Prerare_submission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.194582Z",
     "start_time": "2019-01-04T04:06:48.966401Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['submissions', 'test_set', 'track_features', 'training_set']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "\n",
    "import keggler as kg\n",
    "from helpers import *\n",
    "#import helpers\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "import gc\n",
    "gc.enable()\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)\n",
    "\n",
    "import os, psutil\n",
    "import glob\n",
    "\n",
    "\n",
    "import logging as logging\n",
    "def ini_log(filename):\n",
    "    logger = logging.getLogger(__name__)\n",
    "    ## avoid multiple printouts due to same handlers added several times\n",
    "    if not logger.handlers:\n",
    "        logger.setLevel(logging.DEBUG)\n",
    "\n",
    "        handlers = [#logging.StreamHandler(None), \n",
    "            logging.FileHandler(filename, 'a')\n",
    "        ]\n",
    "\n",
    "        fmt=logging.Formatter('%(asctime)-15s: %(levelname)s  %(message)s')\n",
    "        for h in handlers:\n",
    "            h.setFormatter(fmt)\n",
    "            logger.addHandler(h)\n",
    "    return logger\n",
    "        \n",
    "log = ini_log('out.log')\n",
    "\n",
    "#PATH='data_mini/'\n",
    "#prefix='_mini'\n",
    "\n",
    "PATH='data/'\n",
    "SUB_PATH='test_set/' #test_set #training_set\n",
    "\n",
    "v_data='v2'\n",
    "v_xtra='v4'\n",
    "v_model=['m05i{}_6f'.format(i) for i in range(5)]\n",
    "\n",
    "print(os.listdir(PATH))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.230725Z",
     "start_time": "2019-01-04T04:06:54.197620Z"
    }
   },
   "outputs": [],
   "source": [
    "half_1_logs = sorted(glob.glob(PATH + SUB_PATH + \"outDD_\"+v_data+\"*.h5\"))\n",
    "half_1_xtra = sorted(glob.glob(PATH + SUB_PATH + \"outDD_\"+v_xtra+\"*.h5\"))\n",
    "half_2_logs = sorted(glob.glob(PATH + SUB_PATH + \"subDD_\"+v_data+\"*.h5\"))\n",
    "half_2_xtra = sorted(glob.glob(PATH + SUB_PATH + \"subDD_\"+v_xtra+\"*.h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.241154Z",
     "start_time": "2019-01-04T04:06:54.233319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/test_set/outDD_v2_00.h5',\n",
       " 'data/test_set/outDD_v2_01.h5',\n",
       " 'data/test_set/outDD_v2_02.h5']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_1_logs[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.250329Z",
     "start_time": "2019-01-04T04:06:54.243872Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/test_set/subDD_v2_00.h5',\n",
       " 'data/test_set/subDD_v2_01.h5',\n",
       " 'data/test_set/subDD_v2_02.h5']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_2_logs[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.258987Z",
     "start_time": "2019-01-04T04:06:54.253956Z"
    }
   },
   "outputs": [],
   "source": [
    "n_trk = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.274243Z",
     "start_time": "2019-01-04T04:06:54.262176Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def read_list_of_files(l1, l2, l1_xtr=None, l2_xtr=None, n_trk=1):\n",
    "    df_1 = pd.concat([read_log(f) for f in l1], axis=0, ignore_index=True)\n",
    "#     print(df_1.shape)\n",
    "    if l1_xtr:\n",
    "        df_xtr = pd.concat([read_log(f) for f in l1_xtr], axis=0, ignore_index=True)\n",
    "        df_1 = pd.concat([df_1, df_xtr], axis=1)\n",
    "        del df_xtr\n",
    "    \n",
    "    df_2 = pd.concat([read_log(f) for f in l2], axis=0, ignore_index=True)\n",
    "#     print(df_2.shape)\n",
    "    if l2_xtr:\n",
    "        df_xtr = pd.concat([read_log(f) for f in l2_xtr], axis=0, ignore_index=True)\n",
    "        df_2 = pd.concat([df_2, df_xtr], axis=1)\n",
    "        \n",
    "#     print(df_1.shape, df_2.shape)\n",
    "\n",
    "#     df_1['session_id'] = df_1['session_id'].astype('category')\n",
    "    le = LabelEncoder().fit(df_1['session_id'])\n",
    "    df_1['session_id'] = le.transform(df_1['session_id'])\n",
    "    df_1.rename({'not_skipped': 'skip_4'}, axis=1, inplace=True)\n",
    "\n",
    "#     df_2['session_id'] = df_2['session_id'].astype('category')\n",
    "    df_2['session_id'] = le.transform(df_2['session_id'])\n",
    "    df_2.rename({'not_skipped': 'skip_4'}, axis=1, inplace=True)\n",
    "    \n",
    "    del le\n",
    "    \n",
    "#     display(df_2.head(30))\n",
    "    \n",
    "    # transform the data\n",
    "    return get_XY((df_1, df_2), aggs, reset_index=True, \n",
    "                  list_musik_qualities_=list_musik_qualities,\n",
    "                  aggs_music_qualities_=aggs_music_qualities,\n",
    "                  i_=list(range(n_trk)),\n",
    "                  aggs_trkvec_=aggs_trkvec, list_trkvec_=list_trkvec\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.288631Z",
     "start_time": "2019-01-04T04:06:54.276558Z"
    }
   },
   "outputs": [],
   "source": [
    "def pred_series_of_lists(list_preds, y_length, i_2fill=-2):\n",
    "    # transform predictions into a dataframe\n",
    "    tmp_preds_constant_mdl = pd.DataFrame({'pred_{}'.format(i): list_preds[i] for i in range(len(list_preds))}).astype(np.uint8)\n",
    "    # add a column with the residual desired length of complete session\n",
    "    tmp_preds_constant_mdl['len'] = y_length.values - len(list_preds)\n",
    "    # create a series with lists\n",
    "    series_of_lists = tmp_preds_constant_mdl.apply(lambda x: x.iloc[:-1].tolist() + [x.iloc[i_2fill]]*x['len'], axis=1)\n",
    "    del tmp_preds_constant_mdl\n",
    "    return series_of_lists\n",
    "\n",
    "def make_predictions(l1, l2, l1_xtr=None, l2_xtr=None, n_trk=1):\n",
    "    X_trn, [target_length], X_trk = read_list_of_files(l1, l2, l1_xtr=l1_xtr, l2_xtr=l2_xtr, n_trk=n_trk)\n",
    "\n",
    "    l_prob = []\n",
    "    for i_ in tqdm(list(range(n_trk))):\n",
    "        X = pd.concat([X_trn, X_trk[i_]], axis=1)\n",
    "        if len(l_prob) > 0:\n",
    "            X = pd.concat([X, pd.DataFrame({'pred_trk{}'.format(j): l_prob[j] \n",
    "                                            for j in range(len(l_prob))\n",
    "                                           })],\n",
    "                          axis=1)\n",
    "        import joblib\n",
    "        prob_pred = np.zeros(shape=(len(X),))\n",
    "        for v in v_model:\n",
    "            mfn = 'models/model_{}_{}_{}.pkl'.format(v_data, v, i_)\n",
    "    #         print(mfn)\n",
    "            mdl = joblib.load(mfn)\n",
    "            prob_pred += mdl.predict_proba(X)[:,1]\n",
    "\n",
    "        prob_pred /= len(v_model)\n",
    "\n",
    "        l_prob.append(prob_pred)\n",
    "        \n",
    "#     print([p>0.50 for p in l_prob])\n",
    "    return pred_series_of_lists([p>0.50 for p in l_prob], target_length, i_2fill=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-04T04:06:54.299411Z",
     "start_time": "2019-01-04T04:06:54.292161Z"
    }
   },
   "outputs": [],
   "source": [
    "#!rm data/submissions/sub_v1.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-01-04T04:06:49.008Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
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     ]
    }
   ],
   "source": [
    "n_part = 66\n",
    "\n",
    "for i, (a1, a2, a1_xtr, a2_xtr) in tqdm(enumerate(zip(np.array(half_1_logs).reshape(n_part,-1),\n",
    "                  np.array(half_2_logs).reshape(n_part,-1),\n",
    "                  np.array(half_1_xtra).reshape(n_part,-1),\n",
    "                  np.array(half_2_xtra).reshape(n_part,-1),  \n",
    "                 ))):\n",
    "#     print(i)\n",
    "#     print(a1, a2)\n",
    "#     print(a1_xtr, a2_xtr)\n",
    "    x1 = make_predictions(a1.tolist(), a2.tolist(), \n",
    "                          l1_xtr=a1_xtr.tolist(), l2_xtr=a2_xtr.tolist(), \n",
    "                          n_trk=10)\n",
    "    gc.collect()\n",
    "    log.info(psutil.Process(os.getpid()).memory_info().rss / 1024**2)\n",
    "    \n",
    "    x1.apply(lambda l: ''.join(map(str,l))).to_csv(PATH+'submissions/sub_'+v_data+v_xtra+'_'+v_model[0]+'.txt', index=False, mode='a')\n",
    "    gc.collect()\n",
    "    log.info(psutil.Process(os.getpid()).memory_info().rss / 1024**2)\n",
    "#     if i >=1:\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-01-04T04:06:49.021Z"
    }
   },
   "outputs": [],
   "source": [
    "!ls -ltr data/submissions/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-01-04T04:06:49.027Z"
    }
   },
   "outputs": [],
   "source": [
    "!wc -l\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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