competition_notebooks/kaggle/plasticc/notebooks/rapids_lsst_gpu_only_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "GPU_id = 0\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7.2+0.g3ebd286.dirty\n"
     ]
    }
   ],
   "source": [
    "import cudf as gd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "from functools import partial\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from termcolor import colored\n",
    "from cudf_workaround import cudf_groupby_aggs\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set()\n",
    "print(gd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is a 2 minutes end-to-end gpu-only demos the 8th place solution (8/1094) of Rapids.ai for the __[PLAsTiCC Astronomical Classification](https://www.kaggle.com/c/PLAsTiCC-2018/leaderboard)__.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "[1. Global variables](#global)<br>\n",
    "[2. Functions](#func)<br>\n",
    "[3. ETL & Visualizations](#etl)<br>\n",
    "[4. Model training](#train)<br>\n",
    "[5. Conclusions](#conclusions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"global\"></a>\n",
    "## 1. Global variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Original data download and description __[link](https://www.kaggle.com/c/PLAsTiCC-2018/data)__**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = '../data'\n",
    "#PATH = '/raid/data/ml/lsst/input'\n",
    "#PATH = '../lsst/input'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tested on V100 with 32 GB GPU memory. Please reset this variable if memory capacity is smaller, and the input data will be down sampled accordingly.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "GPU_MEMORY = 32 # GB. \n",
    "#GPU_MEMORY = 16 # GB. Both 32 and 16 GB have been tested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_ROWS = 453653104 # number of rows in test data\n",
    "# no skip if your gpu has 32 GB memory\n",
    "# otherwise, skip rows porportionally\n",
    "OVERHEAD = 1.2 # cudf 0.7 introduces 20% memory overhead comparing to cudf 0.4\n",
    "SKIP_ROWS = int((1 - GPU_MEMORY/(32.0*OVERHEAD))*TEST_ROWS) \n",
    "GPU_RUN_TIME = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"func\"></a>\n",
    "## 2. Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter(x,y,values,xlabel='x',ylabel='y',title=None):\n",
    "    colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
    "    colors = np.array([colors[i] for i in values])\n",
    "    ps = []\n",
    "    bs = []\n",
    "    bands = ['passband_%s'%i for i in ['u', 'g', 'r', 'i', 'z','y']]\n",
    "    for i in sorted(np.unique(values)):\n",
    "        mask = values==i\n",
    "        if len(x[mask]):\n",
    "            p = plt.scatter(x[mask],y[mask],c=colors[mask])\n",
    "            ps.append(p)\n",
    "            bs.append(bands[i])\n",
    "    plt.legend(ps,bs,scatterpoints=1)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    \n",
    "    plt.xlim([np.min(x)-10,np.min(x)+1500])\n",
    "    plt.ylabel('y: %s'%ylabel)\n",
    "    plt.xlabel('x: %s'%xlabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_weighted_logloss(y_true, y_preds, classes, class_weights):\n",
    "    \"\"\"\n",
    "    refactor from\n",
    "    @author olivier https://www.kaggle.com/ogrellier\n",
    "    multi logloss for PLAsTiCC challenge\n",
    "    \"\"\"\n",
    "    y_p = y_preds.reshape(y_true.shape[0], len(classes), order='F')\n",
    "    y_ohe = pd.get_dummies(y_true)\n",
    "    y_p = np.clip(a=y_p, a_min=1e-15, a_max=1 - 1e-15)\n",
    "    y_p_log = np.log(y_p)\n",
    "    y_log_ones = np.sum(y_ohe.values * y_p_log, axis=0)\n",
    "    nb_pos = y_ohe.sum(axis=0).values.astype(float)\n",
    "    class_arr = np.array([class_weights[k] for k in sorted(class_weights.keys())])\n",
    "    y_w = y_log_ones * class_arr / nb_pos\n",
    "\n",
    "    loss = - np.sum(y_w) / np.sum(class_arr)\n",
    "    return loss\n",
    "\n",
    "def xgb_multi_weighted_logloss(y_predicted, y_true, classes, class_weights):\n",
    "    loss = multi_weighted_logloss(y_true.get_label(), y_predicted, \n",
    "                                  classes, class_weights)\n",
    "    return 'wloss', loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU ETL functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To save GPU memory, we drop the column as soon as it is done with groupby\n",
    "# this hits performance a little but avoids GPU OOM.\n",
    "def groupby_aggs(df,aggs,col):\n",
    "    res = None\n",
    "    for i,j in aggs.items():\n",
    "        for k in j:\n",
    "            #print(i,k)\n",
    "            tmp = df.groupby(col,as_index=False).agg({i:[k]})\n",
    "            if res is None:\n",
    "                res = tmp\n",
    "            else:\n",
    "                res = res.merge(tmp,on=[col],how='left')\n",
    "        df.drop_column(i)\n",
    "    return res\n",
    "\n",
    "def etl_gpu(df,df_meta):\n",
    "    aggs = {\n",
    "        'passband': ['mean'], \n",
    "        'detected': ['mean'],\n",
    "        'mjd':['max','min'],\n",
    "    }\n",
    "    agg_df = groupby_aggs(df,aggs,'object_id')\n",
    "    # at this step, columns ['passband','detected','mjd'] are deleted \n",
    "    \n",
    "    df['flux_ratio_sq'] = df['flux'] / df['flux_err']\n",
    "    df['flux_ratio_sq'] = df['flux_ratio_sq'].applymap(lambda x: math.pow(x,2))\n",
    "    df['flux_by_flux_ratio_sq'] = df['flux'] * df['flux_ratio_sq']\n",
    "    \n",
    "    aggs2 = {\n",
    "        'flux_ratio_sq':['sum'],\n",
    "        'flux_by_flux_ratio_sq':['sum'],\n",
    "        'flux': ['min', 'max', 'mean'],\n",
    "        'flux_err': ['min', 'max', 'mean'],\n",
    "    }\n",
    "    agg_df2 = groupby_aggs(df,aggs2,'object_id')\n",
    "    agg_df = agg_df.merge(agg_df2,on=['object_id'],how='left')\n",
    "    del agg_df2\n",
    "\n",
    "    agg_df['flux_diff'] = agg_df['max_flux'] - agg_df['min_flux']\n",
    "    agg_df['flux_dif2'] = (agg_df['max_flux'] - agg_df['min_flux']) / agg_df['mean_flux']\n",
    "    agg_df['flux_w_mean'] = agg_df['sum_flux_by_flux_ratio_sq'] / agg_df['sum_flux_ratio_sq']\n",
    "    agg_df['flux_dif3'] = (agg_df['max_flux'] - agg_df['min_flux']) / agg_df['flux_w_mean']\n",
    "    \n",
    "    agg_df['mjd_diff'] = agg_df['max_mjd'] - agg_df['min_mjd']\n",
    "    agg_df.drop_column('max_mjd')\n",
    "    agg_df.drop_column('min_mjd')\n",
    "    \n",
    "    for col in ['ra','decl','gal_l','gal_b']:\n",
    "        df_meta.drop_column(col)\n",
    "    \n",
    "    df_meta = df_meta.merge(agg_df,on=['object_id'],how='left')\n",
    "    return df_meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"etl\"></a>\n",
    "## 3. ETL & Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data for ETL part 1\n",
    "**GPU load data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.9 s, sys: 7.14 s, total: 25.1 s\n",
      "Wall time: 25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = time.time()\n",
    "step = 'load data part1'\n",
    "ts_cols = ['object_id', 'mjd', 'passband', 'flux', 'flux_err', 'detected']\n",
    "ts_dtypes = ['int32', 'float32', 'int32', 'float32','float32','int32']\n",
    "\n",
    "train_gd = gd.read_csv('%s/training_set.csv'%PATH,\n",
    "            names=ts_cols,dtype=ts_dtypes,skiprows=1)\n",
    "test_gd = gd.read_csv('%s/test_set.csv'%PATH,\n",
    "            names=ts_cols,dtype=ts_dtypes,skiprows=1+SKIP_ROWS) # skip the header\n",
    "GPU_RUN_TIME[step] = time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oid = 615\n",
    "train = train_gd.to_pandas()\n",
    "mask = train.object_id== oid\n",
    "scatter(train.loc[mask,'mjd'].values,\n",
    "                train.loc[mask,'flux'].values,\n",
    "                values=train.loc[mask,'passband'].values,\n",
    "                xlabel='time',ylabel='flux',title='object %d class 42'%oid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETL part 1 with 100x  speedup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 16 ms, total: 20 ms\n",
      "Wall time: 19.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# to save memory, we need to move dataframe to cpu and only keep the columns we need\n",
    "test_gd = test_gd[['object_id','flux']]\n",
    "train_gd = train_gd[['object_id','flux']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.1 s, sys: 1.33 s, total: 12.4 s\n",
      "Wall time: 5.05 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# GPU\n",
    "step = 'ETL part1'\n",
    "start = time.time()\n",
    "aggs = {'flux':['skew']}\n",
    "test_gd = cudf_groupby_aggs(test_gd,group_id_col='object_id',aggs=aggs)\n",
    "train_gd = cudf_groupby_aggs(train_gd,group_id_col='object_id',aggs=aggs)\n",
    "GPU_RUN_TIME[step] = time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 80 ms, sys: 20 ms, total: 100 ms\n",
      "Wall time: 104 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "test_flux_skew_gd = test_gd.sort_values(by='object_id')\n",
    "train_flux_skew_gd = train_gd.sort_values(by='object_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data for the ETL part 2 with 11x speedup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.1 s, sys: 6.89 s, total: 25 s\n",
      "Wall time: 25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# read data on gpu\n",
    "step = 'load data part2'\n",
    "start = time.time()\n",
    "ts_cols = ['object_id', 'mjd', 'passband', 'flux', 'flux_err', 'detected']\n",
    "ts_dtypes = ['int32', 'float32', 'int32', 'float32','float32','int32']\n",
    "\n",
    "test_gd = gd.read_csv('%s/test_set.csv'%PATH,\n",
    "            names=ts_cols,dtype=ts_dtypes,skiprows=1+SKIP_ROWS) # skip the header\n",
    "train_gd = gd.read_csv('%s/training_set.csv'%PATH,\n",
    "            names=ts_cols,dtype=ts_dtypes,skiprows=1)\n",
    "\n",
    "cols = ['object_id', 'ra', 'decl', 'gal_l', 'gal_b', 'ddf',\n",
    "       'hostgal_specz', 'hostgal_photoz', 'hostgal_photoz_err', \n",
    "       'distmod','mwebv', 'target']\n",
    "dtypes = ['int32']+['float32']*4+['int32']+['float32']*5+['int32']\n",
    "\n",
    "train_meta_gd = gd.read_csv('%s/training_set_metadata.csv'%PATH,\n",
    "            names=cols,dtype=dtypes,skiprows=1)\n",
    "del cols[-1],dtypes[-1]\n",
    "test_meta_gd = gd.read_csv('%s/test_set_metadata.csv'%PATH,\n",
    "            names=cols,dtype=dtypes,skiprows=1)\n",
    "GPU_RUN_TIME[step] = time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETL part2 with 9x ~ 12x speedup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.56 s, sys: 1.58 s, total: 3.15 s\n",
      "Wall time: 6.29 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# GPU\n",
    "start = time.time()\n",
    "step = 'ETL part2'\n",
    "train_final_gd = etl_gpu(train_gd,train_meta_gd)\n",
    "train_final_gd = train_final_gd.merge(train_flux_skew_gd,on=['object_id'],how='left')\n",
    "test_final_gd = etl_gpu(test_gd,test_meta_gd)\n",
    "del test_gd,test_meta_gd\n",
    "test_final_gd = test_final_gd.merge(test_flux_skew_gd,on=['object_id'],how='left')\n",
    "GPU_RUN_TIME[step] = time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"train\"></a>\n",
    "## 4. Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train and validation with 5x speedup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_final_gd['target'].to_array()\n",
    "classes = sorted(np.unique(y))    \n",
    "# Taken from Giba's topic : https://www.kaggle.com/titericz\n",
    "# https://www.kaggle.com/c/PLAsTiCC-2018/discussion/67194\n",
    "# with Kyle Boone's post https://www.kaggle.com/kyleboone\n",
    "class_weights = {c: 1 for c in classes}\n",
    "class_weights.update({c:2 for c in [64, 15]})\n",
    "\n",
    "lbl = LabelEncoder()\n",
    "y = lbl.fit_transform(y)\n",
    "\n",
    "cols = [i for i in test_final_gd.columns if i not in ['object_id','target']]\n",
    "for col in cols:\n",
    "    train_final_gd[col] = train_final_gd[col].fillna(0).astype('float32')\n",
    "\n",
    "for col in cols:\n",
    "    test_final_gd[col] = test_final_gd[col].fillna(0).astype('float32')\n",
    "    \n",
    "X = train_final_gd[cols].as_matrix()\n",
    "Xt = test_final_gd[cols].as_matrix()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.1,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_params = {\n",
    "            'objective': 'multi:softprob', \n",
    "            'tree_method': 'hist', \n",
    "            'nthread': 16, \n",
    "            'num_class':14,\n",
    "            'max_depth': 7, \n",
    "            'silent':1,\n",
    "            'subsample':0.7,\n",
    "            'colsample_bytree': 0.7,}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_loss = partial(xgb_multi_weighted_logloss, \n",
    "                        classes=classes, \n",
    "                        class_weights=class_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPU\n",
    "gpu_params = cpu_params.copy()\n",
    "gpu_params.update({'objective': 'multi:softprob',\n",
    "                   'tree_method': 'gpu_hist', \n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-merror:0.333758\ttrain-merror:0.284865\teval-wloss:2.01486\ttrain-wloss:1.91747\n",
      "Multiple eval metrics have been passed: 'train-wloss' will be used for early stopping.\n",
      "\n",
      "Will train until train-wloss hasn't improved in 10 rounds.\n",
      "[59]\teval-merror:0.26242\ttrain-merror:0.001416\teval-wloss:1.24072\ttrain-wloss:0.091787\n",
      "\u001b[32mvalidation loss 1.2407\u001b[0m\n",
      "CPU times: user 1min 3s, sys: 5.24 s, total: 1min 8s\n",
      "Wall time: 5.72 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "step = 'training'\n",
    "start = time.time()\n",
    "dtrain = xgb.DMatrix(data=X_train, label=y_train)\n",
    "dvalid = xgb.DMatrix(data=X_test, label=y_test)\n",
    "dtest = xgb.DMatrix(data=Xt)\n",
    "watchlist = [(dvalid, 'eval'), (dtrain, 'train')]\n",
    "clf = xgb.train(gpu_params, dtrain=dtrain,\n",
    "                num_boost_round=60,evals=watchlist,\n",
    "                feval=func_loss,early_stopping_rounds=10,\n",
    "                verbose_eval=1000)\n",
    "yp = clf.predict(dvalid)\n",
    "gpu_loss = multi_weighted_logloss(y_test, yp, classes, class_weights)\n",
    "ysub = clf.predict(dtest)\n",
    "line = 'validation loss %.4f'%gpu_loss\n",
    "print(colored(line,'green'))\n",
    "GPU_RUN_TIME[step] = time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"conclusions\"></a>\n",
    "## 5. Conclustions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'load data part1': 25.04227304458618,\n",
       " 'ETL part1': 5.047987222671509,\n",
       " 'load data part2': 25.019412517547607,\n",
       " 'ETL part2': 6.285659313201904,\n",
       " 'training': 5.7185375690460205}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GPU_RUN_TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6664585198>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = ['load data part1','ETL part1','load data part2','ETL part2','training']\n",
    "GPU_RUN_TIME['Overall'] = sum([GPU_RUN_TIME[i] for i in steps])\n",
    "steps.append('Overall')\n",
    "gpu_time = [GPU_RUN_TIME[i] for i in steps]\n",
    "df = pd.DataFrame({'GPU': gpu_time}, index=steps)\n",
    "df.plot.bar(rot=0,figsize=(20,5), fontsize=15, title='Running time: seconds')"
   ]
  }
 ],
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