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0000000000000000000000000000000000000000..b582efb555447c67c3b731a4ea3a2542a3226e88 --- /dev/null +++ b/iris/tvae/0/mlu-eval.ipynb @@ -0,0 +1,2347 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "982e76f5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T12:44:35.844697Z", + "iopub.status.busy": "2024-07-23T12:44:35.844347Z", + "iopub.status.idle": "2024-07-23T12:44:35.878011Z", + "shell.execute_reply": "2024-07-23T12:44:35.877289Z" + }, + "papermill": { + "duration": 0.048616, + "end_time": "2024-07-23T12:44:35.880030", + "exception": false, + "start_time": "2024-07-23T12:44:35.831414", + "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": "2024-07-23T12:44:35.906904Z", + "iopub.status.busy": "2024-07-23T12:44:35.906101Z", + "iopub.status.idle": "2024-07-23T12:44:35.912961Z", + "shell.execute_reply": "2024-07-23T12:44:35.912132Z" + }, + "papermill": { + "duration": 0.022696, + "end_time": "2024-07-23T12:44:35.914994", + "exception": false, + "start_time": "2024-07-23T12:44:35.892298", + "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": "2024-07-23T12:44:35.938480Z", + "iopub.status.busy": "2024-07-23T12:44:35.938221Z", + "iopub.status.idle": "2024-07-23T12:44:35.942333Z", + "shell.execute_reply": "2024-07-23T12:44:35.941434Z" + }, + "papermill": { + "duration": 0.018262, + "end_time": "2024-07-23T12:44:35.944331", + "exception": false, + "start_time": "2024-07-23T12:44:35.926069", + "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": "2024-07-23T12:44:35.967976Z", + "iopub.status.busy": "2024-07-23T12:44:35.967710Z", + "iopub.status.idle": "2024-07-23T12:44:35.971843Z", + "shell.execute_reply": "2024-07-23T12:44:35.970942Z" + }, + "executionInfo": { + "elapsed": 678, + "status": "ok", + "timestamp": 1696841022168, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "ns5hFcVL2yvs", + "papermill": { + "duration": 0.018375, + "end_time": "2024-07-23T12:44:35.973830", + "exception": false, + "start_time": "2024-07-23T12:44:35.955455", + "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": "2024-07-23T12:44:35.997111Z", + "iopub.status.busy": "2024-07-23T12:44:35.996815Z", + "iopub.status.idle": "2024-07-23T12:44:36.002531Z", + "shell.execute_reply": "2024-07-23T12:44:36.001692Z" + }, + "papermill": { + "duration": 0.019657, + "end_time": "2024-07-23T12:44:36.004515", + "exception": false, + "start_time": "2024-07-23T12:44:35.984858", + "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": "32c7ee59", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T12:44:36.029847Z", + "iopub.status.busy": "2024-07-23T12:44:36.029569Z", + "iopub.status.idle": "2024-07-23T12:44:36.034585Z", + "shell.execute_reply": "2024-07-23T12:44:36.033771Z" + }, + "papermill": { + "duration": 0.020349, + "end_time": "2024-07-23T12:44:36.036602", + "exception": false, + "start_time": "2024-07-23T12:44:36.016253", + "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 = 0\n", + "debug = False\n", + "folder = \"eval\"\n", + "path_prefix = \"../../../../\"\n", + "path = \"eval/iris/tvae/0\"\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": 0.010983, + "end_time": "2024-07-23T12:44:36.058777", + "exception": false, + "start_time": "2024-07-23T12:44:36.047794", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5f45b1d0", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-23T12:44:36.082609Z", + "iopub.status.busy": "2024-07-23T12:44:36.082012Z", + "iopub.status.idle": "2024-07-23T12:44:36.091840Z", + "shell.execute_reply": "2024-07-23T12:44:36.090906Z" + }, + "executionInfo": { + "elapsed": 7, + "status": "ok", + "timestamp": 1696841022169, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "UdvXYv3c3LXy", + "papermill": { + "duration": 0.023881, + "end_time": "2024-07-23T12:44:36.093730", + "exception": false, + "start_time": "2024-07-23T12:44:36.069849", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/working\n", + "/kaggle/working/eval/iris/tvae/0\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": "2024-07-23T12:44:36.118068Z", + "iopub.status.busy": "2024-07-23T12:44:36.117261Z", + "iopub.status.idle": "2024-07-23T12:44:38.065753Z", + "shell.execute_reply": "2024-07-23T12:44:38.064794Z" + }, + "papermill": { + "duration": 1.962972, + "end_time": "2024-07-23T12:44:38.067862", + "exception": false, + "start_time": "2024-07-23T12:44:36.104890", + "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": "2024-07-23T12:44:38.094242Z", + "iopub.status.busy": "2024-07-23T12:44:38.093737Z", + "iopub.status.idle": "2024-07-23T12:44:38.104719Z", + "shell.execute_reply": "2024-07-23T12:44:38.103698Z" + }, + "papermill": { + "duration": 0.026548, + "end_time": "2024-07-23T12:44:38.106759", + "exception": false, + "start_time": "2024-07-23T12:44:38.080211", + "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": "2024-07-23T12:44:38.131232Z", + "iopub.status.busy": "2024-07-23T12:44:38.130928Z", + "iopub.status.idle": "2024-07-23T12:44:38.138114Z", + "shell.execute_reply": "2024-07-23T12:44:38.137144Z" + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1696841022169, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "Vrl2QkoV3o_8", + "papermill": { + "duration": 0.021727, + "end_time": "2024-07-23T12:44:38.140069", + "exception": false, + "start_time": "2024-07-23T12:44:38.118342", + "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": "2024-07-23T12:44:38.163967Z", + "iopub.status.busy": "2024-07-23T12:44:38.163710Z", + "iopub.status.idle": "2024-07-23T12:44:38.263854Z", + "shell.execute_reply": "2024-07-23T12:44:38.262836Z" + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1696841022169, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "TilUuFk9vqMb", + "papermill": { + "duration": 0.114944, + "end_time": "2024-07-23T12:44:38.266302", + "exception": false, + "start_time": "2024-07-23T12:44:38.151358", + "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": "2024-07-23T12:44:38.292728Z", + "iopub.status.busy": "2024-07-23T12:44:38.292424Z", + "iopub.status.idle": "2024-07-23T12:44:42.705410Z", + "shell.execute_reply": "2024-07-23T12:44:42.704507Z" + }, + "executionInfo": { + "elapsed": 3113, + "status": "ok", + "timestamp": 1696841025277, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "7Abt8nStvr9Z", + "papermill": { + "duration": 4.428804, + "end_time": "2024-07-23T12:44:42.707816", + "exception": false, + "start_time": "2024-07-23T12:44:38.279012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-23 12:44:40.070693: 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 12:44:40.070746: 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 12:44:40.072399: 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": "2024-07-23T12:44:42.732668Z", + "iopub.status.busy": "2024-07-23T12:44:42.732041Z", + "iopub.status.idle": "2024-07-23T12:44:42.738775Z", + "shell.execute_reply": "2024-07-23T12:44:42.738068Z" + }, + "papermill": { + "duration": 0.021044, + "end_time": "2024-07-23T12:44:42.740644", + "exception": false, + "start_time": "2024-07-23T12:44:42.719600", + "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": "2024-07-23T12:44:42.767114Z", + "iopub.status.busy": "2024-07-23T12:44:42.766310Z", + "iopub.status.idle": "2024-07-23T12:44:45.387918Z", + "shell.execute_reply": "2024-07-23T12:44:45.387120Z" + }, + "executionInfo": { + "elapsed": 20137, + "status": "ok", + "timestamp": 1696841045408, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "tbaguWxAvtPi", + "papermill": { + "duration": 2.637587, + "end_time": "2024-07-23T12:44:45.390375", + "exception": false, + "start_time": "2024-07-23T12:44:42.752788", + "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": "2024-07-23T12:44:45.417936Z", + "iopub.status.busy": "2024-07-23T12:44:45.417055Z", + "iopub.status.idle": "2024-07-23T12:44:45.424199Z", + "shell.execute_reply": "2024-07-23T12:44:45.423117Z" + }, + "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": 0.023214, + "end_time": "2024-07-23T12:44:45.426180", + "exception": false, + "start_time": "2024-07-23T12:44:45.402966", + "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": "2024-07-23T12:44:45.450414Z", + "iopub.status.busy": "2024-07-23T12:44:45.450132Z", + "iopub.status.idle": "2024-07-23T12:44:45.455196Z", + "shell.execute_reply": "2024-07-23T12:44:45.454308Z" + }, + "papermill": { + "duration": 0.019519, + "end_time": "2024-07-23T12:44:45.457191", + "exception": false, + "start_time": "2024-07-23T12:44:45.437672", + "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": "2024-07-23T12:44:45.483301Z", + "iopub.status.busy": "2024-07-23T12:44:45.482628Z", + "iopub.status.idle": "2024-07-23T12:45:00.544981Z", + "shell.execute_reply": "2024-07-23T12:45:00.543998Z" + }, + "papermill": { + "duration": 15.077364, + "end_time": "2024-07-23T12:45:00.547123", + "exception": false, + "start_time": "2024-07-23T12:44:45.469759", + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../../../../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": "2024-07-23T12:45:00.574917Z", + "iopub.status.busy": "2024-07-23T12:45:00.574596Z", + "iopub.status.idle": "2024-07-23T12:45:01.446660Z", + "shell.execute_reply": "2024-07-23T12:45:01.445746Z" + }, + "executionInfo": { + "elapsed": 588, + "status": "ok", + "timestamp": 1696841049215, + "user": { + "displayName": "Rizqi Nur", + "userId": "09644007964068789560" + }, + "user_tz": -420 + }, + "id": "NgahtU1q9uLO", + "papermill": { + "duration": 0.88885, + "end_time": "2024-07-23T12:45:01.448843", + "exception": false, + "start_time": "2024-07-23T12:45:00.559993", + "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": "2024-07-23T12:45:01.476820Z", + "iopub.status.busy": "2024-07-23T12:45:01.476482Z", + "iopub.status.idle": "2024-07-23T12:46:16.158129Z", + "shell.execute_reply": "2024-07-23T12:46:16.156999Z" + }, + "papermill": { + "duration": 74.698508, + "end_time": "2024-07-23T12:46:16.160415", + "exception": false, + "start_time": "2024-07-23T12:45:01.461907", + "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" + ] + }, + { + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "split df ratio is 0\n", + "../../../../ml-utility-loss/synthetics/iris [5, 0]\n", + "[805, 200]\n", + "[805, 200]\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": "2024-07-23T12:46:16.189979Z", + "iopub.status.busy": "2024-07-23T12:46:16.189055Z", + "iopub.status.idle": "2024-07-23T12:46:16.489737Z", + "shell.execute_reply": "2024-07-23T12:46:16.488669Z" + }, + "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": 0.31785, + "end_time": "2024-07-23T12:46:16.491661", + "exception": false, + "start_time": "2024-07-23T12:46:16.173811", + "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": "2024-07-23T12:46:16.520828Z", + "iopub.status.busy": "2024-07-23T12:46:16.520523Z", + "iopub.status.idle": "2024-07-23T12:46:16.524771Z", + "shell.execute_reply": "2024-07-23T12:46:16.523824Z" + }, + "papermill": { + "duration": 0.021458, + "end_time": "2024-07-23T12:46:16.526751", + "exception": false, + "start_time": "2024-07-23T12:46:16.505293", + "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": "2024-07-23T12:46:16.553206Z", + "iopub.status.busy": "2024-07-23T12:46:16.552886Z", + "iopub.status.idle": "2024-07-23T12:46:16.559882Z", + "shell.execute_reply": "2024-07-23T12:46:16.559020Z" + }, + "papermill": { + "duration": 0.022539, + "end_time": "2024-07-23T12:46:16.561814", + "exception": false, + "start_time": "2024-07-23T12:46:16.539275", + "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": "2024-07-23T12:46:16.588762Z", + "iopub.status.busy": "2024-07-23T12:46:16.588037Z", + "iopub.status.idle": "2024-07-23T12:46:16.642814Z", + "shell.execute_reply": "2024-07-23T12:46:16.641804Z" + }, + "papermill": { + "duration": 0.070607, + "end_time": "2024-07-23T12:46:16.644900", + "exception": false, + "start_time": "2024-07-23T12:46:16.574293", + "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": "2024-07-23T12:46:16.674891Z", + "iopub.status.busy": "2024-07-23T12:46:16.674575Z", + "iopub.status.idle": "2024-07-23T13:49:06.612285Z", + "shell.execute_reply": "2024-07-23T13:49:06.611303Z" + }, + "papermill": { + "duration": 3769.970654, + "end_time": "2024-07-23T13:49:06.629790", + "exception": false, + "start_time": "2024-07-23T12:46:16.659136", + "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': 0.050058625359088185, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.0038189488827085586, 'avg_role_model_g_mag_loss': 0.2928008040332276, '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': 0.05124865138226461, 'n_size': 805, 'n_batch': 202, 'duration': 162.32388854026794, 'duration_batch': 0.8035836066349898, 'duration_size': 0.20164458203759994, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.01380991430953145, 'avg_role_model_std_loss': 1.0332548362238776, 'avg_role_model_mean_pred_loss': 0.00022369780528578076, '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': 0.01380991430953145, 'n_size': 200, 'n_batch': 50, 'duration': 37.204161405563354, 'duration_batch': 0.744083228111267, 'duration_size': 0.18602080702781676, 'avg_pred_std': 0.20243973705917598}\n", + "Epoch 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.013834703466218682, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.00024387830734015945, 'avg_role_model_g_mag_loss': 0.20789108090565442, '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': 0.013961820922045743, 'n_size': 805, 'n_batch': 202, 'duration': 156.58676767349243, 'duration_batch': 0.7751820181856061, 'duration_size': 0.19451772381800303, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.011330255954526365, 'avg_role_model_std_loss': 0.23788032891211286, 'avg_role_model_mean_pred_loss': 8.255381331963463e-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': 0.011330255954526365, 'n_size': 200, 'n_batch': 50, 'duration': 37.045591831207275, 'duration_batch': 0.7409118366241455, 'duration_size': 0.18522795915603638, 'avg_pred_std': 0.2168916241824627}\n", + "Epoch 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.010315183287353388, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.0001199892065505052, 'avg_role_model_g_mag_loss': 0.09883831987701218, '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': 0.01042766306977372, 'n_size': 805, 'n_batch': 202, 'duration': 162.65101838111877, 'duration_batch': 0.8052030612926672, 'duration_size': 0.20205095451070657, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.011883822558447719, 'avg_role_model_std_loss': 0.45994447562930874, 'avg_role_model_mean_pred_loss': 0.00018599521862654455, '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': 0.011883822558447719, 'n_size': 200, 'n_batch': 50, 'duration': 37.52820634841919, 'duration_batch': 0.7505641269683838, 'duration_size': 0.18764103174209595, 'avg_pred_std': 0.20268165171146393}\n", + "Epoch 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.009571485980327085, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.00013191995011554194, 'avg_role_model_g_mag_loss': 0.09425949151888027, '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': 0.009673453772133968, 'n_size': 805, 'n_batch': 202, 'duration': 159.52475118637085, 'duration_batch': 0.7897264910216378, 'duration_size': 0.19816739277810044, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.008419936873251573, 'avg_role_model_std_loss': 0.1574359182455676, 'avg_role_model_mean_pred_loss': 7.123594515633158e-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': 0.008419936873251573, 'n_size': 200, 'n_batch': 50, 'duration': 38.97650623321533, 'duration_batch': 0.7795301246643066, 'duration_size': 0.19488253116607665, 'avg_pred_std': 0.22664070010185242}\n", + "Epoch 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.008276314181723973, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 9.216505732865434e-05, 'avg_role_model_g_mag_loss': 0.08228672831695272, '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': 0.008357117456052833, 'n_size': 805, 'n_batch': 202, 'duration': 159.72294282913208, 'duration_batch': 0.7907076377679806, 'duration_size': 0.1984135935765616, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.009074561491142958, 'avg_role_model_std_loss': 0.08358777020010166, 'avg_role_model_mean_pred_loss': 0.00011296471567812993, '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': 0.009074561491142958, 'n_size': 200, 'n_batch': 50, 'duration': 37.664777994155884, 'duration_batch': 0.7532955598831177, 'duration_size': 0.18832388997077942, 'avg_pred_std': 0.25696665436029437}\n", + "Epoch 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.008860537945039814, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 0.00016087141088814343, 'avg_role_model_g_mag_loss': 0.10597761826855796, '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': 0.008940959945704314, 'n_size': 805, 'n_batch': 202, 'duration': 157.78362035751343, 'duration_batch': 0.7811070314728388, 'duration_size': 0.19600449733852598, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.008069937156978995, 'avg_role_model_std_loss': 0.09032030399492214, 'avg_role_model_mean_pred_loss': 5.787474323771358e-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': 0.008069937156978995, 'n_size': 200, 'n_batch': 50, 'duration': 37.049257040023804, 'duration_batch': 0.7409851408004761, 'duration_size': 0.18524628520011902, 'avg_pred_std': 0.24754563540220262}\n", + "Epoch 6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.007722100725969421, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 8.35489591843433e-05, 'avg_role_model_g_mag_loss': 0.0910686688416678, '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': 0.00779350242627747, 'n_size': 805, 'n_batch': 202, 'duration': 162.37695503234863, 'duration_batch': 0.8038463120413298, 'duration_size': 0.2017105031457747, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.009678735376510303, 'avg_role_model_std_loss': 0.2973533032249838, 'avg_role_model_mean_pred_loss': 0.00013776582317397867, 'avg_role_model_g_mag_loss': 0.0, 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"Epoch 17\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.005085343570764418, 'avg_role_model_std_loss': nan, 'avg_role_model_mean_pred_loss': 4.416938581179032e-05, 'avg_role_model_g_mag_loss': 0.07605684064184055, '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': 0.005129217085417374, 'n_size': 805, 'n_batch': 202, 'duration': 159.41030383110046, 'duration_batch': 0.7891599199559429, 'duration_size': 0.19802522215043536, 'avg_pred_std': nan}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.006535529452376068, 'avg_role_model_std_loss': 0.09938975967265833, 'avg_role_model_mean_pred_loss': 0.00011342970194588186, '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, 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"output_type": "stream", + "text": [ + "Eval loss {'role_model': 'tvae', 'n_size': 200, 'n_batch': 50, 'role_model_metrics': {'avg_loss': 0.007712974138557911, 'avg_g_mag_loss': 0.03224484422340197, 'avg_g_cos_loss': 0.01151576804434626, 'pred_duration': 0.49635744094848633, 'grad_duration': 0.2787814140319824, 'total_duration': 0.7751388549804688, 'pred_std': 0.22546058893203735, 'std_loss': 0.007603875361382961, 'mean_pred_loss': 7.94629449956119e-05, 'pred_rmse': 0.0878235399723053, 'pred_mae': 0.06582590192556381, 'pred_mape': 0.12693996727466583, 'grad_rmse': 0.2004992961883545, 'grad_mae': 0.085373155772686, 'grad_mape': 0.9616652727127075}, 'non_role_model_metrics': {'avg_loss': 0, 'avg_g_mag_loss': 0, 'avg_g_cos_loss': 0, 'avg_pred_duration': 0, 'avg_grad_duration': 0, 'avg_total_duration': 0, 'avg_pred_std': 0, 'avg_std_loss': 0, 'avg_mean_pred_loss': 0}, 'avg_metrics': {'avg_loss': 0.007712974138557911, 'avg_g_mag_loss': 0.03224484422340197, 'avg_g_cos_loss': 0.01151576804434626, 'avg_pred_duration': 0.49635744094848633, 'avg_grad_duration': 0.2787814140319824, 'avg_total_duration': 0.7751388549804688, 'avg_pred_std': 0.22546058893203735, 'avg_std_loss': 0.007603875361382961, 'avg_mean_pred_loss': 7.94629449956119e-05}, 'min_metrics': {'avg_loss': 0.007712974138557911, 'avg_g_mag_loss': 0.03224484422340197, 'avg_g_cos_loss': 0.01151576804434626, 'pred_duration': 0.49635744094848633, 'grad_duration': 0.2787814140319824, 'total_duration': 0.7751388549804688, 'pred_std': 0.22546058893203735, 'std_loss': 0.007603875361382961, 'mean_pred_loss': 7.94629449956119e-05, 'pred_rmse': 0.0878235399723053, 'pred_mae': 0.06582590192556381, 'pred_mape': 0.12693996727466583, 'grad_rmse': 0.2004992961883545, 'grad_mae': 0.085373155772686, 'grad_mape': 0.9616652727127075}, 'model_metrics': {'tvae': {'avg_loss': 0.007712974138557911, 'avg_g_mag_loss': 0.03224484422340197, 'avg_g_cos_loss': 0.01151576804434626, 'pred_duration': 0.49635744094848633, 'grad_duration': 0.2787814140319824, 'total_duration': 0.7751388549804688, 'pred_std': 0.22546058893203735, 'std_loss': 0.007603875361382961, 'mean_pred_loss': 7.94629449956119e-05, 'pred_rmse': 0.0878235399723053, 'pred_mae': 0.06582590192556381, 'pred_mape': 0.12693996727466583, 'grad_rmse': 0.2004992961883545, 'grad_mae': 0.085373155772686, 'grad_mape': 0.9616652727127075}}}\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": "2024-07-23T13:49:06.666213Z", + "iopub.status.busy": "2024-07-23T13:49:06.665875Z", + "iopub.status.idle": "2024-07-23T13:49:06.669943Z", + "shell.execute_reply": "2024-07-23T13:49:06.669119Z" + }, + "papermill": { + "duration": 0.024522, + "end_time": "2024-07-23T13:49:06.671893", + "exception": false, + "start_time": "2024-07-23T13:49:06.647371", + "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": "2024-07-23T13:49:06.705836Z", + "iopub.status.busy": "2024-07-23T13:49:06.705523Z", + "iopub.status.idle": "2024-07-23T13:49:06.724494Z", + "shell.execute_reply": "2024-07-23T13:49:06.723603Z" + }, + "papermill": { + "duration": 0.038439, + "end_time": "2024-07-23T13:49:06.726378", + "exception": false, + "start_time": "2024-07-23T13:49:06.687939", + "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": "2024-07-23T13:49:06.759873Z", + "iopub.status.busy": "2024-07-23T13:49:06.759601Z", + "iopub.status.idle": "2024-07-23T13:49:07.037014Z", + "shell.execute_reply": "2024-07-23T13:49:07.036124Z" + }, + "papermill": { + "duration": 0.296642, + "end_time": "2024-07-23T13:49:07.038999", + "exception": false, + "start_time": "2024-07-23T13:49:06.742357", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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": "2024-07-23T13:49:07.075651Z", + "iopub.status.busy": "2024-07-23T13:49:07.075343Z", + "iopub.status.idle": "2024-07-23T13:49:44.268565Z", + "shell.execute_reply": "2024-07-23T13:49:44.267725Z" + }, + "papermill": { + "duration": 37.214347, + "end_time": "2024-07-23T13:49:44.270984", + "exception": false, + "start_time": "2024-07-23T13:49:07.056637", + "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": "2024-07-23T13:49:44.308137Z", + "iopub.status.busy": "2024-07-23T13:49:44.307778Z", + "iopub.status.idle": "2024-07-23T13:49:44.328271Z", + "shell.execute_reply": "2024-07-23T13:49:44.327449Z" + }, + "papermill": { + "duration": 0.041345, + "end_time": "2024-07-23T13:49:44.330348", + "exception": false, + "start_time": "2024-07-23T13:49:44.289003", + "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
tvae0.0030390.0121950.0077130.2767020.0853730.9616650.2004990.0000790.4949610.0658260.126940.0878240.2254610.0076040.771662
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" + ], + "text/plain": [ + " avg_g_cos_loss avg_g_mag_loss avg_loss grad_duration grad_mae \\\n", + "tvae 0.003039 0.012195 0.007713 0.276702 0.085373 \n", + "\n", + " grad_mape grad_rmse mean_pred_loss pred_duration pred_mae \\\n", + "tvae 0.961665 0.200499 0.000079 0.494961 0.065826 \n", + "\n", + " pred_mape pred_rmse pred_std std_loss total_duration \n", + "tvae 0.12694 0.087824 0.225461 0.007604 0.771662 " + ] + }, + "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": "2024-07-23T13:49:44.365722Z", + "iopub.status.busy": "2024-07-23T13:49:44.365234Z", + "iopub.status.idle": "2024-07-23T13:49:44.631323Z", + "shell.execute_reply": "2024-07-23T13:49:44.630376Z" + }, + "papermill": { + "duration": 0.285871, + "end_time": "2024-07-23T13:49:44.633273", + "exception": false, + "start_time": "2024-07-23T13:49:44.347402", + "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": "2024-07-23T13:49:44.671483Z", + "iopub.status.busy": "2024-07-23T13:49:44.671170Z", + "iopub.status.idle": "2024-07-23T13:50:21.523166Z", + "shell.execute_reply": "2024-07-23T13:50:21.522373Z" + }, + "papermill": { + "duration": 36.873966, + "end_time": "2024-07-23T13:50:21.525490", + "exception": false, + "start_time": "2024-07-23T13:49:44.651524", + "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": "2024-07-23T13:50:21.562982Z", + "iopub.status.busy": "2024-07-23T13:50:21.562672Z", + "iopub.status.idle": "2024-07-23T13:50:21.576621Z", + "shell.execute_reply": "2024-07-23T13:50:21.575953Z" + }, + "papermill": { + "duration": 0.034952, + "end_time": "2024-07-23T13:50:21.578448", + "exception": false, + "start_time": "2024-07-23T13:50:21.543496", + "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": "2024-07-23T13:50:21.614189Z", + "iopub.status.busy": "2024-07-23T13:50:21.613721Z", + "iopub.status.idle": "2024-07-23T13:50:21.619041Z", + "shell.execute_reply": "2024-07-23T13:50:21.618205Z" + }, + "papermill": { + "duration": 0.025895, + "end_time": "2024-07-23T13:50:21.621125", + "exception": false, + "start_time": "2024-07-23T13:50:21.595230", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'tvae': 0.742495599836111}\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": "2024-07-23T13:50:21.656396Z", + "iopub.status.busy": "2024-07-23T13:50:21.656130Z", + "iopub.status.idle": "2024-07-23T13:50:22.012532Z", + "shell.execute_reply": "2024-07-23T13:50:22.011666Z" + }, + "papermill": { + "duration": 0.376696, + "end_time": "2024-07-23T13:50:22.014622", + "exception": false, + "start_time": "2024-07-23T13:50:21.637926", + "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": "2024-07-23T13:50:22.053496Z", + "iopub.status.busy": "2024-07-23T13:50:22.053213Z", + "iopub.status.idle": "2024-07-23T13:50:22.391998Z", + "shell.execute_reply": "2024-07-23T13:50:22.391137Z" + }, + "papermill": { + "duration": 0.360764, + "end_time": "2024-07-23T13:50:22.394040", + "exception": false, + "start_time": "2024-07-23T13:50:22.033276", + "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": "2024-07-23T13:50:22.433063Z", + "iopub.status.busy": "2024-07-23T13:50:22.432776Z", + "iopub.status.idle": "2024-07-23T13:50:22.605489Z", + "shell.execute_reply": "2024-07-23T13:50:22.604454Z" + }, + "papermill": { + "duration": 0.194565, + "end_time": "2024-07-23T13:50:22.607690", + "exception": false, + "start_time": "2024-07-23T13:50:22.413125", + "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": "2024-07-23T13:50:22.648564Z", + "iopub.status.busy": "2024-07-23T13:50:22.648277Z", + "iopub.status.idle": "2024-07-23T13:50:22.894930Z", + "shell.execute_reply": "2024-07-23T13:50:22.894115Z" + }, + "papermill": { + "duration": 0.268911, + "end_time": "2024-07-23T13:50:22.896999", + "exception": false, + "start_time": "2024-07-23T13:50:22.628088", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "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": 0.019703, + "end_time": "2024-07-23T13:50:22.936625", + "exception": false, + "start_time": "2024-07-23T13:50:22.916922", + "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": 3950.916467, + "end_time": "2024-07-23T13:50:25.281001", + "environment_variables": {}, + "exception": null, + "input_path": "eval/iris/tvae/0/mlu-eval.ipynb", + "output_path": "eval/iris/tvae/0/mlu-eval.ipynb", + "parameters": { + "allow_same_prediction": true, + "dataset": "iris", + "dataset_name": "iris", + 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