diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..844156418aae673f55457e4cf4068df65e67e7bd --- /dev/null +++ b/.gitignore @@ -0,0 +1,9 @@ +/src/*.egg-info/ +*__pycache__* +/.vscode/ + +/data/ +/.old/ +/outputs*/ +/logs/ +hf_token.txt \ No newline at end of file diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000000000000000000000000000000000000..0623110a1bbcb3aa477587541e120cef3ddf457c --- /dev/null +++ b/Readme.md @@ -0,0 +1,42 @@ +# Large Language Models adaptation using post-hoc calibration (PHC) and supervised fine-tuning (SFT) + +This repo contains the code to run adaptation of generative language models. The `scripts` directory contains the scripts to be run and everything (datasets, number of adaptation samples, base model) can be configured using the `scripts/env.sh` file. + +## Adaptation methods + +Supported adaptation methods are distributed across different scripts files: +- `scripts/cal_vs_samples.sh`: Compute the logits for the current model and then run Post-hoc calibration (PHC) with affine calibration for four different variants: + - Vector Scaling (**PHC-VS**): The matrix of the affine transformation is assumed to be diagonal. + - Direction Preserving (**PHC-DP**): The matrix of the affine transformation is replaced by a scalar, $\alpha$. + - Bias Only (**PHC-BO**): Same as PHC-DP, but with the weight $\alpha$ fixed at 1. + - Temperature Scaling (**PHC-TS**): Same as PHC-DP but with the bias term fixed at 0. +- `scripts/lora_vs_samples.sh`: Run SFT using LoRA on all the datasets. There are three variants implemented depending on how we use the adaptation samples: + - **SFT-w-val**: we leave a portion of the adaptation set for validation, and stop training based on the CE loss on this set. + - **SFT-retrain**: we perform SFT with validation, as above, and keep track of the optimal number of steps. Then, we rerun SFT on the full training set, stopping after that same number of steps. + - **SFT-wo-val**: we use all the available samples for training until convergence based on the training loss. +SFT and computing the model posteriors is implemented using the [litgpt](https://github.com/Lightning-AI/litgpt) library. + +## Datasets and Models + +Currently, there are 5 supported datasets: +- **SST-2**: The Stanford Sentiment Treebank, which includes movie reviews annotated as either positive or negative. +- **AGNews**: a collection of news articles grouped into four categories. +- **DBPedia**: a dataset derived from Wikipedia, where each article is categorized into one of 14 topics. +- **20NewsGroups**: posts from online newsgroups categorized into 20 different topics. +- **Banking77**: customer service queries related to online banking, classified into 77 intent categories. + +All the models of the `litgpt>=0.5.3` library are supported, but for our experiments we used Llama3.2-1B and Qwen2.5-7B. Be sure to configure your token in order to use model that require permission. + +## Installation + +To use this you can clone it and pip-install it direclty but we recommend creating a separate conda enviroment: +```bash +conda create -n llmcal python=3.11 +conda activate llmcal +pip install -r requirements.txt +pip install -e . +``` + +## Cite + +Coming soon in OpenReview! diff --git a/TODO b/TODO new file mode 100644 index 0000000000000000000000000000000000000000..23cad2415e012858c63491ad61f524e6b9a110c5 --- /dev/null +++ b/TODO @@ -0,0 +1,130 @@ +- Terminar de correr todo hasta ahora +- Para DBPedia, comparar DPCal con LoRA en función de la cantidad de muestras entrenando con un conjunto limitado de datos. Repetir para cuando el modelo fue previamente fine-tuneado con datos out-of-domain. Graficar Zero-shot. Ver plot dbpedia_nce. Debería verse que en algún momento LoRA supera a DPCal pero para pocas muestras, debería funcionar bien DPCal. + +DPCal helps on non-finetuned and fine-tuned-on-matched-data models: +Zero-shot/Few-shot/LoraMatchedAns/LoraMatchedFS/BERT + DPCal/HistBinning/VectorScaling/etc + +You can choose DPCal if you have limited budget of data: +Zero-shot/LoramatchedAns/LoraMatchedFS/DPCal vs number of samples + +If you have other data, calibrate after train: +LoraMismatchedAns/LoraMismatchedFS/Instruct/InstructFewShot + DPCal (use zero-shot/zero-shot+dpcal as baseline) + + +sin es con 100 deberia dar mejor que con 70 +el mejor deberia ser sin es calibrado en el 30 + + + + + + + + + + + + + + + +instruct base +instruc + dpcal en todos +instruct + 70-30 lora +instruct + 70-30 lora + dpcal +instruc + 100 lora (no es) +instruct + 100 lora (con steps de lora70-30) + + + +Data split: +- Set 1: first p% of the data +- Set 2: last 100-p% of the data +- Set 3: 100% of the data + +Methods: +1. No Adaptation (baseline): Do not use training data +2. Calibration: Use Set 3 for calibration + +3. LoRA p%: Use Set 1 for training LoRA and Set 2 for validation +4. LoRA 100%: Use Set 3 for training LoRA with number of steps used in method 3. + +5. LoRA p% without ES: Use Set 1 for training LoRA without Early Stopping +6. LoRA 100% without ES: Use Set 3 for training LoRA without Early Stopping + +7. LoRA p% + Cal: Method 3 plus calibration on Set 2. +8. LoRA 100% + Cal: Method 4 plus calibration with parameters of method 7. + +9. LoRA p% without ES + Cal: Method 5 plus calibration on Set 2. +10. LoRA 100% without ES + Cal: Method 6 plus calibration with parameters of method 9. + +* = No ES +x = ES + +Red = LoRA p +Green = LoRA 100 + +Solid (-) = No calibration +Dashed (--) = Temp Scaling +Dotted (:) = DP Calibration + + + +icen laboratorio +cerca del RR, tambien hay bus a la universidad +estacion de orsay +qualcum + + + + + +Cosas para el paper: + +- Hay que argumentar que un modelo robusto es un modelo que puede clasificar bien para cualquier set de respuestas que se le presente +- Capaz podemos venderlo como un framework alternativo a prompt engineering en donde elgís las posibles respuestas y se las das como answers en lugar de en el prompt. + +- Métodos PEFTs son efectivos: https://aclanthology.org/2021.acl-long.172.pdf +- Ejemplo de catastrofic forgeting en medical domain: https://arxiv.org/abs/2203.13381 +- Otro ejemplo de catastrofic forgeting: https://arxiv.org/abs/2308.08747 +- Mixture of Prompts: https://arxiv.org/pdf/2310.02842 +- Filtrado de muestras para hacer in domain finetuning: https://arxiv.org/abs/2410.14745 +- Blog que habla sobre catastrofic forgetting: https://blog.arcee.ai/the-hidden-obstacles-of-domain-adaptation-in-llms/ +- Synthetic generation: https://arxiv.org/abs/2303.00807 +- Training budget guide: https://aclanthology.org/2024.eamt-1.51/ +- Prompt engineering: https://ieeexplore.ieee.org/abstract/document/9908590 +- Paper de continual pretraining: https://arxiv.org/abs/2004.10964 + + + + +No tiene que ver con el paper pero sí con el proyecto de pablo: https://arxiv.org/abs/2310.01444 + +Mostrar NER y NCE con todos los métodos de adaptación +otro plot con LoRA p=1.0 no ES con calibracion +en dbpedia, comparar distribuciones en pocas mustras del sin calibrar y del calibrado. + + + +TODO: +- Hacer y agregar plots de la primera sección de resultados +- Hacer y agregar plots de la segunda sección de resultados +- Análisis de las posteriors de las clases +- Análisis de las posteriors por fuera de la distribución de tokens de interés +- Escribir la primera parte de la sección de entrenamiento (descripción de LoRA y fine-tuning) +- Agregar related works +- Escribir el experimental setup +- agregar las referencias al .bib +- Terminar de correr Qwen y agregar al apéndice + + + + + diff --git a/configs/methods2.yaml b/configs/methods2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..84c392cada2d347798ef2c6bb28477f55885c729 --- /dev/null +++ b/configs/methods2.yaml @@ -0,0 +1,75 @@ +markersize: &markersize 5 +linewidth: &linewidth 4 + +lora_1.0_no_es: + label: "SFT w/o Early Stopping" + color: "tab:blue" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_0.7_no_es: + label: "SFT w/o Early-stopping (70%)" + color: "tab:purple" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_0.7_no_es_plus_dpcal: + label: "SFT SFT w/o Early Stopping (70%) + DP Cal" + color: "tab:brown" + linestyle: "--" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_0.7_no_es_plus_tempscaling: + label: "SFT w/o Early Stopping (70%) + Temp Scaling" + color: "tab:cyan" + linestyle: "--" + marker: null + linewidth: *linewidth + markersize: *markersize + + +lora_1.0: + label: "SFT w/ Early Stopping" + color: "tab:orange" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_dpcal: + label: "SFT w/o Early Stopping\n+ Direction Preserving" + color: "tab:green" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_dpcal_naive: + label: "SFT w/o Early Stopping\n+ Direction Preserving (Naive)" + color: "tab:green" + linestyle: "--" + marker: "x" + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_tempscaling: + label: "SFT w/o Early Stopping\n+ Temperature Scaling" + color: "tab:green" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_tempscaling_naive: + label: "SFT w/o Early Stopping\n+ Temperature Scaling (Naive)" + color: "tab:orange" + linestyle: "--" + marker: "o" + linewidth: *linewidth + markersize: *markersize diff --git a/configs/methods_final.yaml b/configs/methods_final.yaml new file mode 100644 index 0000000000000000000000000000000000000000..896857fd2c3c36efa1aa540f1e31f71acda503ea --- /dev/null +++ b/configs/methods_final.yaml @@ -0,0 +1,136 @@ +markersize: &markersize 6 +linewidth: &linewidth 4 + + +no_adaptation: + label: "NoA" # No Adaptation + color: "black" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +temp_scaling: + label: "PHC-TS" # Temperature Scaling + # color: "tab:green" + # color: "#c6dbef" + color: "#17becf" + # linestyle: "-." + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +dp_calibration: + label: "PHC-DP" # Direction Preserving + # color: "tab:red" + # color: "#9ecae1" + color: "#2e86c1" + # linestyle: "-." + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +bias_shift: + label: "PHC-BO" # Bias Shift + # color: "tab:cyan" + # color: "#6baed6" + color: "#5dade2" + # linestyle: "-." + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +vector_scaling: + label: "PHC-VS" # Vector Scaling + # color: "tab:purple" + # color: "#3182bd" + color: "#1f77b4" + # linestyle: "-." + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_0.7: + label: "SFT-w-val" # "SFT (70% Train + 30% Val)\nw/ Early Stopping" + # label: "SFT (70% Train + 30% Val)\nw/ Early Stopping" + # color: "tab:pink" + # color: "#c7e9c0" + color: "#2ca02c" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0: + label: "SFT-retrain" # "SFT (100% Train)\nw/ Early Stopping" + # label: "SFT (100% Train)\nw/ Early Stopping" + # color: "tab:blue" + # color: "#74c476" + color: "#27ae60" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es: + label: "SFT-wo-val" # "SFT (100% Train)\nw/o Early Stopping" + # label: "SFT (100% Train)\nw/o Early Stopping" + # color: "tab:orange" + # color: "#238b45" + color: "#82e0aa" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_tempscaling: + # label: "SFT retrain + PHC TS" + label: "SFT-wo-val + PHC-TS" + # color: "tab:green" + # color: "#fcbba1" + color: "#e74c3c" + # linestyle: "--" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_dpcal: + # label: "SFT retrain + PHC DP" + label: "SFT-wo-val + PHC-DP" + # color: "tab:red" + # color: "#fc9272" + color: "#c0392b" + # linestyle: "--" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_biasshift: + # label: "SFT retrain + PHC BO" + label: "SFT-wo-val + PHC-BO" + # color: "tab:cyan" + # color: "#fb6a4a" + color: "#cd6155" + # linestyle: "--" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize + +lora_1.0_no_es_plus_vectorscaling: + # label: "SFT retrain + PHC VS" + label: "SFT-wo-val + PHC-VS" + # color: "tab:purple" + # color: "#de2d26" + color: "#d62728" + # linestyle: "--" + linestyle: "-" + marker: null + linewidth: *linewidth + markersize: *markersize \ No newline at end of file diff --git a/lists/agnews/size=128/seed=2/0.0-0.3.txt b/lists/agnews/size=128/seed=2/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3c22aab0ac4c12ea1cb0247b6edbad78b240e40 --- /dev/null +++ b/lists/agnews/size=128/seed=2/0.0-0.3.txt @@ -0,0 +1,77 @@ +126292 +57811 +93714 +116305 +100814 +75979 +23433 +92178 +78581 +104328 +36096 +119546 +74241 +95253 +97186 +6611 +82012 +69708 +124774 +66031 +93429 +29123 +12413 +66183 +56550 +49448 +65056 +38579 +105572 +60527 +66389 +36393 +31445 +97209 +125715 +62561 +124256 +35056 +104698 +21432 +534 +98043 +5229 +95450 +25662 +78598 +89432 +16274 +77362 +19218 +86550 +69230 +111104 +11620 +45324 +13396 +70264 +20065 +84158 +112605 +113394 +78188 +18768 +57344 +61758 +73494 +76050 +105346 +98939 +88258 +60554 +47534 +99759 +88007 +123627 +50875 +58858 diff --git a/lists/agnews/size=128/seed=2/0.0-0.7.txt b/lists/agnews/size=128/seed=2/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..b212fa9a7a926b1d0a86d349a0af7a5e6bc86d86 --- /dev/null +++ b/lists/agnews/size=128/seed=2/0.0-0.7.txt @@ -0,0 +1,180 @@ +126292 +57811 +93714 +116305 +100814 +75979 +23433 +92178 +78581 +104328 +36096 +119546 +74241 +95253 +97186 +6611 +82012 +69708 +124774 +66031 +93429 +29123 +12413 +66183 +56550 +49448 +65056 +38579 +105572 +60527 +66389 +36393 +31445 +97209 +125715 +62561 +124256 +35056 +104698 +21432 +534 +98043 +5229 +95450 +25662 +78598 +89432 +16274 +77362 +19218 +86550 +69230 +111104 +11620 +45324 +13396 +70264 +20065 +84158 +112605 +113394 +78188 +18768 +57344 +61758 +73494 +76050 +105346 +98939 +88258 +60554 +47534 +99759 +88007 +123627 +50875 +58858 +74144 +73217 +65271 +82690 +69870 +39707 +111794 +124929 +15336 +89833 +36223 +45221 +87099 +53697 +84358 +98039 +105659 +89422 +53195 +20743 +62796 +30605 +19416 +31587 +21814 +80637 +34353 +63118 +24185 +103001 +49280 +18280 +48568 +87904 +67757 +27635 +107232 +34434 +14066 +51384 +36018 +99321 +104479 +104449 +120525 +62981 +8399 +42818 +46652 +954 +54819 +44913 +123397 +101045 +47919 +33277 +537 +25894 +33777 +51002 +106747 +108844 +17413 +94089 +67394 +66802 +33625 +54516 +72709 +68450 +5411 +79521 +52134 +57282 +11372 +97818 +27204 +45550 +112086 +89554 +67244 +81174 +67790 +2896 +109946 +99103 +69092 +15352 +113987 +71497 +116107 +32517 +2729 +67085 +114361 +52191 +86559 +86248 +86914 +68478 +109420 +115754 +34632 diff --git a/lists/agnews/size=128/seed=2/0.0-1.0.txt b/lists/agnews/size=128/seed=2/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..94d2fd6275b6faafd842fc7ed6a9065023409cd7 --- /dev/null +++ b/lists/agnews/size=128/seed=2/0.0-1.0.txt @@ -0,0 +1,256 @@ +126292 +57811 +93714 +116305 +100814 +75979 +23433 +92178 +78581 +104328 +36096 +119546 +74241 +95253 +97186 +6611 +82012 +69708 +124774 +66031 +93429 +29123 +12413 +66183 +56550 +49448 +65056 +38579 +105572 +60527 +66389 +36393 +31445 +97209 +125715 +62561 +124256 +35056 +104698 +21432 +534 +98043 +5229 +95450 +25662 +78598 +89432 +16274 +77362 +19218 +86550 +69230 +111104 +11620 +45324 +13396 +70264 +20065 +84158 +112605 +113394 +78188 +18768 +57344 +61758 +73494 +76050 +105346 +98939 +88258 +60554 +47534 +99759 +88007 +123627 +50875 +58858 +74144 +73217 +65271 +82690 +69870 +39707 +111794 +124929 +15336 +89833 +36223 +45221 +87099 +53697 +84358 +98039 +105659 +89422 +53195 +20743 +62796 +30605 +19416 +31587 +21814 +80637 +34353 +63118 +24185 +103001 +49280 +18280 +48568 +87904 +67757 +27635 +107232 +34434 +14066 +51384 +36018 +99321 +104479 +104449 +120525 +62981 +8399 +42818 +46652 +954 +54819 +44913 +123397 +101045 +47919 +33277 +537 +25894 +33777 +51002 +106747 +108844 +17413 +94089 +67394 +66802 +33625 +54516 +72709 +68450 +5411 +79521 +52134 +57282 +11372 +97818 +27204 +45550 +112086 +89554 +67244 +81174 +67790 +2896 +109946 +99103 +69092 +15352 +113987 +71497 +116107 +32517 +2729 +67085 +114361 +52191 +86559 +86248 +86914 +68478 +109420 +115754 +34632 +114895 +110404 +35744 +80841 +13081 +100415 +115590 +78007 +90658 +102502 +37510 +18892 +88412 +39145 +3733 +40938 +870 +50646 +79822 +47153 +65233 +9274 +29894 +34700 +7533 +95722 +56024 +53860 +124542 +53889 +104181 +123521 +81041 +77646 +55565 +95174 +107204 +113671 +116631 +76759 +55841 +31969 +106856 +74644 +51511 +32574 +101889 +124775 +78189 +79178 +31472 +98577 +110611 +1997 +64976 +42524 +78707 +33313 +106468 +25676 +52987 +13647 +102071 +19842 +9367 +15994 +37268 +11051 +37579 +112447 +32596 +76957 +123314 +56255 +119248 +91188 diff --git a/lists/agnews/size=128/seed=2/0.7-1.0.txt b/lists/agnews/size=128/seed=2/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e92e91ea1130a97b8789ea344980656d1b9e8a8 --- /dev/null +++ b/lists/agnews/size=128/seed=2/0.7-1.0.txt @@ -0,0 +1,76 @@ +114895 +110404 +35744 +80841 +13081 +100415 +115590 +78007 +90658 +102502 +37510 +18892 +88412 +39145 +3733 +40938 +870 +50646 +79822 +47153 +65233 +9274 +29894 +34700 +7533 +95722 +56024 +53860 +124542 +53889 +104181 +123521 +81041 +77646 +55565 +95174 +107204 +113671 +116631 +76759 +55841 +31969 +106856 +74644 +51511 +32574 +101889 +124775 +78189 +79178 +31472 +98577 +110611 +1997 +64976 +42524 +78707 +33313 +106468 +25676 +52987 +13647 +102071 +19842 +9367 +15994 +37268 +11051 +37579 +112447 +32596 +76957 +123314 +56255 +119248 +91188 diff --git a/lists/agnews/size=128/seed=3/0.0-0.3.txt b/lists/agnews/size=128/seed=3/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..4f651374fa45f174fb26de12549e2590df6eb8cc --- /dev/null +++ b/lists/agnews/size=128/seed=3/0.0-0.3.txt @@ -0,0 +1,77 @@ +17325 +70416 +101644 +103757 +10755 +48974 +106457 +101137 +99225 +102798 +77287 +17980 +102281 +66364 +26010 +39586 +25362 +55992 +46414 +81171 +70330 +53790 +10261 +99336 +18289 +46519 +106137 +46902 +23530 +76678 +94008 +15144 +106806 +119932 +105677 +25505 +43084 +21581 +99728 +69329 +50530 +52862 +940 +74536 +979 +126670 +105705 +32067 +1388 +47767 +33501 +121156 +38679 +114189 +104626 +4627 +126243 +116596 +17918 +91133 +118037 +61575 +44101 +59664 +98476 +102857 +99982 +119478 +33362 +67745 +103623 +46863 +80988 +19085 +40650 +11212 +50583 diff --git a/lists/agnews/size=128/seed=3/0.0-0.7.txt b/lists/agnews/size=128/seed=3/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..53770c4140d9e325a08edb83c77925113e0ea28f --- /dev/null +++ b/lists/agnews/size=128/seed=3/0.0-0.7.txt @@ -0,0 +1,180 @@ +17325 +70416 +101644 +103757 +10755 +48974 +106457 +101137 +99225 +102798 +77287 +17980 +102281 +66364 +26010 +39586 +25362 +55992 +46414 +81171 +70330 +53790 +10261 +99336 +18289 +46519 +106137 +46902 +23530 +76678 +94008 +15144 +106806 +119932 +105677 +25505 +43084 +21581 +99728 +69329 +50530 +52862 +940 +74536 +979 +126670 +105705 +32067 +1388 +47767 +33501 +121156 +38679 +114189 +104626 +4627 +126243 +116596 +17918 +91133 +118037 +61575 +44101 +59664 +98476 +102857 +99982 +119478 +33362 +67745 +103623 +46863 +80988 +19085 +40650 +11212 +50583 +22270 +117543 +23094 +73296 +9885 +72655 +117992 +72302 +9182 +96920 +56313 +68174 +111656 +50968 +118118 +21992 +7138 +10211 +96948 +122982 +23521 +120394 +101612 +73215 +63351 +45966 +118232 +56232 +76911 +63037 +66304 +105573 +107497 +74066 +115094 +51607 +96907 +1517 +52749 +71981 +120683 +61071 +120487 +66272 +115756 +107034 +63696 +102783 +781 +78063 +97748 +25435 +117563 +4972 +13817 +52938 +89809 +64394 +40961 +107969 +40040 +65317 +102002 +66901 +61337 +47298 +82512 +105283 +69735 +63167 +111525 +8368 +115705 +120897 +33626 +38474 +103532 +97364 +85438 +25396 +41903 +49737 +64397 +124476 +32784 +92090 +13993 +48135 +35223 +26096 +891 +72264 +60825 +121851 +58728 +123037 +10368 +97366 +20966 +51610 +124694 +48007 +19622 diff --git a/lists/agnews/size=128/seed=3/0.0-1.0.txt b/lists/agnews/size=128/seed=3/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..919288b8374c16f1f0c50089565f248a14c0eb5b --- /dev/null +++ b/lists/agnews/size=128/seed=3/0.0-1.0.txt @@ -0,0 +1,256 @@ +17325 +70416 +101644 +103757 +10755 +48974 +106457 +101137 +99225 +102798 +77287 +17980 +102281 +66364 +26010 +39586 +25362 +55992 +46414 +81171 +70330 +53790 +10261 +99336 +18289 +46519 +106137 +46902 +23530 +76678 +94008 +15144 +106806 +119932 +105677 +25505 +43084 +21581 +99728 +69329 +50530 +52862 +940 +74536 +979 +126670 +105705 +32067 +1388 +47767 +33501 +121156 +38679 +114189 +104626 +4627 +126243 +116596 +17918 +91133 +118037 +61575 +44101 +59664 +98476 +102857 +99982 +119478 +33362 +67745 +103623 +46863 +80988 +19085 +40650 +11212 +50583 +22270 +117543 +23094 +73296 +9885 +72655 +117992 +72302 +9182 +96920 +56313 +68174 +111656 +50968 +118118 +21992 +7138 +10211 +96948 +122982 +23521 +120394 +101612 +73215 +63351 +45966 +118232 +56232 +76911 +63037 +66304 +105573 +107497 +74066 +115094 +51607 +96907 +1517 +52749 +71981 +120683 +61071 +120487 +66272 +115756 +107034 +63696 +102783 +781 +78063 +97748 +25435 +117563 +4972 +13817 +52938 +89809 +64394 +40961 +107969 +40040 +65317 +102002 +66901 +61337 +47298 +82512 +105283 +69735 +63167 +111525 +8368 +115705 +120897 +33626 +38474 +103532 +97364 +85438 +25396 +41903 +49737 +64397 +124476 +32784 +92090 +13993 +48135 +35223 +26096 +891 +72264 +60825 +121851 +58728 +123037 +10368 +97366 +20966 +51610 +124694 +48007 +19622 +42419 +88591 +121286 +104441 +74341 +30726 +46493 +78991 +48403 +108653 +124178 +117181 +80117 +71523 +13998 +100693 +116280 +95601 +108705 +27834 +84878 +107896 +104826 +7554 +61471 +15884 +11551 +85245 +54098 +4227 +105982 +24162 +39011 +4555 +73175 +87633 +33747 +88516 +3335 +27814 +16936 +28689 +60061 +84431 +1043 +38653 +45326 +114838 +205 +61034 +57555 +35457 +6672 +100714 +9946 +87344 +101579 +76129 +32275 +194 +25910 +84206 +18980 +107407 +95979 +52597 +10556 +55281 +14549 +56215 +26038 +96873 +79841 +20378 +74958 +93767 diff --git a/lists/agnews/size=128/seed=3/0.7-1.0.txt b/lists/agnews/size=128/seed=3/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..74842f9be24b7a3667fafb7db4b01bd2b4209b13 --- /dev/null +++ b/lists/agnews/size=128/seed=3/0.7-1.0.txt @@ -0,0 +1,76 @@ +42419 +88591 +121286 +104441 +74341 +30726 +46493 +78991 +48403 +108653 +124178 +117181 +80117 +71523 +13998 +100693 +116280 +95601 +108705 +27834 +84878 +107896 +104826 +7554 +61471 +15884 +11551 +85245 +54098 +4227 +105982 +24162 +39011 +4555 +73175 +87633 +33747 +88516 +3335 +27814 +16936 +28689 +60061 +84431 +1043 +38653 +45326 +114838 +205 +61034 +57555 +35457 +6672 +100714 +9946 +87344 +101579 +76129 +32275 +194 +25910 +84206 +18980 +107407 +95979 +52597 +10556 +55281 +14549 +56215 +26038 +96873 +79841 +20378 +74958 +93767 diff --git a/lists/agnews/size=128/seed=4/0.0-0.3.txt b/lists/agnews/size=128/seed=4/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca67bf03451f7a6a5de94ed7fc072fbf640c3214 --- /dev/null +++ b/lists/agnews/size=128/seed=4/0.0-0.3.txt @@ -0,0 +1,77 @@ +56010 +661 +103156 +67353 +63411 +31108 +104842 +7456 +85033 +55459 +58290 +70408 +20715 +51405 +53467 +91088 +69362 +40649 +745 +101406 +40089 +105881 +8948 +92790 +87944 +77310 +105839 +54485 +49140 +78559 +111227 +120575 +68388 +5347 +22283 +73383 +70524 +100429 +114666 +105965 +76828 +69408 +65501 +100830 +115789 +101637 +33985 +103033 +3908 +4079 +74014 +64182 +88643 +26765 +125469 +14221 +31685 +5643 +117240 +22405 +91760 +91261 +127119 +105487 +120138 +111685 +104330 +25394 +61226 +67963 +89729 +62040 +51177 +35001 +58856 +20888 +61237 diff --git a/lists/agnews/size=128/seed=4/0.0-0.7.txt b/lists/agnews/size=128/seed=4/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..c08089d8943a5fe9fb80c77dbbbf9db39246241a --- /dev/null +++ b/lists/agnews/size=128/seed=4/0.0-0.7.txt @@ -0,0 +1,180 @@ +56010 +661 +103156 +67353 +63411 +31108 +104842 +7456 +85033 +55459 +58290 +70408 +20715 +51405 +53467 +91088 +69362 +40649 +745 +101406 +40089 +105881 +8948 +92790 +87944 +77310 +105839 +54485 +49140 +78559 +111227 +120575 +68388 +5347 +22283 +73383 +70524 +100429 +114666 +105965 +76828 +69408 +65501 +100830 +115789 +101637 +33985 +103033 +3908 +4079 +74014 +64182 +88643 +26765 +125469 +14221 +31685 +5643 +117240 +22405 +91760 +91261 +127119 +105487 +120138 +111685 +104330 +25394 +61226 +67963 +89729 +62040 +51177 +35001 +58856 +20888 +61237 +67485 +99337 +54136 +85887 +97550 +64579 +35078 +30380 +31796 +36085 +1968 +42655 +61579 +116837 +36025 +95692 +11915 +821 +79161 +69354 +5929 +105747 +9593 +19924 +123914 +645 +23602 +72383 +12522 +35966 +70208 +103839 +101839 +98203 +111136 +6263 +73971 +28953 +63727 +29102 +75537 +78281 +62492 +20273 +124663 +123858 +108351 +28603 +103462 +2741 +32666 +35783 +87017 +19889 +55249 +27810 +118210 +76541 +7021 +51822 +88891 +111148 +42200 +21130 +123503 +3771 +16915 +67085 +15967 +49367 +74294 +41367 +39510 +87055 +113421 +125316 +6692 +40144 +121924 +29771 +92753 +122234 +101809 +118945 +70098 +29617 +90463 +46611 +14424 +20881 +99757 +87607 +81465 +72213 +97797 +79802 +46962 +86215 +97284 +97088 +120725 +55058 +6468 diff --git a/lists/agnews/size=128/seed=4/0.0-1.0.txt b/lists/agnews/size=128/seed=4/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..f110955c9e6902bdb5db8cd6b00581792ac18f68 --- /dev/null +++ b/lists/agnews/size=128/seed=4/0.0-1.0.txt @@ -0,0 +1,256 @@ +56010 +661 +103156 +67353 +63411 +31108 +104842 +7456 +85033 +55459 +58290 +70408 +20715 +51405 +53467 +91088 +69362 +40649 +745 +101406 +40089 +105881 +8948 +92790 +87944 +77310 +105839 +54485 +49140 +78559 +111227 +120575 +68388 +5347 +22283 +73383 +70524 +100429 +114666 +105965 +76828 +69408 +65501 +100830 +115789 +101637 +33985 +103033 +3908 +4079 +74014 +64182 +88643 +26765 +125469 +14221 +31685 +5643 +117240 +22405 +91760 +91261 +127119 +105487 +120138 +111685 +104330 +25394 +61226 +67963 +89729 +62040 +51177 +35001 +58856 +20888 +61237 +67485 +99337 +54136 +85887 +97550 +64579 +35078 +30380 +31796 +36085 +1968 +42655 +61579 +116837 +36025 +95692 +11915 +821 +79161 +69354 +5929 +105747 +9593 +19924 +123914 +645 +23602 +72383 +12522 +35966 +70208 +103839 +101839 +98203 +111136 +6263 +73971 +28953 +63727 +29102 +75537 +78281 +62492 +20273 +124663 +123858 +108351 +28603 +103462 +2741 +32666 +35783 +87017 +19889 +55249 +27810 +118210 +76541 +7021 +51822 +88891 +111148 +42200 +21130 +123503 +3771 +16915 +67085 +15967 +49367 +74294 +41367 +39510 +87055 +113421 +125316 +6692 +40144 +121924 +29771 +92753 +122234 +101809 +118945 +70098 +29617 +90463 +46611 +14424 +20881 +99757 +87607 +81465 +72213 +97797 +79802 +46962 +86215 +97284 +97088 +120725 +55058 +6468 +83499 +43074 +121850 +19593 +86934 +37918 +48514 +4073 +16491 +53242 +22366 +125071 +26034 +29935 +74711 +10215 +106041 +12983 +20487 +127550 +59780 +88941 +35900 +59193 +95727 +58233 +88062 +98263 +115333 +72896 +60558 +30209 +122890 +70724 +66927 +88804 +4557 +45258 +98542 +123534 +53113 +47123 +93184 +37367 +112132 +118271 +89217 +50934 +20305 +51638 +18010 +93085 +35994 +92395 +101051 +119049 +127242 +121311 +48410 +100233 +680 +52333 +94444 +62839 +53320 +63161 +39348 +90430 +71115 +66663 +10630 +1375 +52315 +23723 +30329 +34764 diff --git a/lists/agnews/size=128/seed=4/0.7-1.0.txt b/lists/agnews/size=128/seed=4/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..b69b9c70155fb23a72c3c9d71ffbe7d055ef4e60 --- /dev/null +++ b/lists/agnews/size=128/seed=4/0.7-1.0.txt @@ -0,0 +1,76 @@ +83499 +43074 +121850 +19593 +86934 +37918 +48514 +4073 +16491 +53242 +22366 +125071 +26034 +29935 +74711 +10215 +106041 +12983 +20487 +127550 +59780 +88941 +35900 +59193 +95727 +58233 +88062 +98263 +115333 +72896 +60558 +30209 +122890 +70724 +66927 +88804 +4557 +45258 +98542 +123534 +53113 +47123 +93184 +37367 +112132 +118271 +89217 +50934 +20305 +51638 +18010 +93085 +35994 +92395 +101051 +119049 +127242 +121311 +48410 +100233 +680 +52333 +94444 +62839 +53320 +63161 +39348 +90430 +71115 +66663 +10630 +1375 +52315 +23723 +30329 +34764 diff --git a/lists/agnews/size=128/seed=6/0.0-0.3.txt b/lists/agnews/size=128/seed=6/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..d67b10b39ff684fa74c7a1b9c3715045068b80fe --- /dev/null +++ b/lists/agnews/size=128/seed=6/0.0-0.3.txt @@ -0,0 +1,77 @@ +46284 +62317 +106055 +24713 +34127 +89606 +3759 +6210 +88672 +83034 +35304 +63831 +36285 +126915 +28118 +96827 +80738 +97233 +35552 +110930 +88923 +124866 +21484 +81226 +102427 +49371 +42175 +94666 +100199 +20434 +6751 +96915 +23117 +71763 +85356 +35880 +39303 +106443 +83444 +114400 +114496 +116753 +41393 +52008 +40795 +10259 +71027 +117004 +58598 +53591 +12662 +108255 +105672 +96954 +41942 +117460 +79677 +3213 +92063 +89798 +23374 +10482 +54255 +78534 +89612 +125969 +86985 +120477 +82249 +87949 +66957 +88701 +70302 +73552 +116621 +97293 +6443 diff --git a/lists/agnews/size=128/seed=6/0.0-0.7.txt b/lists/agnews/size=128/seed=6/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..759d898decaab90b306453f6ba2798c4014d15ba --- /dev/null +++ b/lists/agnews/size=128/seed=6/0.0-0.7.txt @@ -0,0 +1,180 @@ +46284 +62317 +106055 +24713 +34127 +89606 +3759 +6210 +88672 +83034 +35304 +63831 +36285 +126915 +28118 +96827 +80738 +97233 +35552 +110930 +88923 +124866 +21484 +81226 +102427 +49371 +42175 +94666 +100199 +20434 +6751 +96915 +23117 +71763 +85356 +35880 +39303 +106443 +83444 +114400 +114496 +116753 +41393 +52008 +40795 +10259 +71027 +117004 +58598 +53591 +12662 +108255 +105672 +96954 +41942 +117460 +79677 +3213 +92063 +89798 +23374 +10482 +54255 +78534 +89612 +125969 +86985 +120477 +82249 +87949 +66957 +88701 +70302 +73552 +116621 +97293 +6443 +56621 +40131 +123790 +51959 +32121 +81078 +103506 +115880 +103743 +72342 +3153 +88900 +103181 +33808 +91533 +93418 +28478 +85641 +59852 +81235 +122069 +35103 +39096 +20262 +27921 +67846 +20447 +82132 +86467 +98576 +90871 +73221 +39920 +31592 +127106 +25055 +2995 +16974 +62119 +9482 +59337 +81126 +12107 +96998 +110792 +65911 +47250 +4436 +49784 +44000 +102053 +40880 +21380 +13543 +17956 +110438 +94927 +24793 +125499 +38229 +76185 +106608 +4190 +53148 +2353 +118364 +27846 +75134 +73730 +20534 +33733 +9117 +75461 +113013 +57812 +70402 +15965 +39899 +49469 +13707 +3443 +39201 +89012 +77329 +60155 +17964 +14866 +51616 +38844 +93997 +6571 +89746 +84195 +59847 +90916 +104709 +122945 +19151 +26696 +95652 +22458 +54751 +37503 diff --git a/lists/agnews/size=128/seed=6/0.0-1.0.txt b/lists/agnews/size=128/seed=6/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..fd4aadcc7e0872bafa1e7536814643f5dd856440 --- /dev/null +++ b/lists/agnews/size=128/seed=6/0.0-1.0.txt @@ -0,0 +1,256 @@ +46284 +62317 +106055 +24713 +34127 +89606 +3759 +6210 +88672 +83034 +35304 +63831 +36285 +126915 +28118 +96827 +80738 +97233 +35552 +110930 +88923 +124866 +21484 +81226 +102427 +49371 +42175 +94666 +100199 +20434 +6751 +96915 +23117 +71763 +85356 +35880 +39303 +106443 +83444 +114400 +114496 +116753 +41393 +52008 +40795 +10259 +71027 +117004 +58598 +53591 +12662 +108255 +105672 +96954 +41942 +117460 +79677 +3213 +92063 +89798 +23374 +10482 +54255 +78534 +89612 +125969 +86985 +120477 +82249 +87949 +66957 +88701 +70302 +73552 +116621 +97293 +6443 +56621 +40131 +123790 +51959 +32121 +81078 +103506 +115880 +103743 +72342 +3153 +88900 +103181 +33808 +91533 +93418 +28478 +85641 +59852 +81235 +122069 +35103 +39096 +20262 +27921 +67846 +20447 +82132 +86467 +98576 +90871 +73221 +39920 +31592 +127106 +25055 +2995 +16974 +62119 +9482 +59337 +81126 +12107 +96998 +110792 +65911 +47250 +4436 +49784 +44000 +102053 +40880 +21380 +13543 +17956 +110438 +94927 +24793 +125499 +38229 +76185 +106608 +4190 +53148 +2353 +118364 +27846 +75134 +73730 +20534 +33733 +9117 +75461 +113013 +57812 +70402 +15965 +39899 +49469 +13707 +3443 +39201 +89012 +77329 +60155 +17964 +14866 +51616 +38844 +93997 +6571 +89746 +84195 +59847 +90916 +104709 +122945 +19151 +26696 +95652 +22458 +54751 +37503 +114182 +61901 +38881 +15209 +16292 +19738 +92407 +62266 +113820 +22995 +70986 +96105 +41518 +62764 +30808 +16922 +111722 +53481 +108535 +98321 +4222 +37512 +56778 +97632 +221 +15962 +38584 +61581 +9325 +56978 +89751 +34277 +122338 +72073 +663 +123651 +107641 +5468 +125220 +72698 +71408 +46471 +108811 +77513 +2341 +70640 +80361 +10014 +91518 +78408 +106071 +114521 +67690 +89591 +21958 +28388 +119863 +98034 +53255 +125797 +100163 +14496 +99614 +50193 +73057 +115834 +109825 +71231 +81368 +33307 +111033 +120862 +92759 +65473 +39450 +100242 diff --git a/lists/agnews/size=128/seed=6/0.7-1.0.txt b/lists/agnews/size=128/seed=6/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..7fe4ecdabee791834d0a370241270cc9fd91b029 --- /dev/null +++ b/lists/agnews/size=128/seed=6/0.7-1.0.txt @@ -0,0 +1,76 @@ +114182 +61901 +38881 +15209 +16292 +19738 +92407 +62266 +113820 +22995 +70986 +96105 +41518 +62764 +30808 +16922 +111722 +53481 +108535 +98321 +4222 +37512 +56778 +97632 +221 +15962 +38584 +61581 +9325 +56978 +89751 +34277 +122338 +72073 +663 +123651 +107641 +5468 +125220 +72698 +71408 +46471 +108811 +77513 +2341 +70640 +80361 +10014 +91518 +78408 +106071 +114521 +67690 +89591 +21958 +28388 +119863 +98034 +53255 +125797 +100163 +14496 +99614 +50193 +73057 +115834 +109825 +71231 +81368 +33307 +111033 +120862 +92759 +65473 +39450 +100242 diff --git a/lists/agnews/size=128/seed=8/0.0-0.7.txt b/lists/agnews/size=128/seed=8/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..a29fd79f2da9508020b8dc7ece9260371c822f6d --- /dev/null +++ b/lists/agnews/size=128/seed=8/0.0-0.7.txt @@ -0,0 +1,180 @@ +62744 +113690 +96524 +115982 +97017 +64627 +71957 +92563 +46779 +77187 +41197 +5736 +98241 +28670 +20195 +95666 +31656 +49504 +9879 +70423 +31546 +89919 +22750 +61483 +49085 +118748 +68402 +23585 +10740 +18656 +15867 +104194 +18705 +61431 +48574 +114072 +124159 +2076 +85129 +40014 +1811 +43430 +78088 +75980 +118948 +121411 +65573 +99765 +25638 +77627 +18047 +38672 +46041 +55661 +101475 +96827 +26556 +115706 +114141 +52960 +81868 +105213 +86934 +114952 +60595 +78684 +98957 +105642 +102281 +83205 +110364 +95392 +26657 +59075 +40202 +122503 +127324 +20744 +43818 +26607 +29690 +910 +29601 +56172 +60867 +121903 +61011 +50334 +75054 +60028 +48305 +59198 +38381 +25674 +48922 +89047 +2011 +116091 +57142 +17800 +126890 +50476 +120265 +37298 +4154 +34419 +33457 +27062 +36737 +44661 +6429 +71563 +61732 +119711 +83842 +123865 +47850 +3998 +44352 +97244 +28808 +103806 +80302 +114184 +76641 +51299 +28638 +127486 +85124 +53890 +4508 +46737 +7046 +92404 +35067 +80235 +13304 +33348 +17580 +57362 +106753 +74051 +113831 +125065 +106616 +108469 +65674 +33679 +68191 +78935 +50040 +69126 +64837 +51225 +37635 +7180 +124920 +16849 +124728 +108426 +9962 +81300 +97565 +112226 +55886 +94322 +95818 +47138 +58894 +12974 +102835 +60283 +78680 +47406 +42544 +100041 +72124 +72540 +68245 +19851 diff --git a/lists/agnews/size=128/seed=8/0.0-1.0.txt b/lists/agnews/size=128/seed=8/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..af806be0544dd747734b18ea38106ac257a744d1 --- /dev/null +++ b/lists/agnews/size=128/seed=8/0.0-1.0.txt @@ -0,0 +1,256 @@ +62744 +113690 +96524 +115982 +97017 +64627 +71957 +92563 +46779 +77187 +41197 +5736 +98241 +28670 +20195 +95666 +31656 +49504 +9879 +70423 +31546 +89919 +22750 +61483 +49085 +118748 +68402 +23585 +10740 +18656 +15867 +104194 +18705 +61431 +48574 +114072 +124159 +2076 +85129 +40014 +1811 +43430 +78088 +75980 +118948 +121411 +65573 +99765 +25638 +77627 +18047 +38672 +46041 +55661 +101475 +96827 +26556 +115706 +114141 +52960 +81868 +105213 +86934 +114952 +60595 +78684 +98957 +105642 +102281 +83205 +110364 +95392 +26657 +59075 +40202 +122503 +127324 +20744 +43818 +26607 +29690 +910 +29601 +56172 +60867 +121903 +61011 +50334 +75054 +60028 +48305 +59198 +38381 +25674 +48922 +89047 +2011 +116091 +57142 +17800 +126890 +50476 +120265 +37298 +4154 +34419 +33457 +27062 +36737 +44661 +6429 +71563 +61732 +119711 +83842 +123865 +47850 +3998 +44352 +97244 +28808 +103806 +80302 +114184 +76641 +51299 +28638 +127486 +85124 +53890 +4508 +46737 +7046 +92404 +35067 +80235 +13304 +33348 +17580 +57362 +106753 +74051 +113831 +125065 +106616 +108469 +65674 +33679 +68191 +78935 +50040 +69126 +64837 +51225 +37635 +7180 +124920 +16849 +124728 +108426 +9962 +81300 +97565 +112226 +55886 +94322 +95818 +47138 +58894 +12974 +102835 +60283 +78680 +47406 +42544 +100041 +72124 +72540 +68245 +19851 +68949 +14846 +81196 +60230 +25424 +50982 +29689 +64334 +49123 +118626 +58023 +93225 +36369 +4362 +90627 +101082 +98759 +101939 +77723 +64795 +4549 +109733 +92562 +35584 +118448 +10126 +69770 +43877 +79794 +46329 +122547 +89780 +26155 +69520 +29750 +12862 +125129 +86756 +57701 +120492 +115233 +25857 +52279 +121424 +104437 +100666 +76845 +64263 +87496 +85220 +42732 +21581 +67462 +16755 +85251 +10046 +88714 +96103 +111916 +107359 +31992 +16638 +115254 +32649 +37340 +68162 +116149 +68837 +107632 +72402 +53631 +2177 +90682 +45106 +78503 +109227 diff --git a/lists/agnews/size=128/seed=8/0.7-1.0.txt b/lists/agnews/size=128/seed=8/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf44836ffe25d7895d8532a2e232ea4f06b572ea --- /dev/null +++ b/lists/agnews/size=128/seed=8/0.7-1.0.txt @@ -0,0 +1,76 @@ +68949 +14846 +81196 +60230 +25424 +50982 +29689 +64334 +49123 +118626 +58023 +93225 +36369 +4362 +90627 +101082 +98759 +101939 +77723 +64795 +4549 +109733 +92562 +35584 +118448 +10126 +69770 +43877 +79794 +46329 +122547 +89780 +26155 +69520 +29750 +12862 +125129 +86756 +57701 +120492 +115233 +25857 +52279 +121424 +104437 +100666 +76845 +64263 +87496 +85220 +42732 +21581 +67462 +16755 +85251 +10046 +88714 +96103 +111916 +107359 +31992 +16638 +115254 +32649 +37340 +68162 +116149 +68837 +107632 +72402 +53631 +2177 +90682 +45106 +78503 +109227 diff --git a/lists/agnews/size=128/seed=9/0.0-0.3.txt b/lists/agnews/size=128/seed=9/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..21a0f2e07f617aec1b74f86e88225044b0c555f4 --- /dev/null +++ b/lists/agnews/size=128/seed=9/0.0-0.3.txt @@ -0,0 +1,77 @@ +59930 +116325 +27821 +100717 +78074 +88974 +78483 +70970 +34345 +3875 +79314 +81720 +63950 +85212 +104792 +15008 +93 +72450 +46721 +64654 +37576 +55690 +57051 +8337 +4929 +49686 +80841 +106434 +61847 +121702 +17550 +108405 +99264 +774 +51699 +108073 +14537 +102309 +27610 +84378 +105162 +86250 +112221 +59889 +39843 +72534 +78005 +9855 +122540 +68269 +15139 +67024 +52559 +90406 +104774 +40008 +3990 +13995 +108650 +51285 +47023 +44227 +30830 +2128 +84892 +45232 +50196 +9353 +25051 +113561 +125026 +115010 +124386 +113248 +114363 +64426 +89784 diff --git a/lists/agnews/size=128/seed=9/0.0-0.7.txt b/lists/agnews/size=128/seed=9/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebf1885045d7bb9eb349e9e1255dcd1fc4a23e3f --- /dev/null +++ b/lists/agnews/size=128/seed=9/0.0-0.7.txt @@ -0,0 +1,180 @@ +59930 +116325 +27821 +100717 +78074 +88974 +78483 +70970 +34345 +3875 +79314 +81720 +63950 +85212 +104792 +15008 +93 +72450 +46721 +64654 +37576 +55690 +57051 +8337 +4929 +49686 +80841 +106434 +61847 +121702 +17550 +108405 +99264 +774 +51699 +108073 +14537 +102309 +27610 +84378 +105162 +86250 +112221 +59889 +39843 +72534 +78005 +9855 +122540 +68269 +15139 +67024 +52559 +90406 +104774 +40008 +3990 +13995 +108650 +51285 +47023 +44227 +30830 +2128 +84892 +45232 +50196 +9353 +25051 +113561 +125026 +115010 +124386 +113248 +114363 +64426 +89784 +63059 +111220 +39343 +3549 +89189 +75615 +57171 +98848 +5792 +64166 +114861 +74468 +16669 +103421 +5997 +110957 +7419 +30127 +64438 +26369 +8027 +31206 +26187 +87576 +65028 +18732 +21244 +25084 +42357 +56511 +14103 +5362 +93131 +102709 +24723 +73070 +104375 +51980 +103601 +537 +55520 +12541 +51644 +53896 +73629 +113311 +21077 +32692 +82482 +73168 +120123 +92405 +91400 +23645 +91948 +23245 +14257 +84672 +5748 +104648 +62822 +114536 +57439 +59050 +398 +4029 +127430 +120778 +101794 +112156 +121307 +20514 +80792 +4624 +96408 +78746 +63622 +106075 +56401 +69632 +25010 +83105 +77507 +76134 +81645 +107890 +48428 +44494 +66014 +93774 +83374 +41968 +54384 +103136 +48170 +106169 +33782 +101358 +86246 +31941 +109756 +4030 +19080 diff --git a/lists/agnews/size=128/seed=9/0.0-1.0.txt b/lists/agnews/size=128/seed=9/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..59a984123dca89ed1b8a265d88905947ce7ef4f1 --- /dev/null +++ b/lists/agnews/size=128/seed=9/0.0-1.0.txt @@ -0,0 +1,256 @@ +59930 +116325 +27821 +100717 +78074 +88974 +78483 +70970 +34345 +3875 +79314 +81720 +63950 +85212 +104792 +15008 +93 +72450 +46721 +64654 +37576 +55690 +57051 +8337 +4929 +49686 +80841 +106434 +61847 +121702 +17550 +108405 +99264 +774 +51699 +108073 +14537 +102309 +27610 +84378 +105162 +86250 +112221 +59889 +39843 +72534 +78005 +9855 +122540 +68269 +15139 +67024 +52559 +90406 +104774 +40008 +3990 +13995 +108650 +51285 +47023 +44227 +30830 +2128 +84892 +45232 +50196 +9353 +25051 +113561 +125026 +115010 +124386 +113248 +114363 +64426 +89784 +63059 +111220 +39343 +3549 +89189 +75615 +57171 +98848 +5792 +64166 +114861 +74468 +16669 +103421 +5997 +110957 +7419 +30127 +64438 +26369 +8027 +31206 +26187 +87576 +65028 +18732 +21244 +25084 +42357 +56511 +14103 +5362 +93131 +102709 +24723 +73070 +104375 +51980 +103601 +537 +55520 +12541 +51644 +53896 +73629 +113311 +21077 +32692 +82482 +73168 +120123 +92405 +91400 +23645 +91948 +23245 +14257 +84672 +5748 +104648 +62822 +114536 +57439 +59050 +398 +4029 +127430 +120778 +101794 +112156 +121307 +20514 +80792 +4624 +96408 +78746 +63622 +106075 +56401 +69632 +25010 +83105 +77507 +76134 +81645 +107890 +48428 +44494 +66014 +93774 +83374 +41968 +54384 +103136 +48170 +106169 +33782 +101358 +86246 +31941 +109756 +4030 +19080 +34050 +46465 +72763 +29058 +113219 +11412 +43955 +10609 +105032 +8466 +100662 +85873 +28144 +103156 +126669 +12602 +119839 +22556 +108284 +6465 +32616 +19528 +59337 +36023 +69830 +92556 +32141 +12031 +46071 +108585 +45824 +111483 +109904 +74713 +34809 +125537 +127439 +7773 +58005 +85329 +71465 +28761 +120247 +31642 +59977 +47107 +82563 +34445 +12020 +2365 +29508 +111396 +92649 +98431 +104647 +48793 +105671 +103594 +8178 +27549 +35342 +46316 +98770 +22881 +38254 +69837 +17020 +63863 +79243 +54750 +41688 +88004 +116552 +126607 +21332 +69320 diff --git a/lists/agnews/size=128/seed=9/0.7-1.0.txt b/lists/agnews/size=128/seed=9/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..2485bbd1e19f9c655c3b0415fe754d343bea82fe --- /dev/null +++ b/lists/agnews/size=128/seed=9/0.7-1.0.txt @@ -0,0 +1,76 @@ +34050 +46465 +72763 +29058 +113219 +11412 +43955 +10609 +105032 +8466 +100662 +85873 +28144 +103156 +126669 +12602 +119839 +22556 +108284 +6465 +32616 +19528 +59337 +36023 +69830 +92556 +32141 +12031 +46071 +108585 +45824 +111483 +109904 +74713 +34809 +125537 +127439 +7773 +58005 +85329 +71465 +28761 +120247 +31642 +59977 +47107 +82563 +34445 +12020 +2365 +29508 +111396 +92649 +98431 +104647 +48793 +105671 +103594 +8178 +27549 +35342 +46316 +98770 +22881 +38254 +69837 +17020 +63863 +79243 +54750 +41688 +88004 +116552 +126607 +21332 +69320 diff --git a/lists/agnews/size=8/seed=0/0.0-0.3.txt b/lists/agnews/size=8/seed=0/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..89aa4912c1077c5eb019f46d719c902542b507eb --- /dev/null +++ b/lists/agnews/size=8/seed=0/0.0-0.3.txt @@ -0,0 +1,5 @@ +39155 +8934 +31196 +45484 +36446 diff --git a/lists/agnews/size=8/seed=0/0.0-0.7.txt b/lists/agnews/size=8/seed=0/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..35f2e586e0e24ac55f9b20b097fa065a5e5285de --- /dev/null +++ b/lists/agnews/size=8/seed=0/0.0-0.7.txt @@ -0,0 +1,12 @@ +39155 +8934 +31196 +45484 +36446 +27069 +98154 +38190 +76908 +114510 +20161 +62713 diff --git a/lists/agnews/size=8/seed=0/0.0-1.0.txt b/lists/agnews/size=8/seed=0/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..119aaf186df8c7c0e908033099efe11d06a0a87d --- /dev/null +++ b/lists/agnews/size=8/seed=0/0.0-1.0.txt @@ -0,0 +1,16 @@ +39155 +8934 +31196 +45484 +36446 +27069 +98154 +38190 +76908 +114510 +20161 +62713 +32678 +55068 +46296 +67446 diff --git a/lists/agnews/size=8/seed=0/0.7-1.0.txt b/lists/agnews/size=8/seed=0/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..5fac5f6592846e63fbdfa8546c922d96888b1ae0 --- /dev/null +++ b/lists/agnews/size=8/seed=0/0.7-1.0.txt @@ -0,0 +1,4 @@ +32678 +55068 +46296 +67446 diff --git a/lists/agnews/size=8/seed=1/0.0-0.3.txt b/lists/agnews/size=8/seed=1/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..fddf15695c6dea235900465fff3909b94463dd1c --- /dev/null +++ b/lists/agnews/size=8/seed=1/0.0-0.3.txt @@ -0,0 +1,5 @@ +23737 +115866 +74029 +119768 +104853 diff --git a/lists/agnews/size=8/seed=1/0.0-0.7.txt b/lists/agnews/size=8/seed=1/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..bedef855e6bc2df1f538a837edb9d213d846be6e --- /dev/null +++ b/lists/agnews/size=8/seed=1/0.0-0.7.txt @@ -0,0 +1,12 @@ +23737 +115866 +74029 +119768 +104853 +12606 +22683 +59347 +48713 +107261 +56098 +5056 diff --git a/lists/agnews/size=8/seed=1/0.0-1.0.txt b/lists/agnews/size=8/seed=1/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb236e831f48a0dacfc699cc6a94e6247264ca25 --- /dev/null +++ b/lists/agnews/size=8/seed=1/0.0-1.0.txt @@ -0,0 +1,16 @@ +23737 +115866 +74029 +119768 +104853 +12606 +22683 +59347 +48713 +107261 +56098 +5056 +47061 +72767 +18365 +119473 diff --git a/lists/agnews/size=8/seed=1/0.7-1.0.txt b/lists/agnews/size=8/seed=1/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b056ffbc73f159a7cab97cc9f9295f90303284d --- /dev/null +++ b/lists/agnews/size=8/seed=1/0.7-1.0.txt @@ -0,0 +1,4 @@ +47061 +72767 +18365 +119473 diff --git a/lists/agnews/size=8/seed=2/0.0-0.3.txt b/lists/agnews/size=8/seed=2/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..aea50c7bf92b3e58404102a8a122491f77a0e7f2 --- /dev/null +++ b/lists/agnews/size=8/seed=2/0.0-0.3.txt @@ -0,0 +1,5 @@ +126292 +57811 +93714 +116305 +100814 diff --git a/lists/agnews/size=8/seed=2/0.0-0.7.txt b/lists/agnews/size=8/seed=2/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ed9908a65ac22350e106392c4070a0e2a15e627 --- /dev/null +++ b/lists/agnews/size=8/seed=2/0.0-0.7.txt @@ -0,0 +1,12 @@ +126292 +57811 +93714 +116305 +100814 +75979 +23433 +92178 +78581 +104328 +36096 +119546 diff --git a/lists/agnews/size=8/seed=2/0.0-1.0.txt b/lists/agnews/size=8/seed=2/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..30856d05f5a814bf008fd9b4ec52d7e03241f58c --- /dev/null +++ b/lists/agnews/size=8/seed=2/0.0-1.0.txt @@ -0,0 +1,16 @@ +126292 +57811 +93714 +116305 +100814 +75979 +23433 +92178 +78581 +104328 +36096 +119546 +74241 +95253 +97186 +6611 diff --git a/lists/agnews/size=8/seed=2/0.7-1.0.txt b/lists/agnews/size=8/seed=2/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..71d0afbd7e0af1ef6db0a013c3e51cbd6d902018 --- /dev/null +++ b/lists/agnews/size=8/seed=2/0.7-1.0.txt @@ -0,0 +1,4 @@ +74241 +95253 +97186 +6611 diff --git a/lists/agnews/size=8/seed=3/0.0-0.3.txt b/lists/agnews/size=8/seed=3/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..9aa9c27a60851bcee54127fe79ccd0955c047035 --- /dev/null +++ b/lists/agnews/size=8/seed=3/0.0-0.3.txt @@ -0,0 +1,5 @@ +17325 +70416 +101644 +103757 +10755 diff --git a/lists/agnews/size=8/seed=3/0.0-0.7.txt b/lists/agnews/size=8/seed=3/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..7ff750f7da8b2a97d3c9732621678e1a13ab3146 --- /dev/null +++ b/lists/agnews/size=8/seed=3/0.0-0.7.txt @@ -0,0 +1,12 @@ +17325 +70416 +101644 +103757 +10755 +48974 +106457 +101137 +99225 +102798 +77287 +17980 diff --git a/lists/agnews/size=8/seed=3/0.0-1.0.txt b/lists/agnews/size=8/seed=3/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..a350dc291fc4714db8e3e9b5ddbd6a827d1b6b0a --- /dev/null +++ b/lists/agnews/size=8/seed=3/0.0-1.0.txt @@ -0,0 +1,16 @@ +17325 +70416 +101644 +103757 +10755 +48974 +106457 +101137 +99225 +102798 +77287 +17980 +102281 +66364 +26010 +39586 diff --git a/lists/agnews/size=8/seed=3/0.7-1.0.txt b/lists/agnews/size=8/seed=3/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..00ee5d4b49e9f3f2b08880e05b970027ffbabb5c --- /dev/null +++ b/lists/agnews/size=8/seed=3/0.7-1.0.txt @@ -0,0 +1,4 @@ +102281 +66364 +26010 +39586 diff --git a/lists/agnews/size=8/seed=4/0.0-0.3.txt b/lists/agnews/size=8/seed=4/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e60593ffcbebe97bcfec6eb74ffb9c4c02c7930 --- /dev/null +++ b/lists/agnews/size=8/seed=4/0.0-0.3.txt @@ -0,0 +1,5 @@ +56010 +661 +103156 +67353 +63411 diff --git a/lists/agnews/size=8/seed=5/0.0-0.3.txt b/lists/agnews/size=8/seed=5/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..3a36609698a680f474fdd1fd9c35ab4fa10a6595 --- /dev/null +++ b/lists/agnews/size=8/seed=5/0.0-0.3.txt @@ -0,0 +1,5 @@ +33883 +25778 +85655 +71399 +77521 diff --git a/lists/agnews/size=8/seed=5/0.0-0.7.txt b/lists/agnews/size=8/seed=5/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..4a2bac72d6aaa5354be0f10397faf76c279377b4 --- /dev/null +++ b/lists/agnews/size=8/seed=5/0.0-0.7.txt @@ -0,0 +1,12 @@ +33883 +25778 +85655 +71399 +77521 +24208 +98868 +2144 +111351 +11711 +41649 +118957 diff --git a/lists/agnews/size=8/seed=5/0.0-1.0.txt b/lists/agnews/size=8/seed=5/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf629ff129611609476cc238c2f7cab682a66a1c --- /dev/null +++ b/lists/agnews/size=8/seed=5/0.0-1.0.txt @@ -0,0 +1,16 @@ +33883 +25778 +85655 +71399 +77521 +24208 +98868 +2144 +111351 +11711 +41649 +118957 +100590 +112134 +102107 +48457 diff --git a/lists/agnews/size=8/seed=5/0.7-1.0.txt b/lists/agnews/size=8/seed=5/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..f846cd5dbde1b3faf805878f22674408c5e7ead9 --- /dev/null +++ b/lists/agnews/size=8/seed=5/0.7-1.0.txt @@ -0,0 +1,4 @@ +100590 +112134 +102107 +48457 diff --git a/lists/agnews/size=8/seed=6/0.0-0.3.txt b/lists/agnews/size=8/seed=6/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..d7a2a69709d21eb73f087528ed469afe31dac42f --- /dev/null +++ b/lists/agnews/size=8/seed=6/0.0-0.3.txt @@ -0,0 +1,5 @@ +46284 +62317 +106055 +24713 +34127 diff --git a/lists/agnews/size=8/seed=6/0.0-0.7.txt b/lists/agnews/size=8/seed=6/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..25e3eb45a621fb6f402c4a707fab4b924d8a024f --- /dev/null +++ b/lists/agnews/size=8/seed=6/0.0-0.7.txt @@ -0,0 +1,12 @@ +46284 +62317 +106055 +24713 +34127 +89606 +3759 +6210 +88672 +83034 +35304 +63831 diff --git a/lists/agnews/size=8/seed=6/0.0-1.0.txt b/lists/agnews/size=8/seed=6/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..6dfc0ac41866768cd009cb73ce4ff9df82d8091e --- /dev/null +++ b/lists/agnews/size=8/seed=6/0.0-1.0.txt @@ -0,0 +1,16 @@ +46284 +62317 +106055 +24713 +34127 +89606 +3759 +6210 +88672 +83034 +35304 +63831 +36285 +126915 +28118 +96827 diff --git a/lists/agnews/size=8/seed=7/0.0-0.3.txt b/lists/agnews/size=8/seed=7/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..7c7688b9e2f2d7a62a2ac3426463536f366bc7f7 --- /dev/null +++ b/lists/agnews/size=8/seed=7/0.0-0.3.txt @@ -0,0 +1,5 @@ +10118 +13700 +89501 +69446 +9051 diff --git a/lists/agnews/size=8/seed=7/0.0-0.7.txt b/lists/agnews/size=8/seed=7/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..5683f949ac2e24332db53462075a27ef58074600 --- /dev/null +++ b/lists/agnews/size=8/seed=7/0.0-0.7.txt @@ -0,0 +1,12 @@ +10118 +13700 +89501 +69446 +9051 +51885 +80113 +22792 +76211 +112145 +115190 +42091 diff --git a/lists/agnews/size=8/seed=7/0.0-1.0.txt b/lists/agnews/size=8/seed=7/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..37b4504f3af5aa1cb0534b093b74f23084af11e9 --- /dev/null +++ b/lists/agnews/size=8/seed=7/0.0-1.0.txt @@ -0,0 +1,16 @@ +10118 +13700 +89501 +69446 +9051 +51885 +80113 +22792 +76211 +112145 +115190 +42091 +119757 +83197 +99235 +109283 diff --git a/lists/agnews/size=8/seed=7/0.7-1.0.txt b/lists/agnews/size=8/seed=7/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..15201774bfee1430642d0572b1bfe2fc95b3b92c --- /dev/null +++ b/lists/agnews/size=8/seed=7/0.7-1.0.txt @@ -0,0 +1,4 @@ +119757 +83197 +99235 +109283 diff --git a/lists/agnews/size=8/seed=8/0.0-0.3.txt b/lists/agnews/size=8/seed=8/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..221009304c9cb0cd3910de59dd3c4a3ba3247fae --- /dev/null +++ b/lists/agnews/size=8/seed=8/0.0-0.3.txt @@ -0,0 +1,5 @@ +62744 +113690 +96524 +115982 +97017 diff --git a/lists/agnews/size=8/seed=8/0.0-0.7.txt b/lists/agnews/size=8/seed=8/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..d8d3b6349df7334729d12191ed6c094f09fcc629 --- /dev/null +++ b/lists/agnews/size=8/seed=8/0.0-0.7.txt @@ -0,0 +1,12 @@ +62744 +113690 +96524 +115982 +97017 +64627 +71957 +92563 +46779 +77187 +41197 +5736 diff --git a/lists/agnews/size=8/seed=8/0.0-1.0.txt b/lists/agnews/size=8/seed=8/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..b443268b9bfa14ba3a5401105aa72fc54fad603a --- /dev/null +++ b/lists/agnews/size=8/seed=8/0.0-1.0.txt @@ -0,0 +1,16 @@ +62744 +113690 +96524 +115982 +97017 +64627 +71957 +92563 +46779 +77187 +41197 +5736 +98241 +28670 +20195 +95666 diff --git a/lists/agnews/size=8/seed=8/0.7-1.0.txt b/lists/agnews/size=8/seed=8/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..0de7c9d403ac1ad3c727eb09eaee3b819d47ef05 --- /dev/null +++ b/lists/agnews/size=8/seed=8/0.7-1.0.txt @@ -0,0 +1,4 @@ +98241 +28670 +20195 +95666 diff --git a/lists/agnews/size=8/seed=9/0.0-0.3.txt b/lists/agnews/size=8/seed=9/0.0-0.3.txt new file mode 100644 index 0000000000000000000000000000000000000000..47b1ccc03acbd28b00c5a6fee1f0ceb7c3be7ad1 --- /dev/null +++ b/lists/agnews/size=8/seed=9/0.0-0.3.txt @@ -0,0 +1,5 @@ +59930 +116325 +27821 +100717 +78074 diff --git a/lists/agnews/size=8/seed=9/0.0-0.7.txt b/lists/agnews/size=8/seed=9/0.0-0.7.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a4016f98fb9f7775afedd128d2cd1e7ca3393fe --- /dev/null +++ b/lists/agnews/size=8/seed=9/0.0-0.7.txt @@ -0,0 +1,12 @@ +59930 +116325 +27821 +100717 +78074 +88974 +78483 +70970 +34345 +3875 +79314 +81720 diff --git a/lists/agnews/size=8/seed=9/0.0-1.0.txt b/lists/agnews/size=8/seed=9/0.0-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..745a252c845eab322f5a3c2af1b41ba3b8e39102 --- /dev/null +++ b/lists/agnews/size=8/seed=9/0.0-1.0.txt @@ -0,0 +1,16 @@ +59930 +116325 +27821 +100717 +78074 +88974 +78483 +70970 +34345 +3875 +79314 +81720 +63950 +85212 +104792 +15008 diff --git a/lists/agnews/size=8/seed=9/0.7-1.0.txt b/lists/agnews/size=8/seed=9/0.7-1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..19a7b8c5b2b146129de516729b92e4712379e948 --- /dev/null +++ b/lists/agnews/size=8/seed=9/0.7-1.0.txt @@ -0,0 +1,4 @@ +63950 +85212 +104792 +15008 diff --git a/notebooks/corr_noa_calloss.ipynb b/notebooks/corr_noa_calloss.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..02bd32e1b7adfc219ef82afa30cd04f1b435442b --- /dev/null +++ b/notebooks/corr_noa_calloss.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from scipy.stats import pearsonr" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sorted_datasets = [\"sst2\", \"agnews\", \"dbpedia\", \"20newsgroups\", \"banking77\"]\n", + "dataset2name = {\n", + " \"sst2\": {\"label\": \"SST-2\", \"color\": \"tab:blue\"},\n", + " \"agnews\": {\"label\": \"AG News\", \"color\": \"tab:orange\"},\n", + " \"dbpedia\": {\"label\": \"DBpedia\", \"color\": \"tab:green\"},\n", + " \"20newsgroups\": {\"label\": \"20 Newsgroups\", \"color\": \"tab:red\"},\n", + " \"banking77\": {\"label\": \"Banking77\", \"color\": \"tab:purple\"},\n", + "}\n", + "models = [(\"llama3.2-1b-instruct\", \"Llama3.2-1B\", \"o\"), (\"qwen2.5-7b-instruct\", \"Qwen2.5-7B\", \"x\")]\n", + "\n", + "def read_results(model, metric, cal_method):\n", + " df = pd.read_json(f\"../outputs/results_paper/{model}/{metric}.jsonl\", lines=True)\n", + " df_cal = df[(df[\"size\"] == 256) & (df[\"method\"] == cal_method)].drop(columns=[\"size\", \"min_result\"]).copy()\n", + " df_cal[\"uncal\"] = 0.\n", + " for dataset in sorted_datasets:\n", + " df_cal.loc[df_cal[\"dataset\"] == dataset, \"uncal\"] = df.loc[(df[\"dataset\"] == dataset) & (df[\"method\"] == \"no_adaptation\"), \"result\"].values[0]\n", + " df_cal[\"delta\"] = df_cal[\"uncal\"] - df_cal[\"result\"]\n", + " df_cal[\"delta_perc\"] = 100 * df_cal[\"delta\"] / df_cal[\"uncal\"]\n", + " df_cal = df_cal.rename(columns={\"result\": \"Calibrated\", \"uncal\": \"NoA\", \"delta\": \"Calibration Loss\", \"delta_perc\": \"Relative Calibration Loss\"})\n", + " return df_cal\n", + "\n", + "def plot(metric, method, y=\"cal_loss\"):\n", + "\n", + " x = \"NoA\"\n", + " fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n", + " for model, model_name, ax in [(\"llama3.2-1b-instruct\", \"Llama3.2-1B\", axes[0]), (\"qwen2.5-7b-instruct\", \"Qwen2.5-7B\", axes[1])]:\n", + " df = read_results(model, metric, method)\n", + " corr, p = pearsonr(df[x].values, df[y].values)\n", + " for dataset in sorted_datasets:\n", + " df_dataset = df[df[\"dataset\"] == dataset]\n", + " ax.plot(df_dataset[x], df_dataset[y], marker=\"o\", label=dataset2name[dataset])\n", + " ax.set_title(f\"{model_name}\\nCorrelation: {corr:.3f} (p={p:.2e})\")\n", + " ax.set_xlabel(x)\n", + " ax.grid(True)\n", + "\n", + " axes[0].set_ylabel(y) \n", + " axes[1].legend(title=\"Dataset\", bbox_to_anchor=(1.05, 1), loc='upper left')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " fig.savefig(f\"../outputs/results_paper/corr_{metric}_{method}.png\")\n", + "\n", + "def plot_all_in_one(method, y):\n", + "\n", + " x = \"NoA\"\n", + " fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + " for metric, ax in zip([\"nce\", \"ner\"], axes):\n", + " x_values, y_values = [], []\n", + " for model, _, marker in models:\n", + " df = read_results(model, metric, method)\n", + " x_values.append(df[x].values)\n", + " y_values.append(df[y].values)\n", + " for dataset in sorted_datasets:\n", + " df_dataset = df[df[\"dataset\"] == dataset]\n", + " ax.plot(df_dataset[x], df_dataset[y], linestyle=\"none\", marker=marker, color=dataset2name[dataset][\"color\"])\n", + " x_values = np.concatenate(x_values)\n", + " y_values = np.concatenate(y_values)\n", + " corr, p = pearsonr(x_values, y_values)\n", + " ax.set_title(f\"{metric.upper()}\")\n", + " # text box with correlation at upper left\n", + " p_text = \"p < 0.00001\" if p < 0.00001 else f\"p = {p:.2e}\"\n", + " ax.text(0.05, 0.95, f\"Correlation: {corr:.2f}\\n({p_text})\", transform=ax.transAxes, fontsize=10, verticalalignment='top', bbox=dict(facecolor='white', alpha=0.5))\n", + " ax.set_xlabel(x)\n", + " ax.grid(True)\n", + "\n", + " axes[0].set_ylabel(y)\n", + "\n", + " # Gather handles and labels from all axes\n", + " custom_handles = []\n", + " for dataset in sorted_datasets:\n", + " custom_handles.append(\n", + " plt.Line2D([0], [0], color=dataset2name[dataset][\"color\"], linestyle=\"-\", linewidth=3, label=dataset2name[dataset][\"label\"])\n", + " ) \n", + " for model, model_name, marker in models:\n", + " custom_handles.append(\n", + " plt.Line2D([0], [0], color=\"black\", linestyle=\"none\", marker=marker, markersize=5, label=model_name)\n", + " )\n", + " fig.legend(handles=custom_handles, loc='upper right', bbox_to_anchor=(1.17, .95), title_fontsize=12, fontsize=10)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " fig.savefig(f\"../outputs/results_paper/corr_allinone_{method}.png\")\n", + "\n", + "\n", + "# metric = \"nce\"\n", + "metric = \"ner\"\n", + "# plot(metric, \"dp_calibration\", \"Relative Calibration Loss\")\n", + "# plot_all_in_one(\"dp_calibration\", \"Relative Calibration Loss\")\n", + "plot_all_in_one(\"dp_calibration\", \"Calibrated\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sst2. Min count: 196. Class: 0\n", + "agnews. Min count: 92. Class: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dbpedia. Min count: 35. Class: 11\n", + "20newsgroups. Min count: 25. Class: 0\n", + "banking77. Min count: 6. Class: 1\n" + ] + } + ], + "source": [ + "datset2test = {\n", + " \"sst2\": 400,\n", + " \"agnews\": 400,\n", + " \"dbpedia\": 700,\n", + " \"20newsgroups\": 800,\n", + " \"banking77\": 1000\n", + "}\n", + "\n", + "for dataset, test_size in datset2test.items():\n", + " df = pd.read_csv(f\"../data/{dataset}/all.csv\", header=0, index_col=0)\n", + " test_list = np.loadtxt(f\"../lists/{dataset}/test_{test_size}.txt\", dtype=int)\n", + " labels = df.loc[test_list,\"label\"].values\n", + " counts = np.bincount(labels)\n", + " print(f\"{dataset}. Min count: {np.min(counts)}. Class: {np.argmin(counts)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmcal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/data.ipynb b/notebooks/data.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..76ddaf0e56f27293dac887671e8f3a3e9c84589b --- /dev/null +++ b/notebooks/data.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 3, 3, 1, 2, 2, 2, 0, 0, 1, 1, 0, 0, 2, 1, 2, 2, 1, 1, 2, 3,\n", + " 1, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 4, 1, 0, 0, 2, 0, 0, 0, 0, 3,\n", + " 0, 0, 2, 0, 0, 1, 0, 1, 2, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 3, 1, 0,\n", + " 1, 2, 2, 1, 2, 2, 1, 0, 1])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = \"banking77\"\n", + "T = 3\n", + "seed = 0\n", + "data = pd.read_csv(f\"../data/{dataset}/all.csv\", index_col=0, header=0)\n", + "train_lst = np.loadtxt(f\"../lists/{dataset}/size={int(2**T)}/seed={seed}/0.0-1.0.txt\")\n", + "labels = data.loc[train_lst,\"label\"]\n", + "np.bincount(labels)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmcal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/distributions.ipynb b/notebooks/distributions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..48f735800d6bf5e8f8dd85a9a9d2c2ab8b409a5a --- /dev/null +++ b/notebooks/distributions.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.special import log_softmax, softmax\n", + "import matplotlib.pyplot as plt\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def load_data(dataset, size, seed):\n", + " test_samples = {\"sst2\": 400, \"agnews\": 400, \"dbpedia\": 700}[dataset]\n", + " uncal_logits = pd.read_csv(f'../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-1.0/0.7-1.0/test={dataset}/list=test_{test_samples}/logits.csv', header=None, index_col=0).values.astype(float)\n", + " uncal_logits = log_softmax(uncal_logits, axis=1)\n", + " uncal_labels = pd.read_csv(f'../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-1.0/0.7-1.0/test={dataset}/list=test_{test_samples}/labels.csv', header=None, index_col=0).values.astype(int).flatten()\n", + " cal_logits = pd.read_csv(f'../outputs/lora_plus_tempscaling/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-1.0/0.7-1.0/0.7-1.0/test={dataset}/list=test_{test_samples}/logits.csv', header=None, index_col=0).values.astype(float)\n", + " cal_logits = log_softmax(cal_logits, axis=1)\n", + " cal_labels = pd.read_csv(f'../outputs/lora_plus_tempscaling/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-1.0/0.7-1.0/0.7-1.0/test={dataset}/list=test_{test_samples}/labels.csv', header=None, index_col=0).values.astype(int).flatten()\n", + " alpha = float(torch.load(f'../outputs/lora_plus_tempscaling/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/last.ckpt',weights_only=False)[\"model\"][\"alpha\"])\n", + "\n", + " uncal_logits_train = pd.read_csv(f'../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0/logits.csv', header=None, index_col=0).values.astype(float)\n", + " uncal_logits_train = log_softmax(uncal_logits_train, axis=1)\n", + " uncal_labels_train = pd.read_csv(f'../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0/labels.csv', header=None, index_col=0).values.astype(int).flatten()\n", + " cal_logits_train = log_softmax(uncal_logits_train * alpha, axis=1)\n", + " cal_labeles_train = uncal_labels_train\n", + "\n", + " \n", + " ce_cal_vs_uncal = -np.mean(np.sum(np.exp(cal_logits_train) * uncal_logits_train, axis=1))\n", + " ce_data_vs_uncal = -np.mean(uncal_logits_train[range(len(uncal_labels_train)), uncal_labels_train])\n", + " \n", + " # print(f\"CE(q_hat, q) = {ce_cal_vs_uncal:.4f}\")\n", + " # print(f\"CE(data, q) = {ce_data_vs_uncal:.4f}\")\n", + " # print(f\"Rel err = {abs(ce_cal_vs_uncal - ce_data_vs_uncal)/ce_cal_vs_uncal:.4f}\")\n", + " # print()\n", + " rel_err = abs(ce_cal_vs_uncal - ce_data_vs_uncal)/ce_cal_vs_uncal\n", + " return uncal_logits, uncal_labels, cal_logits, cal_labels, alpha, rel_err\n", + "\n", + "# rel_err = 0\n", + "# # for size in [8, 16, 32, 64, 128, 256]:\n", + "# for size in [8]:\n", + "# for seed in range(5):\n", + "# uncal_logits, uncal_labels, cal_logits, cal_labels, alpha, rel_err = load_data(\"agnews\", size, seed)\n", + "# rel_err += rel_err\n", + "# print(alpha)\n", + "# # print(rel_err/5)\n", + "\n", + "# print((~np.isclose(log_softmax(uncal_logits * alpha, axis=1),log_softmax(cal_logits,axis=1))).sum())\n", + "# print(alpha)\n", + "uncal_logits, uncal_labels, cal_logits, cal_labels, alpha, rel_err = load_data(\"agnews\", 8, 0)\n", + "nbins = 20\n", + "fig, ax = plt.subplots(uncal_logits.shape[1], 1, figsize=(10, 10), sharex=True)\n", + "for i in range(uncal_logits.shape[1]):\n", + " for j in range(uncal_logits.shape[1]):\n", + " # i = j\n", + " # hist, edges = np.histogram(uncal_logits.max(axis=1), bins=nbins, density=True)\n", + " # ax[i].plot(edges[:-1], hist, label=f'winner', alpha=0.5, color=f'k', linestyle=':')\n", + " hist, edges = np.histogram(uncal_logits[uncal_labels==j, i]-uncal_logits[uncal_labels==j, 0], bins=nbins, density=True)\n", + " ax[i].plot(edges[:-1], hist, label=f'uncal: {j}', alpha=0.5, color=f'C{j}', linestyle='-')\n", + " hist, edges = np.histogram(cal_logits[cal_labels==j, i]-cal_logits[cal_labels==j, 0], bins=nbins, density=True)\n", + " ax[i].plot(edges[:-1], hist, label=f'cal: {j}', alpha=0.5, color=f'C{j}', linestyle='--')\n", + " ax[i].set_ylabel(f'class {i}')\n", + " # ax[i].set_xlim(-20, 0)\n", + "ax[-1].legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + }, + { + "data": { + "text/plain": [ + "array([,\n", + " ], dtype=object)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from expected_cost.utils import plot_hists\n", + "\n", + "uncal_logits, uncal_labels, cal_logits, cal_labels, alpha, rel_err = load_data(\"sst2\", 32, 3)\n", + "uncal_logits -= uncal_logits[:,0].reshape(-1,1)\n", + "print(alpha)\n", + "plot_hists(uncal_labels, uncal_logits, nbins=20, group_by='score')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ,\n", + " ,\n", + " ], dtype=object)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "uncal_logits, uncal_labels, cal_logits, cal_labels, alpha, rel_err = load_data(\"agnews\", 8, 2)\n", + "cal_logits -= cal_logits[:,0].reshape(-1,1)\n", + "plot_hists(cal_labels, cal_logits, nbins=20, group_by='score')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmcal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/fails.ipynb b/notebooks/fails.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4ee01ca650ce41e535564d93518a6d4be8eae9d8 --- /dev/null +++ b/notebooks/fails.ipynb @@ -0,0 +1,562 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.special import log_softmax\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test priors: [0.49 0.51]\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
stepsalphasbetasnerncetrain_priors
seed
015680.272969[0.0, 0.0]0.1632650.344810[0.47368421052631576, 0.5263157894736842]
115760.280585[0.0, 0.0]0.1734690.374176[0.631578947368421, 0.3684210526315789]
214670.350538[0.0, 0.0]0.1632650.377032[0.47368421052631576, 0.5263157894736842]
311.000000[0.0, 0.0]0.1377550.662122[0.42105263157894735, 0.5789473684210527]
414240.436886[0.0, 0.0]0.1581630.364137[0.7368421052631579, 0.2631578947368421]
\n", + "
" + ], + "text/plain": [ + " steps alphas betas ner nce \\\n", + "seed \n", + "0 1568 0.272969 [0.0, 0.0] 0.163265 0.344810 \n", + "1 1576 0.280585 [0.0, 0.0] 0.173469 0.374176 \n", + "2 1467 0.350538 [0.0, 0.0] 0.163265 0.377032 \n", + "3 1 1.000000 [0.0, 0.0] 0.137755 0.662122 \n", + "4 1424 0.436886 [0.0, 0.0] 0.158163 0.364137 \n", + "\n", + " train_priors \n", + "seed \n", + "0 [0.47368421052631576, 0.5263157894736842] \n", + "1 [0.631578947368421, 0.3684210526315789] \n", + "2 [0.47368421052631576, 0.5263157894736842] \n", + "3 [0.42105263157894735, 0.5789473684210527] \n", + "4 [0.7368421052631579, 0.2631578947368421] " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def load_steps(dataset, size, method=\"dpcal\", old=True):\n", + " steps = []\n", + " for seed in range(5):\n", + " filename = f\"../outputs/{'old/' if old else ''}lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/state.ckpt\"\n", + " state = torch.load(filename, weights_only=False)\n", + " steps.append(state[\"best_step_count\"])\n", + " return steps\n", + "\n", + "def load_params(dataset, size, method=\"dpcal\", old=True):\n", + " alphas, betas = [], []\n", + " for seed in range(5):\n", + " filename = f\"../outputs/{'old/' if old else ''}lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/state.ckpt\"\n", + " state = torch.load(filename, weights_only=False)\n", + " alphas.append(state['best_model']['alpha'].cpu().item())\n", + " betas.append(state['best_model']['beta'].cpu().numpy().astype(float))\n", + " return alphas, betas\n", + "\n", + "def load_priors(dataset, size):\n", + " priors = []\n", + " for seed in range(5):\n", + " filename = f\"../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0\"\n", + " labels = pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten()\n", + " priors.append(np.bincount(labels) / len(labels))\n", + " return priors\n", + "\n", + "def compute_ner(dataset, size, method=\"dpcal\", old=True):\n", + " ner = []\n", + " for seed in range(5):\n", + " filename = f\"../outputs/{'old/' if old else ''}lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/test={dataset}/list=test_400\"\n", + " logits = pd.read_csv(f\"{filename}/logits.csv\", header=None, index_col=0).values.astype(float)\n", + " labels = pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten()\n", + " priors = np.bincount(labels) / len(labels)\n", + " er = np.mean(logits.argmax(axis=1) != labels)\n", + " er_priors = np.mean(priors.argmax() != labels)\n", + " ner.append(er / er_priors)\n", + " return ner\n", + "\n", + "def compute_nce(dataset, size, method=\"dpcal\", old=True):\n", + " nce = []\n", + " for seed in range(5):\n", + " filename = f\"../outputs/{'old/' if old else ''}lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/test={dataset}/list=test_400\"\n", + " logits = pd.read_csv(f\"{filename}/logits.csv\", header=None, index_col=0).values.astype(float)\n", + " logits = log_softmax(logits, axis=1)\n", + " labels = pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten()\n", + " priors = np.bincount(labels) / len(labels)\n", + " ce = -np.mean(logits[np.arange(len(labels)), labels])\n", + " ce_priors = -np.mean(np.log(priors[labels]))\n", + " nce.append(ce / ce_priors)\n", + " return nce\n", + "\n", + "def load_test_priors(dataset, size, method=\"dpcal\", old=True):\n", + " # filename = f\"../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0\"\n", + " filename = f\"../outputs/{'old/' if old else ''}lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed=0/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/test={dataset}/list=test_400\"\n", + " labels = pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten()\n", + " priors = np.bincount(labels) / len(labels)\n", + " return priors\n", + "\n", + "\n", + "dataset = \"sst2\"\n", + "size = 32\n", + "method = \"tempscaling\"\n", + "old = False\n", + "steps = load_steps(dataset, size, method, old=old)\n", + "alphas, betas = load_params(dataset, size, method, old=old)\n", + "train_priors = load_priors(dataset, size)\n", + "ner = compute_ner(dataset, size, method, old=old)\n", + "nce = compute_nce(dataset, size, method, old=old)\n", + "test_priors = load_test_priors(dataset, size, method, old=old)\n", + "print(\"Test priors:\", test_priors)\n", + "\n", + "pd.DataFrame({\n", + " \"steps\": steps,\n", + " \"alphas\": alphas,\n", + " \"betas\": betas,\n", + " \"ner\": ner,\n", + " \"nce\": nce,\n", + " \"train_priors\": train_priors\n", + "}, index=pd.Index(range(5), name=\"seed\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test priors: [0.23 0.2625 0.2575 0.25 ]\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
stepsalphasbetasnerncetrain_priors
seed
021.000133[0.0, 0.0, 0.0, 0.0]0.3050850.772099[1.0]
121.000152[0.0, 0.0, 0.0, 0.0]0.1593220.350738[0.25, 0.0, 0.5, 0.25]
226870.085351[0.0, 0.0, 0.0, 0.0]0.3830510.737915[0.0, 0.5, 0.5]
35670.664049[0.0, 0.0, 0.0, 0.0]0.2338980.401422[0.25, 0.5, 0.0, 0.25]
411961.521518[0.0, 0.0, 0.0, 0.0]0.2305080.685006[0.0, 0.25, 0.25, 0.5]
\n", + "
" + ], + "text/plain": [ + " steps alphas betas ner nce \\\n", + "seed \n", + "0 2 1.000133 [0.0, 0.0, 0.0, 0.0] 0.305085 0.772099 \n", + "1 2 1.000152 [0.0, 0.0, 0.0, 0.0] 0.159322 0.350738 \n", + "2 2687 0.085351 [0.0, 0.0, 0.0, 0.0] 0.383051 0.737915 \n", + "3 567 0.664049 [0.0, 0.0, 0.0, 0.0] 0.233898 0.401422 \n", + "4 1196 1.521518 [0.0, 0.0, 0.0, 0.0] 0.230508 0.685006 \n", + "\n", + " train_priors \n", + "seed \n", + "0 [1.0] \n", + "1 [0.25, 0.0, 0.5, 0.25] \n", + "2 [0.0, 0.5, 0.5] \n", + "3 [0.25, 0.5, 0.0, 0.25] \n", + "4 [0.0, 0.25, 0.25, 0.5] " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = \"agnews\"\n", + "size = 8\n", + "method = \"tempscaling\"\n", + "old = False\n", + "steps = load_steps(dataset, size, method, old=old)\n", + "alphas, betas = load_params(dataset, size, method, old=old)\n", + "train_priors = load_priors(dataset, size)\n", + "ner = compute_ner(dataset, size, method, old=old)\n", + "nce = compute_nce(dataset, size, method, old=old)\n", + "test_priors = load_test_priors(dataset, size, method, old=old)\n", + "print(\"Test priors:\", test_priors)\n", + "\n", + "pd.DataFrame({\n", + " \"steps\": steps,\n", + " \"alphas\": alphas,\n", + " \"betas\": betas,\n", + " \"ner\": ner,\n", + " \"nce\": nce,\n", + " \"train_priors\": train_priors\n", + "}, index=pd.Index(range(5), name=\"seed\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calibrated CE: 0.20321426528170855\n", + "Calibrated NCE: 0.35259800781306166\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_distributions(dataset, size, seed, nbins=20, method=\"dpcal\"):\n", + "\n", + " # Miscalibrated posteriors\n", + " filename = f\"../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0\"\n", + " miscal_logits = pd.read_csv(f\"{filename}/logits.csv\", header=None, index_col=0).values.astype(float)\n", + " miscal_logits = log_softmax(miscal_logits, axis=1)\n", + " miscal_labels = pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten()\n", + "\n", + " # Calibrated posteriors\n", + " filename = f\"../outputs/old/lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/state.ckpt\"\n", + " state = torch.load(filename, weights_only=False)\n", + " alpha, beta = state[\"best_model\"][\"alpha\"].cpu().numpy(), state[\"best_model\"][\"beta\"].cpu().numpy()\n", + " cal_logits = log_softmax(miscal_logits * alpha + beta, axis=1)\n", + " cal_labels = miscal_labels.copy()\n", + "\n", + " ce = -np.mean(cal_logits[np.arange(len(cal_labels)), cal_labels])\n", + " priors = np.bincount(cal_labels) / len(cal_labels)\n", + " ce_priors = -np.mean(np.log(priors[cal_labels]))\n", + " print(\"Calibrated CE:\", ce)\n", + " print(\"Calibrated NCE:\", ce / ce_priors)\n", + "\n", + " num_classes = miscal_logits.shape[1]\n", + " fig, ax = plt.subplots(num_classes, 1, sharex=True)\n", + " for i in range(num_classes):\n", + " for j in range(num_classes):\n", + " # hist, edges = np.histogram(miscal_logits[miscal_labels==j, i]-miscal_logits[miscal_labels==j, 0], bins=nbins, density=True)\n", + " # ax[i].plot(edges[:-1], hist, label=f'class {j} pre-cal', alpha=0.5, color=f'C{j}', linestyle='-')\n", + " # hist, edges = np.histogram(cal_logits[cal_labels==j, i]-cal_logits[cal_labels==j, 0], bins=nbins, density=True)\n", + " # ax[i].plot(edges[:-1], hist, label=f'class {j} post-cal', alpha=0.5, color=f'C{j}', linestyle='--')\n", + " hist, edges = np.histogram(miscal_logits[miscal_labels==j, i], bins=nbins, density=True)\n", + " ax[i].plot(edges[:-1], hist, label=f'class {j} pre-cal', alpha=0.5, color=f'C{j}', linestyle='-')\n", + " hist, edges = np.histogram(cal_logits[cal_labels==j, i], bins=nbins, density=True)\n", + " ax[i].plot(edges[:-1], hist, label=f'class {j} post-cal', alpha=0.5, color=f'C{j}', linestyle='--')\n", + " # hist, edges, bar = ax[i].hist(miscal_logits[miscal_labels==j, i], bins=nbins, density=True, label=f'class {j} pre-cal', alpha=0.5, color=f'C{j}')\n", + " # print(hist, edges)\n", + "\n", + " # print(miscal_logits[miscal_labels==j, i])\n", + " # ax[i].hist(cal_logits[cal_labels==j, i], bins=nbins, density=True, label=f'class {j} post-cal', alpha=0.5, color=f'C{j}')\n", + " ax[i].set_ylabel(f'class {i}')\n", + " ax[-1].legend(loc='upper right', bbox_to_anchor=(1.4, 1))\n", + " \n", + "plot_distributions(dataset, size, 4, nbins=20, method=method)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calibrated CE: 0.20321426528170855\n", + "Calibrated NCE: 0.35259800781306166\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_scores(dataset, size, seed, nbins=20, method=\"dpcal\"):\n", + "\n", + " # Miscalibrated posteriors\n", + " filename = f\"../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0\"\n", + " miscal_logits = pd.read_csv(f\"{filename}/logits.csv\", header=None, index_col=0).values.astype(float)\n", + " miscal_logits = log_softmax(miscal_logits, axis=1)\n", + " miscal_labels = pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten()\n", + "\n", + " # Calibrated posteriors\n", + " filename = f\"../outputs/old/lora_plus_{method}/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/0.7-1.0/state.ckpt\"\n", + " state = torch.load(filename, weights_only=False)\n", + " alpha, beta = state[\"best_model\"][\"alpha\"].cpu().numpy(), state[\"best_model\"][\"beta\"].cpu().numpy()\n", + " cal_logits = log_softmax(miscal_logits * alpha + beta, axis=1)\n", + " cal_labels = miscal_labels.copy()\n", + "\n", + " ce = -np.mean(cal_logits[np.arange(len(cal_labels)), cal_labels])\n", + " priors = np.bincount(cal_labels) / len(cal_labels)\n", + " ce_priors = -np.mean(np.log(priors[cal_labels]))\n", + " print(\"Calibrated CE:\", ce)\n", + " print(\"Calibrated NCE:\", ce / ce_priors)\n", + "\n", + " num_classes = miscal_logits.shape[1]\n", + " fig, ax = plt.subplots(num_classes, 1, sharex=True)\n", + " for i in range(num_classes):\n", + " miscal_scores = -miscal_logits[miscal_labels==i,i]\n", + " hist, edges = np.histogram(miscal_scores, bins=nbins, density=True)\n", + " ax[i].plot(edges[:-1], hist, label=f'pre-cal', alpha=0.5, color=f'tab:blue', linestyle='-')\n", + " cal_scores = -cal_logits[cal_labels==i,i]\n", + " hist, edges = np.histogram(cal_scores, bins=nbins, density=True)\n", + " ax[i].plot(edges[:-1], hist, label=f'post-cal', alpha=0.5, color=f'tab:orange', linestyle='-')\n", + " ax[i].set_ylabel(f'class {i}')\n", + " ax[-1].legend(loc='upper right', bbox_to_anchor=(1.4, 1))\n", + " \n", + "plot_scores(dataset, size, 4, nbins=20, method=method)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calibrated CE: 2.2178437021873257e-06\n", + "Calibrated NCE: 3.848190824339659e-06\n", + "alpha: Parameter containing:\n", + "tensor(9.0929, dtype=torch.float64, requires_grad=True)\n" + ] + } + ], + "source": [ + "from psrcal.calibration import AffineCalLogLoss\n", + "import torch\n", + "\n", + "def calibrate(dataset, size, seed):\n", + "\n", + " # Miscalibrated posteriors\n", + " filename = f\"../outputs/finetune_lora/llama3.2-1b-instruct/{dataset}/size={size}/seed={seed}/lora_ans_no_es/0.0-0.7/0.0-0.3/test={dataset}/list=0.7-1.0\"\n", + " miscal_logits = pd.read_csv(f\"{filename}/logits.csv\", header=None, index_col=0).values.astype(float)\n", + " miscal_logits = torch.from_numpy(log_softmax(miscal_logits, axis=1))\n", + " miscal_labels = torch.from_numpy(pd.read_csv(f\"{filename}/labels.csv\", header=None, index_col=0).values.astype(int).flatten())\n", + "\n", + " model = AffineCalLogLoss(miscal_logits, miscal_labels, bias=True, scale=True, priors=None)\n", + " model.train()\n", + "\n", + " cal_logits = model.calibrate(miscal_logits).cpu().detach().numpy()\n", + " cal_labels = miscal_labels.cpu().detach().numpy()\n", + "\n", + " ce = -np.mean(cal_logits[np.arange(len(cal_labels)), cal_labels])\n", + " priors = np.bincount(cal_labels) / len(cal_labels)\n", + " ce_priors = -np.mean(np.log(priors[cal_labels]))\n", + " print(\"Calibrated CE:\", ce)\n", + " print(\"Calibrated NCE:\", ce / ce_priors)\n", + " print(\"alpha:\", model.temp)\n", + "\n", + "calibrate(\"sst2\", 32, 4)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmcal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/priors.ipynb b/notebooks/priors.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..eaccf51c234c53029178ddb01c4fc93a49a142a9 --- /dev/null +++ b/notebooks/priors.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_lists = {\n", + " \"sst2\": \"test_400\",\n", + " \"agnews\": \"test_400\",\n", + " \"dbpedia\": \"test_700\",\n", + " \"20newsgroups\": \"test_800\",\n", + " \"banking77\": \"test_1000\",\n", + "}\n", + "\n", + "dataset2name = {\n", + " \"sst2\": \"SST-2\",\n", + " \"agnews\": \"AG News\",\n", + " \"dbpedia\": \"DBpedia\",\n", + " \"20newsgroups\": \"20 Newsgroups\",\n", + " \"banking77\": \"Banking77\",\n", + "}\n", + "\n", + "def plot_priors(datasets):\n", + "\n", + " # fig, ax = plt.subplots(1, len(datasets), figsize=(5*len(datasets), 5))\n", + " fig = plt.figure(figsize=(5*len(datasets), 5))\n", + " cum_width = 0\n", + "\n", + " factor = 1.\n", + " for i, dataset in enumerate(datasets):\n", + " test_list = np.loadtxt(f\"../lists/{dataset}/{test_lists[dataset]}.txt\", dtype=int)\n", + " df = pd.read_csv(f\"../data/{dataset}/all.csv\", header=0, index_col=0)\n", + " labels = df.loc[:,\"label\"].values\n", + " priors = np.bincount(labels[test_list]) / len(test_list)\n", + " n_classes = priors.size\n", + " \n", + " width = n_classes / 50\n", + " ax = fig.add_axes([cum_width, 0, width * factor, 1])\n", + " cum_width += width * factor + 0.05\n", + " factor = 0.8 * factor\n", + " ax.bar(range(len(priors)), np.sort(priors)[::-1])\n", + " ax.set_title(dataset2name[dataset], fontsize=28)\n", + " ax.set_xlim(-1, n_classes)\n", + " ax.set_xticks([])\n", + " ax.set_yticks(ax.get_yticks())\n", + " ax.set_yticklabels(ax.get_yticklabels(), fontsize=15)\n", + "\n", + " \n", + " # fig.suptitle(\"Prior Probabilities for each dataset sorted by number\", fontsize=16, y=1.12)\n", + "\n", + " # plt.tight_layout()\n", + "\n", + "\n", + "plot_priors([\"sst2\", \"agnews\", \"dbpedia\", \"20newsgroups\", \"banking77\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmcal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/samples.ipynb b/notebooks/samples.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..492c9d860b0c6e475f060db5fb674b34c0819e46 --- /dev/null +++ b/notebooks/samples.ipynb @@ -0,0 +1,60 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of samples: [ 16. 32. 64. 128. 256.] (x2)\n", + "Number of samples: [ 8. 16. 32. 64. 128.] (x4)\n", + "Number of samples: [ 4. 8. 16. 32. 64.] (x14)\n", + "Number of samples: [ 4. 8. 16. 32. 64.] (x20)\n", + "Number of samples: [ 2. 4. 8. 16. 32.] (x77)\n" + ] + } + ], + "source": [ + "# This is equivalent to round factor / log2(num_classes) to the nearest power of 2\n", + "factor = np.array([16, 32, 64, 128, 256])\n", + "for num_classes in [2, 4, 14, 20, 77]:\n", + " scale = factor / np.log2(num_classes)\n", + " nearest_power_of_2 = 2 ** np.round(np.log2(scale)) # round to nearest power of 2\n", + " print(\"Number of samples: \", nearest_power_of_2, f\"(x{num_classes})\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llmcal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..05f15d53caaa2f17d5151c30c46f064ceb4b30d2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,22 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "llmcal" +version = "0.0.1" +authors = [ + { name="Lautaro Estienne", email="lestienne@fi.uba.ar" }, +] +description = "Direction Preserving Calibration on LLMs" +readme = "README.md" +requires-python = ">=3.11" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +[project.urls] +Homepage = "https://github.com/LautaroEst/llmcal" +Issues = "https://github.com/LautaroEst/llmcal/issues" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e7ca72e5dbb3505ac2c2d306d6dea014bfea0ee6 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,8 @@ +litgpt[all]==0.5.3 +fire +scikit-learn +matplotlib +jupyter +nbclassic +psrcal +expected_cost \ No newline at end of file diff --git a/scripts/all.sh b/scripts/all.sh new file mode 100644 index 0000000000000000000000000000000000000000..ced6cc364012139a4b9218cfba89304d7eaa3b52 --- /dev/null +++ b/scripts/all.sh @@ -0,0 +1,6 @@ +#!/bin/bash -ex + +./scripts/prepare_data.sh +./scripts/cal_vs_samples.sh +./scripts/lora_vs_samples.sh +./scripts/lora_plus_cal_vs_samples.sh \ No newline at end of file