{
  "creado": "2026-09-30T17:55:30Z",
  "corte": "119-C3",
  "tipo_de_ejecucion": "pasada",
  "motor": "matrixai.dense.tabm_cpu",
  "configuracion_del_motor": {
    "declarada_por_la_enmienda_1": {
      "k": 8,
      "d_block": 256,
      "n_blocks": 2
    },
    "leida_del_motor": {
      "k": 8,
      "d_block": 256,
      "n_blocks": 2
    },
    "constantes": {
      "k": "K_CABEZAS",
      "d_block": "D_BLOCK",
      "n_blocks": "N_BLOCKS"
    },
    "modulo": "matrixai_engines.motores.densa_tabm",
    "receta_del_protocolo": {
      "k": {
        "valor": 8,
        "de": "protocolo_119_v4_enmienda_1.json: que_cambia"
      },
      "d_block": {
        "valor": 256,
        "de": "protocolo_119_v4_enmienda_1.json: que_cambia"
      },
      "n_blocks": {
        "valor": 2,
        "de": "protocolo_119_v4_enmienda_1.json: que_cambia"
      },
      "dropout": {
        "valor": 0.1,
        "de": "protocolo_119_v4.json: arquitectura.backbone_ensamblado.parametros_por_omision_de_TabM_make.dropout (la enmienda 1 solo cambia k y d_block)"
      },
      "d_embedding": {
        "valor": 24,
        "de": "protocolo_119_v4.json: arquitectura.embeddings_numericas.parametros_por_omision_del_constructor.d_embedding (también el de las categóricas: embeddings_categoricas.decision_del_119)"
      },
      "n_frequencies": {
        "valor": 48,
        "de": "protocolo_119_v4.json: arquitectura.embeddings_numericas.parametros_por_omision_del_constructor.n_frequencies"
      },
      "frequency_init_scale": {
        "valor": 0.01,
        "de": "protocolo_119_v4.json: arquitectura.embeddings_numericas.parametros_por_omision_del_constructor.frequency_init_scale"
      },
      "optimizador": {
        "valor": "adamw",
        "de": "protocolo_119_v4.json: receta_entrenamiento.optimizador"
      },
      "tasa_de_aprendizaje": {
        "valor": 0.002,
        "de": "protocolo_119_v4.json: receta_entrenamiento.learning_rate"
      },
      "weight_decay": {
        "valor": 0.0003,
        "de": "protocolo_119_v4.json: receta_entrenamiento.weight_decay"
      },
      "recorte_de_gradiente": {
        "valor": 1.0,
        "de": "protocolo_119_v4.json: receta_entrenamiento.recorte_de_gradiente.norma_maxima"
      },
      "lote": {
        "valor": 256,
        "de": "protocolo_119_v4.json: receta_entrenamiento.batch_size (aplicado: min(256, filas de train))"
      },
      "paciencia": {
        "valor": 16,
        "de": "protocolo_119_v4.json: receta_entrenamiento.parada_temprana.paciencia_epocas"
      },
      "fraccion_del_presupuesto_para_entrenar": {
        "valor": 0.75,
        "de": "protocolo_119_v4.json: receta_entrenamiento.epocas_maximas.aplicado_aqui («0,75 del presupuesto de pared»)"
      },
      "semilla_del_ruido_de_cuantiles": {
        "valor": 0,
        "de": "protocolo_119_v4.json: arquitectura.preprocesado_numericas.ruido_antes_de_ajustar.distribucion"
      },
      "para_tras_epocas_sin_mejora": {
        "valor": 17,
        "de": "protocolo_119_v4_enmienda_2.json: se_fija.paciencia"
      },
      "metrica_de_parada": {
        "valor": "validation_loss_del_ensamblado",
        "de": "protocolo_119_v4_enmienda_2.json: se_fija.parada_temprana («la pérdida del ENSAMBLADO en validación»), con el nombre que le da el motor"
      }
    },
    "constantes_de_la_receta_leidas_del_motor": {
      "k": 8,
      "d_block": 256,
      "n_blocks": 2,
      "dropout": 0.1,
      "d_embedding": 24,
      "n_frequencies": 48,
      "frequency_init_scale": 0.01,
      "tasa_de_aprendizaje": 0.002,
      "weight_decay": 0.0003,
      "recorte_de_gradiente": 1.0,
      "lote": 256,
      "paciencia": 16,
      "fraccion_del_presupuesto_para_entrenar": 0.75,
      "semilla_del_ruido_de_cuantiles": 0
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    "receta_medida_en_un_ajuste": {
      "usado": {
        "optimizador": "adamw",
        "tasa_de_aprendizaje": 0.002,
        "weight_decay": 0.0003,
        "k": 8,
        "d_block": 256,
        "n_blocks": 2,
        "dropout": 0.1,
        "d_embedding": 24,
        "n_frequencies": 48,
        "frequency_init_scale": 0.01,
        "recorte_de_gradiente": 1.0,
        "lote": 256,
        "semilla_del_ruido_de_cuantiles": 0,
        "fraccion_del_presupuesto_para_entrenar": 0.75
      },
      "d_embedding_de_la_preparacion": 24,
      "declarado_por_el_predictor": {
        "paciencia": 16,
        "para_tras_epocas_sin_mejora": 17,
        "metrica_de_parada": "validation_loss_del_ensamblado"
      },
      "clase_del_optimizador": "torch.optim.adamw.AdamW",
      "filas_de_train": 300,
      "presupuesto_s": 20.0,
      "lotes_de_entrenamiento_vistos": [
        44,
        256
      ],
      "epocas": 28,
      "como_se_mide": "un ajuste de verdad (MotorDensaTabM().fit, 300 filas al azar, 1 hilo) con espías en torch.optim.Optimizer.__init__, RedTabMPLR.__init__/forward, torch.nn.utils.clip_grad_norm_ y preparacion_tabm.ajustar_preparacion; el plazo, por el que declara el predictor. La paciencia y la métrica de parada, por lo que DECLARA el predictor"
    }
  },
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      "version": "119-C0.v4",
      "digest_sha256_declarado": "50fc56a1c688c057cca643953c7659e73731915f59c1cf32f6419c14992299a4",
      "cuadra_con_su_digest": true
    },
    "enmienda_1": {
      "enmienda": "119-C0.v4.e1",
      "digest_sha256_declarado": "bcd27c8370c2880ac1e5049e5b5dfd797084e898c847223d4d35e7e45eb0c2ec",
      "cuadra_con_su_digest": true,
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        "digest_sha256": "50fc56a1c688c057cca643953c7659e73731915f59c1cf32f6419c14992299a4"
      },
      "cita_el_v4": true
    },
    "bloques_identicos_v2_v4": {
      "particion": true,
      "presupuesto": true,
      "regla_de_cierre": true,
      "datasets": true
    },
    "como_se_comprueba": "autodigest = sha256 del JSON sin `digest_sha256`, claves ordenadas, separadores compactos, sin escapar; los bloques, por su JSON canónico"
  },
  "enmienda_2": {
    "ruta": "protocolo_119_v4_enmienda_2.json",
    "presente": true,
    "cuadra": true,
    "problemas": [],
    "enmienda": "119-C0.v4.e2",
    "sha256_del_fichero": "fcb154c67e2d7f0e06ec62d9ba843de4766b76d50fa1f622cb94032eace4d118",
    "digest_sha256_declarado": "c35a737d5cdcea30bca08cbff8cb4f5e338e0f67b73aea09f6f9d2b38493ed33",
    "de": {
      "protocolo": "119-C0.v4",
      "digest_sha256": "50fc56a1c688c057cca643953c7659e73731915f59c1cf32f6419c14992299a4",
      "enmienda_anterior": {
        "enmienda": "119-C0.v4.e1",
        "digest_sha256": "bcd27c8370c2880ac1e5049e5b5dfd797084e898c847223d4d35e7e45eb0c2ec"
      }
    }
  },
  "allstate_declarado": "Allstate_Claims_Severity entrena las épocas que quepan en el plazo (≈10, menos que las 17 de la regla) y se queda con la mejor época vista: el plazo lo corta, como a la densa de hoy. La paciencia (16) casi no podrá actuar. Se declara en el resultado de C3 junto a su cifra.",
  "allstate_medido": {
    "medido": true,
    "detalle": [
      {
        "repeticion": 0,
        "pliegue": 0,
        "estado": "completed",
        "epocas_ejecutadas": 5,
        "mejor_epoca": 4,
        "parado_por_plazo": true
      },
      {
        "repeticion": 0,
        "pliegue": 1,
        "estado": "completed",
        "epocas_ejecutadas": 5,
        "mejor_epoca": 4,
        "parado_por_plazo": true
      },
      {
        "repeticion": 0,
        "pliegue": 2,
        "estado": "completed",
        "epocas_ejecutadas": 5,
        "mejor_epoca": 4,
        "parado_por_plazo": true
      },
      {
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        "pliegue": 3,
        "estado": "completed",
        "epocas_ejecutadas": 5,
        "mejor_epoca": 4,
        "parado_por_plazo": true
      },
      {
        "repeticion": 0,
        "pliegue": 4,
        "estado": "completed",
        "epocas_ejecutadas": 5,
        "mejor_epoca": 4,
        "parado_por_plazo": true
      }
    ],
    "epocas_min": 5,
    "epocas_max": 5,
    "minimo_de_la_regla": 17,
    "todos_por_debajo_del_minimo": true
  },
  "parada_temprana_declarada": {
    "registrado_en_el_v4": "validation_loss / la metrica de validacion del arnes -- mismo criterio que 118-C3.receta",
    "fija_la_enmienda_2": "sobre la pérdida del ENSAMBLADO en validación (log-loss de las probabilidades promediadas de las k cabezas; en regresión, error cuadrático de la media), como la fuente («optimal for the ensemble, not for individual MLPs»; evaluate() de example.ipynb). El protocolo decía «validation_loss», ambiguo. Se entrena con la media de las k pérdidas, sin cambio.",
    "metrica_que_declaran_los_intentos": [
      "validation_loss_del_ensamblado"
    ],
    "por_tarea": {
      "regression": "como la fuente: el error cuadrático del ensamblado (la fuente, −RMSE: ordenan igual)",
      "binary_classification": "NO como la fuente: log-loss del ensamblado; la fuente para con su accuracy",
      "multiclass_classification": "NO como la fuente: log-loss del ensamblado; la fuente para con su accuracy"
    },
    "en_clasificacion_no_es_como_la_fuente": "la fuente para en clasificación con la ACCURACY del ensamblado (accuracy_score de la media de las k predicciones), que tiene mesetas y empates que cuenta como «sin mejora»; este motor para con la LOG-LOSS de las probabilidades promediadas, que no los tiene. Cambia qué época se guarda y cuándo actúa la paciencia, o sea los números. El código cumple lo REGISTRADO (el v4 decía validation_loss); lo que no es cierto es el «como la fuente» de la enmienda 2 en clasificación. No se cambia: la enmienda va sellada y cambiar la parada después de auditar sería otra receta",
    "cita_de_la_fuente": "score = (-(sklearn.metrics.mean_squared_error(y_true, y_pred) ** 0.5) if task_type == 'regression' else sklearn.metrics.accuracy_score(y_true, y_pred.argmax(1)))  (example.ipynb, evaluate(), rama main)",
    "conjuntos_de_clasificacion_en_esta_ejecucion": [
      "APSFailure",
      "Amazon_employee_access",
      "Internet-Advertisements",
      "KDDCup09_appetency",
      "PhishingWebsites",
      "balance-scale",
      "climate-model-simulation-crashes",
      "connect-4",
      "diabetes",
      "dresses-sales",
      "jm1",
      "kc2",
      "kick",
      "mfeat-factors",
      "micro-mass",
      "okcupid-stem",
      "optdigits",
      "ozone-level-8hr",
      "pc1",
      "pc4",
      "pendigits",
      "sick",
      "wilt",
      "yeast"
    ],
    "de_donde_sale": "auditoría 3 del 119 (30-09), hallazgo I3"
  },
  "procedencia": {
    "medido": "2026-09-30T17:11:17Z",
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      "matrixAI": {
        "commit": "f54caf3a1ae0295541f42ce4733024dd35627cb2",
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      "matrixai_core": "1.11.1",
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      "xgboost": "3.4.1",
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        "como_se_midio": "`sys.modules` del proceso que sella, resuelto a rutas absolutas. Medido, no declarado.",
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    "wine_quality": {
      "plan_digest": "fe76feaff18816a77d8978ec9957f0ddcc65d905f88f8336f12d639dce9eb107",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "jm1": {
      "plan_digest": "483d31b32e5d296cb9157784951ce916385b23c7acf21f75d43da9a69af00bd3",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "pendigits": {
      "plan_digest": "85f9dfb58797a389e50f89951229b463b9ccf0e8928ea895e2e21dfa79adbd6c",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "PhishingWebsites": {
      "plan_digest": "00ace3a7c2d2102269404e165d9eda59c775f36d2c66ac4086bd308e398f4ed7",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "house_sales": {
      "plan_digest": "48688476f7799492cdaea94081751cbc697d740f65d03d7b16d5b08ab3149470",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "house_16H": {
      "plan_digest": "35ed9717d3061f760636d578379a2722aeccd220fea45f216839f8913b56edb1",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "Amazon_employee_access": {
      "plan_digest": "a09ed2195c75e10687418e976301c2bf34472d37d9ee721766132a88ff5ea678",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "KDDCup09_appetency": {
      "plan_digest": "57fdb98aad1d7a2e1dbc57b120d45971854b50086ed232db0d01598809dfa8c9",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "okcupid-stem": {
      "plan_digest": "4c57aabc2174acedf40c324da4ecdda38516bf559792a05a0748e8ab3977a2a7",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "connect-4": {
      "plan_digest": "ca82cc9250a77a600c0a6999e6361b9f86a88da4a3c9d5ec73b5c14be201fbba",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "kick": {
      "plan_digest": "e07336e0e1c8c47657c603470339e671d04f9a92a76227b7468b9af258e35df6",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "APSFailure": {
      "plan_digest": "78607228fe74d3f53d5562389384874383c5382ff764a594fdbce7354b15c8c4",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    },
    "Allstate_Claims_Severity": {
      "plan_digest": "303bb345c00a4be24c4edfb0157bdeb4cee120908d231ac6a56759e0e1d965cb",
      "identica_a_la_v2": true,
      "lectura_identica_a_la_v2": true
    }
  },
  "pliegues_digest_por_dataset": {
    "dresses-sales": "fbe3c37e1b0bc4ee3cfb1d6c7cfdfc591b0dfdac1529eba17bdde562870e87f2",
    "kc2": "dbe92641ff8053a36b13b633603c190b26240492806c21f02a9f4a48a08661ec",
    "climate-model-simulation-crashes": "e6f7c6f60991e0b4bb0674d69a6e61bcecc2e2e03f0c57cab0bd99796dcec58b",
    "micro-mass": "eebfeb3e5c71d1e315435609ea727d38c3c9576b9bc55fb6352ea4d1947830ad",
    "balance-scale": "2caa0f277c4add94f23aa9c48adb4d2931bd9274c4ed2e3ce267008c60e6693f",
    "diabetes": "28e173b0b865abefc0dedc14d9c3547708a4908dcbeabd26db20f4b438f317ac",
    "pc1": "96afedb5703e69f2217370f9b97decde129959bf2e4159d32699af3ca5766fbc",
    "Moneyball": "194e93efaef877b75537d05a015d1e36da05867e557b5b1c628300b3d5024058",
    "pc4": "9b827a5fdf9b42cd920de7e421521bb8d27c0c33974c6f21b4d56ac081bcd56b",
    "house_prices_nominal": "83a6503149b9aed0828be351289d58830a9710c3d734697f40d093257779c31d",
    "yeast": "267f667072f9a4b84f29e06e743241eec6b38720d278240ce2d0dac788341bbe",
    "us_crime": "3afe244f33ecf46139226960a75c3bae80458ccb947dbe643c241732ff9cbbd8",
    "mfeat-factors": "6df11b8abadb2a768130d1aa36f6374c1340f76fbfbf3c5fa52fdf45116dd3e7",
    "ozone-level-8hr": "f0d0de2ae24202c952d55fe8368d5bde8fd3d56452cf6a72b15a9185e43dcad5",
    "Internet-Advertisements": "e2022a5fd34def26d78a7f0cda0cbd1ec98ba94c71e11969bb2c8a9d1eae1dd8",
    "sick": "adc3b92df7028e8e04dcb39b8c80b4deed2f141e6860e5628ffc1f77e6691a03",
    "SAT11-HAND-runtime-regression": "2f449d8bc1d07f66d84649641b668c3b7935ff944a3886c68d2499224ce088d7",
    "wilt": "4509ee3153149b60cbefeeabaa1cab10bb1535dbccaf6759913e244327eb887d",
    "optdigits": "c233366dba5451e7ac99978558fc077686c8db68f36295c534d020c7daf50f79",
    "wine_quality": "5177627272017d935aca0e75f029e87c9e54924dbe0d10b71da2e5976c19986a",
    "jm1": "14d7eb3357921e662ee000ce627253ccc96957d2dd9efb89a96946d04b7416e9",
    "pendigits": "6966b04b47f128fca3e4ed65b48e7d1f610facec021013c9c5592ed86997b7a1",
    "PhishingWebsites": "aebc903b945bd751247ea8e60ad2f3436b5fa5d15828d98f940bc67df071c9dd",
    "house_sales": "be842483553b684c9aba06e39d1ddafa48313645f760f996841bf97ba5ab61b1",
    "house_16H": "77de9ce67dbb2e7292dba8ec270043c56a5bc2ebd0b6ad88ef2e8974a21f3f5b",
    "Amazon_employee_access": "3d7c079d01c7a368ddb001b16a8b6676e950c6a30c258f8ba1e8cecd7ab20c7e",
    "KDDCup09_appetency": "01beec6a4213cb9164e61636e371e006ecf02c2e027020a2f9fce77e4e4d7992",
    "okcupid-stem": "e4b09395e3fba1c31bef4d96cd929896707366b7529629ae8012151f0c7b406e",
    "connect-4": "b19d911dfd600f39b8e81797cf11b489670ce02e0c72b91d1132622ce0fce677",
    "kick": "a7bc4871021148e6f2192216db9a1961868270ca7629bda92eddef76fd8b174c",
    "APSFailure": "1e0e2019b5f23f4205026b7d556fd5a432e84fea3bb72b0d1f899e96a2a58cf6",
    "Allstate_Claims_Severity": "33f078c1f6b15df339dd8c082dd808482a583ca3a220e61af27e8e6352a5f2be"
  },
  "pliegues_digest_que_es": "digest de la asignación de pliegues (entrena/valida por repetición): la v2 no lo registró, así que con la v2 solo se compara `particion_por_dataset` (plan_digest incluido)",
  "tiempos_del_padre_por_dataset": {
    "dresses-sales": {
      "carga_s": 0.048,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "kc2": {
      "carga_s": 0.032,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "climate-model-simulation-crashes": {
      "carga_s": 0.032,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "micro-mass": {
      "carga_s": 1.305,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "balance-scale": {
      "carga_s": 0.025,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "diabetes": {
      "carga_s": 0.031,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "pc1": {
      "carga_s": 0.064,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "Moneyball": {
      "carga_s": 0.05,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "pc4": {
      "carga_s": 0.129,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "house_prices_nominal": {
      "carga_s": 0.341,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "yeast": {
      "carga_s": 0.08,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "us_crime": {
      "carga_s": 0.54,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "mfeat-factors": {
      "carga_s": 0.904,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "ozone-level-8hr": {
      "carga_s": 0.363,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "Internet-Advertisements": {
      "carga_s": 8.86,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "sick": {
      "carga_s": 0.281,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "SAT11-HAND-runtime-regression": {
      "carga_s": 1.156,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "wilt": {
      "carga_s": 0.148,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "optdigits": {
      "carga_s": 0.743,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "wine_quality": {
      "carga_s": 0.252,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "jm1": {
      "carga_s": 1.069,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "pendigits": {
      "carga_s": 0.718,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "PhishingWebsites": {
      "carga_s": 1.02,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "house_sales": {
      "carga_s": 1.565,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "house_16H": {
      "carga_s": 1.087,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "Amazon_employee_access": {
      "carga_s": 4.451,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "KDDCup09_appetency": {
      "carga_s": 45.001,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "okcupid-stem": {
      "carga_s": 24.496,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "connect-4": {
      "carga_s": 6.241,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "kick": {
      "carga_s": 8.857,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "APSFailure": {
      "carga_s": 35.34,
      "preparacion_s": 0.0,
      "n_preparaciones": 0
    },
    "Allstate_Claims_Severity": {
      "carga_s": 53.145,
      "preparacion_s": 165.091,
      "n_preparaciones": 5
    }
  },
  "tiempos_del_padre_que_son": "carga_s = leer el ARFF y particionar (una vez por conjunto); preparacion_s = preparar_para_motor y construir las particiones de los intentos EJECUTADOS. Ninguno está dentro de wall_s, que es solo ejecutar_intento_aislado",
  "criterio_de_los_conjuntos": "los 32 NO sellados del protocolo v4 (idénticos en particion/presupuesto/regla_de_cierre/datasets a protocolo_exploratorio_v2.json, comprobado al arrancar), o el subconjunto de --solo o --humo, declarado arriba. Los 8 sellados se niegan",
  "datasets_declarados": [
    {
      "data_id": 23381,
      "nombre": "dresses-sales",
      "cubo": "pequeno",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "Class",
      "n_clases": 2,
      "clase_positiva": "2",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 1063,
      "nombre": "kc2",
      "cubo": "pequeno",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "problems",
      "n_clases": 2,
      "clase_positiva": "yes",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 40994,
      "nombre": "climate-model-simulation-crashes",
      "cubo": "pequeno",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "outcome",
      "n_clases": 2,
      "clase_positiva": "0",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 1515,
      "nombre": "micro-mass",
      "cubo": "pequeno",
      "tarea": "multiclass_classification",
      "sellado": false,
      "objetivo": "Class",
      "n_clases": 20,
      "clase_positiva": "8",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 11,
      "nombre": "balance-scale",
      "cubo": "pequeno",
      "tarea": "multiclass_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 3,
      "clase_positiva": "B",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 37,
      "nombre": "diabetes",
      "cubo": "pequeno",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 2,
      "clase_positiva": "tested_positive",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 1068,
      "nombre": "pc1",
      "cubo": "pequeno",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "defects",
      "n_clases": 2,
      "clase_positiva": "true",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 41021,
      "nombre": "Moneyball",
      "cubo": "pequeno",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "RS",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 1049,
      "nombre": "pc4",
      "cubo": "pequeno",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "c",
      "n_clases": 2,
      "clase_positiva": "TRUE",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 42563,
      "nombre": "house_prices_nominal",
      "cubo": "pequeno",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "SalePrice",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 181,
      "nombre": "yeast",
      "cubo": "pequeno",
      "tarea": "multiclass_classification",
      "sellado": false,
      "objetivo": "class_protein_localization",
      "n_clases": 10,
      "clase_positiva": "ERL",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 42730,
      "nombre": "us_crime",
      "cubo": "pequeno",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "ViolentCrimesPerPop",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 12,
      "nombre": "mfeat-factors",
      "cubo": "pequeno",
      "tarea": "multiclass_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 10,
      "clase_positiva": "1",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 1487,
      "nombre": "ozone-level-8hr",
      "cubo": "mediano",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "Class",
      "n_clases": 2,
      "clase_positiva": "2",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 40978,
      "nombre": "Internet-Advertisements",
      "cubo": "mediano",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 2,
      "clase_positiva": "ad",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 38,
      "nombre": "sick",
      "cubo": "mediano",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "Class",
      "n_clases": 2,
      "clase_positiva": "sick",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 41980,
      "nombre": "SAT11-HAND-runtime-regression",
      "cubo": "mediano",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "runtime",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 40983,
      "nombre": "wilt",
      "cubo": "mediano",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 2,
      "clase_positiva": "2",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 28,
      "nombre": "optdigits",
      "cubo": "mediano",
      "tarea": "multiclass_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 10,
      "clase_positiva": "0",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 287,
      "nombre": "wine_quality",
      "cubo": "mediano",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "quality",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 1053,
      "nombre": "jm1",
      "cubo": "mediano",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "defects",
      "n_clases": 2,
      "clase_positiva": "true",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 32,
      "nombre": "pendigits",
      "cubo": "mediano",
      "tarea": "multiclass_classification",
      "sellado": false,
      "objetivo": "class",
      "n_clases": 10,
      "clase_positiva": "3",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 4534,
      "nombre": "PhishingWebsites",
      "cubo": "mediano",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "Result",
      "n_clases": 2,
      "clase_positiva": "-1",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 42731,
      "nombre": "house_sales",
      "cubo": "grande",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "price",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 574,
      "nombre": "house_16H",
      "cubo": "grande",
      "tarea": "regression",
      "sellado": false,
      "objetivo": "price",
      "n_clases": null,
      "clase_positiva": null,
      "estratificado_por_el_objetivo": false
    },
    {
      "data_id": 4135,
      "nombre": "Amazon_employee_access",
      "cubo": "grande",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "target",
      "n_clases": 2,
      "clase_positiva": "0",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 1111,
      "nombre": "KDDCup09_appetency",
      "cubo": "grande",
      "tarea": "binary_classification",
      "sellado": false,
      "objetivo": "APPETENCY",
      "n_clases": 2,
      "clase_positiva": "1",
      "estratificado_por_el_objetivo": true
    },
    {
      "data_id": 42734,
      "nombre": "okcupid-stem",
      "cubo": "grande",
      "tarea": "multiclass_classification",
      "sellado": false,
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    "clave_de_cada_registro": "entorno_digest, motor_digest, presupuesto_wall_s, estado que cuente como medida y datos_sha256 del ARFF (_reusable_c3)"
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  "lo_que_no_cubre_el_digest": {
    "como_se_calcula": "cierre ESTÁTICO de imports de pasada_119_c3.py dentro de los dos repos (ast de cada fichero, TODOS sus import, también los de dentro de funciones), más los compartidos de C5 y los tres del motor. Es un superconjunto de lo que corre: medido el 29-09, un intento de kc2 en proceso cargó 124 módulos de los repos y los 124 estaban en el cierre (190)",
    "bibliotecas_de_terceros_que_NO_invalidan": [
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      "onnxmltools",
      "onnxruntime",
      "platform",
      "scipy",
      "threadpoolctl",
      "tokenizers",
      "xgboost"
    ],
    "bibliotecas_de_terceros": "solo por versión: python, numpy, scikit-learn, torch entran en el digest; las demás constan en procedencia.versiones_de_bibliotecas pero un cambio en ellas NO invalida la caché. Su código tampoco entra, ni el de la biblioteca estándar",
    "imports_dinamicos_en_el_cierre": [
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      "core:matrixai/compiler/torch_forward.py:64: import_module('torch')",
      "core:matrixai/export/equivalence.py:373: import_module('numpy')",
      "core:matrixai/export/equivalence.py:420: import_module('onnxruntime')",
      "core:matrixai/export/onnx_exporter.py:1794: import_module('onnx')",
      "core:matrixai/export/onnx_exporter.py:1795: import_module('onnx.numpy_helper')",
      "core:matrixai/export/onnx_exporter.py:1796: import_module('onnx.helper')",
      "core:matrixai/export/onnx_exporter.py:1805: import_module('numpy')",
      "core:matrixai/parameters/tensor_bridge.py:145: import_module('torch')",
      "core:matrixai/parameters/tensor_bridge.py:168: import_module('torch')",
      "core:matrixai/parameters/tensor_bridge.py:222: import_module('torch')",
      "core:matrixai/training/torch_trainer.py:264: import_module('torch')",
      "engines:matrixai_engines/procedencia.py:244: importlib.import_module(modulo)"
    ],
    "imports_dinamicos": "un import_module/__import__ con una cadena no se sigue; los listados (medidos en el cierre) cargan bibliotecas de terceros",
    "datos": "los ARFF NO entran en el digest del entorno: su sha256 va en la clave de cada registro (`datos_sha256`, que `_reusable_c3` compara). pasada_v2_113_resultado.json no entra: no cambia lo que ejecuta un intento, solo la comparación, que se recalcula en cada ejecución",
    "enmienda_2": "no entra: fija cómo se CUENTAN los fallos, no lo que se ejecuta; el veredicto se recalcula en cada ejecución",
    "modulos_de_los_repos_cargados_por_este_proceso_fuera_del_digest": [],
    "que_dice_la_lista_de_arriba": "COTA INFERIOR: los módulos del padre; el hijo de multiprocessing.spawn importa los suyos, que el cierre estático cubre salvo import dinámico. Tiene que estar vacía"
  },
  "lectura_de_los_datos": {
    "23381": {
      "nombre": "dresses-sales",
      "objetivo": "Class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "1063": {
      "nombre": "kc2",
      "objetivo": "problems",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "40994": {
      "nombre": "climate-model-simulation-crashes",
      "objetivo": "outcome",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "1515": {
      "nombre": "micro-mass",
      "objetivo": "Class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "11": {
      "nombre": "balance-scale",
      "objetivo": "class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "37": {
      "nombre": "diabetes",
      "objetivo": "class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "1068": {
      "nombre": "pc1",
      "objetivo": "defects",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "41021": {
      "nombre": "Moneyball",
      "objetivo": "RS",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "1049": {
      "nombre": "pc4",
      "objetivo": "c",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "42563": {
      "nombre": "house_prices_nominal",
      "objetivo": "SalePrice",
      "normalizaciones": [
        "declaración nominal de 'MSZoning': comillas y espacios recortados",
        "declaración nominal de 'Street': comillas y espacios recortados",
        "declaración nominal de 'Alley': comillas y espacios recortados",
        "declaración nominal de 'LotShape': comillas y espacios recortados",
        "declaración nominal de 'LandContour': comillas y espacios recortados",
        "declaración nominal de 'Utilities': comillas y espacios recortados",
        "declaración nominal de 'LotConfig': comillas y espacios recortados",
        "declaración nominal de 'LandSlope': comillas y espacios recortados",
        "declaración nominal de 'Neighborhood': comillas y espacios recortados",
        "declaración nominal de 'Condition1': comillas y espacios recortados",
        "declaración nominal de 'Condition2': comillas y espacios recortados",
        "declaración nominal de 'BldgType': comillas y espacios recortados",
        "declaración nominal de 'HouseStyle': comillas y espacios recortados",
        "declaración nominal de 'RoofStyle': comillas y espacios recortados",
        "declaración nominal de 'RoofMatl': comillas y espacios recortados",
        "declaración nominal de 'Exterior1st': comillas y espacios recortados",
        "declaración nominal de 'Exterior2nd': comillas y espacios recortados",
        "declaración nominal de 'MasVnrType': comillas y espacios recortados",
        "declaración nominal de 'ExterQual': comillas y espacios recortados",
        "declaración nominal de 'ExterCond': comillas y espacios recortados",
        "declaración nominal de 'Foundation': comillas y espacios recortados",
        "declaración nominal de 'BsmtQual': comillas y espacios recortados",
        "declaración nominal de 'BsmtCond': comillas y espacios recortados",
        "declaración nominal de 'BsmtExposure': comillas y espacios recortados",
        "declaración nominal de 'BsmtFinType1': comillas y espacios recortados",
        "declaración nominal de 'BsmtFinType2': comillas y espacios recortados",
        "declaración nominal de 'Heating': comillas y espacios recortados",
        "declaración nominal de 'HeatingQC': comillas y espacios recortados",
        "declaración nominal de 'CentralAir': comillas y espacios recortados",
        "declaración nominal de 'Electrical': comillas y espacios recortados",
        "declaración nominal de 'KitchenQual': comillas y espacios recortados",
        "declaración nominal de 'Functional': comillas y espacios recortados",
        "declaración nominal de 'FireplaceQu': comillas y espacios recortados",
        "declaración nominal de 'GarageType': comillas y espacios recortados",
        "declaración nominal de 'GarageFinish': comillas y espacios recortados",
        "declaración nominal de 'GarageQual': comillas y espacios recortados",
        "declaración nominal de 'GarageCond': comillas y espacios recortados",
        "declaración nominal de 'PavedDrive': comillas y espacios recortados",
        "declaración nominal de 'PoolQC': comillas y espacios recortados",
        "declaración nominal de 'Fence': comillas y espacios recortados",
        "declaración nominal de 'MiscFeature': comillas y espacios recortados",
        "declaración nominal de 'SaleType': comillas y espacios recortados",
        "declaración nominal de 'SaleCondition': comillas y espacios recortados"
      ],
      "columnas_excluidas": [
        "Id"
      ],
      "motivos_de_exclusion": {
        "Id": "identificador de fila: el catálogo declara 80 columnas y el ARFF trae 81; el núcleo la clasifica `identifier` con 1460 valores distintos en 1460 filas (unique_ratio=1.0)"
      },
      "avisos": [],
      "catalogo_leido": true
    },
    "181": {
      "nombre": "yeast",
      "objetivo": "class_protein_localization",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "42730": {
      "nombre": "us_crime",
      "objetivo": "ViolentCrimesPerPop",
      "normalizaciones": [
        "columnas STRING excluidas: communityname"
      ],
      "columnas_excluidas": [
        "communityname"
      ],
      "motivos_de_exclusion": {
        "communityname": "atributo STRING: `scipy` no lo soporta y convertirlo en categórica sería meterle al modelo texto libre que identifica la fila"
      },
      "avisos": [],
      "catalogo_leido": true
    },
    "12": {
      "nombre": "mfeat-factors",
      "objetivo": "class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "1487": {
      "nombre": "ozone-level-8hr",
      "objetivo": "Class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "40978": {
      "nombre": "Internet-Advertisements",
      "objetivo": "class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "38": {
      "nombre": "sick",
      "objetivo": "Class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "41980": {
      "nombre": "SAT11-HAND-runtime-regression",
      "objetivo": "runtime",
      "normalizaciones": [
        "declaración nominal de 'algorithm': comillas y espacios recortados",
        "columnas STRING excluidas: row_id"
      ],
      "columnas_excluidas": [
        "row_id"
      ],
      "motivos_de_exclusion": {
        "row_id": "atributo STRING: `scipy` no lo soporta y convertirlo en categórica sería meterle al modelo texto libre que identifica la fila"
      },
      "avisos": [],
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    },
    "40983": {
      "nombre": "wilt",
      "objetivo": "class",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
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      "catalogo_leido": true
    },
    "28": {
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      "catalogo_leido": true
    },
    "287": {
      "nombre": "wine_quality",
      "objetivo": "quality",
      "normalizaciones": [],
      "columnas_excluidas": [],
      "motivos_de_exclusion": {},
      "avisos": [],
      "catalogo_leido": true
    },
    "1053": {
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    "32": {
      "nombre": "pendigits",
      "objetivo": "class",
      "normalizaciones": [
        "espacios de alineación recortados en los datos"
      ],
      "columnas_excluidas": [],
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    },
    "4534": {
      "nombre": "PhishingWebsites",
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    "42731": {
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      ],
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    },
    "4135": {
      "nombre": "Amazon_employee_access",
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    "1111": {
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      "objetivo": "APPETENCY",
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      "nombre": "okcupid-stem",
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    "41138": {
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    "42571": {
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  "resultados": [
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    },
    {
      "dataset": "pendigits",
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    {
      "dataset": "PhishingWebsites",
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    },
    {
      "dataset": "house_sales",
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    },
    {
      "dataset": "house_16H",
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    },
    {
      "dataset": "Amazon_employee_access",
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    {
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      "pliegues_del_motor_nuevo_sin_intento_de_la_densa_v2": [
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    },
    {
      "dataset": "okcupid-stem",
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    {
      "dataset": "connect-4",
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    {
      "dataset": "kick",
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    },
    {
      "dataset": "APSFailure",
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    {
      "dataset": "Allstate_Claims_Severity",
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      "pliegues_del_motor_nuevo_sin_intento_de_la_densa_v2": [
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  ],
  "veredicto": {
    "campo": "los motores de la v2 (pasada_v2_113_resultado.json) con sus resultados de esa pasada, y en el sitio de la densa v2, el motor nuevo (matrixai.dense.tabm_cpu) -- SOLO en los conjuntos medidos aquí",
    "cumplidos_con_el_motor_nuevo": {
      "cumplidos": 31,
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      "fraccion": 0.96875,
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      "detalle": [
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          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "Amazon_employee_access",
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          "distancia_en_puntos": 0.0
        },
        {
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        {
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        {
          "dataset": "Moneyball",
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          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "PhishingWebsites",
          "cumple": true,
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          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "SAT11-HAND-runtime-regression",
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        {
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        },
        {
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        {
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          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "diabetes",
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        {
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        {
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        },
        {
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        {
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        {
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        {
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        {
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        {
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        {
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          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "ozone-level-8hr",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "xgboost",
          "distancia_en_puntos": 0.42894736842105097
        },
        {
          "dataset": "pc1",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 1.3592233009708687
        },
        {
          "dataset": "pc4",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "pendigits",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "sick",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "us_crime",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 0.6471814381588836
        },
        {
          "dataset": "wilt",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "wine_quality",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.tabm_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "yeast",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 1.593959731543626
        }
      ]
    },
    "cumplidos_de_la_densa_v2_en_los_mismos_conjuntos": {
      "cumplidos": 11,
      "datasets": 32,
      "fraccion": 0.34375,
      "cumple_la_regla": false,
      "detalle": [
        {
          "dataset": "APSFailure",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 0.4976518958428433
        },
        {
          "dataset": "Allstate_Claims_Severity",
          "cumple": false,
          "perdido_por_fallo": true,
          "mejor": "xgboost",
          "distancia_en_puntos": null
        },
        {
          "dataset": "Amazon_employee_access",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "lightgbm",
          "distancia_en_puntos": 18.808220360640792
        },
        {
          "dataset": "Internet-Advertisements",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "sklearn.lineal",
          "distancia_en_puntos": 1.487691437968952
        },
        {
          "dataset": "KDDCup09_appetency",
          "cumple": false,
          "perdido_por_fallo": true,
          "mejor": "catboost",
          "distancia_en_puntos": null
        },
        {
          "dataset": "Moneyball",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 6.230511611905431
        },
        {
          "dataset": "PhishingWebsites",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "lightgbm",
          "distancia_en_puntos": 0.47582298557116154
        },
        {
          "dataset": "SAT11-HAND-runtime-regression",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "xgboost",
          "distancia_en_puntos": 6.381574417576086
        },
        {
          "dataset": "balance-scale",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.torch_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "climate-model-simulation-crashes",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 2.760942760942753
        },
        {
          "dataset": "connect-4",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 4.137063351095316
        },
        {
          "dataset": "diabetes",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 1.0234567901234448
        },
        {
          "dataset": "dresses-sales",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "lightgbm",
          "distancia_en_puntos": 2.639573070607537
        },
        {
          "dataset": "house_16H",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "lightgbm",
          "distancia_en_puntos": 16.48329022695964
        },
        {
          "dataset": "house_prices_nominal",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 11.93748844863648
        },
        {
          "dataset": "house_sales",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 1.6278757081763962
        },
        {
          "dataset": "jm1",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 2.7503169767899016
        },
        {
          "dataset": "kc2",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "matrixai.dense.torch_cpu",
          "distancia_en_puntos": 0.0
        },
        {
          "dataset": "kick",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 2.3194505826168332
        },
        {
          "dataset": "mfeat-factors",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "sklearn.lineal",
          "distancia_en_puntos": 2.9000000000000026
        },
        {
          "dataset": "micro-mass",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 27.079646017699112
        },
        {
          "dataset": "okcupid-stem",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 1.8962292015358817
        },
        {
          "dataset": "optdigits",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "sklearn.hgb",
          "distancia_en_puntos": 0.8362989323843495
        },
        {
          "dataset": "ozone-level-8hr",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "xgboost",
          "distancia_en_puntos": 2.4697368421052635
        },
        {
          "dataset": "pc1",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 4.548004314994603
        },
        {
          "dataset": "pc4",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 2.4580439814814725
        },
        {
          "dataset": "pendigits",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "lightgbm",
          "distancia_en_puntos": 0.6033045323632025
        },
        {
          "dataset": "sick",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 6.090027020388122
        },
        {
          "dataset": "us_crime",
          "cumple": true,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 1.4623198244844926
        },
        {
          "dataset": "wilt",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 3.202748852312176
        },
        {
          "dataset": "wine_quality",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "xgboost",
          "distancia_en_puntos": 5.616222992414633
        },
        {
          "dataset": "yeast",
          "cumple": false,
          "perdido_por_fallo": false,
          "mejor": "catboost",
          "distancia_en_puntos": 2.2651006711409405
        }
      ]
    },
    "sube_los_cumplidos": true,
    "n_conjuntos": 32,
    "n_mejora": 27,
    "n_inferioridad": 1,
    "n_sin_comparacion": 2,
    "conjuntos_con_mejora": [
      "APSFailure",
      "Amazon_employee_access",
      "Moneyball",
      "PhishingWebsites",
      "SAT11-HAND-runtime-regression",
      "balance-scale",
      "connect-4",
      "diabetes",
      "dresses-sales",
      "house_16H",
      "house_prices_nominal",
      "house_sales",
      "jm1",
      "kc2",
      "kick",
      "mfeat-factors",
      "micro-mass",
      "okcupid-stem",
      "optdigits",
      "ozone-level-8hr",
      "pc1",
      "pc4",
      "pendigits",
      "sick",
      "us_crime",
      "wilt",
      "wine_quality"
    ],
    "conjuntos_con_inferioridad": [
      "Internet-Advertisements"
    ],
    "conjuntos_sin_comparacion": [
      "Allstate_Claims_Severity",
      "KDDCup09_appetency"
    ],
    "conjuntos_con_fallos_del_motor_nuevo": [],
    "mejoras_superan_inferioridades": true,
    "cumple_la_regla_de_subida": true,
    "regla_de_subida": "protocolo_119_v4.json, veredicto.regla_de_subida: sube a confirmación (C4) si cumple las DOS condiciones sobre los 32 no sellados frente a la densa v2 en los MISMOS conjuntos -- (1) el número de conjuntos donde el motor nuevo está a <=2 puntos del mejor es MAYOR que el de la densa v2, y (2) sus mejoras superan a sus inferioridades (diferencia emparejada por repetición y pliegue, bootstrap de 1000 remuestras de pliegues, semilla 0)",
    "como_se_cuentan_los_fallos": {
      "fuente": "decisión del supervisor tras la auditoría 2 de 119-C3 (29-09), registrada en la enmienda 2 del protocolo v4 (`se_fija.fallos_en_el_veredicto`; ver `enmienda_2`)",
      "que_es_un_fallo": "un intento ESPERADO por la partición (repetición y pliegue que `propuesta.pliegues` trae) que no está en el artefacto, cuyo estado no cuenta como medida (ESTADOS_QUE_CUENTAN_COMO_MEDIDA), o que se completó sin la métrica de cierre",
      "a_condicion_1": "un conjunto con CUALQUIER fallo del motor nuevo cuenta como PERDIDO (no cumple), como dice regla_de_cierre.definicion_de_mejor. Mismo criterio para la densa v2",
      "b_condicion_2": "fallos del motor nuevo con la densa v2 completa = INFERIORIDAD; si la densa v2 falló TODOS sus intentos (Allstate, KDDCup09) = «sin comparación», ni mejora ni inferioridad, y solo cuenta en la condición 1",
      "c_emparejada": "la diferencia emparejada solo sobre repeticiones y pliegues COMPLETOS en los dos motores",
      "caso_no_previsto": "fallos del motor nuevo Y fallos parciales de la densa v2: la decisión no lo cubre; se aplica (c) y se marca `caso_no_previsto`. En la v2 real ningún conjunto no sellado tiene fallos PARCIALES de la densa (probado)",
      "d_registro": "`veredicto_por_conjunto` lleva los fallos de cada motor: cuántos y por qué"
    },
    "conjuntos_con_intentos_reintentados": [],
    "intentos_reintentados_por_conjunto": {},
    "que_son_los_intentos_reintentados": "intentos que en una ejecución ANTERIOR no contaron como medida (fallidos, cancelados…) y una posterior volvió a medir: `resultados` y el veredicto cuentan el reintento (la regla de la casa es reintentar lo que no está completed: un tope por carga ajena no se cementa), y aquí queda lo que pasó antes, con su error y su hora (`intentos_reintentados`). Sin esto, un conjunto perdido una noche salía cumplido la siguiente sin que el artefacto lo dijera (auditoría 3 del 119, I4)"
  },
  "digest_solo_de_resultados": "02015734df07110daab713fa798dad376dd33e307d329d715feee1590a6fe110",
  "que_cubre_digest_resultados_crudos": "TODO el objeto de salida salvo este mismo campo —incluidos `alcance_y_veredicto`, el plan y la procedencia—, no solo `resultados`. El nombre es historico y NO se cambia: lo citan cinco artefactos y el `evidencia_digest` de la cartera aprobada. El digest de los resultados crudos a secas es `digest_solo_de_resultados`.",
  "digest_resultados_crudos": "640137070dc30a282a1cfc4d7702fd5f06b4e24ca9e2d720950b0c0b8bd995ce"
}