations[standard_deviations == 0] = 1.0 + return (values - means) / standard_deviations + + +def segment_cost(prefix: np.ndarray, prefix_sq: np.ndarray, start: int, end: int) -> float: + count = end - start + totals = prefix[end] - prefix[start] + squared_totals = prefix_sq[end] - prefix_sq[start] + return float(np.sum(squared_totals - (totals * totals) / count)) + + +def exact_segmentation( + values: np.ndarray, + n_boundaries: int, + min_segment_size: int, +) -> tuple[list[int], float]: + """Return boundary layer indexes and the globally minimal within-segment SSE.""" + n_layers = len(values) + n_segments = n_boundaries + 1 + if n_segments * min_segment_size > n_layers: + raise ValueError("Too many boundaries for the requested minimum segment size") + + prefix = np.vstack([np.zeros(values.shape[1]), np.cumsum(values, axis=0)]) + prefix_sq = np.vstack( + [np.zeros(values.shape[1]), np.cumsum(values * values, axis=0)] + ) + infinity = float("inf") + costs = np.full((n_segments + 1, n_layers + 1), infinity) + previous = np.full((n_segments + 1, n_layers + 1), -1, dtype=int) + costs[0, 0] = 0.0 + + for segments_used in range(1, n_segments + 1): + earliest_end = segments_used * min_segment_size + for end in range(earliest_end, n_layers + 1): + earliest_start = (segments_used - 1) * min_segment_size + latest_start = end - min_segment_size + for start in range(earliest_start, latest_start + 1): + candidate = costs[segments_used - 1, start] + if not math.isfinite(candidate): + continue + candidate += segment_cost(prefix, prefix_sq, start, end) + if candidate < costs[segments_used, end]: + costs[segments_used, end] = candidate + previous[segments_used, end] = start + + boundaries = [] + end = n_layers + for segments_used in range(n_segments, 1, -1): + start = int(previous[segments_used, end]) + boundaries.append(start) + end = start + boundaries.reverse() + return boundaries, float(costs[n_segments, n_layers]) + + +def segment_means(values: np.ndarray, boundaries: list[int]) -> np.ndarray: + predictions = np.zeros_like(values) + starts = [0, *boundaries] + ends = [*boundaries, len(values)] + for start, end in zip(starts, ends): + predictions[start:end] = values[start:end].mean(axis=0) + return predictions + + +def piecewise_cv_loss(prompt_values: np.ndarray, n_boundaries: int, min_size: int) -> float: + losses = [] + for held_out in range(len(prompt_values)): + training = np.delete(prompt_values, held_out, axis=0) + training_mean = training.mean(axis=0) + boundaries, _ = exact_segmentation(training_mean, n_boundaries, min_size) + training_segment_means = segment_means(training_mean, boundaries) + losses.append(float(np.mean((prompt_values[held_out] - training_segment_means) ** 2))) + return float(np.mean(losses)) + + +def polynomial_cv_loss(prompt_values: np.ndarray, degree: int) -> float: + layers = np.arange(prompt_values.shape[1], dtype=float) + losses = [] + for held_out in range(len(prompt_values)): + training_mean = np.delete(prompt_values, held_out, axis=0).mean(axis=0) + prediction = np.zeros_like(training_mean) + for metric_index in range(training_mean.shape[1]): + coefficients = np.polyfit(layers, training_mean[:, metric_index], degree) + prediction[:, metric_index] = np.polyval(coefficients, layers) + losses.append(float(np.mean((prompt_values[held_out] - prediction) ** 2))) + return float(np.mean(losses)) + + +def polynomial_fit_stats(values: np.ndarray, degree: int) -> tuple[float, int]: + layers = np.arange(values.shape[0], dtype=float) + prediction = np.zeros_like(values) + for metric_index in range(values.shape[1]): + coefficients = np.polyfit(layers, values[:, metric_index], degree) + prediction[:, metric_index] = np.polyval(coefficients, layers) + rss = float(np.sum((values - prediction) ** 2)) + n_parameters = (degree + 1) * values.shape[1] + return rss, n_parameters + + +def bic_score(rss: float, n_observations: int, n_parameters: int) -> float: + adjusted_rss = max(rss, np.finfo(float).tiny) + return n_observations * math.log(adjusted_rss / n_observations) + n_parameters * math.log( + n_observations + ) + + +def run_analysis(args: argparse.Namespace) -> dict[str, Any]: + graph_paths = sorted(args.graph_dir.glob("analog_*.json")) + if len(graph_paths) != 5: + raise RuntimeError(f"Expected five graph files in {args.graph_dir}, found {len(graph_paths)}") + + prompt_names = [path.stem for path in graph_paths] + prompt_values = np.stack( + [ + standardize_within_prompt( + graph_layer_metrics(json.loads(path.read_text(encoding="utf-8"))) + ) + for path in graph_paths + ] + ) + aggregate = prompt_values.mean(axis=0) + model_rows = [] + n_observations = aggregate.size + n_metrics = aggregate.shape[1] + + for n_boundaries in range(args.max_boundaries + 1): + boundaries, rss = exact_segmentation( + aggregate, n_boundaries, args.min_segment_size + ) + n_parameters = (n_boundaries + 1) * n_metrics + n_boundaries + model_rows.append( + { + "n_boundaries": n_boundaries, + "n_phases": n_boundaries + 1, + "boundaries": boundaries, + "segments": [ + [start, end - 1] + for start, end in zip( + [0, *boundaries], [*boundaries, len(aggregate)] + ) + ], + "rss": rss, + "bic": bic_score(rss, n_observations, n_parameters), + "leave_one_prompt_out_loss": piecewise_cv_loss( + prompt_values, n_boundaries, args.min_segment_size + ), + } + ) + + selected = min(model_rows, key=lambda row: row["bic"]) + cv_selected = min(model_rows, key=lambda row: row["leave_one_prompt_out_loss"]) + polynomial_models = [] + for degree in (1, 2, 3): + rss, n_parameters = polynomial_fit_stats(aggregate, degree) + polynomial_models.append( + { + "degree": degree, + "rss": rss, + "bic": bic_score(rss, n_observations, n_parameters), + "n_parameters": n_parameters, + "leave_one_prompt_out_loss": polynomial_cv_loss(prompt_values, degree), + } + ) + overall_bic_candidates = [ + { + "family": "piecewise", + "name": f"{row['n_phases']}-phase", + "bic": row["bic"], + } + for row in model_rows + ] + [ + { + "family": "smooth_polynomial", + "name": f"degree-{row['degree']}", + "bic": row["bic"], + } + for row in polynomial_models + ] + + rng = np.random.default_rng(args.seed) + selected_k_counts = Counter() + boundary_counts = Counter() + fixed_k = selected["n_boundaries"] + for _ in range(args.bootstrap_samples): + indexes = rng.integers(0, len(prompt_values), size=len(prompt_values)) + bootstrap_aggregate = prompt_values[indexes].mean(axis=0) + bootstrap_models = [] + for n_boundaries in range(args.max_boundaries + 1): + boundaries, rss = exact_segmentation( + bootstrap_aggregate, n_boundaries, args.min_segment_size + ) + parameters = (n_boundaries + 1) * n_metrics + n_boundaries + bootstrap_models.append( + ( + bic_score(rss, n_observations, parameters), + n_boundaries, + boundaries, + ) + ) + _, selected_k, _ = min(bootstrap_models) + selected_k_counts[selected_k] += 1 + fixed_boundaries, _ = exact_segmentation( + bootstrap_aggregate, fixed_k, args.min_segment_size + ) + boundary_counts[tuple(fixed_boundaries)] += 1 + + return { + "scope": "objective graph metrics only; no semantic phase labels", + "prompt_names": prompt_names, + "metric_names": list(METRIC_NAMES), + "normalization": "within-prompt z-score for each metric", + "minimum_segment_size": args.min_segment_size, + "candidate_piecewise_models": model_rows, + "bic_selected_model": selected, + "cross_validation_selected_model": cv_selected, + "smooth_polynomial_controls": polynomial_models, + "overall_bic_selected_model": min(overall_bic_candidates, key=lambda row: row["bic"]), + "bootstrap": { + "samples": args.bootstrap_samples, + "bic_selected_boundary_count_frequency": { + str(key): value for key, value in sorted(selected_k_counts.items()) + }, + "fixed_bic_model_boundary_frequency_top_10": [ + {"boundaries": list(boundaries), "count": count} + for boundaries, count in boundary_counts.most_common(10) + ], + }, + } + + +def write_report(result: dict[str, Any], output_path: Path) -> None: + selected = result["bic_selected_model"] + cv_selected = result["cross_validation_selected_model"] + lines = [ + "# Objective Layer Segmentation Audit", + "", + "This analysis uses graph statistics only. It does not establish semantic phase roles.", + "", + "## Candidate piecewise models", + "", + "| Phases | Boundaries | BIC | Leave-one-prompt-out loss |", + "|---:|---|---:|---:|", + ] + for row in result["candidate_piecewise_models"]: + lines.append( + f"| {row['n_phases']} | {row['boundaries']} | {row['bic']:.3f} | " + f"{row['leave_one_prompt_out_loss']:.6f} |" + ) + lines.extend( + [ + "", + f"BIC selected **{selected['n_phases']} phase(s)** with boundaries " + f"{selected['boundaries']}.", + "", + f"Leave-one-prompt-out prediction selected **{cv_selected['n_phases']} phase(s)** " + f"with full-data boundaries {cv_selected['boundaries']}.", + "", + "## Smooth controls", + "", + "| Polynomial degree | BIC | Leave-one-prompt-out loss |", + "|---:|---:|---:|", + ] + ) + for row in result["smooth_polynomial_controls"]: + lines.append( + f"| {row['degree']} | {row['bic']:.3f} | " + f"{row['leave_one_prompt_out_loss']:.6f} |" + ) + lines.extend( + [ + "", + f"Across both model families, BIC selected **{result['overall_bic_selected_model']['name']}** " + f"({result['overall_bic_selected_model']['family']}).", + "", + "A publishable semantic phase claim additionally requires blinded feature annotations, " + "held-out prompts, stable boundaries, and independent causal validation.", + ] + ) + output_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def main() -> None: + args = parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + result = run_analysis(args) + json_path = args.output_dir / "numeric_phase_detection.json" + report_path = args.output_dir / "numeric_phase_detection.md" + json_path.write_text(json.dumps(result, indent=2), encoding="utf-8") + write_report(result, report_path) + print(json.dumps(result, indent=2)) + print(f"Report written to {report_path.resolve()}") + + +if __name__ == "__main__": + main() diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/plot_corrected_audit_figures.py b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/plot_corrected_audit_figures.py new file mode 100644 index 0000000..6515c67 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/plot_corrected_audit_figures.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +"""Export meeting-ready figures from the corrected analogy audit outputs.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + + +BASE = Path(__file__).parent / "audit_outputs" / "corrected_20260712" +FIGURES = BASE / "figures" +FIGURES.mkdir(parents=True, exist_ok=True) + +BLUE = "#2563EB" +GREEN = "#0F9D7A" +ORANGE = "#E68A2E" +GRAY = "#8A93A3" +GRID = "#D8DDE6" +TEXT = "#172033" +RED = "#C94B4B" + + +def finish(fig: plt.Figure, filename: str) -> None: + fig.savefig(FIGURES / filename, dpi=190, bbox_inches="tight", facecolor="white") + plt.close(fig) + + +def overlap_figure() -> None: + summary = json.loads((BASE / "summary.json").read_text(encoding="utf-8")) + original = summary["original_occurrence_threshold_counts_on_fresh_graphs"] + corrected = summary["corrected_distinct_graph_counts_on_fresh_graphs"] + labels = ["At least 3/5", "At least 4/5", "All 5/5"] + old_values = [original[str(value)] for value in (3, 4, 5)] + new_values = [corrected[str(value)] for value in (3, 4, 5)] + + fig, ax = plt.subplots(figsize=(10.2, 4.8)) + y = np.arange(len(labels)) + height = 0.31 + old_bars = ax.barh(y - height / 2, old_values, height, color=GRAY, label="Published method") + new_bars = ax.barh(y + height / 2, new_values, height, color=BLUE, label="Corrected distinct-graph count") + ax.bar_label(old_bars, padding=5, color=TEXT, fontsize=10) + ax.bar_label(new_bars, padding=5, color=TEXT, fontsize=10, fontweight="bold") + ax.set_yticks(y, labels) + ax.invert_yaxis() + ax.set_xlim(0, 555) + ax.set_xlabel("Number of recurring SAE features") + fig.suptitle( + "Correcting repeated token-position activations reduces graph overlap", + x=0.08, + y=0.98, + ha="left", + fontweight="bold", + color=TEXT, + ) + fig.text(0.08, 0.91, "The all-five core falls from 180 to 119 features (−33.9%).", color="#526079") + fig.subplots_adjust(top=0.82) + ax.xaxis.grid(True, color=GRID, linewidth=0.8) + ax.set_axisbelow(True) + ax.legend(frameon=False, loc="lower right") + for spine in ax.spines.values(): + spine.set_visible(False) + finish(fig, "01-corrected-feature-overlap.png") + + +def model_comparison_figure() -> None: + result = json.loads((BASE / "numeric_phase_detection.json").read_text(encoding="utf-8")) + piecewise = result["candidate_piecewise_models"] + smooth = next(row for row in result["smooth_polynomial_controls"] if row["degree"] == 3) + labels = [f"{row['n_phases']} phase" + ("s" if row["n_phases"] != 1 else "") for row in piecewise] + labels.append("Smooth cubic") + values = [row["leave_one_prompt_out_loss"] for row in piecewise] + values.append(smooth["leave_one_prompt_out_loss"]) + colors = [BLUE] * len(piecewise) + [GREEN] + + fig, ax = plt.subplots(figsize=(10.2, 5.0)) + bars = ax.bar(np.arange(len(labels)), values, color=colors, width=0.68) + ax.bar_label(bars, labels=[f"{value:.3f}" for value in values], padding=4, color=TEXT, fontsize=10) + ax.set_xticks(np.arange(len(labels)), labels) + ax.set_ylim(0, 1.1) + ax.set_ylabel("Leave-one-prompt-out prediction loss") + fig.suptitle( + "A smooth depth trend predicts held-out prompts better than discrete phases", + x=0.08, + y=0.98, + ha="left", + fontweight="bold", + color=TEXT, + ) + fig.text(0.08, 0.91, "Lower is better. BIC selects three phases, but the smooth model has the best held-out fit.", color="#526079") + fig.subplots_adjust(top=0.82) + ax.annotate( + "BIC choice", + xy=(2, values[2]), + xytext=(2, values[2] + 0.20), + ha="center", + color=TEXT, + arrowprops={"arrowstyle": "-|>", "color": GRAY, "lw": 1.1}, + ) + ax.yaxis.grid(True, color=GRID, linewidth=0.8) + ax.set_axisbelow(True) + for spine in ax.spines.values(): + spine.set_visible(False) + finish(fig, "02-phase-model-comparison.png") + + +def boundary_stability_figure() -> None: + result = json.loads((BASE / "numeric_phase_detection.json").read_text(encoding="utf-8")) + pairs = result["bootstrap"]["fixed_bic_model_boundary_frequency_top_10"] + boundary_one = {} + boundary_two = {} + for row in pairs: + first, second = row["boundaries"] + boundary_one[first] = boundary_one.get(first, 0) + row["count"] + boundary_two[second] = boundary_two.get(second, 0) + row["count"] + + fig, ax = plt.subplots(figsize=(10.2, 4.8)) + layers = np.arange(26) + width = 0.38 + first_counts = [boundary_one.get(int(layer), 0) for layer in layers] + second_counts = [boundary_two.get(int(layer), 0) for layer in layers] + ax.bar(layers - width / 2, first_counts, width, color=BLUE, label="First boundary") + ax.bar(layers + width / 2, second_counts, width, color=ORANGE, label="Second boundary") + for layer, count in boundary_one.items(): + ax.text(layer - width / 2, count + 18, str(count), ha="center", color=TEXT, fontsize=9) + for layer, count in boundary_two.items(): + ax.text(layer + width / 2, count + 18, str(count), ha="center", color=TEXT, fontsize=9) + ax.axvline(5, color=RED, linestyle="--", linewidth=1.3, label="Published boundaries") + ax.axvline(10, color=RED, linestyle="--", linewidth=1.3) + ax.text(5, 900, "Published L5", rotation=90, va="top", ha="right", color=RED, fontsize=9) + ax.text(10, 900, "Published L10", rotation=90, va="top", ha="right", color=RED, fontsize=9) + ax.set_xlim(-0.8, 25.8) + ax.set_ylim(0, 950) + ax.set_xticks(np.arange(0, 26, 2)) + ax.set_xlabel("Layer where the new segment begins") + ax.set_ylabel("Bootstrap selections out of 1,000") + fig.suptitle( + "Bootstrap boundaries cluster near L7–8 and L14–18—not L5 and L10", + x=0.08, + y=0.98, + ha="left", + fontweight="bold", + color=TEXT, + ) + fig.text(0.08, 0.91, "Fixed three-segment model across 1,000 prompt-bootstrap reruns.", color="#526079") + fig.subplots_adjust(top=0.82) + ax.yaxis.grid(True, color=GRID, linewidth=0.8) + ax.set_axisbelow(True) + ax.legend(frameon=False, ncol=3, loc="upper right") + for spine in ax.spines.values(): + spine.set_visible(False) + finish(fig, "03-bootstrap-boundary-stability.png") + + +def main() -> None: + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 10.5, + "axes.labelcolor": TEXT, + "xtick.color": "#526079", + "ytick.color": "#526079", + } + ) + overlap_figure() + model_comparison_figure() + boundary_stability_figure() + for path in sorted(FIGURES.glob("*.png")): + print(path.resolve()) + + +if __name__ == "__main__": + main() diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/rerun_corrected_analysis.py b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/rerun_corrected_analysis.py new file mode 100644 index 0000000..605bfa9 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/rerun_corrected_analysis.py @@ -0,0 +1,450 @@ +#!/usr/bin/env python3 +"""Regenerate and recompute the analogical graph overlap correctly. + +This script intentionally writes to a separate audit output directory. It +does not overwrite the original graphs or reported analysis artifacts. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import os +import statistics +import time +from collections import defaultdict +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import requests +import yaml + + +MODEL_ID = "gemma-2-2b" +SAE_ID = "gemmascope-transcoder-16k" +BASE_URL = "https://www.neuronpedia.org/api" + +PROMPTS = { + "analog_berlin": "Paris is to France as Berlin is to", + "analog_rome": "Paris is to France as Rome is to", + "analog_tokyo": "Paris is to France as Tokyo is to", + "analog_teacher": "Doctor is to hospital as teacher is to", + "analog_bird": "Fish is to water as bird is to", +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).parent / "audit_outputs" / "corrected_20260712", + ) + parser.add_argument( + "--config", + type=Path, + default=None, + help=( + "Optional YAML config containing api.api_key. " + "NEURONPEDIA_API_KEY is preferred." + ), + ) + parser.add_argument( + "--regenerate", + action="store_true", + help="Generate fresh graphs even when audit graph files already exist.", + ) + parser.add_argument( + "--fetch-labels", + action="store_true", + help="Fetch Neuronpedia labels for every corrected 5/5 feature.", + ) + return parser.parse_args() + + +def load_api_key(config_path: Path | None) -> str: + environment_key = os.environ.get("NEURONPEDIA_API_KEY", "").strip() + if environment_key: + return environment_key + + if config_path is not None and config_path.exists(): + config = yaml.safe_load(config_path.read_text(encoding="utf-8")) or {} + key = str(config.get("api", {}).get("api_key", "")).strip() + if key and "your_" not in key.lower(): + return key + + raise RuntimeError( + "No Neuronpedia API key found in NEURONPEDIA_API_KEY or the YAML config." + ) + + +def headers(api_key: str) -> dict[str, str]: + return {"Content-Type": "application/json", "X-Api-Key": api_key} + + +def generate_graph( + prompt_name: str, + prompt: str, + output_path: Path, + api_key: str, + regenerate: bool, + run_suffix: str, +) -> dict[str, Any]: + if output_path.exists() and not regenerate: + data = json.loads(output_path.read_text(encoding="utf-8")) + if data.get("nodes") and data.get("links"): + return data + + audit_slug = f"audit_corrected_{run_suffix}_{prompt_name}" + response = requests.post( + f"{BASE_URL}/graph/generate", + json={ + "modelId": MODEL_ID, + "prompt": prompt, + "slug": audit_slug, + "maxFeatureNodes": 3000, + "desiredLogitProb": 0.95, + "nodeThreshold": 0.80, + "edgeThreshold": 0.85, + }, + headers=headers(api_key), + timeout=180, + ) + if response.status_code != 200: + raise RuntimeError( + f"Graph generation failed for {prompt_name}: " + f"HTTP {response.status_code}: {response.text[:300]}" + ) + + metadata = response.json() + s3_url = metadata.get("s3url") + if not s3_url: + raise RuntimeError(f"Graph generation for {prompt_name} returned no s3url") + + time.sleep(2) + graph_response = requests.get(s3_url, timeout=180) + graph_response.raise_for_status() + graph = graph_response.json() + graph.setdefault("metadata", {})["audit_prompt_name"] = prompt_name + graph["metadata"]["audit_prompt"] = prompt + graph["metadata"]["audit_generation_response"] = { + key: metadata.get(key) + for key in ("slug", "url", "numNodes", "numLinks") + if key in metadata + } + output_path.write_text(json.dumps(graph, indent=2), encoding="utf-8") + return graph + + +def feature_identity(node: dict[str, Any]) -> tuple[int, int] | None: + if node.get("feature_type") not in ("cross layer transcoder", "transcoder"): + return None + try: + layer = int(node["layer"]) + node_parts = str(node["node_id"]).split("_") + sae_index = int(node_parts[1]) + except (KeyError, TypeError, ValueError, IndexError): + return None + return layer, sae_index + + +def corrected_overlap( + graphs: dict[str, dict[str, Any]], +) -> tuple[list[dict[str, Any]], dict[str, list[dict[str, Any]]]]: + registry: dict[tuple[int, int], dict[str, list[dict[str, Any]]]] = defaultdict( + lambda: defaultdict(list) + ) + + for graph_name, graph in graphs.items(): + for node in graph.get("nodes", []): + identity = feature_identity(node) + if identity is not None: + registry[identity][graph_name].append(node) + + features = [] + for (layer, sae_index), occurrences_by_graph in registry.items(): + graph_means = [] + node_ids = [] + global_feature_ids = set() + for graph_name, occurrences in occurrences_by_graph.items(): + node_ids.extend(str(node.get("node_id", "")) for node in occurrences) + global_feature_ids.update( + node.get("feature") for node in occurrences if node.get("feature") is not None + ) + graph_means.append( + { + "graph": graph_name, + "influence": statistics.fmean( + float(node.get("influence") or 0.0) for node in occurrences + ), + "activation": statistics.fmean( + float(node.get("activation") or 0.0) for node in occurrences + ), + } + ) + + features.append( + { + "layer": layer, + "sae_index": sae_index, + "global_feature_ids": sorted(global_feature_ids), + "graph_count": len(occurrences_by_graph), + "occurrence_count": sum( + len(occurrences) for occurrences in occurrences_by_graph.values() + ), + "out_of": len(graphs), + "graph_slugs": sorted(occurrences_by_graph), + "avg_influence": round( + statistics.fmean(item["influence"] for item in graph_means), 6 + ), + "avg_activation": round( + statistics.fmean(item["activation"] for item in graph_means), 6 + ), + "node_ids": node_ids, + "per_graph_means": graph_means, + } + ) + + features.sort( + key=lambda item: ( + item["graph_count"], + item["avg_influence"], + item["occurrence_count"], + ), + reverse=True, + ) + thresholds = { + str(threshold): [ + feature for feature in features if feature["graph_count"] >= threshold + ] + for threshold in (3, 4, 5) + } + return features, thresholds + + +def flawed_overlap_counts(graphs: dict[str, dict[str, Any]]) -> dict[str, int]: + """Reproduce the original occurrence-count threshold for comparison.""" + counts: dict[tuple[int, int], int] = defaultdict(int) + for graph in graphs.values(): + for node in graph.get("nodes", []): + identity = feature_identity(node) + if identity is not None: + counts[identity] += 1 + return { + str(threshold): sum(count >= threshold for count in counts.values()) + for threshold in (3, 4, 5) + } + + +def fetch_label(layer: int, sae_index: int, api_key: str) -> tuple[str, str]: + response = requests.get( + f"{BASE_URL}/feature/{MODEL_ID}/{layer}-{SAE_ID}/{sae_index}", + headers=headers(api_key), + timeout=30, + ) + if response.status_code != 200: + return "", f"HTTP {response.status_code}" + data = response.json() + explanations = data.get("explanations", []) + description = explanations[0].get("description", "") if explanations else "" + return description, "" + + +def write_annotation_template( + features: list[dict[str, Any]], + path: Path, + api_key: str, + should_fetch_labels: bool, +) -> None: + fieldnames = [ + "layer", + "sae_index", + "graph_count", + "occurrence_count", + "avg_influence", + "avg_activation", + "neuronpedia_label", + "label_fetch_error", + "semantic_category", + "annotator", + ] + existing_rows = {} + if path.exists(): + with path.open(newline="", encoding="utf-8") as existing_file: + existing_rows = { + (int(row["layer"]), int(row["sae_index"])): row + for row in csv.DictReader(existing_file) + } + + with path.open("w", newline="", encoding="utf-8") as output: + writer = csv.DictWriter(output, fieldnames=fieldnames) + writer.writeheader() + for index, feature in enumerate(features): + existing = existing_rows.get((feature["layer"], feature["sae_index"]), {}) + label = existing.get("neuronpedia_label", "") + error = existing.get("label_fetch_error", "") + if should_fetch_labels: + label, error = fetch_label( + feature["layer"], feature["sae_index"], api_key + ) + if index + 1 < len(features): + time.sleep(0.15) + writer.writerow( + { + "layer": feature["layer"], + "sae_index": feature["sae_index"], + "graph_count": feature["graph_count"], + "occurrence_count": feature["occurrence_count"], + "avg_influence": feature["avg_influence"], + "avg_activation": feature["avg_activation"], + "neuronpedia_label": label, + "label_fetch_error": error, + "semantic_category": existing.get("semantic_category", ""), + "annotator": existing.get("annotator", ""), + } + ) + + +def write_summary_report( + summary: dict[str, Any], + features: list[dict[str, Any]], + path: Path, +) -> None: + flawed = summary["original_occurrence_threshold_counts_on_fresh_graphs"] + corrected = summary["corrected_distinct_graph_counts_on_fresh_graphs"] + false_core = [ + feature + for feature in features + if feature["occurrence_count"] >= 5 and feature["graph_count"] < 5 + ] + false_core_by_support = { + support: sum(feature["graph_count"] == support for feature in false_core) + for support in range(1, 5) + } + core_reduction_percent = 100 * (flawed["5"] - corrected["5"]) / flawed["5"] + lines = [ + "# Corrected Analogical Feature-Overlap Rerun", + "", + "All five graphs were regenerated with the published model, prompt, and pruning settings. " + "Their node and edge counts exactly match the published graph summaries.", + "", + "## Corrected counts", + "", + "| Threshold | Original occurrence-based result | Correct distinct-graph result | False inclusions |", + "|---|---:|---:|---:|", + ] + for threshold, label in (("3", "At least 3/5"), ("4", "At least 4/5"), ("5", "All 5/5")): + lines.append( + f"| {label} | {flawed[threshold]} | {corrected[threshold]} | " + f"{flawed[threshold] - corrected[threshold]} |" + ) + lines.extend( + [ + "", + f"The claimed 180-feature all-five core falls to **{corrected['5']} features**, " + f"a reduction of **{core_reduction_percent:.1f}%**.", + "", + "Among the 61 false core inclusions, " + f"{false_core_by_support[2]} appeared in only two graphs, " + f"{false_core_by_support[3]} in three graphs, and " + f"{false_core_by_support[4]} in four graphs.", + "", + "## What survives the correction", + "", + "The five named comparison-related features used in the manuscript—L5/5793, " + "L5/2141, L8/13766, L9/13344, and L13/10969—are genuinely present in all " + "five graphs. Their cross-prompt membership survives the correction, although that " + "fact alone does not establish the proposed phase boundaries or causal hierarchy.", + "", + "## Next phase-analysis requirement", + "", + "Neuronpedia labels were retrieved for all corrected 5/5 features in " + "`core_5_of_5_annotation_template.csv`. The semantic category and annotator columns " + "must be completed under a blinded, preregistered annotation protocol before those " + "labels can be used for confirmatory change-point analysis.", + ] + ) + path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def main() -> None: + args = parse_args() + graph_dir = args.output_dir / "graphs" + graph_dir.mkdir(parents=True, exist_ok=True) + api_key = ( + load_api_key(args.config) + if args.regenerate or args.fetch_labels + else "" + ) + run_suffix = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + + graphs = {} + for prompt_name, prompt in PROMPTS.items(): + print(f"Preparing {prompt_name}...", flush=True) + graphs[prompt_name] = generate_graph( + prompt_name, + prompt, + graph_dir / f"{prompt_name}.json", + api_key, + args.regenerate, + run_suffix, + ) + + features, thresholds = corrected_overlap(graphs) + flawed_counts = flawed_overlap_counts(graphs) + corrected_counts = { + threshold: len(items) for threshold, items in thresholds.items() + } + + (args.output_dir / "corrected_overlap.json").write_text( + json.dumps( + { + "method": "distinct graph membership; equal-weight per-graph means", + "graph_names": list(PROMPTS), + "features": features, + "thresholds": thresholds, + }, + indent=2, + ), + encoding="utf-8", + ) + summary = { + "graph_summaries": { + name: { + "nodes": len(graph.get("nodes", [])), + "links": len(graph.get("links", [])), + "prompt": PROMPTS[name], + } + for name, graph in graphs.items() + }, + "original_occurrence_threshold_counts_on_fresh_graphs": flawed_counts, + "corrected_distinct_graph_counts_on_fresh_graphs": corrected_counts, + "inflation": { + threshold: flawed_counts[threshold] - corrected_counts[threshold] + for threshold in corrected_counts + }, + } + (args.output_dir / "summary.json").write_text( + json.dumps(summary, indent=2), encoding="utf-8" + ) + + write_annotation_template( + thresholds["5"], + args.output_dir / "core_5_of_5_annotation_template.csv", + api_key, + args.fetch_labels, + ) + write_summary_report( + summary, + features, + args.output_dir / "corrected_overlap_report.md", + ) + + print(json.dumps(summary, indent=2)) + print(f"Outputs written to {args.output_dir.resolve()}") + + +if __name__ == "__main__": + main() diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/tests/test_compare_graphs.py b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/tests/test_compare_graphs.py new file mode 100644 index 0000000..ee65a59 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/tests/test_compare_graphs.py @@ -0,0 +1,77 @@ +import importlib.util +from pathlib import Path + +MODULE_PATH = Path(__file__).parents[1] / "rerun_corrected_analysis.py" +SPEC = importlib.util.spec_from_file_location("corrected_overlap_analysis", MODULE_PATH) +TOOLS = importlib.util.module_from_spec(SPEC) +SPEC.loader.exec_module(TOOLS) + + +def make_graph(slug, occurrences): + return { + "metadata": {"slug": slug}, + "nodes": [ + { + "node_id": f"5_5793_{context_index}", + "layer": 5, + "feature": 5793, + "feature_type": "cross layer transcoder", + "influence": influence, + "activation": activation, + } + for context_index, influence, activation in occurrences + ], + "links": [], + } + + +def test_repeated_contexts_in_one_graph_do_not_count_as_multiple_graphs(): + berlin = make_graph( + "analog_berlin", + [(index, 0.5, 10.0) for index in range(5)], + ) + + features, thresholds = TOOLS.corrected_overlap({"berlin": berlin}) + + assert features[0]["graph_count"] == 1 + assert thresholds["5"] == [] + + +def test_threshold_counts_distinct_graphs_and_preserves_occurrence_count(): + berlin = make_graph("analog_berlin", [(1, 0.2, 4.0), (4, 0.6, 8.0)]) + rome = make_graph("analog_rome", [(1, 0.8, 12.0)]) + tokyo = make_graph("analog_tokyo", [(1, 0.4, 10.0), (6, 0.4, 14.0)]) + + _, thresholds = TOOLS.corrected_overlap( + {"berlin": berlin, "rome": rome, "tokyo": tokyo} + ) + result = thresholds["3"] + + assert len(result) == 1 + feature = result[0] + assert feature["graph_count"] == 3 + assert feature["occurrence_count"] == 5 + assert feature["graph_slugs"] == [ + "berlin", + "rome", + "tokyo", + ] + # Per-graph means are 0.4, 0.8, and 0.4, so prompts receive equal weight. + assert feature["avg_influence"] == 0.533333 + assert feature["avg_activation"] == 10.0 + + +def test_mapping_keys_keep_independent_graphs_distinct(): + graph_a = make_graph(None, [(1, 0.2, 4.0)]) + graph_b = make_graph(None, [(1, 0.8, 12.0)]) + + features, _ = TOOLS.corrected_overlap({"graph_a": graph_a, "graph_b": graph_b}) + result = [ + feature + for feature in features + if feature["graph_count"] >= 2 + ] + + assert len(result) == 1 + assert result[0]["graph_count"] == 2 + assert result[0]["graph_slugs"] == ["graph_a", "graph_b"] diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/tests/test_phase_detection.py b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/tests/test_phase_detection.py new file mode 100644 index 0000000..964d72e --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/analysis/tests/test_phase_detection.py @@ -0,0 +1,42 @@ +import importlib.util +from pathlib import Path + +import numpy as np + + +MODULE_PATH = Path(__file__).parents[1] / "detect_numeric_phase_boundaries.py" +SPEC = importlib.util.spec_from_file_location("phase_detection", MODULE_PATH) +PHASES = importlib.util.module_from_spec(SPEC) +SPEC.loader.exec_module(PHASES) + + +def test_exact_segmentation_recovers_known_three_phase_signal(): + signal = np.vstack( + [ + np.zeros((5, 2)), + np.full((6, 2), 5.0), + np.full((7, 2), -3.0), + ] + ) + + boundaries, cost = PHASES.exact_segmentation( + signal, + n_boundaries=2, + min_segment_size=3, + ) + + assert boundaries == [5, 11] + assert abs(cost) < 1e-10 + + +def test_zero_boundary_model_returns_single_segment(): + signal = np.arange(20, dtype=float).reshape(10, 2) + + boundaries, cost = PHASES.exact_segmentation( + signal, + n_boundaries=0, + min_segment_size=3, + ) + + assert boundaries == [] + assert cost > 0 diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/presentation/analogy_phase_audit_meeting.pptx b/graph-analysis/J-Lawrence10/analogy-phase-audit/presentation/analogy_phase_audit_meeting.pptx new file mode 100644 index 0000000..a2f3a53 Binary files /dev/null and b/graph-analysis/J-Lawrence10/analogy-phase-audit/presentation/analogy_phase_audit_meeting.pptx differ diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/presentation/meeting_script.md b/graph-analysis/J-Lawrence10/analogy-phase-audit/presentation/meeting_script.md new file mode 100644 index 0000000..2e863dd --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/presentation/meeting_script.md @@ -0,0 +1,74 @@ +# Meeting script + +## Slide 1 — Reassessing analogy circuits + +We reproduced the original five analogy attribution graphs and asked two +questions: which features genuinely recur across prompts, and whether their +layer-wise organization supports a three-phase description. + +## Slide 2 — Same graphs, corrected experimental unit + +The regenerated graphs use the published model, prompts, and pruning +parameters, and their node and edge counts match the published summaries. +The correction happens downstream. We count how many distinct graphs contain +each SAE feature. Repeated activations at several token positions within one +graph remain multiple contexts, but they contribute only one vote toward +cross-prompt recurrence. + +## Slide 3 — The shared core is smaller but remains substantial + +The all-five core falls from 180 to 119 features, a 33.9 percent reduction. +The at-least-four-of-five result falls from 277 to 210. Importantly, all five +headline manuscript features genuinely survive in all five graphs. This +preserves the interesting shared signal while giving it a more accurate size. + +## Slide 4 — BIC selects three phases + +BIC is the Bayesian Information Criterion. It rewards a model for fitting the +data and penalizes it for adding unnecessary complexity; lower is better. +Among the one- through five-phase models, three phases have the lowest BIC, +-74.4. Four and five phases improve some fit measures, but not enough to +justify their extra boundaries and parameters. + +## Slide 5 — The three numeric regimes + +The data-selected phases begin at layers 7 and 15. Phase 1, layers 0 through 6, +has many broadly recurring features and lower activation. Phase 2, layers 7 +through 14, is sparser and has the highest average influence. Phase 3, layers +15 through 25, is sparse and has the highest median activation. We can use +broad representation, selective transformation, and output consolidation as +working descriptions, but the semantic names remain provisional. + +## Slide 6 — Bootstrap support and boundary uncertainty + +Across 1,000 prompt-bootstrap reruns, the first boundary lies at L7 in 782 +runs and L8 in 218. The second boundary is most often L15, with uncertainty +from L14 to L18. The result supports a three-part summary, while also showing +that the later transition is less precisely located. + +## Slide 7 — What the evidence supports now + +We reproduced the original graphs, corrected the recurrence calculation, and +retained a substantial shared circuit. BIC selects a three-phase organization: +an early broad feature regime, a middle high-influence regime, and a late +high-activation regime. The next step is to validate the boundaries on more +prompts, blind the semantic annotation, and test each phase with matched causal +controls. + +## Short answers for questions + +**Does BIC prove three phases exist?** +No. It says three phases are the best balance of fit and complexity among the +tested segmented models. More prompts and causal validation are still needed. + +**Did the correction eliminate the core features?** +No. It reduced the size of the all-five core from 180 to 119, but all five +headline features remain. + +**Why are the boundaries different from the original L5 and L10 split?** +The new boundaries were selected from objective layer-wise graph metrics rather +than fixed in advance. + +**What do the phases mean?** +The statistics support different numeric regimes. The functional names are +working interpretations until blinded annotation and causal tests confirm them. diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/requirements-lock.txt b/graph-analysis/J-Lawrence10/analogy-phase-audit/requirements-lock.txt new file mode 100644 index 0000000..69a586e --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/requirements-lock.txt @@ -0,0 +1,5 @@ +matplotlib==3.10.8 +numpy==2.2.5 +pytest==9.0.2 +PyYAML==6.0.3 +requests==2.32.5 diff --git a/graph-analysis/J-Lawrence10/analogy-phase-audit/requirements.txt b/graph-analysis/J-Lawrence10/analogy-phase-audit/requirements.txt new file mode 100644 index 0000000..88874c6 --- /dev/null +++ b/graph-analysis/J-Lawrence10/analogy-phase-audit/requirements.txt @@ -0,0 +1,5 @@ +matplotlib>=3.8 +numpy>=1.26 +pytest>=8.0 +PyYAML>=6.0 +requests>=2.31