dataset_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "087c3ed3-3066-4943-933c-f31067e2db42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from operator import index\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7141b8cb-44e0-4395-bad5-dc1da956f1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/max/.local/lib/python3.8/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import xgboost as xgb\n",
    "#import tensorflow as tf\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_validate, ShuffleSplit\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import median_absolute_error\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('once', '.*sliced data.*', )\n",
    "warnings.filterwarnings('once', '.*Maximum iterations*', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9de6292d-a0f3-43b4-8d11-7e8f2273415c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def prepare_dataset(path : str, workload_name : str):\n",
    "    dataset = {}\n",
    "    files = os.listdir(path)\n",
    "\n",
    "    for file in files:\n",
    "        with open(path+\"/\"+file, \"r\") as f:\n",
    "            raw_str = f.read()\n",
    "        data = json.loads(raw_str)\n",
    "\n",
    "        if \"layers\" in data.keys():\n",
    "            del data[\"layers\"]\n",
    "\n",
    "        if \"relay\" in data.keys():\n",
    "            del data[\"relay\"]\n",
    "\n",
    "        if \"conv2d\" in workload_name:\n",
    "            data[\"kernel_0\"] = data[\"kernel\"][0]\n",
    "            data[\"kernel_1\"] = data[\"kernel\"][1]\n",
    "            del data[\"kernel\"]\n",
    "            data[\"dilation_0\"] = data[\"dilation\"][0]\n",
    "            data[\"dilation_1\"] = data[\"dilation\"][1]\n",
    "            del data[\"dilation\"]\n",
    "\n",
    "            if not \"kernel layout\" in data.keys():\n",
    "                data[\"kernel layout\"] = \"OIHW\"\n",
    "\n",
    "        elif workload_name == \"dense\":\n",
    "            data[\"features\"] = data[\"input shape\"][1]\n",
    "            del data[\"input shape\"]\n",
    "            del data[\"output shape\"]\n",
    "            #print()\n",
    "\n",
    "        elif workload_name in [\"max_pool2d\", \"avg_pool2d\"]:\n",
    "            data[\"pool_0\"] = data[\"pool_size\"][0]\n",
    "            data[\"pool_1\"] = data[\"pool_size\"][1]\n",
    "            del data[\"pool_size\"]\n",
    "\n",
    "        if workload_name in [\"max_pool2d\", \"avg_pool2d\", \"conv2d\", \"dilated_conv2d\", \"depthwise_conv2d\"]:\n",
    "            del data[\"padding\"]\n",
    "            data[\"C_I\"] = data[\"input shape\"][3]\n",
    "            data[\"H_I\"] = data[\"input shape\"][1]\n",
    "            data[\"W_I\"] = data[\"input shape\"][2]\n",
    "            del data[\"input shape\"]\n",
    "\n",
    "            data[\"C_O\"] = data[\"output shape\"][3]\n",
    "            data[\"H_O\"] = data[\"output shape\"][1]\n",
    "            data[\"W_O\"] = data[\"output shape\"][2]\n",
    "            del data[\"output shape\"]\n",
    "\n",
    "            key = \"strides\"\n",
    "            if \"stride\" in data.keys():\n",
    "                key = \"stride\"\n",
    "            #print(key)\n",
    "            data[\"strides_0\"] = data[key][0]\n",
    "            data[\"strides_1\"] = data[key][1]\n",
    "            del data[\"strides\"]\n",
    "            if \"stride\" in data.keys():\n",
    "                del data[\"stride\"]\n",
    "\n",
    "        dataset[file] = data\n",
    "\n",
    "    return dataset\n",
    "\n",
    "def create_dataframe(dataset : dict, workload_name : str, labels = [\"time\", \"power\", \"memory\"]):\n",
    "    df = pd.DataFrame.from_dict(dataset, orient='index')\n",
    "    categoricals = [\n",
    "        \"output dtype\",\n",
    "        \"compute dtype\",\n",
    "        \"workload\",\n",
    "        ]\n",
    "    if workload_name in [\"conv2d\", \"max_pool2d\", \"avg_pool2d\", \"depthwise_conv2d\", \"dilated_conv2d\"]:\n",
    "        if \"data_layout\" in list(df.columns):\n",
    "            categoricals += [\n",
    "                #\"padding\",\n",
    "                \"data_layout\",\n",
    "            ]\n",
    "        else:    \n",
    "            categoricals += [\n",
    "                #\"padding\",\n",
    "                \"data layout\",\n",
    "            ]\n",
    "        \n",
    "\n",
    "    if \"conv2d\" in workload_name:\n",
    "        if \"kernel_layout\" in list(df.columns):\n",
    "            categoricals += [\n",
    "                \"kernel_layout\",\n",
    "            ]\n",
    "        if \"kernel layout\" in list(df.columns):\n",
    "            categoricals += [\n",
    "                \"kernel layout\",\n",
    "            ]\n",
    "\n",
    "    for col in categoricals:\n",
    "        oh = pd.get_dummies(df[col], prefix=col, drop_first=False)\n",
    "        df = pd.concat([df, oh], axis=1).drop(col, axis=1)\n",
    "    \n",
    "    features = list(df.columns)\n",
    "    #labels = [\"time\", \"power\", \"memory\"]\n",
    "    for label in labels:\n",
    "        idx = features.index(label)\n",
    "        del features[idx]\n",
    "    del idx\n",
    "    df = df.drop_duplicates(subset=features)\n",
    "\n",
    "    label_data = []\n",
    "    for label in labels:\n",
    "        label_data.append(df[label])\n",
    "        df.pop(label)\n",
    "\n",
    "    output = pd.concat(label_data, axis=1)\n",
    "\n",
    "    return df, output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f44f9b9a-f244-405d-9845-f80b1e8623d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " cuda_980ti\t\t\t     'llvm -mcpu=haswell_i7-4790'\n",
      " cuda_A100\t\t\t      opencl_Mali-T628_MP6\n",
      " cuda_K80\t\t\t      opencl_Mali-T628_MP6_no_power\n",
      "'llvm -mcpu=haswell_E5-2680'\t      rasp4b\n",
      "'llvm -mcpu=haswell_E5-2680_faulty'\n"
     ]
    }
   ],
   "source": [
    "!ls dataset/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "267fba38-a25d-4e19-8f41-b1ed8e88d706",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./dataset/cuda_980ti/avg_pool2d/\n",
      "./dataset/cuda_980ti/conv2d/\n",
      "./dataset/cuda_980ti/dense/\n",
      "./dataset/cuda_980ti/depthwise_conv2d/\n",
      "./dataset/cuda_980ti/dilated_conv2d/\n",
      "./dataset/cuda_980ti/max_pool2d/\n",
      "./dataset/cuda_A100/avg_pool2d/\n",
      "./dataset/cuda_A100/conv2d/\n",
      "./dataset/cuda_A100/dense/\n",
      "./dataset/cuda_A100/depthwise_conv2d/\n",
      "./dataset/cuda_A100/dilated_conv2d/\n",
      "./dataset/cuda_A100/max_pool2d/\n",
      "./dataset/cuda_K80/avg_pool2d/\n",
      "./dataset/cuda_K80/conv2d/\n",
      "./dataset/cuda_K80/dense/\n",
      "./dataset/cuda_K80/depthwise_conv2d/\n",
      "./dataset/cuda_K80/dilated_conv2d/\n",
      "./dataset/cuda_K80/max_pool2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680/avg_pool2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680/conv2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680/dense/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680/depthwise_conv2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680/dilated_conv2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680/max_pool2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680_faulty/conv2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680_faulty/dense/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680_faulty/depthwise_conv2d/\n",
      "./dataset/llvm -mcpu=haswell_E5-2680_faulty/dilated_conv2d/\n",
      "./dataset/llvm -mcpu=haswell_i7-4790/avg_pool2d/\n",
      "./dataset/llvm -mcpu=haswell_i7-4790/conv2d/\n",
      "./dataset/llvm -mcpu=haswell_i7-4790/max_pool2d/\n",
      "./dataset/opencl_Mali-T628_MP6/avg_pool2d/\n",
      "./dataset/opencl_Mali-T628_MP6/conv2d/\n",
      "./dataset/opencl_Mali-T628_MP6/dense/\n",
      "./dataset/opencl_Mali-T628_MP6/depthwise_conv2d/\n",
      "./dataset/opencl_Mali-T628_MP6/dilated_conv2d/\n",
      "./dataset/opencl_Mali-T628_MP6/max_pool2d/\n",
      "./dataset/opencl_Mali-T628_MP6_no_power/conv2d/\n",
      "./dataset/opencl_Mali-T628_MP6_no_power/dense/\n",
      "./dataset/opencl_Mali-T628_MP6_no_power/depthwise_conv2d/\n",
      "./dataset/rasp4b/conv2d/\n",
      "./dataset/rasp4b/dilated_conv2d/\n",
      "found 42 folders with samples, going to train models for each of these targets\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset_base = \"./dataset\"\n",
    "targets = os.listdir(dataset_base)\n",
    "\n",
    "layer_targets = list()\n",
    "for target in targets:\n",
    "    target_path = dataset_base + \"/\" + target\n",
    "    layers = os.listdir(target_path)\n",
    "    \n",
    "    for layer in layers:\n",
    "        dataset_path = target_path + \"/\" + layer + \"/\"\n",
    "        print(dataset_path)\n",
    "        layer_targets.append(dataset_path)\n",
    "\n",
    "print(\"found {0} folders with samples, going to train models for each of these targets\".format(len(layer_targets)))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caf492e1-421b-463d-9734-2fc4e033b25f",
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_targets = [\n",
    "\"./dataset/cuda_980ti/avg_pool2d/\",\n",
    "\"./dataset/cuda_980ti/conv2d/\",\n",
    "\"./dataset/cuda_980ti/dense/\",\n",
    "\"./dataset/cuda_980ti/depthwise_conv2d/\",\n",
    "\"./dataset/cuda_980ti/dilated_conv2d/\",\n",
    "\"./dataset/cuda_980ti/max_pool2d/\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1b50ec1-ef20-47bd-b593-f7b3e9de3a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"xgb\": xgb.XGBRegressor,\n",
    "    \"ert\": ExtraTreesRegressor,\n",
    "    \"dTr\": DecisionTreeRegressor,\n",
    "    \"liR\": LinearRegression,\n",
    "    \"kNN\": KNeighborsRegressor,\n",
    "    \"SVR\": SVR,\n",
    "    \"MLP\": MLPRegressor,\n",
    "}\n",
    "\n",
    "kwargs_dict = {\n",
    "    \"xgb\": None,\n",
    "    \"ert\": {\"n_estimators\":175},\n",
    "    \"dTr\": {\"criterion\":\"mae\", },\n",
    "    \"liR\": {\"normalize\": True, \"n_jobs\":-1},\n",
    "    \"kNN\": {\"n_neighbors\":3, \"weights\":\"distance\"},\n",
    "    \"SVR\": {},\n",
    "    \"MLP\": {\"hidden_layer_sizes\":(100,400,200,100,), \"max_iter\":500},\n",
    "}\n",
    "\n",
    "results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13ab80f8-5890-4568-8ee8-b9c6b10c0e36",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./dataset/cuda_980ti/avg_pool2d/\n",
      "avg_pool2d : cuda_980ti\t:\t contains 4692 samples\n",
      "\t[INFO] Remaining Samples after duplicate elimination:\t4692\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "150\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.6257710168089552, 'max_error': 15.85395, 'mae': 1.577896561294979, 'mape': 1.9005720069056025, 'medae': 0.7851575223541256, 'dataset_min': 0.00764443, 'dataset_max': 22.7072, 'dataset_mean': 3.7938289555333338, 'dataset_median': 1.45204, 'dataset_size': 150}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "300\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.7546991988212559, 'max_error': 17.626747, 'mae': 1.2195044549344791, 'mape': 1.408373192533336, 'medae': 0.5732872058105469, 'dataset_min': 0.006952310000000001, 'dataset_max': 27.1845, 'dataset_mean': 3.511813319866667, 'dataset_median': 1.45204, 'dataset_size': 300}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "450\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.7567772642062568, 'max_error': 19.001062, 'mae': 1.1687347235498171, 'mape': 1.29449468967597, 'medae': 0.4795612487792962, 'dataset_min': 0.006952310000000001, 'dataset_max': 27.1845, 'dataset_mean': 3.3289255929333335, 'dataset_median': 1.45204, 'dataset_size': 450}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "600\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.7882045990095131, 'max_error': 19.164679, 'mae': 1.1112944223092274, 'mape': 1.3003198496420494, 'medae': 0.4734109216403961, 'dataset_min': 0.006952310000000001, 'dataset_max': 27.1845, 'dataset_mean': 3.2344332163666674, 'dataset_median': 1.45204, 'dataset_size': 600}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "750\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8352446368972529, 'max_error': 20.187054, 'mae': 0.976010409213089, 'mape': 1.161967875613108, 'medae': 0.45552450803756717, 'dataset_min': 0.006952310000000001, 'dataset_max': 27.1845, 'dataset_mean': 3.2025548994400004, 'dataset_median': 1.45204, 'dataset_size': 750}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "900\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8479059776986835, 'max_error': 18.921377, 'mae': 0.9311660272817197, 'mape': 1.0571380780337112, 'medae': 0.4277711932296753, 'dataset_min': 0.006952310000000001, 'dataset_max': 27.1845, 'dataset_mean': 3.2124853026222224, 'dataset_median': 1.45204, 'dataset_size': 900}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1050\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8690698820405196, 'max_error': 23.433704, 'mae': 0.8745502870460221, 'mape': 1.1645670763326745, 'medae': 0.43168845405769346, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.3085905682476184, 'dataset_median': 1.45204, 'dataset_size': 1050}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1200\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8637941441311894, 'max_error': 26.30755, 'mae': 0.9031604183250617, 'mape': 1.1484186656456536, 'medae': 0.449068894042969, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.3291847553500005, 'dataset_median': 1.45204, 'dataset_size': 1200}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1350\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8851609219826815, 'max_error': 25.009815, 'mae': 0.8040120977896451, 'mape': 1.0140861037113804, 'medae': 0.37655151863098135, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.2708506505407406, 'dataset_median': 1.45204, 'dataset_size': 1350}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1500\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8940548693949497, 'max_error': 20.164694, 'mae': 0.8023395395446072, 'mape': 1.0376671974552043, 'medae': 0.40178009471244813, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.301799913266667, 'dataset_median': 1.45204, 'dataset_size': 1500}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1650\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8955855894957293, 'max_error': 23.822672, 'mae': 0.7746982797487796, 'mape': 0.9797281595185724, 'medae': 0.4036181799316405, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.2749791628181817, 'dataset_median': 1.45204, 'dataset_size': 1650}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1800\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.8916129698377151, 'max_error': 23.315063, 'mae': 0.7606346781397123, 'mape': 0.8913904911093089, 'medae': 0.3346283492813111, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.3135287985833335, 'dataset_median': 1.45204, 'dataset_size': 1800}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "1950\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.9028089038729302, 'max_error': 22.563272, 'mae': 0.7375965061205937, 'mape': 0.9430898716300186, 'medae': 0.3685459552879333, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.281242543307692, 'dataset_median': 1.45204, 'dataset_size': 1950}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "xgb\n",
      "2100\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.9139805506332663, 'max_error': 18.821875, 'mae': 0.6988020573352558, 'mape': 0.9740915505269168, 'medae': 0.3505789497842789, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.289532160752381, 'dataset_median': 1.45204, 'dataset_size': 2100}\n",
      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "3750\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'xgb', 'r2': 0.9387793868502479, 'max_error': 18.691687, 'mae': 0.6076183510088317, 'mape': 0.900786318937246, 'medae': 0.3279508712768546, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.3122291159253336, 'dataset_median': 1.45204, 'dataset_size': 3750}\n",
      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "cuda_980ti\n",
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      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.8401723311465313, 'max_error': 205.11438, 'mae': 4.462353641335322, 'mape': 0.028479380898012798, 'medae': 2.5430172119140764, 'dataset_min': 65.936, 'dataset_max': 206.60199999999998, 'dataset_mean': 160.58695, 'dataset_median': 152.711, 'dataset_size': 750}\n",
      "cuda_980ti\n",
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      "900\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.8597146698419588, 'max_error': 204.42162, 'mae': 4.132936182901628, 'mape': 0.02650712774666527, 'medae': 2.2645675048828195, 'dataset_min': 65.936, 'dataset_max': 206.60199999999998, 'dataset_mean': 160.8456866666667, 'dataset_median': 152.711, 'dataset_size': 900}\n",
      "cuda_980ti\n",
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      "1050\n",
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      "cuda_980ti\n",
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      "1200\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9101354027772685, 'max_error': 209.54626, 'mae': 3.5883227685962877, 'mape': 0.022964117930326534, 'medae': 2.035429199218754, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.87049416666667, 'dataset_median': 152.711, 'dataset_size': 1200}\n",
      "cuda_980ti\n",
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      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9243878857948581, 'max_error': 205.56418, 'mae': 3.3307914168710266, 'mape': 0.021318307485288512, 'medae': 1.8710627441406302, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.59396407407408, 'dataset_median': 152.711, 'dataset_size': 1350}\n",
      "cuda_980ti\n",
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      "1500\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9183031513044175, 'max_error': 207.2495, 'mae': 3.266308877280726, 'mape': 0.020985218371327028, 'medae': 1.806422363281257, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.54250499999998, 'dataset_median': 152.711, 'dataset_size': 1500}\n",
      "cuda_980ti\n",
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      "1650\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9231676482757041, 'max_error': 206.14893, 'mae': 3.2289502123425438, 'mape': 0.02054645432665433, 'medae': 1.7889831542968864, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.6716572727273, 'dataset_median': 152.711, 'dataset_size': 1650}\n",
      "cuda_980ti\n",
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      "1800\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9136131780898282, 'max_error': 207.56627, 'mae': 3.312818440560208, 'mape': 0.02114326382211674, 'medae': 1.7817509765624777, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.71906444444443, 'dataset_median': 152.711, 'dataset_size': 1800}\n",
      "cuda_980ti\n",
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      "1950\n",
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      "cuda_980ti\n",
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      "2100\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9409130639089396, 'max_error': 207.63869, 'mae': 2.9549569462947827, 'mape': 0.018803865803545483, 'medae': 1.6379458007812389, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.62340214285715, 'dataset_median': 152.711, 'dataset_size': 2100}\n",
      "cuda_980ti\n",
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      "xgb\n",
      "2250\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9341138686432445, 'max_error': 205.37419, 'mae': 2.9377184049141167, 'mape': 0.01882342559816793, 'medae': 1.623147094726562, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.61781844444442, 'dataset_median': 152.711, 'dataset_size': 2250}\n",
      "cuda_980ti\n",
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      "xgb\n",
      "2400\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9421610204383002, 'max_error': 206.50513, 'mae': 2.8096637102640987, 'mape': 0.01800722224776287, 'medae': 1.6602138671875082, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.49493770833334, 'dataset_median': 152.711, 'dataset_size': 2400}\n",
      "cuda_980ti\n",
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      "xgb\n",
      "2550\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9479162819465792, 'max_error': 206.21948, 'mae': 2.772180587492304, 'mape': 0.017747241861432346, 'medae': 1.6288853759765516, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.48875137254902, 'dataset_median': 152.711, 'dataset_size': 2550}\n",
      "cuda_980ti\n",
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      "xgb\n",
      "2700\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.9419479211762092, 'max_error': 207.05943, 'mae': 2.702181562234799, 'mape': 0.01726182024175429, 'medae': 1.4074025878906298, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.6372562962963, 'dataset_median': 152.711, 'dataset_size': 2700}\n",
      "cuda_980ti\n",
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      "power\n",
      "xgb\n",
      "2850\n",
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      "cuda_980ti\n",
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      "3000\n",
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      "cuda_980ti\n",
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      "xgb\n",
      "3150\n",
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      "cuda_980ti\n",
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      "xgb\n",
      "3300\n",
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      "cuda_980ti\n",
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      "xgb\n",
      "3450\n",
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      "cuda_980ti\n",
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      "xgb\n",
      "3600\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'power', 'predictor': 'xgb', 'r2': 0.960356628169811, 'max_error': 203.9823, 'mae': 2.421429517524565, 'mape': 0.015394356127648594, 'medae': 1.4025426025390573, 'dataset_min': 65.936, 'dataset_max': 207.30149999999998, 'dataset_mean': 160.88344930555556, 'dataset_median': 152.711, 'dataset_size': 3600}\n",
      "cuda_980ti\n",
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      "3750\n",
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      "cuda_980ti\n",
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      "memory\n",
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      "150\n",
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      "cuda_980ti\n",
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      "300\n",
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      "ert\n",
      "2100\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'ert', 'r2': 0.8930926007693403, 'max_error': 20.437779257142875, 'mae': 0.7147107977282213, 'mape': 0.43250668965425354, 'medae': 0.27673130285714254, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.289532160752381, 'dataset_median': 1.45204, 'dataset_size': 2100}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "ert\n",
      "2250\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'ert', 'r2': 0.900143134091897, 'max_error': 20.884961314285725, 'mae': 0.6873503101399661, 'mape': 0.40286871559559156, 'medae': 0.24614585714285564, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.2620990307377777, 'dataset_median': 1.45204, 'dataset_size': 2250}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "ert\n",
      "2400\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'ert', 'r2': 0.9024182098214589, 'max_error': 19.964302342857152, 'mae': 0.6737041191967138, 'mape': 0.3905769766248915, 'medae': 0.28628869085714415, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.2528571319333337, 'dataset_median': 1.45204, 'dataset_size': 2400}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "ert\n",
      "2550\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'ert', 'r2': 0.9117977679924453, 'max_error': 21.48511611428573, 'mae': 0.6341868896909175, 'mape': 0.37254397259931665, 'medae': 0.25169645142857133, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.258631113203921, 'dataset_median': 1.45204, 'dataset_size': 2550}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "ert\n",
      "2700\n",
      "{'device': 'cuda_980ti', 'workload': 'avg_pool2d', 'metric': 'time', 'predictor': 'ert', 'r2': 0.9141582030026576, 'max_error': 19.03885605714287, 'mae': 0.6189008536028601, 'mape': 0.35977457022699433, 'medae': 0.22926746285714328, 'dataset_min': 0.006952310000000001, 'dataset_max': 37.962999999999994, 'dataset_mean': 3.2745152525666668, 'dataset_median': 1.45204, 'dataset_size': 2700}\n",
      "cuda_980ti\n",
      "avg_pool2d\n",
      "time\n",
      "ert\n",
      "2850\n"
     ]
    }
   ],
   "source": [
    "for target in layer_targets:\n",
    "    print(target)\n",
    "    tmp = target.split(\"/\")\n",
    "    workload_name = tmp[-2]\n",
    "    device_name = tmp[-3]\n",
    "    files = os.listdir(target)\n",
    "    dataset = {}\n",
    "    \n",
    "    labels = [\"time\", \"power\", \"memory\"]\n",
    "    if device_name == \"rasp4b\" or device_name == \"opencl_Mali-T628_MP6\":\n",
    "        labels = [\"time\", \"power\", \"ws_size\", \"io_size\"]\n",
    "    \n",
    "    print(\"{} : {}\\t:\\t contains {} samples\".format(workload_name, device_name, len(files)))\n",
    "    dataset = prepare_dataset(target, workload_name)\n",
    "    df, output = create_dataframe(dataset, workload_name, labels=labels)\n",
    "    print(\"\\t[INFO] Remaining Samples after duplicate elimination:\\t{}\".format(len(df)))\n",
    "    X = df.to_numpy()\n",
    "    Y = output.to_numpy()\n",
    "    labels = list(output.columns)\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size=0.2)\n",
    "    steps = 25\n",
    "    start = step_size = len(X_train)//steps\n",
    "    end = len(X_train)\n",
    "    #scoring = [\"r2\", \"neg_mean_absolute_error\", \"neg_mean_absolute_percentage_error\", \"max_error\", \"neg_median_absolute_error\"]\n",
    "    for method, constructor in models.items():\n",
    "        for idx, name in enumerate(labels):\n",
    "            for train_size in range(start, end, step_size):\n",
    "                print(device_name)\n",
    "                print(workload_name)\n",
    "                print(name)\n",
    "                print(method)\n",
    "                print(train_size)\n",
    "\n",
    "                if kwargs_dict[method] != None:\n",
    "                    model = make_pipeline(RobustScaler(), constructor(**kwargs_dict[method]))\n",
    "                else:\n",
    "                    model = make_pipeline(RobustScaler(), constructor())\n",
    "\n",
    "                model.fit(X_train[0:train_size], y_train[0:train_size,idx])\n",
    "                pred_test = model.predict(X_test)\n",
    "                r2_test = r2_score(y_test[:,idx], pred_test)\n",
    "                max_error = np.max(np.abs(pred_test))\n",
    "                mae = mean_absolute_error(y_test[:,idx], pred_test)\n",
    "                mape = mean_absolute_percentage_error(y_test[:,idx], pred_test)\n",
    "                medae = median_absolute_error(y_test[:,idx], pred_test)\n",
    "                result = {\n",
    "                    \"device\" : device_name,\n",
    "                    \"workload\" : workload_name,\n",
    "                    \"metric\" : name,\n",
    "                    \"predictor\" : method,\n",
    "                    \"r2\" : r2_test,\n",
    "                    \"max_error\" : max_error,\n",
    "                    \"mae\" : mae,\n",
    "                    \"mape\" : mape,\n",
    "                    \"medae\" : medae,\n",
    "                    \"dataset_min\" : y_train[0:train_size,idx].min(),\n",
    "                    \"dataset_max\" : y_train[0:train_size,idx].max(),\n",
    "                    \"dataset_mean\" : y_train[0:train_size,idx].mean(),\n",
    "                    \"dataset_median\" : np.median(y_train[:,idx]),\n",
    "                    \"dataset_size\": train_size,\n",
    "                }\n",
    "                print(result)\n",
    "                #print(\"R2: {.3f}\\tMAE {.3f}\\tMAPE {.3f}\\tMEDAE {.3f}\\tMaxError {.3f}\".format(result[\"r2\"],result[\"mae\"],result[\"mape\"],result[\"medae\"],result[\"max_error\"]))\n",
    "                results[device_name+\"-\"+workload_name+\"-\"+name+\"-\"+method+\":\"+str(train_size)] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b7822cd-38d6-4849-bfc7-e38fc23f1c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_time = \"size_investigation/results_rbst_scaler_980ti_\"+str(int(time.time()))\n",
    "results = pd.DataFrame.from_dict(results, \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f36e0c3-ded3-4234-b8e4-2f5a6c783dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv(date_time+\"-predictor_results.csv\")\n",
    "results.to_excel(date_time+\"-predictor_results.xlsx\")\n",
    "results.to_html(date_time+\"-predictor_results.html\")\n",
    "results.to_markdown(date_time+\"-predictor_results.md\")\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "658d0ea2-aa03-4cc7-bd36-89ce0f8fc5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4689c78a-bfc0-45a9-880d-b02f70d36046",
   "metadata": {},
   "outputs": [],
   "source": [
    "viz = results.set_index('dataset_size', inplace=False)\n",
    "viz[(viz.metric == \"time\")  & (viz.workload == \"conv2d\")].groupby('predictor')[\"r2\"].plot(kind=\"line\", legend=True, figsize=(15,5))\n",
    "\n",
    "#groupby('predictor').plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a84ffde-46b7-499f-b384-2820f5033102",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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