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0000000000000000000000000000000000000000..33fa7d867093e617592a81e71735c54cb7cf9a1a --- /dev/null +++ b/iris/tvae/2/mlu-eval.ipynb @@ -0,0 +1,2329 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "982e76f5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:07.817445Z", + "iopub.status.busy": "2024-07-23T14:55:07.817156Z", + "iopub.status.idle": "2024-07-23T14:55:07.847506Z", + "shell.execute_reply": "2024-07-23T14:55:07.846645Z" + }, + "papermill": { + "duration": 0.044831, + "end_time": "2024-07-23T14:55:07.849562", + "exception": false, + "start_time": "2024-07-23T14:55:07.804731", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "#joblib.parallel_backend(\"threading\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "675f0b41", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:07.874507Z", + "iopub.status.busy": "2024-07-23T14:55:07.874238Z", + "iopub.status.idle": "2024-07-23T14:55:07.880415Z", + "shell.execute_reply": "2024-07-23T14:55:07.879590Z" + }, + "papermill": { + "duration": 0.020905, + "end_time": "2024-07-23T14:55:07.882417", + "exception": false, + "start_time": "2024-07-23T14:55:07.861512", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n%cd /kaggle/working\\n#!git clone https://github.com/R-N/ml-utility-loss --depth=1 --single-branch --branch=main\\n%cd ml-utility-loss\\n!git pull\\n#!pip install .\\n!pip install . --no-deps --force-reinstall --upgrade\\n#'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "%cd /kaggle/working\n", + "#!git clone https://github.com/R-N/ml-utility-loss --depth=1 --single-branch --branch=main\n", + "%cd ml-utility-loss\n", + "!git pull\n", + "#!pip install .\n", + "!pip install . --no-deps --force-reinstall --upgrade\n", + "#\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5ae30f5c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:07.906157Z", + "iopub.status.busy": "2024-07-23T14:55:07.905404Z", + "iopub.status.idle": "2024-07-23T14:55:07.909330Z", + "shell.execute_reply": "2024-07-23T14:55:07.908662Z" + }, + "papermill": { + "duration": 0.017781, + "end_time": "2024-07-23T14:55:07.911221", + "exception": false, + "start_time": "2024-07-23T14:55:07.893440", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [3,3]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9f42c810", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:07.934919Z", + "iopub.status.busy": "2024-07-23T14:55:07.934280Z", + "iopub.status.idle": "2024-07-23T14:55:07.938104Z", + "shell.execute_reply": "2024-07-23T14:55:07.937300Z" + }, + "executionInfo": { + "elapsed": 678, + "status": "ok", + "timestamp": 1696841022168, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "ns5hFcVL2yvs", + "papermill": { + "duration": 0.017891, + "end_time": "2024-07-23T14:55:07.940106", + "exception": false, + "start_time": "2024-07-23T14:55:07.922215", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "datasets = [\n", + " \"insurance\",\n", + " \"treatment\",\n", + " \"contraceptive\"\n", + "]\n", + "\n", + "study_dir = \"./\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "85d0c8ce", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:07.963357Z", + "iopub.status.busy": "2024-07-23T14:55:07.963117Z", + "iopub.status.idle": "2024-07-23T14:55:07.968588Z", + "shell.execute_reply": "2024-07-23T14:55:07.967759Z" + }, + "papermill": { + "duration": 0.019281, + "end_time": "2024-07-23T14:55:07.970481", + "exception": false, + "start_time": "2024-07-23T14:55:07.951200", + "status": "completed" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "#Parameters\n", + "import os\n", + "\n", + "path_prefix = \"../../../../\"\n", + "\n", + "dataset_dir = os.path.join(path_prefix, \"ml-utility-loss/datasets\")\n", + "dataset_name = \"treatment\"\n", + "model_name=\"ml_utility_2\"\n", + "models = [\"tvae\", \"realtabformer\", \"lct_gan\", \"tab_ddpm_concat\"]\n", + "single_model = \"lct_gan\"\n", + "random_seed = 42\n", + "gp = True\n", + "gp_multiply = True\n", + "folder = \"eval\"\n", + "debug = False\n", + "path = None\n", + "param_index = 0\n", + "allow_same_prediction = True\n", + "log_wandb = False" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8b0d1eb6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:07.995773Z", + "iopub.status.busy": "2024-07-23T14:55:07.994994Z", + "iopub.status.idle": "2024-07-23T14:55:07.999826Z", + "shell.execute_reply": "2024-07-23T14:55:07.999117Z" + }, + "papermill": { + "duration": 0.019512, + "end_time": "2024-07-23T14:55:08.001698", + "exception": false, + "start_time": "2024-07-23T14:55:07.982186", + "status": "completed" + }, + "tags": [ + "injected-parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters\n", + "dataset = \"iris\"\n", + "dataset_name = \"iris\"\n", + "single_model = \"tvae\"\n", + "gp = True\n", + "gp_multiply = True\n", + "random_seed = 2\n", + "debug = False\n", + "folder = \"eval\"\n", + "path_prefix = \"../../../../\"\n", + "path = \"eval/iris/tvae/2\"\n", + "param_index = 0\n", + "allow_same_prediction = True\n", + "log_wandb = False\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd7c02d6", + "metadata": { + "papermill": { + "duration": 0.011082, + "end_time": "2024-07-23T14:55:08.023739", + "exception": false, + "start_time": "2024-07-23T14:55:08.012657", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5f45b1d0", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:08.047190Z", + "iopub.status.busy": "2024-07-23T14:55:08.046905Z", + "iopub.status.idle": "2024-07-23T14:55:08.055906Z", + "shell.execute_reply": "2024-07-23T14:55:08.055017Z" + }, + "executionInfo": { + "elapsed": 7, + "status": "ok", + "timestamp": 1696841022169, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "UdvXYv3c3LXy", + "papermill": { + "duration": 0.022834, + "end_time": "2024-07-23T14:55:08.057758", + "exception": false, + "start_time": "2024-07-23T14:55:08.034924", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/working\n", + "/kaggle/working/eval/iris/tvae/2\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import os\n", + "\n", + "%cd /kaggle/working/\n", + "\n", + "if path is None:\n", + " path = os.path.join(folder, dataset_name, single_model, random_seed)\n", + "Path(path).mkdir(parents=True, exist_ok=True)\n", + "\n", + "%cd {path}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f85bf540", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:08.081052Z", + "iopub.status.busy": "2024-07-23T14:55:08.080765Z", + "iopub.status.idle": "2024-07-23T14:55:10.046540Z", + "shell.execute_reply": "2024-07-23T14:55:10.045586Z" + }, + "papermill": { + "duration": 1.979759, + "end_time": "2024-07-23T14:55:10.048644", + "exception": false, + "start_time": "2024-07-23T14:55:08.068885", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Set seed to \n" + ] + } + ], + "source": [ + "from ml_utility_loss.util import seed\n", + "if single_model:\n", + " model_name=f\"{model_name}_{single_model}\"\n", + "if random_seed is not None:\n", + " seed(random_seed)\n", + " print(\"Set seed to\", seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8489feae", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:10.075511Z", + "iopub.status.busy": "2024-07-23T14:55:10.074681Z", + "iopub.status.idle": "2024-07-23T14:55:10.085424Z", + "shell.execute_reply": "2024-07-23T14:55:10.084712Z" + }, + "papermill": { + "duration": 0.026265, + "end_time": "2024-07-23T14:55:10.087306", + "exception": false, + "start_time": "2024-07-23T14:55:10.061041", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import json\n", + "import os\n", + "\n", + "df = pd.read_csv(os.path.join(dataset_dir, f\"{dataset_name}.csv\"))\n", + "with open(os.path.join(dataset_dir, f\"{dataset_name}.json\")) as f:\n", + " info = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "debcc684", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:10.111184Z", + "iopub.status.busy": "2024-07-23T14:55:10.110911Z", + "iopub.status.idle": "2024-07-23T14:55:10.117487Z", + "shell.execute_reply": "2024-07-23T14:55:10.116651Z" + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1696841022169, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "Vrl2QkoV3o_8", + "papermill": { + "duration": 0.020752, + "end_time": "2024-07-23T14:55:10.119352", + "exception": false, + "start_time": "2024-07-23T14:55:10.098600", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "task = info[\"task\"]\n", + "target = info[\"target\"]\n", + "cat_features = info[\"cat_features\"]\n", + "mixed_features = info[\"mixed_features\"]\n", + "longtail_features = info[\"longtail_features\"]\n", + "integer_features = info[\"integer_features\"]\n", + "\n", + "test = df.sample(frac=0.2, random_state=42)\n", + "train = df[~df.index.isin(test.index)]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7538184a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:10.142887Z", + "iopub.status.busy": "2024-07-23T14:55:10.142635Z", + "iopub.status.idle": "2024-07-23T14:55:10.238916Z", + "shell.execute_reply": "2024-07-23T14:55:10.238229Z" + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1696841022169, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "TilUuFk9vqMb", + "papermill": { + "duration": 0.110481, + "end_time": "2024-07-23T14:55:10.241066", + "exception": false, + "start_time": "2024-07-23T14:55:10.130585", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import ml_utility_loss.synthesizers.tab_ddpm.params as TAB_DDPM_PARAMS\n", + "import ml_utility_loss.synthesizers.lct_gan.params as LCT_GAN_PARAMS\n", + "import ml_utility_loss.synthesizers.realtabformer.params as RTF_PARAMS\n", + "from ml_utility_loss.synthesizers.realtabformer.params.default import GPT2_PARAMS, REALTABFORMER_PARAMS\n", + "from ml_utility_loss.util import filter_dict_2, filter_dict\n", + "\n", + "tab_ddpm_params = getattr(TAB_DDPM_PARAMS, dataset_name).BEST\n", + "lct_gan_params = getattr(LCT_GAN_PARAMS, dataset_name).BEST\n", + "lct_ae_params = filter_dict_2(lct_gan_params, LCT_GAN_PARAMS.default.AE_PARAMS)\n", + "rtf_params = getattr(RTF_PARAMS, dataset_name).BEST\n", + "rtf_params = filter_dict(rtf_params, REALTABFORMER_PARAMS)\n", + "\n", + "lct_ae_embedding_size=lct_gan_params[\"embedding_size\"]\n", + "tab_ddpm_normalization=\"quantile\"\n", + "tab_ddpm_cat_encoding=tab_ddpm_params[\"cat_encoding\"]\n", + "#tab_ddpm_cat_encoding=\"one-hot\"\n", + "tab_ddpm_y_policy=\"default\"\n", + "tab_ddpm_is_y_cond=True" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cca61838", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:10.267241Z", + "iopub.status.busy": "2024-07-23T14:55:10.266598Z", + "iopub.status.idle": "2024-07-23T14:55:14.608892Z", + "shell.execute_reply": "2024-07-23T14:55:14.608054Z" + }, + "executionInfo": { + "elapsed": 3113, + "status": "ok", + "timestamp": 1696841025277, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "7Abt8nStvr9Z", + "papermill": { + "duration": 4.357821, + "end_time": "2024-07-23T14:55:14.611235", + "exception": false, + "start_time": "2024-07-23T14:55:10.253414", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-23 14:55:12.022256: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-07-23 14:55:12.022312: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-07-23 14:55:12.024007: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + ] + } + ], + "source": [ + "from ml_utility_loss.loss_learning.estimator.pipeline import load_lct_ae\n", + "\n", + "# lct_ae = load_lct_ae(\n", + "# dataset_name=dataset_name,\n", + "# model_dir=os.path.join(path_prefix, \"ml-utility-loss/models\"),\n", + "# model_name=\"lct_ae\",\n", + "# df_name=\"df\",\n", + "# )\n", + "lct_ae = None" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6f83b7b6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:14.636219Z", + "iopub.status.busy": "2024-07-23T14:55:14.635652Z", + "iopub.status.idle": "2024-07-23T14:55:14.642308Z", + "shell.execute_reply": "2024-07-23T14:55:14.641425Z" + }, + "papermill": { + "duration": 0.021339, + "end_time": "2024-07-23T14:55:14.644347", + "exception": false, + "start_time": "2024-07-23T14:55:14.623008", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from ml_utility_loss.loss_learning.estimator.pipeline import load_rtf_embed\n", + "\n", + "rtf_embed = load_rtf_embed(\n", + " dataset_name=dataset_name,\n", + " model_dir=os.path.join(path_prefix, \"ml-utility-loss/models\"),\n", + " model_name=\"realtabformer\",\n", + " df_name=\"df\",\n", + " ckpt_type=\"best-disc-model\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0026de74", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:14.669652Z", + "iopub.status.busy": "2024-07-23T14:55:14.669365Z", + "iopub.status.idle": "2024-07-23T14:55:17.256345Z", + "shell.execute_reply": "2024-07-23T14:55:17.255347Z" + }, + "executionInfo": { + "elapsed": 20137, + "status": "ok", + "timestamp": 1696841045408, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "tbaguWxAvtPi", + "papermill": { + "duration": 2.602191, + "end_time": "2024-07-23T14:55:17.258814", + "exception": false, + "start_time": "2024-07-23T14:55:14.656623", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/mixture/_base.py:274: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/mixture/_base.py:274: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from ml_utility_loss.loss_learning.estimator.preprocessing import DataPreprocessor\n", + "\n", + "preprocessor = DataPreprocessor(\n", + " task,\n", + " target=target,\n", + " cat_features=cat_features,\n", + " mixed_features=mixed_features,\n", + " longtail_features=longtail_features,\n", + " integer_features=integer_features,\n", + " lct_ae_embedding_size=lct_ae_embedding_size,\n", + " lct_ae_params=lct_ae_params,\n", + " lct_ae=lct_ae,\n", + " tab_ddpm_normalization=tab_ddpm_normalization,\n", + " tab_ddpm_cat_encoding=tab_ddpm_cat_encoding,\n", + " tab_ddpm_y_policy=tab_ddpm_y_policy,\n", + " tab_ddpm_is_y_cond=tab_ddpm_is_y_cond,\n", + " realtabformer_embedding=rtf_embed,\n", + " realtabformer_params=rtf_params,\n", + ")\n", + "preprocessor.fit(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a9c9b110", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "execution": { + "iopub.execute_input": "2024-07-23T14:55:17.285755Z", + "iopub.status.busy": "2024-07-23T14:55:17.285024Z", + "iopub.status.idle": "2024-07-23T14:55:17.291444Z", + "shell.execute_reply": "2024-07-23T14:55:17.290607Z" + }, + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1696841045411, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "OxUH_GBEv2qK", + "outputId": "76464c90-3baf-4bdc-a955-6f4fddc16b9c", + "papermill": { + "duration": 0.022058, + "end_time": "2024-07-23T14:55:17.293469", + "exception": false, + "start_time": "2024-07-23T14:55:17.271411", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tvae': 24,\n", + " 'realtabformer': (31, 89, Embedding(89, 864), True),\n", + " 'lct_gan': 14,\n", + " 'tab_ddpm_concat': 5}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessor.adapter_sizes" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3cb9ed90", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:17.317831Z", + "iopub.status.busy": "2024-07-23T14:55:17.317582Z", + "iopub.status.idle": "2024-07-23T14:55:17.322422Z", + "shell.execute_reply": "2024-07-23T14:55:17.321426Z" + }, + "papermill": { + "duration": 0.019167, + "end_time": "2024-07-23T14:55:17.324269", + "exception": false, + "start_time": "2024-07-23T14:55:17.305102", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from ml_utility_loss.loss_learning.estimator.pipeline import load_dataset_3_factory\n", + "\n", + "datasetsn = load_dataset_3_factory(\n", + " dataset_dir=os.path.join(path_prefix, \"ml-utility-loss/\"),\n", + " dataset_name=dataset_name,\n", + " preprocessor=preprocessor,\n", + " cache_dir=path_prefix,\n", + " #synth_dir=f\"synthetics2/{single_model}\",\n", + " synth_dir=\"synthetics\",\n", + " real_step=1,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ad1eb833", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:17.348739Z", + "iopub.status.busy": "2024-07-23T14:55:17.348492Z", + "iopub.status.idle": "2024-07-23T14:55:17.406874Z", + "shell.execute_reply": "2024-07-23T14:55:17.406120Z" + }, + "papermill": { + "duration": 0.073007, + "end_time": "2024-07-23T14:55:17.408824", + "exception": false, + "start_time": "2024-07-23T14:55:17.335817", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../../../../ml-utility-loss/ synthetics iris\n", + "Caching in ../../../../iris/_cache_aug_test/tvae/all inf False\n", + "../../../../ml-utility-loss/aug_test/iris 0\n", + "Caching in ../../../../iris/_cache_bs_test/tvae/all inf False\n", + "../../../../ml-utility-loss/bs_test/iris 0\n", + "Caching in ../../../../iris/_cache_synth_test/tvae/all inf False\n", + "../../../../ml-utility-loss/synthetics/iris 200\n", + "200\n" + ] + } + ], + "source": [ + "from ml_utility_loss.loss_learning.estimator.pipeline import load_dataset_4\n", + "\n", + "test_set = load_dataset_4(\n", + " dataset_dir=os.path.join(path_prefix, \"ml-utility-loss/\"),\n", + " dataset_name=dataset_name,\n", + " preprocessor=preprocessor,\n", + " model=single_model,\n", + " cache_dir=path_prefix,\n", + " #synth_dir=f\"synthetics2/{single_model}\",\n", + " synth_dir=\"synthetics\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "14ff8b40", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:17.436822Z", + "iopub.status.busy": "2024-07-23T14:55:17.436541Z", + "iopub.status.idle": "2024-07-23T14:55:17.998409Z", + "shell.execute_reply": "2024-07-23T14:55:17.997475Z" + }, + "executionInfo": { + "elapsed": 588, + "status": "ok", + "timestamp": 1696841049215, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "NgahtU1q9uLO", + "papermill": { + "duration": 0.578427, + "end_time": "2024-07-23T14:55:18.000648", + "exception": false, + "start_time": "2024-07-23T14:55:17.422221", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'bias_weight_decay': 0.05,\n", + " 'Body': 'twin_encoder',\n", + " 'loss_balancer_meta': True,\n", + " 'loss_balancer_log': False,\n", + " 'loss_balancer_lbtw': False,\n", + " 'pma_skip_small': False,\n", + " 'isab_skip_small': False,\n", + " 'layer_norm': False,\n", + " 'pma_layer_norm': False,\n", + " 'attn_residual': True,\n", + " 'tf_n_layers_dec': False,\n", + " 'tf_isab_rank': 0,\n", + " 'tf_layer_norm': False,\n", + " 'tf_pma_start': -1,\n", + " 'head_n_seeds': 0,\n", + " 'dropout': 0,\n", + " 'combine_mode': 'diff_left',\n", + " 'tf_isab_mode': 'separate',\n", + " 'grad_loss_fn': torch.Tensor>,\n", + " 'bias': True,\n", + " 'bias_final': True,\n", + " 'pma_ffn_mode': 'none',\n", + " 'gradient_penalty_mode': {'gradient_penalty': True,\n", + " 'forward_once': False,\n", + " 'calc_grad_m': False,\n", + " 'avg_non_role_model_m': False,\n", + " 'inverse_avg_non_role_model_m': False},\n", + " 'single_model': True,\n", + " 'tf_pma_low': 4,\n", + " 'patience': 10,\n", + " 'grad_clip': 0.7999999999999999,\n", + " 'bias_lr_mul': 1.0,\n", + " 'synth_data': 2,\n", + " 'inds_init_mode': 'fixnorm',\n", + " 'head_activation': torch.nn.modules.activation.ReLU6,\n", + " 'tf_activation': torch.nn.modules.activation.ReLU6,\n", + " 'loss_balancer_beta': 0.7,\n", + " 'loss_balancer_r': 0.96,\n", + " 'aug_train': 0,\n", + " 'bs_train': 0,\n", + " 'real_train': 5,\n", + " 'dataset_size': 256,\n", + " 'batch_size': 4,\n", + " 'epochs': 100,\n", + " 'lr_mul': 0.15,\n", + " 'n_warmup_steps': 120,\n", + " 'Optim': functools.partial(, amsgrad=True),\n", + " 'g_loss_mul': 0.1,\n", + " 'd_model': 32,\n", + " 'attn_activation': ml_utility_loss.activations.LeakyHardtanh,\n", + " 'tf_d_inner': 16,\n", + " 'tf_n_layers_enc': 2,\n", + " 'tf_n_head': 16,\n", + " 'tf_activation_final': ml_utility_loss.activations.LeakyHardsigmoid,\n", + " 'ada_d_hid': 32,\n", + " 'ada_n_layers': 3,\n", + " 'ada_activation': torch.nn.modules.activation.ReLU6,\n", + " 'ada_activation_final': torch.nn.modules.activation.Sigmoid,\n", + " 'head_d_hid': 32,\n", + " 'head_n_layers': 7,\n", + " 'head_n_head': 2,\n", + " 'head_activation_final': torch.nn.modules.activation.Sigmoid,\n", + " 'models': ['tvae'],\n", + " 'fixed_role_model': 'tvae',\n", + " 'max_seconds': 3600,\n", + " 'tf_lora': False,\n", + " 'tf_num_inds': 32,\n", + " 'ada_n_seeds': 0,\n", + " 'gradient_penalty_kwargs': {'mag_loss': True,\n", + " 'mse_mag': True,\n", + " 'mag_corr': False,\n", + " 'seq_mag': False,\n", + " 'cos_loss': False,\n", + " 'mag_corr_kwargs': {'only_sign': False},\n", + " 'cos_loss_kwargs': {'only_sign': True, 'cos_matrix': False},\n", + " 'mse_mag_kwargs': {'target': 0.5, 'multiply': True, 'forgive_over': True}}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ml_utility_loss.loss_learning.estimator.params2 as PARAMS\n", + "from ml_utility_loss.tuning import map_parameters\n", + "from ml_utility_loss.loss_learning.estimator.params.default import update_param_space, update_param_space_2\n", + "import wandb\n", + "\n", + "#\"\"\"\n", + "param_space = {\n", + " **getattr(PARAMS, dataset_name).PARAM_SPACE,\n", + "}\n", + "# params = {\n", + "# **getattr(PARAMS, dataset_name).BESTS[param_index],\n", + "# }\n", + "params = getattr(PARAMS, dataset_name).BEST_DICT[gp][gp_multiply][single_model]\n", + "if isinstance(params, (list, tuple)):\n", + " params = params[param_index]\n", + "params = {\n", + " **getattr(PARAMS, dataset_name).DEFAULTS,\n", + " **params,\n", + "}\n", + "if gp:\n", + " params[\"gradient_penalty_mode\"] = \"ALL\"\n", + " params[\"mse_mag\"] = True\n", + " if gp_multiply:\n", + " params[\"mse_mag_multiply\"] = True\n", + " #params[\"mse_mag_target\"] = 1.0\n", + " else:\n", + " params[\"mse_mag_multiply\"] = False\n", + " #params[\"mse_mag_target\"] = 0.1\n", + "else:\n", + " params[\"gradient_penalty_mode\"] = \"NONE\"\n", + " params[\"mse_mag\"] = False\n", + "params[\"single_model\"] = False\n", + "if models:\n", + " params[\"models\"] = models\n", + "if single_model:\n", + " params[\"fixed_role_model\"] = single_model\n", + " params[\"single_model\"] = True\n", + " params[\"models\"] = [single_model]\n", + "# if params[\"fixed_role_model\"] == \"realtabformer\" and dataset_name == \"treatment\":\n", + "# params[\"batch_size\"] = 2\n", + "params[\"max_seconds\"] = 3600\n", + "params[\"patience\"] = 10\n", + "params[\"epochs\"] = 100\n", + "if debug:\n", + " params[\"epochs\"] = 2\n", + "with open(\"params.json\", \"w\") as f:\n", + " json.dump(params, f)\n", + "params = map_parameters(params, param_space=param_space)\n", + "params" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a48bd9e9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:18.026214Z", + "iopub.status.busy": "2024-07-23T14:55:18.025904Z", + "iopub.status.idle": "2024-07-23T14:55:18.168921Z", + "shell.execute_reply": "2024-07-23T14:55:18.167926Z" + }, + "papermill": { + "duration": 0.158233, + "end_time": "2024-07-23T14:55:18.171070", + "exception": false, + "start_time": "2024-07-23T14:55:18.012837", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../../../../ml-utility-loss/ synthetics iris\n", + "Caching in ../../../../iris/_cache_aug_train/tvae/all inf False\n", + "split df ratio is 0\n", + "../../../../ml-utility-loss/aug_train/iris [0, 0]\n", + "Caching in ../../../../iris/_cache_aug_val/tvae/all inf False\n", + "split df ratio is 1\n", + "../../../../ml-utility-loss/aug_val/iris [0, 0]\n", + "Caching in ../../../../iris/_cache_bs_train/tvae/all inf False\n", + "split df ratio is 0\n", + "../../../../ml-utility-loss/bs_train/iris [0, 0]\n", + "Caching in ../../../../iris/_cache_bs_val/tvae/all inf False\n", + "split df ratio is 1\n", + "../../../../ml-utility-loss/bs_val/iris [0, 0]\n", + "Caching in ../../../../iris/_cache_synth/tvae/all inf False\n", + "Splitting without random!\n", + "Split with reverse index!\n", + "../../../../ml-utility-loss/synthetics/iris [800, 200]\n", + "Caching in ../../../../iris/_cache_real/tvae/all inf False\n", + "split df ratio is 0\n", + "../../../../ml-utility-loss/synthetics/iris [5, 0]\n", + "[805, 200]\n", + "[805, 200]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/ml_utility_loss/loss_learning/estimator/data.py:174: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " index = pd.Series(self.index)\n", + "/opt/conda/lib/python3.10/site-packages/ml_utility_loss/loss_learning/estimator/data.py:174: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " index = pd.Series(self.index)\n", + "/opt/conda/lib/python3.10/site-packages/ml_utility_loss/loss_learning/estimator/data.py:174: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " index = pd.Series(self.index)\n", + "/opt/conda/lib/python3.10/site-packages/ml_utility_loss/loss_learning/estimator/data.py:174: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " index = pd.Series(self.index)\n" + ] + } + ], + "source": [ + "train_set, val_set = datasetsn(model=params[\"fixed_role_model\"], synth_data=params[\"synth_data\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2fcb1418", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "execution": { + "iopub.execute_input": "2024-07-23T14:55:18.198603Z", + "iopub.status.busy": "2024-07-23T14:55:18.198312Z", + "iopub.status.idle": "2024-07-23T14:55:18.473460Z", + "shell.execute_reply": "2024-07-23T14:55:18.472650Z" + }, + "executionInfo": { + "elapsed": 396850, + "status": "error", + "timestamp": 1696841446059, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "_bt1MQc5kpSk", + "outputId": "01c1d3e5-ac64-461d-835a-b76f4a66e6d6", + "papermill": { + "duration": 0.291162, + "end_time": "2024-07-23T14:55:18.475512", + "exception": false, + "start_time": "2024-07-23T14:55:18.184350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating model of type \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[*] Embedding False True\n", + "['tvae'] 1\n" + ] + } + ], + "source": [ + "from ml_utility_loss.loss_learning.estimator.model.pipeline import remove_non_model_params\n", + "from ml_utility_loss.loss_learning.estimator.pipeline import create_model\n", + "from ml_utility_loss.util import filter_dict, clear_memory\n", + "\n", + "clear_memory()\n", + "\n", + "params2 = remove_non_model_params(params)\n", + "adapters = filter_dict(preprocessor.adapter_sizes, params[\"models\"])\n", + "\n", + "model = create_model(\n", + " adapters=adapters,\n", + " #Body=\"twin_encoder\",\n", + " **params2,\n", + ")\n", + "#cf.apply_weight_standardization(model, n_last_layers_ignore=0)\n", + "print(model.models, len(model.adapters))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "938f94fc", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:18.504545Z", + "iopub.status.busy": "2024-07-23T14:55:18.503827Z", + "iopub.status.idle": "2024-07-23T14:55:18.508375Z", + "shell.execute_reply": "2024-07-23T14:55:18.507477Z" + }, + "papermill": { + "duration": 0.021501, + "end_time": "2024-07-23T14:55:18.510346", + "exception": false, + "start_time": "2024-07-23T14:55:18.488845", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "study_name=f\"{model_name}_{dataset_name}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "12fb613e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:18.536001Z", + "iopub.status.busy": "2024-07-23T14:55:18.535718Z", + "iopub.status.idle": "2024-07-23T14:55:18.542905Z", + "shell.execute_reply": "2024-07-23T14:55:18.542115Z" + }, + "papermill": { + "duration": 0.022231, + "end_time": "2024-07-23T14:55:18.544878", + "exception": false, + "start_time": "2024-07-23T14:55:18.522647", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "37313" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def count_parameters(model):\n", + " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "\n", + "count_parameters(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "bd386e57", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:18.570591Z", + "iopub.status.busy": "2024-07-23T14:55:18.570319Z", + "iopub.status.idle": "2024-07-23T14:55:18.624415Z", + "shell.execute_reply": "2024-07-23T14:55:18.623569Z" + }, + "papermill": { + "duration": 0.069371, + "end_time": "2024-07-23T14:55:18.626439", + "exception": false, + "start_time": "2024-07-23T14:55:18.557068", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "========================================================================================================================\n", + "Layer (type:depth-idx) Output Shape Param #\n", + "========================================================================================================================\n", + "MLUtilitySingle [2, 120, 24] --\n", + "├─Adapter: 1-1 [2, 120, 24] --\n", + "│ └─Sequential: 2-1 [2, 120, 32] --\n", + "│ │ └─FeedForward: 3-1 [2, 120, 32] --\n", + "│ │ │ └─Linear: 4-1 [2, 120, 32] 800\n", + "│ │ │ └─ReLU6: 4-2 [2, 120, 32] --\n", + "│ │ └─FeedForward: 3-2 [2, 120, 32] --\n", + "│ │ │ └─Linear: 4-3 [2, 120, 32] 1,056\n", + "│ │ │ └─ReLU6: 4-4 [2, 120, 32] --\n", + "│ │ └─FeedForward: 3-3 [2, 120, 32] --\n", + "│ │ │ └─Linear: 4-5 [2, 120, 32] 1,056\n", + "│ │ │ └─Sigmoid: 4-6 [2, 120, 32] --\n", + "├─Adapter: 1-2 [2, 30, 24] (recursive)\n", + "│ └─Sequential: 2-2 [2, 30, 32] (recursive)\n", + "│ │ └─FeedForward: 3-4 [2, 30, 32] (recursive)\n", + "│ │ │ └─Linear: 4-7 [2, 30, 32] (recursive)\n", + "│ │ │ └─ReLU6: 4-8 [2, 30, 32] --\n", + "│ │ └─FeedForward: 3-5 [2, 30, 32] (recursive)\n", + "│ │ │ └─Linear: 4-9 [2, 30, 32] (recursive)\n", + "│ │ │ └─ReLU6: 4-10 [2, 30, 32] --\n", + "│ │ └─FeedForward: 3-6 [2, 30, 32] (recursive)\n", + "│ │ │ └─Linear: 4-11 [2, 30, 32] (recursive)\n", + "│ │ │ └─Sigmoid: 4-12 [2, 30, 32] --\n", + "├─TwinEncoder: 1-3 [2, 128] --\n", + "│ └─Encoder: 2-3 [2, 4, 32] --\n", + "│ │ └─ModuleList: 3-8 -- (recursive)\n", + "│ │ │ └─EncoderLayer: 4-13 [2, 120, 32] --\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-1 [2, 120, 32] --\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-1 [2, 32, 32] 1,024\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-2 [2, 32, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-1 [2, 32, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-2 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-3 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-4 [2, 16, 32, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-1 [2, 16, 32, 120] --\n", + "│ │ │ │ │ │ └─Linear: 7-5 [2, 32, 32] 1,056\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-6 [2, 32, 32] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-3 [2, 120, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-7 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-8 [2, 32, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-9 [2, 32, 32] 1,024\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-10 [2, 16, 120, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-2 [2, 16, 120, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-11 [2, 120, 32] 1,056\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-12 [2, 120, 32] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-2 [2, 120, 32] --\n", + "│ │ │ │ │ └─Linear: 6-4 [2, 120, 16] 528\n", + "│ │ │ │ │ └─ReLU6: 6-5 [2, 120, 16] --\n", + "│ │ │ │ │ └─Linear: 6-6 [2, 120, 32] 544\n", + "│ │ │ └─EncoderLayer: 4-14 [2, 4, 32] --\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-3 [2, 120, 32] --\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-7 [2, 32, 32] 1,024\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-8 [2, 32, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-13 [2, 32, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-14 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-15 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-16 [2, 16, 32, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-3 [2, 16, 32, 120] --\n", + "│ │ │ │ │ │ └─Linear: 7-17 [2, 32, 32] 1,056\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-18 [2, 32, 32] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-9 [2, 120, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-19 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-20 [2, 32, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-21 [2, 32, 32] 1,024\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-22 [2, 16, 120, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-4 [2, 16, 120, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-23 [2, 120, 32] 1,056\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-24 [2, 120, 32] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-4 [2, 120, 32] --\n", + "│ │ │ │ │ └─Linear: 6-10 [2, 120, 16] 528\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-11 [2, 120, 16] --\n", + "│ │ │ │ │ └─Linear: 6-12 [2, 120, 32] 544\n", + "│ │ │ │ └─PoolingByMultiheadAttention: 5-5 [2, 4, 32] --\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-13 [2, 4, 32] 128\n", + "│ │ │ │ │ └─SimpleMultiHeadAttention: 6-14 [2, 4, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-25 [2, 4, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-26 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─Linear: 7-27 [2, 120, 32] 1,024\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-28 [2, 16, 4, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-5 [2, 16, 4, 120] --\n", + "│ │ │ │ │ │ └─Linear: 7-29 [2, 4, 32] 1,056\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-30 [2, 4, 32] --\n", + "│ └─Encoder: 2-4 [2, 4, 32] (recursive)\n", + "│ │ └─ModuleList: 3-8 -- (recursive)\n", + "│ │ │ └─EncoderLayer: 4-15 [2, 30, 32] (recursive)\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-6 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-15 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-16 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-31 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-32 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-33 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-34 [2, 16, 32, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-6 [2, 16, 32, 30] --\n", + "│ │ │ │ │ │ └─Linear: 7-35 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-36 [2, 32, 32] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-17 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-37 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-38 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-39 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-40 [2, 16, 30, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-7 [2, 16, 30, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-41 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-42 [2, 30, 32] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-7 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ └─Linear: 6-18 [2, 30, 16] (recursive)\n", + "│ │ │ │ │ └─ReLU6: 6-19 [2, 30, 16] --\n", + "│ │ │ │ │ └─Linear: 6-20 [2, 30, 32] (recursive)\n", + "│ │ │ └─EncoderLayer: 4-16 [2, 4, 32] (recursive)\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-8 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-21 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-22 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-43 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-44 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-45 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-46 [2, 16, 32, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-8 [2, 16, 32, 30] --\n", + "│ │ │ │ │ │ └─Linear: 7-47 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-48 [2, 32, 32] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-23 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-49 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-50 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-51 [2, 32, 32] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-52 [2, 16, 30, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-9 [2, 16, 30, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-53 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-54 [2, 30, 32] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-9 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ └─Linear: 6-24 [2, 30, 16] (recursive)\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-25 [2, 30, 16] --\n", + "│ │ │ │ │ └─Linear: 6-26 [2, 30, 32] (recursive)\n", + "│ │ │ │ └─PoolingByMultiheadAttention: 5-10 [2, 4, 32] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-27 [2, 4, 32] (recursive)\n", + "│ │ │ │ │ └─SimpleMultiHeadAttention: 6-28 [2, 4, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-55 [2, 4, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-56 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-57 [2, 30, 32] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-58 [2, 16, 4, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-10 [2, 16, 4, 30] --\n", + "│ │ │ │ │ │ └─Linear: 7-59 [2, 4, 32] (recursive)\n", + "│ │ │ │ │ │ └─LeakyHardtanh: 7-60 [2, 4, 32] --\n", + "├─Head: 1-4 [2] --\n", + "│ └─Sequential: 2-5 [2, 1] --\n", + "│ │ └─FeedForward: 3-9 [2, 32] --\n", + "│ │ │ └─Linear: 4-17 [2, 32] 4,128\n", + "│ │ │ └─ReLU6: 4-18 [2, 32] --\n", + "│ │ └─FeedForward: 3-10 [2, 32] --\n", + "│ │ │ └─Linear: 4-19 [2, 32] 1,056\n", + "│ │ │ └─ReLU6: 4-20 [2, 32] --\n", + "│ │ └─FeedForward: 3-11 [2, 32] --\n", + "│ │ │ └─Linear: 4-21 [2, 32] 1,056\n", + "│ │ │ └─ReLU6: 4-22 [2, 32] --\n", + "│ │ └─FeedForward: 3-12 [2, 32] --\n", + "│ │ │ └─Linear: 4-23 [2, 32] 1,056\n", + "│ │ │ └─ReLU6: 4-24 [2, 32] --\n", + "│ │ └─FeedForward: 3-13 [2, 32] --\n", + "│ │ │ └─Linear: 4-25 [2, 32] 1,056\n", + "│ │ │ └─ReLU6: 4-26 [2, 32] --\n", + "│ │ └─FeedForward: 3-14 [2, 32] --\n", + "│ │ │ └─Linear: 4-27 [2, 32] 1,056\n", + "│ │ │ └─ReLU6: 4-28 [2, 32] --\n", + "│ │ └─FeedForward: 3-15 [2, 1] --\n", + "│ │ │ └─Linear: 4-29 [2, 1] 33\n", + "│ │ │ └─Sigmoid: 4-30 [2, 1] --\n", + "========================================================================================================================\n", + "Total params: 37,313\n", + "Trainable params: 37,313\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 0.12\n", + "========================================================================================================================\n", + "Input size (MB): 0.03\n", + "Forward/backward pass size (MB): 1.57\n", + "Params size (MB): 0.15\n", + "Estimated Total Size (MB): 1.75\n", + "========================================================================================================================" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torchinfo import summary\n", + "\n", + "role_model = params[\"fixed_role_model\"]\n", + "s = train_set[0][role_model]\n", + "summary(model[role_model], input_size=((2, *s[0].shape), (2, *s[1].shape)), depth=9) # 8 max" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "0f42c4d1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T14:55:18.654661Z", + "iopub.status.busy": "2024-07-23T14:55:18.654373Z", + "iopub.status.idle": "2024-07-23T15:58:07.385734Z", + "shell.execute_reply": "2024-07-23T15:58:07.384868Z" + }, + "papermill": { + "duration": 3768.763884, + "end_time": "2024-07-23T15:58:07.404008", + "exception": false, + "start_time": "2024-07-23T14:55:18.640124", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 datasets [805, 200, 200]\n", + "Creating model of type \n", + "[*] Embedding False True\n", + "g_loss_mul 0.1\n", + "Epoch 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.06562852033917208, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.0036339827326506984, 'avg_role_model_g_mag_loss': 0.0034513882135752566, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.1490358881744909, 'n_size': 805, 'n_batch': 202, 'duration': 159.1750249862671, 'duration_batch': 0.787995173199342, 'duration_size': 0.1977329502934995, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.05958062722347677, 'avg_role_model_std_loss': 25.847225275039673, 'avg_role_model_mean_pred_loss': 0.0014786072486077729, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.05958062722347677, 'n_size': 200, 'n_batch': 50, 'duration': 37.40110778808594, 'duration_batch': 0.7480221557617187, 'duration_size': 0.18700553894042968, 'avg_pred_std': 0.016021974510513248}\n", + "Epoch 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.025021375682037206, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.0007260728629798112, 'avg_role_model_g_mag_loss': 0.0008932074450928232, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.030302588582495862, 'n_size': 805, 'n_batch': 202, 'duration': 158.4990313053131, 'duration_batch': 0.7846486698282827, 'duration_size': 0.19689320659045106, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.014578261756105349, 'avg_role_model_std_loss': 0.2350980525921659, 'avg_role_model_mean_pred_loss': 0.00014072804498510116, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.014578261756105349, 'n_size': 200, 'n_batch': 50, 'duration': 36.78789305686951, 'duration_batch': 0.7357578611373902, 'duration_size': 0.18393946528434754, 'avg_pred_std': 0.26685153376311066}\n", + "Epoch 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.01234830404712778, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.0001894594463176996, 'avg_role_model_g_mag_loss': 0.00017999495894457243, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.015197739432571652, 'n_size': 805, 'n_batch': 202, 'duration': 160.46673154830933, 'duration_batch': 0.7943897601401452, 'duration_size': 0.19933755471839668, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.007489000000641681, 'avg_role_model_std_loss': 0.11842312298656907, 'avg_role_model_mean_pred_loss': 3.78229050792811e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.007489000000641681, 'n_size': 200, 'n_batch': 50, 'duration': 39.36057734489441, 'duration_batch': 0.7872115468978882, 'duration_size': 0.19680288672447205, 'avg_pred_std': 0.25609073400497434}\n", + "Epoch 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.009948341047061573, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.00015684919342139032, 'avg_role_model_g_mag_loss': 0.00027181627657278933, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011637375818461365, 'n_size': 805, 'n_batch': 202, 'duration': 157.31365442276, 'duration_batch': 0.778780467439406, 'duration_size': 0.19542068872392548, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.007549566647649044, 'avg_role_model_std_loss': 0.07792455582901311, 'avg_role_model_mean_pred_loss': 5.662406800752251e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.007549566647649044, 'n_size': 200, 'n_batch': 50, 'duration': 36.818788290023804, 'duration_batch': 0.736375765800476, 'duration_size': 0.184093941450119, 'avg_pred_std': 0.25310478687286375}\n", + "Epoch 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.00854702929713798, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 7.037709092305226e-05, 'avg_role_model_g_mag_loss': 7.150810405681408e-05, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012079614621799682, 'n_size': 805, 'n_batch': 202, 'duration': 157.6829833984375, 'duration_batch': 0.7806088287051361, 'duration_size': 0.19587948248253106, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.0080631831753999, 'avg_role_model_std_loss': 0.05836149943875057, 'avg_role_model_mean_pred_loss': 4.185925984108829e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.0080631831753999, 'n_size': 200, 'n_batch': 50, 'duration': 36.99217462539673, 'duration_batch': 0.7398434925079346, 'duration_size': 0.18496087312698364, 'avg_pred_std': 0.25894770845770837}\n", + "Epoch 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.009346609180200244, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.00012343654117647225, 'avg_role_model_g_mag_loss': 0.00014534823665892857, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.018135114811774437, 'n_size': 805, 'n_batch': 202, 'duration': 161.5463948249817, 'duration_batch': 0.799734627846444, 'duration_size': 0.200678751335381, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 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0.7745192050933838, 'pred_std': 0.22179843485355377, 'std_loss': 0.010140529833734035, 'mean_pred_loss': 3.5592227504821494e-05, 'pred_rmse': 0.07672078907489777, 'pred_mae': 0.05445937067270279, 'pred_mape': 0.11239900439977646, 'grad_rmse': 0.056929074227809906, 'grad_mae': 0.04146219417452812, 'grad_mape': 0.7870627641677856}, 'model_metrics': {'tvae': {'avg_loss': 0.005886079169576988, 'avg_g_mag_loss': 0.013331892481277806, 'avg_g_cos_loss': 0.0021675284715820453, 'pred_duration': 0.4982330799102783, 'grad_duration': 0.27628612518310547, 'total_duration': 0.7745192050933838, 'pred_std': 0.22179843485355377, 'std_loss': 0.010140529833734035, 'mean_pred_loss': 3.5592227504821494e-05, 'pred_rmse': 0.07672078907489777, 'pred_mae': 0.05445937067270279, 'pred_mape': 0.11239900439977646, 'grad_rmse': 0.056929074227809906, 'grad_mae': 0.04146219417452812, 'grad_mape': 0.7870627641677856}}}\n" + ] + } + ], + "source": [ + "import torch\n", + "from ml_utility_loss.loss_learning.estimator.pipeline import train, train_2\n", + "from ml_utility_loss.loss_learning.estimator.process_simple import train_epoch, eval as _eval\n", + "from ml_utility_loss.params import GradientPenaltyMode\n", + "from ml_utility_loss.util import clear_memory\n", + "import time\n", + "#torch.autograd.set_detect_anomaly(True)\n", + "\n", + "del model\n", + "clear_memory()\n", + "\n", + "#opt = params[\"Optim\"](model.parameters())\n", + "loss = train_2(\n", + " [train_set, val_set, test_set],\n", + " preprocessor=preprocessor,\n", + " #whole_model=model,\n", + " #optim=opt,\n", + " log_dir=\"logs\",\n", + " checkpoint_dir=None,\n", + " verbose=True,\n", + " allow_same_prediction=allow_same_prediction,\n", + " wandb=wandb if log_wandb else None,\n", + " study_name=study_name,\n", + " **params\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9b514a07", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:07.439862Z", + "iopub.status.busy": "2024-07-23T15:58:07.439551Z", + "iopub.status.idle": "2024-07-23T15:58:07.444133Z", + "shell.execute_reply": "2024-07-23T15:58:07.443341Z" + }, + "papermill": { + "duration": 0.024806, + "end_time": "2024-07-23T15:58:07.446028", + "exception": false, + "start_time": "2024-07-23T15:58:07.421222", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "model = loss[\"whole_model\"]\n", + "opt = loss[\"optim\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "331a49e1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:07.479354Z", + "iopub.status.busy": "2024-07-23T15:58:07.479097Z", + "iopub.status.idle": "2024-07-23T15:58:07.497917Z", + "shell.execute_reply": "2024-07-23T15:58:07.497318Z" + }, + "papermill": { + "duration": 0.037872, + "end_time": "2024-07-23T15:58:07.499792", + "exception": false, + "start_time": "2024-07-23T15:58:07.461920", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import torch\n", + "from copy import deepcopy\n", + "\n", + "torch.save(deepcopy(model.state_dict()), \"model.pt\")\n", + "#torch.save(deepcopy(opt.state_dict()), \"optim.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "123b4b17", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:07.533056Z", + "iopub.status.busy": "2024-07-23T15:58:07.532783Z", + "iopub.status.idle": "2024-07-23T15:58:07.750472Z", + "shell.execute_reply": "2024-07-23T15:58:07.749583Z" + }, + "papermill": { + "duration": 0.236595, + "end_time": "2024-07-23T15:58:07.752395", + "exception": false, + "start_time": "2024-07-23T15:58:07.515800", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = loss[\"history\"]\n", + "history.to_csv(\"history.csv\")\n", + "history[[\"avg_loss_train\", \"avg_loss_test\"]].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2586ba0a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:07.788710Z", + "iopub.status.busy": "2024-07-23T15:58:07.788416Z", + "iopub.status.idle": "2024-07-23T15:58:45.651967Z", + "shell.execute_reply": "2024-07-23T15:58:45.650954Z" + }, + "papermill": { + "duration": 37.884807, + "end_time": "2024-07-23T15:58:45.654643", + "exception": false, + "start_time": "2024-07-23T15:58:07.769836", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "from ml_utility_loss.loss_learning.estimator.pipeline import eval\n", + "#eval_loss = loss[\"eval_loss\"]\n", + "\n", + "batch_size = params[\"batch_size_low\"] if \"batch_size_low\" in params else params[\"batch_size\"]\n", + "\n", + "eval_loss = eval(\n", + " test_set, model,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "187137f6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:45.691653Z", + "iopub.status.busy": "2024-07-23T15:58:45.691332Z", + "iopub.status.idle": "2024-07-23T15:58:45.711975Z", + "shell.execute_reply": "2024-07-23T15:58:45.711094Z" + }, + "papermill": { + "duration": 0.041294, + "end_time": "2024-07-23T15:58:45.713808", + "exception": false, + "start_time": "2024-07-23T15:58:45.672514", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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avg_g_cos_lossavg_g_mag_lossavg_lossgrad_durationgrad_maegrad_mapegrad_rmsemean_pred_losspred_durationpred_maepred_mapepred_rmsepred_stdstd_losstotal_duration
tvae0.0038790.0026580.0058860.2703170.0414620.7870630.0569290.0000360.4985240.0544590.1123990.0767210.2217980.0101410.768841
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" + ], + "text/plain": [ + " avg_g_cos_loss avg_g_mag_loss avg_loss grad_duration grad_mae \\\n", + "tvae 0.003879 0.002658 0.005886 0.270317 0.041462 \n", + "\n", + " grad_mape grad_rmse mean_pred_loss pred_duration pred_mae \\\n", + "tvae 0.787063 0.056929 0.000036 0.498524 0.054459 \n", + "\n", + " pred_mape pred_rmse pred_std std_loss total_duration \n", + "tvae 0.112399 0.076721 0.221798 0.010141 0.768841 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "metrics = pd.DataFrame(eval_loss[\"model_metrics\"]).T\n", + "metrics.to_csv(\"eval.csv\")\n", + "metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "123d305b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:45.748470Z", + "iopub.status.busy": "2024-07-23T15:58:45.748202Z", + "iopub.status.idle": "2024-07-23T15:58:45.998381Z", + "shell.execute_reply": "2024-07-23T15:58:45.997444Z" + }, + "papermill": { + "duration": 0.270015, + "end_time": "2024-07-23T15:58:46.000557", + "exception": false, + "start_time": "2024-07-23T15:58:45.730542", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from ml_utility_loss.util import clear_memory\n", + "clear_memory()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a3eecc2a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:58:46.038307Z", + "iopub.status.busy": "2024-07-23T15:58:46.037975Z", + "iopub.status.idle": "2024-07-23T15:59:23.231005Z", + "shell.execute_reply": "2024-07-23T15:59:23.229997Z" + }, + "papermill": { + "duration": 37.214408, + "end_time": "2024-07-23T15:59:23.233462", + "exception": false, + "start_time": "2024-07-23T15:58:46.019054", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caching in ../../../../iris/_cache_aug_test/tvae/all inf False\n", + "Caching in ../../../../iris/_cache_bs_test/tvae/all inf False\n", + "Caching in ../../../../iris/_cache_synth_test/tvae/all inf False\n" + ] + } + ], + "source": [ + "#\"\"\"\n", + "from ml_utility_loss.loss_learning.estimator.process import pred, pred_2\n", + "from ml_utility_loss.util import stack_samples\n", + "\n", + "#samples = test_set[list(range(len(test_set)))]\n", + "#y = {m: pred(model[m], s) for m, s in samples.items()}\n", + "y = pred_2(model, test_set, batch_size=batch_size)\n", + "#\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6ab51db8", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:59:23.271026Z", + "iopub.status.busy": "2024-07-23T15:59:23.270722Z", + "iopub.status.idle": "2024-07-23T15:59:23.284285Z", + "shell.execute_reply": "2024-07-23T15:59:23.283584Z" + }, + "papermill": { + "duration": 0.034762, + "end_time": "2024-07-23T15:59:23.286234", + "exception": false, + "start_time": "2024-07-23T15:59:23.251472", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "from ml_utility_loss.util import transpose_dict\n", + "\n", + "os.makedirs(\"pred\", exist_ok=True)\n", + "y2 = transpose_dict(y)\n", + "for k, v in y2.items():\n", + " df = pd.DataFrame(v)\n", + " df.to_csv(f\"pred/{k}.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "d81a30f1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:59:23.321003Z", + "iopub.status.busy": "2024-07-23T15:59:23.320728Z", + "iopub.status.idle": "2024-07-23T15:59:23.325514Z", + "shell.execute_reply": "2024-07-23T15:59:23.324642Z" + }, + "papermill": { + "duration": 0.024378, + "end_time": "2024-07-23T15:59:23.327368", + "exception": false, + "start_time": "2024-07-23T15:59:23.302990", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'tvae': 0.7587717612087727}\n" + ] + } + ], + "source": [ + "print({k: sum(v[\"pred\"])/len(v[\"pred\"]) for k, v in y.items()})" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3b3ff322", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:59:23.361833Z", + "iopub.status.busy": "2024-07-23T15:59:23.361557Z", + "iopub.status.idle": "2024-07-23T15:59:23.694326Z", + "shell.execute_reply": "2024-07-23T15:59:23.693417Z" + }, + "papermill": { + "duration": 0.352462, + "end_time": "2024-07-23T15:59:23.696431", + "exception": false, + "start_time": "2024-07-23T15:59:23.343969", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ml_utility_loss.loss_learning.visualization import plot_pred_density_2\n", + "\n", + "_ = plot_pred_density_2(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e79e4b0f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:59:23.734703Z", + "iopub.status.busy": "2024-07-23T15:59:23.734399Z", + "iopub.status.idle": "2024-07-23T15:59:24.034016Z", + "shell.execute_reply": "2024-07-23T15:59:24.033115Z" + }, + "papermill": { + "duration": 0.321023, + "end_time": "2024-07-23T15:59:24.036153", + "exception": false, + "start_time": "2024-07-23T15:59:23.715130", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ml_utility_loss.loss_learning.visualization import plot_density_3\n", + "\n", + "_ = plot_density_3(y2[\"pred\"], next(iter(y2[\"y\"].values())))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "745adde1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:59:24.075460Z", + "iopub.status.busy": "2024-07-23T15:59:24.075149Z", + "iopub.status.idle": "2024-07-23T15:59:24.327064Z", + "shell.execute_reply": "2024-07-23T15:59:24.326146Z" + }, + "papermill": { + "duration": 0.274122, + "end_time": "2024-07-23T15:59:24.329242", + "exception": false, + "start_time": "2024-07-23T15:59:24.055120", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ml_utility_loss.loss_learning.visualization import plot_box_3\n", + "\n", + "_ = plot_box_3(y2[\"pred\"], next(iter(y2[\"y\"].values())))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "eabe1bab", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T15:59:24.368733Z", + "iopub.status.busy": "2024-07-23T15:59:24.367973Z", + "iopub.status.idle": "2024-07-23T15:59:24.621244Z", + "shell.execute_reply": "2024-07-23T15:59:24.620318Z" + }, + "papermill": { + "duration": 0.275019, + "end_time": "2024-07-23T15:59:24.623275", + "exception": false, + "start_time": "2024-07-23T15:59:24.348256", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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IWLjqRXBwcGVPAVmWMZlMaDQ166MoV5AFJ5aoTkTEwrmejIwMBg8ejI+PDyEhIXz88cflbiVou0Q9f/48kiSxatUqHnjgAWrVqkW7du3YtWuX3TklNaf57rvv6NSpE35+fgQHB/Pcc8/Z9WfYvHkzkiTx+++/07FjRzw9Pdm+fXu5/hbVGdtPiPCiVhOUmnDVUeBkGfRZlfMowzd4TEwMO3bsYO3atWzYsIFt27Zx4MABp/0Z3nrrLSZMmMDBgwe58847GTRoEEajEchvTvPkk09y+PBhli9fzvbt2+3qxhkMBt577z0OHTrEmjVrOH/+PCNGjCj0OpMmTWLGjBmcOHGCu+++22nzry6IPbhqSLXegzNkw/+Vrv6Z05lyFbQ+JQ7LyMjgm2++YenSpUoZoq+//rrUddtKw4QJE+jbty8A7777LnfddRdnzpyhZcuWTJ8+ncGDByvWYvPmzfn000/p0aMH8+fPx8vLi1GjRinXatKkCZ9++imdO3cmMzMTX19f5bn//Oc/PPTQQ06bd3XD1nPqyo9LpVtw8+bNIzw8HC8vLyIjI9m7d2+x41esWEHLli3x8vKibdu2rFu3zu75zMxMxo8fT8OGDfH29qZ169aFKri6CrEH51rOnTuHwWCgS5cuyrGAgABatGjhtNewtaZCQkIAlCXmoUOHWLx4Mb6+vsqjd+/eSnMasJRI/8c//kGjRo3w8/OjR48eAEphTiuOGtDUJGqEBbd8+XJiYmJYsGABkZGRzJkzh969e3Pq1Cnq169faPzOnTsZNGgQ06dP57HHHmPp0qX079+fAwcO0KZNG8CyhPnrr7/4/vvvCQ8P588//2Ts2LGEhobalbJ2BdV6D86jlsWSqqzXriLYljiyFmE055kb1uY0r776aqHzGjVqRFZWllJZZMmSJUrF4d69exdqJFOwAY07ozea0RlN+Hnl/21rRCbD7NmzeeGFFxg5cqRiadWqVYtFixY5HP/JJ5/Qp08fJk6cSKtWrXjvvfe45557mDt3rjJm586dDB8+nJ49exIeHs6YMWNo165diZahM9CoqnE+qiRZlomV8ShlNdcmTZrg4eFh17cgLS2N06dPu+qvYkdJzWlOnjzJ9evXmTFjBvfffz8tW7Z02AC6pjFv0xl6zdzCV9vPARRqeSjLhY85i0oTOL1eT1xcHNHR0fmTUamIjo4u5LmysmvXLrvxYKnFZTu+W7durF27litXriDLMps2beL06dM8/PDDRc5Fp9ORnp5u9ygPljZ4lp+NrgzPrqH4+fkxfPhwJk6cyKZNmzh27BijR49GpVKVueR1eSipOU2jRo3QarVKg5u1a9fy3nvvuXxeVRlZllm65yIpmTq+3GpZxjv67neVQVBpApeamorJZCIoKMjueFBQEElJSQ7PSUpKKnH8Z599RuvWrWnYsCFarZY+ffowb968YstHT58+nYCAAOURFhZW7vtS9uFM1cyCqybMnj2bqKgoHnvsMaKjo7n33ntp1apVhfQeKKk5Tb169Vi8eDErVqygdevWzJgxg5kzZ7p8XlUZndHMU50aUs/Xk2e7WD5Xtntu1u8lV+Wj3tYeXGZmprI/YaW0TVpcxWeffcbu3btZu3YtjRs3ZuvWrYwbN47Q0NBC1p+VyZMnExMTo/yenp5ebpET+aiuxc/Pz64DfVZWFu+++y5jxowp8dzz58/b/W67LAoPDy+0TAoMDCx0rHPnzvz5559FvsagQYMYNGhQka/Ts2fPGtWVPtdg4tE2ITzaJgRJsvwtrAInSZbPi9EklyVSqEyUWeASEhIYP348mzdvJjc3VzkuyzKSJBVqylsUdevWRa1Wk5ycbHc8OTm5yAjz4ODgYsfn5OQwZcoUVq9erbj67777bg4ePMjMmTOLFDhPT088PT1LNe+S0FRnR0M1ID4+npMnT9KlSxfS0tL4z3/+A8Djjz9eyTMTOCLXkG8AybLld6vVppIkReBc9Xkps8ANGTIEWZZZtGgRQUFB5d770Gq1dOzYkdjYWPr37w9YvFWxsbFFNtyNiooiNjbWLmp9w4YNREVFAZYgS4PBgEplv/JWq9WFLE1XoVGCfcUenKuYOXMmp06dUt5D27Zt48SJEyU2hxFUPDkGe4MnS2/ER2uRHZUKVFVtiXro0CHi4uKcEnsUExPD8OHD6dSpE126dGHOnDlkZWUxcuRIAIYNG0aDBg2URr2vvfYaPXr0YNasWfTt25dly5axf/9+Fi5cCFiWxz169GDixIl4e3vTuHFjtmzZwrfffsvs2bNve76lQezBuZYOHToQFxdX6HhOTk6JzWEEFU9unsD5eWnIyDWSozfh7aEGLBacSgnFqSIC17lzZy5duuQUgRs4cCApKSlMnTqVpKQk2rdvz/r16xVHwsWLF+2ssW7durF06VLefvttpkyZQvPmzVmzZo0SAwewbNkyJk+ezODBg7lx4waNGzfmgw8+4KWXXrrt+ZaGah0LV40RzWGqHkaTWfmir+2jJSPXSJbeSGAtSzychOs/L2XubH/27FleeuklhgwZQps2bQr1fnSHvLrydLa3cjNLz+WbOfh6aYioWzWDOa2dz8PDw/H29q7s6QicTE5ODufPn6/0zvaZOiMJKVloNSqa1/fleGI6sgwhgV4k3srFW6vGU6PiVraBoABP6vuVfq4u62yfkpLC2bNnlWUkWCK+y+pkcFc0StHLqrsHZ/1Sys7OFgLnhlizJtRqdaXOw7o89fJQoVJJeHmoyNGbydJZiheopHwLrsp4UUeNGkWHDh344YcfbsvJ4K5Y9+AMVXgPTq1WExgYqETZ16pVS/w7uglms5mUlBRq1apV6TXmcvRWgbMIbS2thhy9nkxF4CSXL1HL/Be4cOECa9euFfsdRVBd9uCsoTUilcj9UKlUNGrUqNK/tHRGe4Hz0Wq4jl6pJKKSpPxA36oicL169eLQoUNC4IqgYD6qVfCqGpIkERISQv369TEYDJU9HYET0Wq1hUKlKhpZlpUYOC8Py1y8tfZLZkkCtdWLWlXCRP7xj3/w+uuvc+TIEdq2bVvIyeDqih1VHZVKyovYtsTCqVWVuw9SEmq1utL3agTuh85oRpYtIuapsby/tBoVHhoJg9E+kwGqkAVnDbewRpDbIpwMFjzUKvRGi4vcU5QUFdRAdAYz644m8lPcZV59sDlDujYGLMvUW0bLikElSahUrrXgymzHms3mIh9C3CyIfFRBTSfHYGLl/stcy9Axf/NZ5bjtMlUlScoS1VVVfcskcAaDAY1Gw9GjR10zGzdB5KMKajrZeiNPdWpIsL8XL/dsqhy3pmmBfZhIldiD8/DwoFGjRsJSK4F8C67qxsIJBK4kJ6+KyKu9mttZbV4eKmWPWqoAL2qZl6hvvfUWU6ZM4caNG66Yj1vgoRb5qIKaS67BhNlscSJYPahWJEli44lkRi3ex6oDl5Ulqquq+pZ5C3zu3LmcOXOG0NBQGjduXKi2vDNbuFVXqkssnEDgCvIzGNQOY/GW77tESqaOb3aeZ0z3Jspxk1lWMoGcRZkFzlraSFA0GuFkEFQS2XojX2w5x4/7LzHugWaK97Ji52ARuFpax+FH4x5oxvzNZ3m5Z1NlmSrLlpJJzg46KPP1pk2b5uQpuB/qapCPKnBPbmUblB4I8zefrRSBs9aAs5ZFKsiQro3t5uXKqr7lFsy4uDhOnDgBwF133UWHDh2cNqnqjkc1yEcVuCcms8xTnRqyKu6ynfeyopBlWclBLZi5UBSWmnCuqepbZoG7du0azz77LJs3byYwMBCAW7du8cADD7Bs2TLq1avn7DlWO8QenKCyMJllHm0TwrOdw2hcp+LLdVkzGFSq/BzUksjzybmkqm+ZvaivvPIKGRkZHDt2jBs3bnDjxg2OHj1Kenq6w4a4NZFq3R9VUK2xikRlve+s+29FLU8d4cqqvmW24NavX8/GjRtp1aqVcqx169bMmzev2N6jNYnqlo8qcB+swlZZ36vK/lspl6fg2hVPuVK1CibYgyUIuKIau1QH8gtfCgtOUHHkC1zlvO9y9JZab7U8Sm87WS04Kfko3Lro1PmUWeB69erFa6+9xtWrV5VjV65c4fXXX+fBBx906uSqM9Wh8KXA/bAKXGV8sdqVSNKWXlrUKgnPm38TuPJpWPQI3Ehw2pzKLHBz584lPT2d8PBwmjZtStOmTYmIiCA9PZ3PPvvMaROr7oh8VEFFYzbnh1q48n23YPNZoqbH8v3uC3bHcwwmZDlPsDSlX6Jq0y8Q8fsgVDnXwacu1LrDaXMt8x5cWFgYBw4cYOPGjZw8eRKAVq1aFdlUuaYi8lEFFY2tF9Ka+uTsqr65BhNfbU9wGGdX1vAQANIuU3vlU6izr6Gv0xLt0NXgFeC0+ZYrDk6SJB566CEeeughp03E3RB7cIKKpuB7zRWpT8a8OLufHMTZlZTBUIiMZPimH+r0S+j8I7j2+DLCnGi9QTkFLjY2ltjYWK5du1bIsbBo0SKnTKy6IxpACyqaQgLngtQnk8kSZ/ePu0NpHWrfrs82B7VEsm/Ad/3hxlnM/mEkPPoDWm/nx9CWeQ/u3Xff5eGHHyY2NpbU1FRu3rxp9xBYEPmogoqm4HvNFY5U6zJYxv7iZrPMqvgrjFq8jzXxV4q/SG46fP8EXDsOvsFkDfoJg2+oSzy/ZRb4BQsWsHjxYoYOHer0ybgTIh9VUNEUDJR1xfZIUXvKOqOZlfsvk5KpY+HWcwzvFu74AvosWPoMXI0H7ztg2M+oAppCSpZLYvfKbMHp9Xq6devm/Jm4GcKCE1Q0Bd9rrkh9supbwUsbzGae6tSQ+n6eRefAGnWwbDBc3AWeATB0NdRvWbUCfZ9//nmWLl3q9Im4G2IPTlDRFFziuSL1qSgLzpi3N/fji1GOK5iYDLBiBJzbBB4+MGQlhLYHcGlV3zIvUXNzc1m4cCEbN27k7rvvLpTVMHv2bKdNrjpTXfqjCtyHQhackwQjS2fE20ONSiUVeU1jXtcYh15bswlWvwin1oHGC55bBmFdlKcLVvV1ZmhLmS24w4cP0759e1QqFUePHiU+Pl55HDx4sMwTmDdvHuHh4Xh5eREZGcnevXuLHb9ixQpatmyJl5cXbdu2Zd26dYXGnDhxgn79+hEQEICPjw+dO3fm4kXnpoCUhDUfFUQsnKBiKLQH54QlanJ6LnM3naHbjL/4fveFIgXOkHfcWq7fZlLwy6tw9CdQecAz30FEd7shtl/+zrbiymzBbdq0yWkvvnz5cmJiYliwYAGRkZHMmTOH3r17c+rUKerXr19o/M6dOxk0aBDTp0/nscceY+nSpfTv358DBw7Qpk0bAM6ePct9993H6NGjeffdd/H39+fYsWN4eXk5bd6lRaO2NLkVsXCCisBqwalUFl253e9Vo8lMSoZOcR7M33yWyAhLnFpB7VQsONuViizDH5Mh/nuQVPDk/+DOwgU5bKv6OvujIsmu6PRQSiIjI+ncuTNz584FLIn8YWFhvPLKK0yaNKnQ+IEDB5KVlcWvv/6qHOvatSvt27dnwYIFADz77LN4eHjw3XfflXte6enpBAQEkJaWhr+/f8knFMGZa5nk6E00rlsLf6/CBQoEAmdifb95eajINZi5w1dLg0Dvcl8vOT2Xa+k61h1NZNWBy7zSqzmdwmsrnenbNszPOLC+dqM6tQjwznuvx/4Hts2y/Nx/AbQfVORrnUhMx2iSaR7kW6o4utJ+Rsu8RHUWer2euLg4uxQvlUpFdHQ0u3btcnjOrl27CqWE9e7dWxlvNpv57bffuPPOO+nduzf169cnMjKSNWvWFDsXnU5Henq63cMZKJ5U4WgQVADWlYJWY/lY346TwWSWSc3UAfBomxBWjb2XIV0bF/letm7DeFj34LbOzBe3vrOKFTfIryji7NVOpQlcamoqJpOJoKAgu+NBQUEkJSU5PCcpKanY8deuXSMzM5MZM2bQp08f/vzzTwYMGMATTzzBli1bipzL9OnTCQgIUB5hYWG3eXcWRD6qoCIxFdgHu53A2euZukJLXFm275tgu/izCp9GpYLd8+Gv9yxPPPQedH6+xNdzVVXfShM4V2BNG3v88cd5/fXXad++PZMmTeKxxx5TlrCOmDx5Mmlpacrj0qVLTpmPyEcVVCQFLbjyvu9MZpmUPOvNtq9pcR5Uqy55HPoO1udtL/WcDPeWrsq3q6r6llngtm7ditFoLHTcaDSydevWUl+nbt26qNVqkpOT7Y4nJycTHBzs8Jzg4OBix9etWxeNRkPr1q3txrRq1apYL6qnpyf+/v52D2cgYuEEFYWt+NyuBXc9y2K9eXqo8PfO3zsuKhXMWvOw9rmfkX55zXKw2yvQ481Sv6argn3LLHAPPPCAw672aWlpPPDAA6W+jlarpWPHjsTGxirHzGYzsbGxREVFOTwnKirKbjzAhg0blPFarZbOnTtz6tQpuzGnT5+mceOKb58mshkEFYVVGCQpfx/MVI6dEZNZJjVDD0B9P0+sPlFZLjoawGA243fhTxps+icgQ6dRlqVpGeLZFAvOyR+VMoeJFBWId/369UJd7ksiJiaG4cOH06lTJ7p06cKcOXPIyspi5MiRAAwbNowGDRowffp0AF577TV69OjBrFmz6Nu3L8uWLWP//v0sXLhQuebEiRMZOHAg3bt354EHHmD9+vX88ssvbN68uay3etuIfFRBRWEVH7VKuq0N++tZOkxmGa1GRYC3BykZuvzXKGARKr+d+YtGsWORZBPc/Sw8OqtM4madNzi/1HqpBe6JJ54ALDErI0aMwNPTU3nOZDJx+PDhMueoDhw4kJSUFKZOnUpSUhLt27dn/fr1iiPh4sWLqFT5Rma3bt1YunQpb7/9NlOmTKF58+asWbNGiYEDGDBgAAsWLGD69Om8+uqrtGjRgp9++on77ruvTHNzBsKCE1QUVvGxFbiyioW5oPVWQKRMjrZaLuzEb81wJLOe7GaPUevxeZZAvDLiKi9qqQUuIMAS8yLLMn5+fnh758fXaLVaunbtygsvvFDmCYwfP57x48c7fM6R1fX000/z9NNPF3vNUaNGMWrUqDLPxdmIPThBRWEVH7VKUqwhWbaIlqqUaYLXs/SK9RZYq3DcZiEL7vJ+WPIMkjGH9LBe5PSdTy11+SrQWTWx0gTu66+/BiA8PJwJEyaUeTlaE9GU840mEJQVxYKTJPvUJ1lGRcnvO1mW+WbneZbvu8Tz90fQItiv8GvYiI/X9ROofx8I+gxyG97LxQcXEOLhWeic0qIup9VZEmW2JadNmybErZTY5qMaxD6cwIVYYy2t4ma1iEorGLeyDSzfd4mUTB3f7DzvcIwShnLrLOG/D0bKvQUNu3Dlka+RNV5oCuahloEq40VNTk5m6NChhIaGotFoUKvVdg+BPSIWTlARWL8/rUKhbNqX8ns1NVPHU50aEuzvxdgHmjkcYzLLeGRcpMm6QXjkpiIHtYXBKzCoLdtVHrfR/0FV2U4GKyNGjODixYv8+9//JiQkxOlde9wNjUrCgCwcDQKXYrXgrNsiasnyvitNZkBGroFcg5m+bUOY8HCLokt7pV8lYt1zeGQnkRvYDNVzq9B6B2K8kZb32rdhwVWVMJHt27ezbds22rdv79yZuCm/HU7k+90XeaF7BGO6F1HpVCC4TayWmtUSksrglUzNtHhO7/DRFha3vF9V2anUXTMQbcZFdP6NSXhkKU196tpnMdyOBVdVclHDwsKoxAIk1Y6ley+Skqlj0fbzlT0VgRtTyIJTlqjFf1ZzDSYycy2ZSXV8tQ7HqHS3qLv6WbS3zqD3CSXhkR8w+gQj26xM1CrptlZzKhWsO5rIsK/2FmoofTuUWeDmzJnDpEmTOH/+vNMm4c6M7BZBPV9PnotsVNlTEbgx1r0rlc0SFUpOXrcG8gZ4ezjsRi/pM4lYPxxt6jGM3vVIeHQpBr+GyvMGU4EqIuVELUlK3bnPN525rWvZUuYl6sCBA8nOzqZp06bUqlWrUMlyR2lcNZnBXRtxb7O6eHq4VV0DQRXDaklpCnpRi7HgDCYzaTkGAOr6ObDe9NkErB6KNiUek1dtEh5dgj6gifK0LOfnod6OBxUsFuAL3SP4evv5opvWlIMyC9ycOXOc9uI1AWvxPp3BLGLhBC7Dundl3ctSwi6KseCuZ+qRZajlqaaWtoAUGHXw41C0l3di8vAlpd9Scv1bAijVdyG/ku/tWnCSJDGme1On71OXWeCGDx/u1Am4Ox5qFeq8Zh06oxlvrQilETgXWZYVJ4Om4BK1CAvObJa5nmVZntb1LRCgazLCT6PhzEZkjTfney9GVb8d5BqRJIuIWoWzyF4MVYRyzers2bO8/fbbDBo0iGvXrgHw+++/c+zYMadOzl2w1tTKNZgqeSYCd8RWxPIDffOzaBxxI1uP2YySVK9gNsPPY+HEL6DWcuvxb8gO7qIUjFDbBK9DEb0YqhBlFrgtW7bQtm1b9uzZw6pVq8jMzATg0KFDTJs2zekTdAesy9RcoxA4gfOxWlOSlB8eUpwFJ8v55cjr2npOZRl+ex0OLweVBp75FkN4DwA7b6ntcGftwbmKMs9q0qRJvP/++2zYsAGtNv+P06tXL3bv3u3UybkLisAZRLqWwPlYRcy2J6mqmD249BwjBqOlV2/tWnmfYVmGP96CuMWABE8shBaPFHqNQhZcwV4MVYwyC9yRI0cYMGBAoeP169cnNTXVKZNyN8QSVeBKFPGxUR6roeXIi2otR17HV5vv9No8HXbPs/zc7zNo86TdOUoqmM1ryMj2vRiqIGWeVWBgIImJiYWOx8fH06BBA6dMyt2wxhcZTbKyZyEQOAuTg+VjUV7Ur7afY9DC3fx+NJE6PnnW2/Y5sOVDy8+PfAT3DC3ytdQqCYn8OofOyGJwJWUWuGeffZY333yTpKQkJEnCbDazY8cOJkyYwLBhw1wxx6rNqfUWr1MxqFUSHhrLGyDXKARO4FwcCVxRqU9fbk0gJVPHT3GXLftme7+EjXl75w9Og8gXi30t29cwGG0dD24icP/3f/9Hy5YtCQsLIzMzk9atW9O9e3e6devG22+/7Yo5Vl22fww/DIS140ss2+Clse7DiWWqwLkUZ8EVfFsOjWpMPV9PhncLh/glsG6C5Yn7J8D9MSW+lsa2BJjJGiJSNcUNyhEHp9Vq+fLLL/n3v//N0aNHyczMpEOHDjRv3twV86va1GkOkhoO/QAetSwNbov4JvPyUJORaxQCJ3A6tuXKrdjuldkGmD9xTwO6N69H2NX1li9mgK5joVfpjBPbQHVrmlZV9aBCOQTOSqNGjWjUqIbnV7Z6zOJt+ul52P8VaGsV2U0o39EglqgC52I0OVii5llasmxf1VeWwe/iRgI2jgXZDPcMg97/V+omMRqVpNQHNlTxGDgopcDFxMTw3nvv4ePjQ0xM8Wbs7NmznTKxakPbp8CQDWtfgZ2fgdYXek4qNExJ2RKxcAInY5YLe1EhP+PAZJbJe/uhvbiN4NiXkcxGaPs0PDanbO39bMTMGhtnbTRdFSmVwMXHx2MwGJSfi6KqbjS6nHuGgT7L0tF783TLcrVAR29PjQpJsuyJ6I3mKv2mEFQv8hPt7d9T1hRBpUruxd3csXYYKpOO3KaP4NV/PqjKljpouwenN7qJBbdp0yaHPwts6PqyReT+eg82/NuyXO38vPK0JEl4alTkGszkGk1C4AROwxrrVjAUzao7JrMMVw/CkqdRGXPIaNiD7L5f4KUu3DmrIBKFrUIrVt2syntwVXdm1ZHuE+C+vCX8b/+Cgz/YPZ2f0SCWqQLnUZQFpywnk0/AdwNAl46uQVcuRC8ETfk6YFmsNXvRq/ZeVGvT59KwatWqck/GLXhwqsWS2/uFJWlZWwtaPw6g1ITTCUeDwImYirDg1JKENi0Bn3VPQ84NaNCR649/j6x3XLm3NDjq11BVsxiglBZcQECA8vD39yc2Npb9+/crz8fFxREbG6s0h67RSBL0mQEdhli8VCtHw+k/AWHBCZyPLOdnExQUGm3mZSLWDUKVdQ2C2sDglchaX6CgDVY6VCrLVkvBrfZqb8FZmz4DvPnmmzzzzDMsWLBAaRNoMpkYO3Ys/v7+rplldUOlgn98CoYcOPoTLB8Cg1fg1eh+AHRGM7Is11ynjMBpGB2USgIgI4n6awaiybqKsXYzNENXQ607QJ9T7tdyZL1V5SwGKMce3KJFi5gwYYJdD1S1Wk1MTAyLFi1y6uSqNSo1DPgCWjwKJh38MAht4n5UKsvmrE6kbAmcgMPladZ1+LY/mlsJ6H3DSH3iR/CtD3BbDaOs3lJbOavK1huUQ+CMRiMnT54sdPzkyZOYy9m9fd68eYSHh+Pl5UVkZCR79+4tdvyKFSto2bIlXl5etG3blnXr1hU59qWXXkKSpMopta72gKe+hiYPgCELvn8K/1snALFMFTgHU0EHQ24afD8AUk5g8g3h3KNLMfqGOOW1VA4starsQYVyCNzIkSMZPXo0s2fPZvv27Wzfvp1Zs2bx/PPPM3LkyDJPYPny5cTExDBt2jQOHDhAu3bt6N27t1IpuCA7d+5k0KBBjB49mvj4ePr370///v05evRoobGrV69m9+7dhIaGlnleTsPDC55dAo2iQJdG6C+D8Lx5WmQ0CJxCfpoWoMuEJU9D4iGoVZf0p1di8G/sOE26HIaXVURtl6RVOQYOALmMmEwm+cMPP5RDQ0NlSZJkSZLk0NBQ+cMPP5SNRmNZLyd36dJFHjdunN31Q0ND5enTpzsc/8wzz8h9+/a1OxYZGSm/+OKLdscuX74sN2jQQD569KjcuHFj+eOPPy71nNLS0mRATktLK/2NlETOLVn+oocsT/OX9R82ky+dOeq8awtqLDcydfLhS7fkhMRUWV78mCxP85fl6WGynHhYvpllee5cSqYy/tKNLPnwpVtycnpOqa5/LT1XPnzplnz40i350o0sWZZl+VxKpnIs8VbpruNsSvsZLbMFp1KpeOONN7hy5Qq3bt3i1q1bXLlyhTfeeMNuX6406PV64uLiiI6Otrt+dHQ0u3btcnjOrl277MYD9O7d22682Wxm6NChTJw4kbvuuqtMc3IZXgEwZBXmeq3wyL5G0Opn4Nalyp6VoJpjkmUkk56g9WMgYaslVXDIKghu67C7/e30bFcsOJtjbrcHZ4u/v/9teU5TU1MxmUwEBQXZHQ8KCiIpKcnhOUlJSSWO//DDD9FoNLz66qsFT3eITqcjPT3d7uESat2BPPRndAFN8Mi8jPzt45CR7JrXElQbTGaZTJ2xXA4Ak9FIw82v4X1+I2i84Lnl0LATYFMy6XZUzQaHMXBVfA+uXNVEVq5cyY8//sjFixfR6/V2zx04cMApEysvcXFxfPLJJxw4cKDU7uvp06fz7rvvunhmFtT+QST84wcarXkS7Y2z8F1/GPGbxYUvqJHM33yGb3Ze4IXuEWXrC2o24/fn69RK+A1Z5YE0cAmE36c8XVzjmYIpWKXBKnC2Hyu3s+A+/fRTRo4cSVBQEPHx8XTp0oU6depw7tw5HnnkkZIvYEPdunVRq9UkJ9tbMcnJyQQHBzs8Jzg4uNjx27Zt49q1azRq1AiNRoNGo+HChQv861//Ijw83OE1J0+eTFpamvK4dMm1S0dNYBgJjy7F7BME145b0mhy01z6moKqy7e7LpCSqWPR9vOlP0mW4fc3qHV8ObKkJv2xhdDcfuvG6lgtqjdqWaluWQxQDoH7/PPPWbhwIZ999hlarZY33niDDRs28Oqrr5KWVrYPqVarpWPHjsTGxirHzGYzsbGxREVFOTwnKirKbjzAhg0blPFDhw7l8OHDHDx4UHmEhoYyceJE/vjjD4fX9PT0VJbbt7vsLg1eHmr0/uGkPPEj1KoDiQdh6UBLipegxjGwcxj1fD0ZFtW4dCfIsqXM+L4vkZG43GM2phaPFRpmteBk+fbi35TrKXFw+UJX1S24Mi9RL168SLdu3QDw9vYmIyMDsAhL165dmTt3bpmuFxMTw/Dhw+nUqRNdunRhzpw5ZGVlKSEnw4YNo0GDBkyfPh2A1157jR49ejBr1iz69u3LsmXL2L9/PwsXLgSgTp061KlTx+41PDw8CA4OpkWLFmW9XZdgLX6Z6d+MoKGr4Zt/wMVdsGwwDFpmCS0R1Bj6tAnm4dbB1PEtZY7o1v/Cjk8ASOn5IbeaDcC/iCwDK2YZbleLCoaEVPUsBiiHBRccHMyNGzcAS1Vfay/UhISEcn1LDBw4kJkzZzJ16lTat2/PwYMHWb9+veJIuHjxol0Xr27durF06VIWLlxIu3btWLlyJWvWrKFNmzZlfu3Kwi4nNaQdDP4JPHzg3CZYMQJMhsqdoKDCMJtlJU6tVM6AnXNh0wcA7G85kVutngMcLx9t80YLLlPLo0vWQF/ruVXdeoNyWHC9evVi7dq1dOjQgZEjR/L666+zcuVK9u/fX6aqI7aMHz+e8ePHO3xu8+bNhY49/fTTPP3006W+/vnz58s1L1dRqPhlWGeL92vJU3D6d1g1Bp78X5mLEQqqH7a5pCUmAu3/Gv58C4CZhqdZlRDFVz2smQyOxcZa1be8nlRbISz4GlXdgwrlELiFCxcqKVnjxo2jTp067Ny5k379+vHii8W3HBNYkCQJrUaFzrb4ZcT98Mx3sOw5OLbKUhW432eFa+AUIEtn5IstZ/lx/2XG92rGkK6l3McRVAmMNqpWrAgdWg6/vg7A7tBhLE/ux7geTfJzUYswyX4/msiyvZd4qWcTRt/XpNxxcJJkX64cqkEWA2VcohqNRt5//327mLNnn32WTz/9lFdeeQWttvx1pmoaDtsI3vkwPPUVSCo4+L2lBHox78gsnZGE1Cx+2HuJpPRc5m8+6+ppC5yMtfUeFCNwx9fCmpcBmYy7R+HzyHssGtmFgZ0aKW8PR0tUgOX7LpGSqeN/WxNua562118Tf4VRi/fxy6Grt3XNiqBMAqfRaPjoo48wGotvdCwoGa+iil+2fhz6z7f8vPcLiP2Pw/Ot4ibL8FSnhtTz9eSlHk1cOWWBCzCaSrDg/t4AK0eBbIL2Q7jcdZqybtTnnStJRQvcsLw+qEMKeGjLanvZXn/pnoukZOr4bveFMl6l4inzEvXBBx9ky5YtRcaUCUqHZ3HFL9s9awkZ+S0Gts+2VAXuPlF5Oltv5Px1i7j5eKp57O4QHm0TQvMg34qavsBJ2O3BFdS3hG2WWoJmA9z1BLmPfIwxJb+em7VtX1HLU4DnIhvT4876+Hje3n6urcCN69WM+ZvP8nLPMgQlVxJlFrhHHnmESZMmceTIETp27IiPj4/d8/369XPa5NwZxYIrqvhl59GWgpl/vgV/vW/xskaNJVtvsdzMZou4hdfxIeF6Ftk6Ezl6k+KhFVQPDDYWnJ2n89I+S2ykMRfufASeWEhmjtnhuZpivJneee+HnLwvUpmyb8KtO5rIqrjLvPJgc4Z0baw8qgNlFrixY8cCjvufSpKEySTqnJUGT41aacyrM5odC1O38RZLbvP/wR+T0au8SAh7ErMZauWJm0ol4e2htgicwUTtir8VgQMWbU9g4dZzJTp+jI724BIPw5InLTUEI3rA04tB7UGWzj4Q3GAs3sEAli9Sq8e+PD15PTUqVu6/TEqmjvmbz1YbYbNSZj+v2Wwu8iHErWwozaCLqw3X4w3oZika4PF7DH6n11DLU01EnrhB4W9pQeWzYMtZktJzmbfpTLHj7LyoZiDlVH7qXlhXGPSDEvidqbPsfVstNn0pOstLkqSsFnL1ZrvjpcHPy4PxvZrRINC7WixJC1L1A1ncGOWNV9w3qySR23MaN1oPRUImbMvrhKdssnPZe2tFM5uqhjX9akS38GLH2XpRtennLRVmslMhpD0M/hG0li2gHL0Js9kSNeTrqck71yJYRTkYrCiB5eWw4ACGdwtnx6Re1c56gzIsUXNycoiNjeWxxyw5b5MnT0an0ynPq9Vq3nvvPby8RJpRabG88QzFClOuwcS51GxMUe+hMeXif2oF6p9GgfYHaGZJrrYNHNYZTXhqxD5cZfPY3aE83DqY+v7F9x+17rt5ZF4lYt1zSJmJUL81DF1tqSGYh9V689FqlC836/K2JIHz9lBzEwM5+pr3BVhqC+6bb77hiy++UH6fO3cuO3fuJD4+nvj4eL7//nvmz5/vkkm6K/kpW46XqLkGE+dSsjCZZbw9Nfg8PR9a9weTHpYNgfM7gKKXIYLKR19McyGjyYwsgyY7hYjfn0ObeRlz7SYwdE2h8llZVoHz1FBQz0oUOG3N3cIotcAtWbKEMWPG2B1bunQpmzZtYtOmTfz3v//lxx9/dPoE3RkvjeXPrzeaC+UK2ombVkVEXV/UGg944kto3huMObD0GbgcZ7mW2IerUli9lbZe0oIYzTLq3FtErB+MZ9o59L4N0A9eA372BV1lWSZLbxE4X0+NUiXESolL1DyL3miSlT2/qp+D4BxKLXBnzpyhbdu2yu9eXl6obNKIunTpwvHjx507OzdHo1YpG8a2Hq5cg4mE1HxxC6/jk/8m1mjhmW8gojvoMy0dlJKOCEdDFcPqELXdYyuIITuNwNXP4nXjJJkedUh45AdMfg0Kjcsx5O+/WbyiBQSuBIeBSiXhabXwa1izo1IL3K1bt+z23FJSUuyCfc1ms93zgtJRcJmqM1rEzWiS8fKwiFuhpGYPb3j2B2jYxeJt+7Y/tdLP5l1HCFxVIF/gihAUfTZeKwYRmnWcG7Ivz8tvow8Id5jNYN1/8/XUIElSIYutYI6oI6xfgE6qXl5tKLXANWzY0GFrPiuHDx+mYcOGTplUTULZOzOY0Bkty1KruEXUdSBuVjx9YfAKCL4bslPx+uFJPDIuYjTJxS6LBBWDdYkqyw5EzqiD5YPxuLybXLUv//SYRmSkpdS4o4oiWTrLl5ZPnve0oJ6VJum9pgaAl1rgHn30UaZOnUpubm6h53Jycnj33Xfp27evUydXE7Duj1hzS40mGc+SxM2Kd6BlQ7peS6SMqzRZ9xyarCSxTK0C2FpKdgJnMsCKkXD2L8wetbjy6LfMem04/TtYlqYFLThZlhUHgzU8pKDFVtIeHOQ7GqxU8TqVTqPUAjdlyhRu3LhBixYt+O9//8vPP//Mzz//zEcffUSLFi24efMmU6ZMceVc3RLbJarBWAZxs+JTB4b9DLUj0GZcJGLdc+huOe5IJqgcrBkHmE2w+iU49RuoPUl85Guygzqh1aiUfbSCApetNyHLFhGzvlcKZi6USuBqqAVX6ji4oKAgdu7cycsvv8ykSZOU6r2SJPHQQw/x+eefF2rnJygZT42KdUcTWbn/Ms92CePVB5vjUdZCgn7BMHwtpq/64JV2BtWKZ2C06NRVmdjqlN5ktqw9f3kNjq4ElQYGfkeabxSYZSWOEfI71VspaL1BYadCSU4GsIigVqMqNmzFHSnTJykiIoL169eTkpLC7t272b17NykpKaxfv54mTUSpnvKgUkmsOmDJ9fsp7nLZxc1KYCNyBq3C4F0P7fXjlurAugznTlZQLgxGE/wxGeK/s9T6e/J/mJo9nB/kq1YpVlhBJ4AS4GtTDcRWzxwVoiyKmmjFlevTdMcdd9ClSxe6dOnCHXcIK+F2eaVXcxoEejP2gWa3dR2voDtJeGQpRs9AuBKX16kr2zmTFJSagr1JfHbMgD0LLL88Pg/uGmCXZqVWScqy02RXwlwmOy/7wNfLxoKzEbTSLE+teGnzP+7l6YtaHRG5qFWAIV0bOyXXT6NWYa7fkoRHvkf29IMLO+DHoRavnaDCsNW3egfnErDf0gGLvrOgvaVJjC5vqajVWITGGlJquweXbbDsv2nUkl36ne0eXFnKhtdET6oQODfD20NNbt27SXtiqaWvw5mNloqwJlGFuaKwSlSdo4sI3v+R5ZeH3oPOzytjrBacVm3vOLANE8nMLbz/BvZWW2mXpyCWqAI3wPotnVGvk6XUjtoTTv4Ka16yePEELkeWZWqfWkbo7ncASO7wT8xRr9iNsW72e1gtOAde1EwHDgYrViOuLBach1rFH8eSGLV4Hz/uv1Tq86ozQuDcDLueq016WtK6VBo4ssLSlammhbJXBkdX0mDbmwCk3j2Ga/e8rtRus2IVOG2eU8nqCbV6UU1mWclK8XEgcFYrrrhil45YEWdpQvP1jttrQlNdEALnZliXITqjGbNZhhaWctdIKjjwDfwxRYicKzn5G+o1LyEhc73VEG52+zdIUqFsBmWJmldwQcr7JFodFFl6I7Jsed46xharsBVXrtwRr/RqTmiA1207tKoLZS5ZLqjaaDWWkAOTWUZnNFsi2Ns8aenv8PM42P05aH2h11uVPVX34+xfsGIEkmziZrMnSLz3fXw1anKNxkJJ91Yng0cBC87qRM1yEB5iizWaqDQxcLZUp34KzkBYcG6INb/VLmWrwxB4dKbl560fwfaPK2FmbsyFnfDDc2DSY2r5Dy53n4mkUuGRZ33ZWnDWOnBgCfQGCoWJOArwteWXQ4mMWryPVfFXXHI77oIQODekyAKHXV6A6HctP298B/YsdMrrLdh8lqjpsXxfDfpkuoQrcbDkGUuNvmYPYej/Jag0SEh4WPsn2GQQ6G26YVlLH9mGiZjMMjl5hUsd7b8BLNtr6U36zc7zLrop90AInBui1IZzVKL6vn9C9zcsP/8+EeK/v63XytIZ+Wp7AolpuczffPa2rlUtSToK3z0B+gwIvx8GfgdqLWDxdFqdCLYWnOJgsNlbsw0TsXpPPT1URWa2jOvVjJAAL8bVkL208lIlBG7evHmEh4fj5eVFZGQke/fuLXb8ihUraNmyJV5eXrRt25Z169YpzxkMBt58803atm2Lj48PoaGhDBs2jKtXr7r6NqoMtp7U/207R9T/FbCuHpgCXccBYPr5FTb/tKDEaxaMzrdiNMk81akh9Xw9q2XXpbJgreyRmqnjs7/+5ukPviNn0T8g9xY07JzXAcvbzofjoQhc/kG9yd6DCvZ7aRm5BqBo6w1gWFQ4uyY/WKP208pDpQvc8uXLiYmJYdq0aRw4cIB27drRu3dvrl275nD8zp07GTRoEKNHjyY+Pp7+/fvTv39/pVZddnY2Bw4c4N///jcHDhxg1apVnDp1qkY1pLYmb8syfLHlHInpuXxu275OkqD3B6xSPYQaM/cdmQKnfi/yegu3nqXLB7F8ufVc4ScleLRNCItGdHb7D9vcv87Q87+b+Wp7Aht27OMT/VS89TcguC0MXgmefkB+LThJshW4Eiw4la3A5e2/aYUP8HapdIGbPXs2L7zwAiNHjqR169YsWLCAWrVqsWjRIofjP/nkE/r06cPEiRNp1aoV7733Hvfccw9z584FICAggA0bNvDMM8/QokULunbtyty5c4mLi+PixYsVeWuVhqUJjcWKs1pXo++PKDiIM53f5XepOxpM8ONwOLvJ4fW+3JpASqaOhY4Ezs0x2+SGfrf7AimZOrbsP8y3mvcJlW5ws1aEpSafd6AyzmrB2e7B2Ra+LBgDZ8VqxFm7ZRXlQRWUnkoVOL1eT1xcHNHR0coxlUpFdHQ0u3btcnjOrl277MYD9O7du8jxAGlpaUiSRGBgoMPndTod6enpdo/qjtXRYLWuBnZuVGjMI20b0nDkYtLC+4BJB8ueg4u7C42ziuRTnQpXbHbnwokZuQZm/nmKrnlL/Gc6hdHcJ5clntMJ1F1B59eI3OdWg09du/OskihJli8ba6yaVeCsy9WC8W22KVheHqrS1wQUFEml/gVTU1MxmUyF6sgFBQWRlOS4aGNSUlKZxufm5vLmm28yaNAg/P39HY6ZPn06AQEByiMsLKwcd1O1KJh3aCqq+YlKw5UHP7P0WDVkw5Kn4coBuyFWkXy0TUih091Y38jSmVix/zJJ6RYHymN3erM2cBa1s85h9gsl4dEfyNLWK3SeUisx73dlmWqUkeX8kvIFHQi2WQnF7b8JSo9bf0UYDAaeeeYZZFkutmfr5MmTSUtLUx6XLlX/PD2rwK07aomX+mGv4+X5uqOJjPj2MD9EfACN7wNdOnz/BCSXrkOabYenohwR1RWDycxTnRpS38+TV+4LptHvw/G+fgzZpz7GIWsw+IWRazAVed/WP411Kao3mTGYZGQ5z8NayILL/1kInHOoVIGrW7cuarWa5ORku+PJyckEBwc7PCc4OLhU463iduHCBTZs2FCk9Qbg6emJv7+/3aO6Yw32XbnfUkxz0Y4Eu/0kKyv3X+Zaho65267Cc8ugQSfIuQnfPg7XLWEfVpFcdzSx0PnubMEZTGYebRPC10PaMvDMRGpdO4DRMwDT4FVog1qgUln21nQFquTm/5Utfx1rQr3BZFY8qI7CP2y/LIoK8BWUjUoVOK1WS8eOHYmNjVWOmc1mYmNjiYqKcnhOVFSU3XiADRs22I23itvff//Nxo0bqVOnjmtuoApj/bBY989aBvvRbcZfhYJxrRbKyz2bWryAQ1ZCUFvIugbf9MNw/bwikiv3Xy62Y5e7GHAfbzhN5AcbWR1/Bcmkp9GGF5HOb8fk4cv5Pt+jCm4DFB1vqDgZ8vTK1pPqyINqxbpE9daqylTIUlA0lb5EjYmJ4csvv+Sbb77hxIkTvPzyy2RlZTFy5EgAhg0bxuTJk5Xxr732GuvXr2fWrFmcPHmSd955h/379zN+/HjAIm5PPfUU+/fvZ8mSJZhMJpKSkkhKSkKv11fKPVYFDly8pewlQf5y8tE2IXw90ibEw7s2DF0Nde+E9MskfdabrvX1+HpquJmlo/lbv/PqD/HKdW2dDO6gbzqjiaV7LpKcoWPFnvOEbXoFv8ubkDXenO+9mNz67ZSQjiIzRhQvqgVbgSuYZG/LL4euMmrxPv48llzoOUH5qHSBGzhwIDNnzmTq1Km0b9+egwcPsn79esWRcPHiRRIT85dG3bp1Y+nSpSxcuJB27dqxcuVK1qxZQ5s2lm/VK1eusHbtWi5fvkz79u0JCQlRHjt37qyUe6wsIur58FOcxfoCqOfryUs9LL0zbFerhSwv33ow7GeuUJ8wknj96kQCScfaHOq3w/lB07alr91hD05Csli1Ph78r/bXBJz/HbNKy41+i8kO7mIn6N4eatYdTaT/vB12lrFcQOqVPTijnF8HzkEVkB/y0q9qbMqbC5Bkd3hXOpn09HQCAgJIS0ur9vtx3+++wOebztC/QwMeaRPCncG+eGrU6IwmTidlKuPuCvUvVB12wZq/GHBwNEHc4LgcwbO6KaTjQ792oXw6qANgWZ6duWa5TssQv/I3zaki6I1mTiWmE7pjCnVOLkGW1FyI/gK5xaNk5hrRqCVahVjeEzqjiftmbCIlU0dogBc7Jz8IwM0sPZdv5uDrpSGirg9Gk5kTiZYGQN5aFTl6M43uqEVALQ+71/5+9wXmbz7Lyz2bun3Q9O1S2s+o2Ml0c6zlcc5cyyBHbybXYMZToy7UQd1oltEWELh7O3XkVrOfuOPXp2mdm8D3XjPZ2HkB43rfzejF+9h08hp92gbzcg9LPqQ7fFVKyATved8ibkhc6vkJGY0fRp23z2a7N+apUTMoMowf9lxiWLdw5bgSB5f3f406P7PEmkTvaIla00oZVQTV++tWUGqsTUt0eftFBftvGgsqXh76wKacf2QJRs8A7uYUYy6/xa20dDadvIYZWH/EvZpMq7d+SL2jXwJw5f6PSG9mSfGzljEquPc/LCqcRSM6E92qvjImv2dw/riCguZoiSpwPkLgagj5CfgWITOZCwpc/u8Ltpwl8oONSlhIbp1WnO/9HSYPH3wTd+L/y/P0aBaACohunR90XXDvqdqx4xNUWz8E4GrUu9xsMbBQJyqpQOpGoLcHvx9NZNDCPcz68xRzNp7m0U+2se5oot3+pO3SXaVCZClUEOKvXEOwxsXlGi0WXMGYOGv+47WMXL7alkByhiUsxEpO/facf3gxZrUX3uc38rHH5/w8tivvPn6XMqZaL1H3fgkbpgIwVzWY7+Q+QOF80IIVdDVqFasPXCElU8eK/ZdZstvigf1u1wWeXrBTcRjYWmwFc1AFrkP8pWsIVktEZ7D0ajAWsuDMfLHlLH0/2U6rED/q+XrSKsTPLsB35fVGvCZPwCh5EJjwGw23TcRodINOXQeXwroJAHwlPcnM7L6KuGvVKiVQFxw3eRnXqxkNAr0Z90BTRt8XQZCfJxKQnKFTwnJsRc3R/pvANYi/dA3BQ50fPKozmu3a04HFgvtqu6VqyOnkTBaN6MyJxAwlwBcsWQ+/ZLdmkvQ6sqSm9t8/Ebg5v4lNtbTgjq229KoAiHyZ1C4T8fXUkGMwse5oIh4alZ04OSouYG3cPeLeCF7q2ZQ9b0Xz5iMtaRDordTI8xACVymIv3QNQlmmGkzKHpxtiZ6n87IeXuzRhCB/iwWnAlqFWOqcWbMi6nV5kks9PkZGwu/IN2Svm8Kor/eyZE81i986/Qf89DzIZtZI0SzyHcOjbUPx9lCTqTOycv9lPFT2Xa1Km2FgFT2rV9TD5hrVPZSmOiH+0jUIxdFgNPFT3GVL1PxxixfUaDbT5678wpX1/Dw5nZyJGUsWxKjF+wCLyK3cf5kfciO5ct8MACITlzAod1mJ9eLScw12vQkqlXNbYPlQMBtZL91HTM4IFuTN37Y8lEYtOSwtXlbs9uCEBVdhiL90DcIqcFk6o1K8cfk+S+UU28bEasnSDGV8r2aEBngBKEvV73ZZzvtu1wVuthxEUrd3AIjxWMmMkC3kGkwYHeSrpmUbmL/5LPc6yIetSGRZhot74IdBlhp4LfpypPOH1PH15smOlnp31vJQfduG4KFW4anOdzSoyvmJ0apVStGCn+Iul3yCwCkIgatBKO0E9Wae7GhJsn+xu2WPyGDMX7JaMxqGdG3MzskPMjSqcZEFL1PvGkVSJ0sTm14XP+Wj99+gw3828O2u83bjMvVGJWm/sprTfBJ7mqHvf4n+2yfAkAVNe2F+chF97g5zWO/OWqjSGRacJEmsOmC5//9tqxld5asCQuBqENZgX4C+bUPYMvEBhkXZR847+gBbLRorvp4ahuadJ8uQ0n48u0KHA/A2XxFt2MQCByJW2c1pdu7czqfGd9EaM6FRNxi4BB0eRY7X5JlrzhA4sHSVt3U8CFyPELgahFol8efxJEYt3sfW0yl4a9VKGpEVTTER9iv3X1Za2q3cf9muPtyryf/ga2NvVJLMTI8FTGuWL3DzNp2h/9wdAJXXnOb6Wb5Svc8dUibX/O6C55aDtlaxe4JW76laJbH+2O0vLws6HgSuRwhcDWNlXnUR230wW89gcRbKqPvCCfa335Oz8lTnMGarR7JKfgC1JNPr2GT++sXSc3XxjvOFxlcoty7Bt4/ja7hOTu2WSEN+Ai9LgvbSvReKLOZpK/YrbAqHCqoPQuBqGOMesASljrVpGGzr4SsuDGJQl0ase+3+Qnty644msnL/ZYZERdB89CJ+l7vhgZFucf+EhK3FNq1xORlJyN/2g7RL5AZY8mpNXrWVpz/fdNYiXNvPFRI6W4Eb27MpIf6i0XJ1Q5RLcoA7lUsqDQmpWWTm9eIM8PagUZ1ads8fuZwGQNgd3gBcupEDwH//OMn2v1Px0KjQGc34emrw9lCTnZvDHOljHlLHgYcPZ/t8T3ZQR+V6bRsGVMRtseSveLpuHUpTLnGF+vzc4X/c37E9dXy1hAZa7qXttD/IyFt2A4oQr9x/mRe6RzCmu9gvq4qU9jMqLDgBGtslajHvCCnvPyvb/07FjCUzQoWlYm1Kpg5JreUd7QTOB0SCIYsG64Yy4+vlDpeBLiM3jQ5bR9OUSyTJtXlWN5mvj1g6xttmcYy4N5x6vp40q+eLCmgd6q94exfvOF9x8xW4BCFwAjxsYrTWHrxa7FjbiiEPtg5CBXhqVJgBL42KID9P7mkUiF7SsurOD9GFdsHLlMmnxv8Qt6/o3rVORZ+FvORpWnOWG/jzcch/yfVpxFOdGrLuaCJPfJ6fBN/3bouHOCPXgBk4lZShLKnHiuVotUcInAA/L41itXy7q3AQrhKgesDeSfBB/za82LMpHmoVAV4aJvZpyR+vd+fAxVukZOr4YlciPa6M5YjchDpSBl+p3mfn/n1E5TVSdgmGXC5+/jjSpT3kqv24PmA5iR6NuJ6p49iVNL7bdYFrGTr+u/4kgFL486WeTWkQ6M0L3SOUsBjh7az+CIET4OOpUfJO2znYH1sTbykH9NX2BLuEeh9PjRI64uvlwZCujQmspVWe1xllknRahure5AyN8DOk8o+DL0H6FT7fdMb5N2IywIrhNLq1j0zZi5eZwqrEO9iat5Te/neqMtRaTMVqkQ7q0ogdk3pZHCl5gi56I1R/hMAJADhzzZJ3euRKWqHnrJ7XggGqPp4aXurRhNAAL7vn7mkUaDfuFn58EvoRN7zCCCWFpZ4f4Jmb6lwBMZtg1Qtwej0GyZPxTGKnLtwua6JJPV+GRjXG11ODDHy59Zwi2NbwGC+NutIzLgTOQwicAICxDsJHrNgGqMpgZ+GMvr8JOyc/aLecO52cWegav50zMyDzTS7LdYmQkpgvv8eSTfGFxpUVWZa5lZXLyYXD4dhqTJKGyw9/yTGPtuiM9gECaTkGHm0TolQL+XLbuUI9TFUqiVcebFZItAXVEyFwAqD0Ufb+Nvt1RVk443s1IyTAC5s6kZiBC8Y7eE7/FtfkQFqqLvFR7rvc+87q27LkLqRmcfbbcbRM+gWjrOItdQw/prUo1KtUBUocnrXZtTW5HuwDnIdFhRcSbUH1RAicoExo1KoSLZwhXRuza/KDGB1EWF6Ug1jbbj438aetdI6PzdNZ9NfRcs3li81n2PDZS3RMXolZlphoeImwewfm7wt6apTHiz2b8mibECUoeWzPpjx2d4hijVa7WnaCUiEETlBmSmvheOYlqXsWqH8255Ca2UEzSJdr0UV1ipnmj8CQW6Y5yLKMeetMXlD9AsBbxlFcb9qfR9oEK2EeQ6MaMzSqMd55ZaLWHU3ki82WzIUvtyXQsHYtxRp1VBxAUP0RAidwGW/1bUWwvxej7ouge/O6yvFMnZH114MYqX+DLNmTe4wHOTCrP3dO+pnxSw+UeF2zWWbT4nd42fwDAO8ZhvCD6UFOJGYUWppaPcBr4q+wKu4yZixv+pd7NiXA24MXezQhJMBLxLy5KSJVywE1LVXLlZjMMjP/PMXK/Ze5K9Sf/RduAhZP684zqXSWjvG1x0d4SQbWmqL4p2Ec/+l/d7HWYe7u/+G1/l8AfGx8ik+MT+CpURFWuxZnUvIdHJ4aiX8/dpfSLR4QnePdhNJ+RoXAOUAInPOQZZkuH8SSkqkjyN+TJ+6x5Hmm5+oVL2dPVTwLPWajlUwsN/bkM59X2D452vEFD/+IvGoMEjLzjf/gQ+OzQNEFAs7P6OuCuxJUNiIXVVAlkCSJsQ80JTTAUoljVV65JtsQjoOeXdjcZgZmVAzUbObl3C/5vkBFYABO/IJ51UtIyKzxeJT/Gp8lJK98kyP6tQt1wR0JqhNVQuDmzZtHeHg4Xl5eREZGsnfv3mLHr1ixgpYtW+Ll5UXbtm1Zt26d3fOyLDN16lRCQkLw9vYmOjqav//+25W3ICiGkfdGsHPygwyLCueVBy1Vbfu1C6VBoDfv92/DwWkPE9l3JFd6zAJgsLSerN+n2l/k742wYiQqTKwwduf1jOcwY+kd4eepAcBLI+GVF5vSr10onw7qUKH3KaiCyJXMsmXLZK1WKy9atEg+duyY/MILL8iBgYFycnKyw/E7duyQ1Wq1/NFHH8nHjx+X3377bdnDw0M+cuSIMmbGjBlyQECAvGbNGvnQoUNyv3795IiICDknJ6dUc0pLS5MBOS0tzSn3KCiZjFyDPOP3E/KUKf+U5Wn+lseW/8qyLMu6M1tk/Tv1ZHmav7zpvUfliDfXyo3f/FVu/Oav8itLD8jf7Tovd5seK3+363wl34WgoijtZ7TS9+AiIyPp3Lkzc+fOBcBsNhMWFsYrr7zCpEmTCo0fOHAgWVlZ/Prrr8qxrl270r59exYsWIAsy4SGhvKvf/2LCRMs3crT0tIICgpi8eLFPPvssyXOSezBVTyyLNN1eizJ6TqeV//G2x5LAPja2JtnPbbiLeew0dSBlw2vY0CjnNcg0Jsdk3pV1rQFlUS12IPT6/XExcURHZ2/oaxSqYiOjmbXLseldXbt2mU3HqB3797K+ISEBJKSkuzGBAQEEBkZWeQ1dTod6enpdg9BxSJJEq/0ao5Ggv+Z+vKx4UkARmr+wFvOYbvpLsYZXlPEzUsjEejtIdKpBMWiKXmI60hNTcVkMhEUFGR3PCgoiJMnTzo8JykpyeH4pKQk5XnrsaLGFGT69Om8++675boHgfMY0rUxQ7o2ZvzSA3xy+Am8JR0vaX5ln/lOXjD8Cx1a1BK8+3gbEeYhKBWVKnBVhcmTJxMTE6P8np6eTlhYWCXOqGYz97l76NqkDu+uVfGjrieXCMLH24u3ercQwiYoE5UqcHXr1kWtVpOcnGx3PDk5meDgYIfnBAcHFzve+v/k5GRCQkLsxrRv397hNT09PfH09CzvbQhcgNWaEwhuh0rdg9NqtXTs2JHY2FjlmNlsJjY2lqioKIfnREVF2Y0H2LBhgzI+IiKC4OBguzHp6ens2bOnyGsKBAI3pQI8usWybNky2dPTU168eLF8/PhxecyYMXJgYKCclJQky7IsDx06VJ40aZIyfseOHbJGo5FnzpwpnzhxQp42bZrDMJHAwED5559/lg8fPiw//vjjIkxEIHAjSvsZrfQ9uIEDB5KSksLUqVNJSkqiffv2rF+/XnESXLx4EZVNq6du3bqxdOlS3n77baZMmULz5s1Zs2YNbdq0Uca88cYbZGVlMWbMGG7dusV9993H+vXr8fIqOupdIBC4H5UeB1cVEXFwAkHVplrEwQkEAoErEQInEAjcFiFwAoHAbal0J0NVxLotKVK2BIKqifWzWZILQQicAzIyMgBENoNAUMXJyMggIKBws3IrwovqALPZzKlTp2jdujWXLl2qtp5Ua8qZuIfKpbrfQ1WcvyzLZGRkEBoaahdGVhBhwTlApVLRoEEDAPz9/avMP2p5EfdQNaju91DV5l+c5WZFOBkEAoHbIgROIBC4LULgisDT05Np06ZV6yoj4h6qBtX9Hqrz/IWTQSAQuC3CghMIBG6LEDiBQOC2CIETCARuixA4gUDgtgiBs+HGjRsMHjwYf39/AgMDGT16NJmZmcWOf+WVV2jRogXe3t40atSIV199lbS0tAqctf18yjJ/gIULF9KzZ0/8/f2RJIlbt25VzGRtmDdvHuHh4Xh5eREZGcnevXuLHb9ixQpatmyJl5cXbdu2Zd26dRU0U8eUZf7Hjh3jySefJDw8HEmSmDNnTsVNtBjKcg9ffvkl999/P7Vr16Z27dpER0eX+G9WWQiBs2Hw4MEcO3aMDRs28Ouvv7J161bGjBlT5PirV69y9epVZs6cydGjR1m8eDHr169n9OjRFTjrfMo6f4Ds7Gz69OnDlClTKmiW9ixfvpyYmBimTZvGgQMHaNeuHb179+batWsOx+/cuZNBgwYxevRo4uPj6d+/P/379+fo0aMVPHMLZZ1/dnY2TZo0YcaMGUU2VqpoynoPmzdvZtCgQWzatIldu3YRFhbGww8/zJUrVyp45qXAtZXTqw/Hjx+XAXnfvn3Ksd9//12WJEm+cuVKqa/z448/ylqtVjYYDK6YZpHc7vw3bdokA/LNmzddOMvCdOnSRR43bpzyu8lkkkNDQ+Xp06c7HP/MM8/Iffv2tTsWGRkpv/jiiy6dZ1GUdf62NG7cWP74449dOLvScTv3IMuybDQaZT8/P/mbb75x1RTLjbDg8ti1axeBgYF06tRJORYdHY1KpWLPnj2lvo61hLJGU7Fpvs6af0Wi1+uJi4sjOjpaOaZSqYiOjmbXrl0Oz9m1a5fdeIDevXsXOd6VlGf+VQ1n3EN2djYGg4E77rjDVdMsN0Lg8khKSqJ+/fp2xzQaDXfccQdJSUmlukZqairvvfdeictCV+CM+Vc0qampmEwmpcGQlaCgoCLnnJSUVKbxrqQ8869qOOMe3nzzTUJDQwt98VQF3F7gJk2ahCRJxT5Onjx526+Tnp5O3759ad26Ne+8887tTzyPipq/QFAeZsyYwbJly1i9enWV7Frn9uWS/vWvfzFixIhixzRp0oTg4OBCm6pGo5EbN26UuBmckZFBnz598PPzY/Xq1Xh4eNzutBUqYv6VRd26dVGr1SQnJ9sdT05OLnLOwcHBZRrvSsoz/6rG7dzDzJkzmTFjBhs3buTuu+925TTLT2VvAlYVrJv0+/fvV4798ccfJW7Sp6WlyV27dpV79OghZ2VlVcRUHVLe+VupTCfD+PHjld9NJpPcoEGDYp0Mjz32mN2xqKioSnUylGX+tlQlJ0NZ7+HDDz+U/f395V27dlXEFMuNEDgb+vTpI3fo0EHes2ePvH37drl58+byoEGDlOcvX74st2jRQt6zZ48syxZxi4yMlNu2bSufOXNGTkxMVB5Go7HKz1+WZTkxMVGOj4+Xv/zySxmQt27dKsfHx8vXr1+vkDkvW7ZM9vT0lBcvXiwfP35cHjNmjBwYGCgnJSXJsizLQ4cOlSdNmqSM37Fjh6zRaOSZM2fKJ06ckKdNmyZ7eHjIR44cqZD53u78dTqdHB8fL8fHx8shISHyhAkT5Pj4ePnvv/+ulPnLctnvYcaMGbJWq5VXrlxp957PyMiorFsoEiFwNly/fl0eNGiQ7OvrK/v7+8sjR460+0dLSEiQAXnTpk2yLOdbPY4eCQkJVX7+sizL06ZNczj/r7/+usLm/dlnn8mNGjWStVqt3KVLF3n37t3Kcz169JCHDx9uN/7HH3+U77zzTlmr1cp33XWX/Ntvv1XYXB1Rlvlb/w0KPnr06FHxE7ehLPfQuHFjh/cwbdq0ip94CYhySQKBwG1xey+qQCCouQiBEwgEbosQOIFA4LYIgRMIBG6LEDiBQOC2CIETCARuixA4gUDgtgiBEwgEbosQOEG1YcSIEQ6rqfTp06eypyaoorh9NRGBe9GnTx++/vpru2NFdVw3GAyFKrvo9Xq0Wm2ZX7e85wkqF2HBCaoVnp6eBAcH2z1q164NgCRJzJ8/n379+uHj48MHH3zAO++8Q/v27fnf//5HRESEUrPs4sWLPP744/j6+uLv788zzzxjVzKoqPME1QshcAK34p133mHAgAEcOXKEUaNGAXDmzBl++uknVq1axcGDBzGbzTz++OPcuHGDLVu2sGHDBs6dO8fAgQPtrlXwPEH1QyxRBdWKX3/9FV9fX7tjU6ZMUbqCPffcc4wcOdLueb1ez7fffku9evUA2LBhA0eOHCEhIYGwsDAAvv32W+666y727dtH586dHZ4nqH4IgRNUKx544AHmz59vd8y22Ylt0x0rjRs3thOpEydOEBYWpogbQOvWrQkMDOTEiROKwBU8T1D9EAInqFb4+PjQrFmzYp8vzbHSvpageiP24AQ1jlatWnHp0iUuXbqkHDt+/Di3bt2idevWlTgzgbMRFpygWqHT6Qq1s9NoNNStW7fU14iOjqZt27YMHjyYOXPmYDQaGTt2LD169HC4xBVUX4QFJ6hWrF+/npCQELvHfffdV6ZrSJLEzz//TO3atenevTvR0dE0adKE5cuXu2jWgspClCwXCARui7DgBAKB2yIETiAQuC1C4AQCgdsiBE4gELgtQuAEAoHbIgROIBC4LULgBAKB2yIETiAQuC1C4AQCgdsiBE4gELgtQuAEAoHbIgROIBC4Lf8Ph/jLKatZk2kAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\"\"\"\n", + "from ml_utility_loss.loss_learning.visualization import plot_grad, plot_grad_2, plot_grad_3\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#plot_grad_2(y, model.models)\n", + "for m in model.models:\n", + " ym = y[m]\n", + " fig, ax = plt.subplots()\n", + " plot_grad_3(ym[\"error\"], ym[\"grad\"], name=f\"{m}_grad\", fig=fig, ax=ax)\n", + "#\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54c0e9f3", + "metadata": { + "papermill": { + "duration": 0.019225, + "end_time": "2024-07-23T15:59:24.662776", + "exception": false, + "start_time": "2024-07-23T15:59:24.643551", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "celltoolbar": "Tags", + "colab": { + "authorship_tag": "ABX9TyOOVfelovKP9fLGU7SvvRie", + "gpuType": "T4", + "mount_file_id": "17POSGAvge8y9DW9WGs2jLkibaRjToayg", + "provenance": [] + }, + "kaggle": { + "accelerator": "gpu", + "dataSources": [], + "dockerImageVersionId": 30648, + "isGpuEnabled": true, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 3860.80093, + "end_time": "2024-07-23T15:59:27.187802", + "environment_variables": {}, + "exception": null, + "input_path": "eval/iris/tvae/2/mlu-eval.ipynb", + "output_path": "eval/iris/tvae/2/mlu-eval.ipynb", + "parameters": { + "allow_same_prediction": true, + "dataset": "iris", + "dataset_name": "iris", + "debug": false, + "folder": "eval", + "gp": true, + "gp_multiply": true, + "log_wandb": false, + "param_index": 0, + "path": "eval/iris/tvae/2", + "path_prefix": "../../../../", + "random_seed": 2, + "single_model": "tvae" + }, + "start_time": "2024-07-23T14:55:06.386872", + "version": "2.5.0" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/iris/tvae/2/model.pt b/iris/tvae/2/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..fc3653469abb7b8bf4e80430df50fc51c5586116 --- /dev/null +++ b/iris/tvae/2/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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