generate sh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "07865f6e-6279-41b4-927e-7829059868fd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.remove(\"template.sh\")\n",
    "\n",
    "seed_list = [2]\n",
    "model_list = [\"deepconvlstm_attn\"]#[\"deepconvlstm_attn\",\"deepconvlstm\",\"attend\"] # deepconvlstm_attn  attend\n",
    "data_list  = [\"hapt\", \"dg\" ,\"wisdm\", \"dsads\" ,\"oppo\" ,\"pamap2\",\"rw\"]#[\"hapt\", \"dg\" ,\"wisdm\", \"dsads\" ,\"oppo\" ,\"pamap2\"] #\"hapt\" \"dg\" \"wisdm\" \"dsads\" \"oppo\" \"pamap2\"\n",
    "defference_list = [True, False]\n",
    "filtering_list = [ True,False]\n",
    "\n",
    "filter_scaling_factor = 1.0\n",
    "mixup_argmax_list = [True]\n",
    "mixup_probability_list = [0.5,1]\n",
    "random_augmentation_prob_list = [0.5,1]\n",
    "\n",
    "with open(\"template.sh\",'a') as f:\n",
    "    for seed in seed_list:\n",
    "        for model in model_list:\n",
    "            for data in data_list:\n",
    "                for filtering in filtering_list:\n",
    "                    for mixup_argmax in mixup_argmax_list:\n",
    "                        for mixup_probability in mixup_probability_list:\n",
    "                            for random_augmentation_prob in random_augmentation_prob_list:\n",
    "\n",
    "                                if filtering:\n",
    " \n",
    "                                    f.write(\n",
    "                                        \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference --filtering --mixup-argmax --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                   data,                     model,    seed    ,                                                           filter_scaling_factor ,      mixup_probability   ,random_augmentation_prob)\n",
    "                                    )\n",
    "                                    f.write('\\n')  \n",
    "                                else:\n",
    "\n",
    "                                    f.write(\n",
    "                                        \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {}  --mixup-argmax --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                   data,                     model,    seed      ,                                       filter_scaling_factor    ,         mixup_probability    ,      random_augmentation_prob)\n",
    "                                    )\n",
    "                                    f.write('\\n')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f05feae2-5a99-4d3f-b6ea-869398819937",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6db8e633-633b-493a-8ed4-d88afc0b4249",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.remove(\"template.sh\")\n",
    "\n",
    "seed_list = [1]\n",
    "model_list = [\"attend\"] # deepconvlstm_attn  attend\n",
    "data_list  = [\"wisdm\"]#[\"hapt\", \"dg\" ,\"wisdm\", \"dsads\" ,\"oppo\" ,\"pamap2\"] #\"hapt\" \"dg\" \"wisdm\" \"dsads\" \"oppo\" \"pamap2\"\n",
    "defference_list = [True]\n",
    "filtering_list = [True]\n",
    "\n",
    "filter_scaling_factor = 1.0\n",
    "mixup_argmax_list = [True]\n",
    "mixup_probability_list = [0.5,1]\n",
    "random_augmentation_prob_list = [0.5,1]\n",
    "\n",
    "with open(\"template.sh\",'a') as f:\n",
    "    for seed in seed_list:\n",
    "        for model in model_list:\n",
    "            for data in data_list:\n",
    "                for difference in defference_list:\n",
    "                    for filtering in filtering_list:\n",
    "                        for mixup_argmax in mixup_argmax_list:\n",
    "                            for mixup_probability in mixup_probability_list:\n",
    "                                for random_augmentation_prob in random_augmentation_prob_list:\n",
    "                                    if difference:\n",
    "                                        if filtering:\n",
    "                                            if mixup_argmax:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference --filtering --mixup-argmax --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed  ,                                                                               filter_scaling_factor ,            mixup_probability  ,        random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')  \n",
    "                                            else:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference --filtering --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed    ,                                                           filter_scaling_factor ,      mixup_probability   ,random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')  \n",
    "                                        else:\n",
    "                                            if mixup_argmax: \n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference --mixup-argmax --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed       ,                                                     filter_scaling_factor   ,          mixup_probability      ,    random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')    \n",
    "                                            else:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference  --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed      ,                                       filter_scaling_factor    ,         mixup_probability    ,      random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')    \n",
    "                                    else:\n",
    "                                        if filtering:\n",
    "                                            if mixup_argmax:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --filtering --mixup-argmax --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed     ,                                                    filter_scaling_factor  ,           mixup_probability     ,     random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')   \n",
    "                                            else:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --filtering  --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed    ,                                         filter_scaling_factor,             mixup_probability   ,       random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')    \n",
    "                                        else:      \n",
    "                                            if mixup_argmax:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --mixup-argmax --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed    ,                                         filter_scaling_factor ,            mixup_probability    ,      random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')    \n",
    "                                                \n",
    "                                            else:\n",
    "                                                f.write(\n",
    "                                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(\n",
    "                                                                               data,                     model,    seed      ,                          filter_scaling_factor    ,         mixup_probability      ,    random_augmentation_prob)\n",
    "                                                )\n",
    "                                                f.write('\\n')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd9e495d-c593-45a0-be15-fde3d64f7ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"template.sh\",'a') as f:\n",
    "    for seed in seed_list:\n",
    "        for model in model_list:\n",
    "            for data in data_list:\n",
    "                for difference in defference_list:\n",
    "                    for filtering in filtering_list:\n",
    "                        if difference:\n",
    "                            if filtering:\n",
    "                                f.write(\n",
    "                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference --filtering --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(data,model,seed)\n",
    "                                )\n",
    "                                f.write('\\n')                    \n",
    "                            else:\n",
    "                                f.write(\n",
    "                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --difference --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(data,model,seed)\n",
    "                                )\n",
    "                                f.write('\\n')    \n",
    "                        else:\n",
    "                            if filtering:\n",
    "                                f.write(\n",
    "                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --filtering --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(data,model,seed)\n",
    "                                )\n",
    "                                f.write('\\n')    \n",
    "                            else:        \n",
    "                                f.write(\n",
    "                                    \"sbatch main.py --data-name {} --shuffle --model-type {} --seed {} --root-path ../../../datasets --filter-scaling-factor {} --mixup-probability {} --random-augmentation-prob {}\".format(data,model,seed)\n",
    "                                )\n",
    "                                f.write('\\n')    "
   ]
  }
 ],
 "metadata": {
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}