KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "postulaciones = pd.read_csv('Sources/postulaciones_for_training.csv').drop('Unnamed: 0', axis=1)\n",
    "postulantes = pd.read_csv('Sources/postulantes_proc.csv').drop('Unnamed: 0', axis=1)\n",
    "avisos = pd.read_csv('Sources/fiuba_6_avisos_detalle_limpio.csv').drop('Unnamed: 0', axis=1)\n",
    "test = pd.read_csv('Sources/test_final_100k.csv')\n",
    "preds = pd.DataFrame()\n",
    "postulaciones = postulaciones.merge(postulantes, on='idpostulante')\n",
    "postulaciones = postulaciones.merge(avisos, on='idaviso')\n",
    "resultados = postulaciones['sepostulo']\n",
    "postulaciones.drop(['titulo','descripcion','idaviso','idpostulante','sepostulo'],axis=1, inplace=True)\n",
    "postulaciones.head()\n",
    "test = test.merge(postulantes, on='idpostulante')\n",
    "test = test.merge(avisos, on='idaviso')\n",
    "test.drop(['titulo','descripcion','idaviso','idpostulante','id'],axis=1, inplace=True)\n",
    "test.head()\n",
    "test = pd.concat([test,postulaciones])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "for col in test.columns:\n",
    "    if not postulaciones[col].dtype == 'O': continue\n",
    "    test[col] = test[col].fillna('na')\n",
    "    le.fit(test[col])\n",
    "    test[col] = le.transform(test[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "postulaciones = test[100000:].fillna(-1)\n",
    "test = test[:100000].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "postulaciones.drop('Unnamed: 0.1',axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "      <th>nivel</th>\n",
       "      <th>tipo_de_trabajo</th>\n",
       "      <th>nivel_laboral</th>\n",
       "      <th>nombre_area</th>\n",
       "      <th>denominacion_empresa</th>\n",
       "      <th>cluster</th>\n",
       "      <th>FEM</th>\n",
       "      <th>MASC</th>\n",
       "      <th>NO_DECLARA</th>\n",
       "      <th>TendenciaSexo</th>\n",
       "      <th>prom_edad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1272</td>\n",
       "      <td>178.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.095588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1272</td>\n",
       "      <td>178.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.095588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1272</td>\n",
       "      <td>178.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.095588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1272</td>\n",
       "      <td>178.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.095588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1272</td>\n",
       "      <td>178.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.095588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fechanacimiento  sexo  nivel  tipo_de_trabajo  nivel_laboral  nombre_area  \\\n",
       "0             29.0     1      5                2              2            2   \n",
       "1             20.0     1     11                2              2            2   \n",
       "2             38.0     2      3                2              2            2   \n",
       "3             -1.0     3     -1                2              2            2   \n",
       "4             22.0     1      5                2              2            2   \n",
       "\n",
       "   denominacion_empresa  cluster   FEM  MASC  NO_DECLARA  TendenciaSexo  \\\n",
       "0                  1272    178.0  61.0  75.0         1.0              2   \n",
       "1                  1272    178.0  61.0  75.0         1.0              2   \n",
       "2                  1272    178.0  61.0  75.0         1.0              2   \n",
       "3                  1272    178.0  61.0  75.0         1.0              2   \n",
       "4                  1272    178.0  61.0  75.0         1.0              2   \n",
       "\n",
       "   prom_edad  \n",
       "0  26.095588  \n",
       "1  26.095588  \n",
       "2  26.095588  \n",
       "3  26.095588  \n",
       "4  26.095588  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postulaciones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "partitions = []\n",
    "partitions_res = []\n",
    "n = 3\n",
    "index = int(postulaciones.shape[0]/n)\n",
    "for i in range(n):\n",
    "    partitions.append(postulaciones[index*i:index*(i+1)])\n",
    "    partitions_res.append(resultados[index*i:index*(i+1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(partitions[0], partitions_res[0], test_size=0.2, random_state=43)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "result = []\n",
    "\n",
    "for i in range(5,100,10):\n",
    "    neigh = KNeighborsClassifier(n_neighbors=i ,n_jobs=-1, algorithm='auto')\n",
    "    neigh.fit(X_train, y_train)\n",
    "    scoree = neigh.score(X_test, y_test, sample_weight=None)\n",
    "    print(i,\" neighboors \",scoree)\n",
    "    result.append([i,scoree])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "s = 0\n",
    "i = 5\n",
    "for x in result:\n",
    "    if x[1] > s:\n",
    "        i = x[0]\n",
    "        s = x[1]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(\"k = \", i )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "JoblibValueError",
     "evalue": "JoblibValueError\n___________________________________________________________________________\nMultiprocessing exception:\n...........................................................................\n/anaconda3/lib/python3.6/runpy.py in _run_module_as_main(mod_name='ipykernel_launcher', alter_argv=1)\n    188         sys.exit(msg)\n    189     main_globals = sys.modules[\"__main__\"].__dict__\n    190     if alter_argv:\n    191         sys.argv[0] = mod_spec.origin\n    192     return _run_code(code, main_globals, None,\n--> 193                      \"__main__\", mod_spec)\n        mod_spec = ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py')\n    194 \n    195 def run_module(mod_name, init_globals=None,\n    196                run_name=None, alter_sys=False):\n    197     \"\"\"Execute a module's code without importing it\n\n...........................................................................\n/anaconda3/lib/python3.6/runpy.py in _run_code(code=<code object <module> at 0x1037a2660, file \"/ana...3.6/site-packages/ipykernel_launcher.py\", line 5>, run_globals={'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': '/anaconda3/lib/python3.6/site-packages/__pycache__/ipykernel_launcher.cpython-36.pyc', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nTh...orts until\\nafter removing the cwd from sys.path.\\n', '__file__': '/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from '/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py'>, ...}, init_globals=None, mod_name='__main__', mod_spec=ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), pkg_name='', script_name=None)\n     80                        __cached__ = cached,\n     81                        __doc__ = None,\n     82                        __loader__ = loader,\n     83                        __package__ = pkg_name,\n     84                        __spec__ = mod_spec)\n---> 85     exec(code, run_globals)\n        code = <code object <module> at 0x1037a2660, file \"/ana...3.6/site-packages/ipykernel_launcher.py\", line 5>\n        run_globals = {'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': '/anaconda3/lib/python3.6/site-packages/__pycache__/ipykernel_launcher.cpython-36.pyc', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nTh...orts until\\nafter removing the cwd from sys.path.\\n', '__file__': '/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from '/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py'>, ...}\n     86     return run_globals\n     87 \n     88 def _run_module_code(code, init_globals=None,\n     89                     mod_name=None, mod_spec=None,\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py in <module>()\n     11     # This is added back by InteractiveShellApp.init_path()\n     12     if sys.path[0] == '':\n     13         del sys.path[0]\n     14 \n     15     from ipykernel import kernelapp as app\n---> 16     app.launch_new_instance()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py in launch_instance(cls=<class 'ipykernel.kernelapp.IPKernelApp'>, argv=None, **kwargs={})\n    653 \n    654         If a global instance already exists, this reinitializes and starts it\n    655         \"\"\"\n    656         app = cls.instance(**kwargs)\n    657         app.initialize(argv)\n--> 658         app.start()\n        app.start = <bound method IPKernelApp.start of <ipykernel.kernelapp.IPKernelApp object>>\n    659 \n    660 #-----------------------------------------------------------------------------\n    661 # utility functions, for convenience\n    662 #-----------------------------------------------------------------------------\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py in start(self=<ipykernel.kernelapp.IPKernelApp object>)\n    473         if self.poller is not None:\n    474             self.poller.start()\n    475         self.kernel.start()\n    476         self.io_loop = ioloop.IOLoop.current()\n    477         try:\n--> 478             self.io_loop.start()\n        self.io_loop.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>\n    479         except KeyboardInterrupt:\n    480             pass\n    481 \n    482 launch_new_instance = IPKernelApp.launch_instance\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)\n    172             )\n    173         return loop\n    174     \n    175     def start(self):\n    176         try:\n--> 177             super(ZMQIOLoop, self).start()\n        self.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>\n    178         except ZMQError as e:\n    179             if e.errno == ETERM:\n    180                 # quietly return on ETERM\n    181                 pass\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)\n    883                 self._events.update(event_pairs)\n    884                 while self._events:\n    885                     fd, events = self._events.popitem()\n    886                     try:\n    887                         fd_obj, handler_func = self._handlers[fd]\n--> 888                         handler_func(fd_obj, events)\n        handler_func = <function wrap.<locals>.null_wrapper>\n        fd_obj = <zmq.sugar.socket.Socket object>\n        events = 5\n    889                     except (OSError, IOError) as e:\n    890                         if errno_from_exception(e) == errno.EPIPE:\n    891                             # Happens when the client closes the connection\n    892                             pass\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 5), **kwargs={})\n    272         # Fast path when there are no active contexts.\n    273         def null_wrapper(*args, **kwargs):\n    274             try:\n    275                 current_state = _state.contexts\n    276                 _state.contexts = cap_contexts[0]\n--> 277                 return fn(*args, **kwargs)\n        args = (<zmq.sugar.socket.Socket object>, 5)\n        kwargs = {}\n    278             finally:\n    279                 _state.contexts = current_state\n    280         null_wrapper._wrapped = True\n    281         return null_wrapper\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=5)\n    435             # dispatch events:\n    436             if events & IOLoop.ERROR:\n    437                 gen_log.error(\"got POLLERR event on ZMQStream, which doesn't make sense\")\n    438                 return\n    439             if events & IOLoop.READ:\n--> 440                 self._handle_recv()\n        self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>\n    441                 if not self.socket:\n    442                     return\n    443             if events & IOLoop.WRITE:\n    444                 self._handle_send()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)\n    467                 gen_log.error(\"RECV Error: %s\"%zmq.strerror(e.errno))\n    468         else:\n    469             if self._recv_callback:\n    470                 callback = self._recv_callback\n    471                 # self._recv_callback = None\n--> 472                 self._run_callback(callback, msg)\n        self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>\n        callback = <function wrap.<locals>.null_wrapper>\n        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]\n    473                 \n    474         # self.update_state()\n    475         \n    476 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py in _run_callback(self=<zmq.eventloop.zmqstream.ZMQStream object>, callback=<function wrap.<locals>.null_wrapper>, *args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})\n    409         close our socket.\"\"\"\n    410         try:\n    411             # Use a NullContext to ensure that all StackContexts are run\n    412             # inside our blanket exception handler rather than outside.\n    413             with stack_context.NullContext():\n--> 414                 callback(*args, **kwargs)\n        callback = <function wrap.<locals>.null_wrapper>\n        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)\n        kwargs = {}\n    415         except:\n    416             gen_log.error(\"Uncaught exception, closing connection.\",\n    417                           exc_info=True)\n    418             # Close the socket on an uncaught exception from a user callback\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py in null_wrapper(*args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})\n    272         # Fast path when there are no active contexts.\n    273         def null_wrapper(*args, **kwargs):\n    274             try:\n    275                 current_state = _state.contexts\n    276                 _state.contexts = cap_contexts[0]\n--> 277                 return fn(*args, **kwargs)\n        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)\n        kwargs = {}\n    278             finally:\n    279                 _state.contexts = current_state\n    280         null_wrapper._wrapped = True\n    281         return null_wrapper\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in dispatcher(msg=[<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>])\n    278         if self.control_stream:\n    279             self.control_stream.on_recv(self.dispatch_control, copy=False)\n    280 \n    281         def make_dispatcher(stream):\n    282             def dispatcher(msg):\n--> 283                 return self.dispatch_shell(stream, msg)\n        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]\n    284             return dispatcher\n    285 \n    286         for s in self.shell_streams:\n    287             s.on_recv(make_dispatcher(s), copy=False)\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in dispatch_shell(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, msg={'buffers': [], 'content': {'allow_stdin': True, 'code': 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 27, 0, 22, 41, 893184, tzinfo=tzutc()), 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'session': '334d994fc64444e39c70701df189b9f3', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'parent_header': {}})\n    228             self.log.warn(\"Unknown message type: %r\", msg_type)\n    229         else:\n    230             self.log.debug(\"%s: %s\", msg_type, msg)\n    231             self.pre_handler_hook()\n    232             try:\n--> 233                 handler(stream, idents, msg)\n        handler = <bound method Kernel.execute_request of <ipykernel.ipkernel.IPythonKernel object>>\n        stream = <zmq.eventloop.zmqstream.ZMQStream object>\n        idents = [b'334d994fc64444e39c70701df189b9f3']\n        msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 27, 0, 22, 41, 893184, tzinfo=tzutc()), 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'session': '334d994fc64444e39c70701df189b9f3', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'parent_header': {}}\n    234             except Exception:\n    235                 self.log.error(\"Exception in message handler:\", exc_info=True)\n    236             finally:\n    237                 self.post_handler_hook()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'334d994fc64444e39c70701df189b9f3'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 27, 0, 22, 41, 893184, tzinfo=tzutc()), 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'session': '334d994fc64444e39c70701df189b9f3', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'parent_header': {}})\n    394         if not silent:\n    395             self.execution_count += 1\n    396             self._publish_execute_input(code, parent, self.execution_count)\n    397 \n    398         reply_content = self.do_execute(code, silent, store_history,\n--> 399                                         user_expressions, allow_stdin)\n        user_expressions = {}\n        allow_stdin = True\n    400 \n    401         # Flush output before sending the reply.\n    402         sys.stdout.flush()\n    403         sys.stderr.flush()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code='from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', silent=False, store_history=True, user_expressions={}, allow_stdin=True)\n    203 \n    204         self._forward_input(allow_stdin)\n    205 \n    206         reply_content = {}\n    207         try:\n--> 208             res = shell.run_cell(code, store_history=store_history, silent=silent)\n        res = undefined\n        shell.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>\n        code = 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)'\n        store_history = True\n        silent = False\n    209         finally:\n    210             self._restore_input()\n    211 \n    212         if res.error_before_exec is not None:\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, *args=('from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)',), **kwargs={'silent': False, 'store_history': True})\n    532             )\n    533         self.payload_manager.write_payload(payload)\n    534 \n    535     def run_cell(self, *args, **kwargs):\n    536         self._last_traceback = None\n--> 537         return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n        self.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>\n        args = ('from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)',)\n        kwargs = {'silent': False, 'store_history': True}\n    538 \n    539     def _showtraceback(self, etype, evalue, stb):\n    540         # try to preserve ordering of tracebacks and print statements\n    541         sys.stdout.flush()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell='from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', store_history=True, silent=False, shell_futures=True)\n   2723                 self.displayhook.exec_result = result\n   2724 \n   2725                 # Execute the user code\n   2726                 interactivity = \"none\" if silent else self.ast_node_interactivity\n   2727                 has_raised = self.run_ast_nodes(code_ast.body, cell_name,\n-> 2728                    interactivity=interactivity, compiler=compiler, result=result)\n        interactivity = 'last_expr'\n        compiler = <IPython.core.compilerop.CachingCompiler object>\n   2729                 \n   2730                 self.last_execution_succeeded = not has_raised\n   2731                 self.last_execution_result = result\n   2732 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.ImportFrom object>, <_ast.Assign object>, <_ast.Expr object>, <_ast.Assign object>], cell_name='<ipython-input-8-67c4373b57e3>', interactivity='none', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<ExecutionResult object at 104741fd0, execution_..._before_exec=None error_in_exec=None result=None>)\n   2845 \n   2846         try:\n   2847             for i, node in enumerate(to_run_exec):\n   2848                 mod = ast.Module([node])\n   2849                 code = compiler(mod, cell_name, \"exec\")\n-> 2850                 if self.run_code(code, result):\n        self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>\n        code = <code object <module> at 0x10c0f0d20, file \"<ipython-input-8-67c4373b57e3>\", line 4>\n        result = <ExecutionResult object at 104741fd0, execution_..._before_exec=None error_in_exec=None result=None>\n   2851                     return True\n   2852 \n   2853             for i, node in enumerate(to_run_interactive):\n   2854                 mod = ast.Interactive([node])\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x10c0f0d20, file \"<ipython-input-8-67c4373b57e3>\", line 4>, result=<ExecutionResult object at 104741fd0, execution_..._before_exec=None error_in_exec=None result=None>)\n   2905         outflag = True  # happens in more places, so it's easier as default\n   2906         try:\n   2907             try:\n   2908                 self.hooks.pre_run_code_hook()\n   2909                 #rprint('Running code', repr(code_obj)) # dbg\n-> 2910                 exec(code_obj, self.user_global_ns, self.user_ns)\n        code_obj = <code object <module> at 0x10c0f0d20, file \"<ipython-input-8-67c4373b57e3>\", line 4>\n        self.user_global_ns = {'In': ['', 'import pandas as pd\\nfrom sklearn import preproce...est.head()\\ntest = pd.concat([test,postulaciones])', 'from sklearn import preprocessing\\nle = preproces...est[col])\\n    test[col] = le.transform(test[col])', 'postulaciones = test[100000:].fillna(-1)\\ntest = test[:100000].fillna(-1)', \"postulaciones.drop('Unnamed: 0.1',axis=1,inplace=True)\", 'postulaciones.head()', 'partitions = []\\npartitions_res = []\\nn = 3\\nindex ...tions_res.append(resultados[index*i:index*(i+1)])', 'from sklearn.model_selection import train_test_s...artitions_res[0], test_size=0.2, random_state=43)', 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)'], 'KNeighborsClassifier': <class 'sklearn.neighbors.classification.KNeighborsClassifier'>, 'Out': {5:    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  }, 'X_test':          fechanacimiento  sexo  nivel  tipo_de_t...      1  30.407378  \n\n[1313644 rows x 13 columns], 'X_train':          fechanacimiento  sexo  nivel  tipo_de_t...      2  27.478095  \n\n[5254573 rows x 13 columns], '_':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '_5':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '__': '', '___': '', '__builtin__': <module 'builtins' (built-in)>, ...}\n        self.user_ns = {'In': ['', 'import pandas as pd\\nfrom sklearn import preproce...est.head()\\ntest = pd.concat([test,postulaciones])', 'from sklearn import preprocessing\\nle = preproces...est[col])\\n    test[col] = le.transform(test[col])', 'postulaciones = test[100000:].fillna(-1)\\ntest = test[:100000].fillna(-1)', \"postulaciones.drop('Unnamed: 0.1',axis=1,inplace=True)\", 'postulaciones.head()', 'partitions = []\\npartitions_res = []\\nn = 3\\nindex ...tions_res.append(resultados[index*i:index*(i+1)])', 'from sklearn.model_selection import train_test_s...artitions_res[0], test_size=0.2, random_state=43)', 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)'], 'KNeighborsClassifier': <class 'sklearn.neighbors.classification.KNeighborsClassifier'>, 'Out': {5:    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  }, 'X_test':          fechanacimiento  sexo  nivel  tipo_de_t...      1  30.407378  \n\n[1313644 rows x 13 columns], 'X_train':          fechanacimiento  sexo  nivel  tipo_de_t...      2  27.478095  \n\n[5254573 rows x 13 columns], '_':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '_5':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '__': '', '___': '', '__builtin__': <module 'builtins' (built-in)>, ...}\n   2911             finally:\n   2912                 # Reset our crash handler in place\n   2913                 sys.excepthook = old_excepthook\n   2914         except SystemExit as e:\n\n...........................................................................\n/Users/federicocavazzoli/Documents/GitHub/DatosTP/<ipython-input-8-67c4373b57e3> in <module>()\n      1 from sklearn.neighbors import KNeighborsClassifier\n      2 neigh = KNeighborsClassifier(n_neighbors=25 ,n_jobs=-1, algorithm='auto')\n      3 neigh.fit(X_train, y_train)\n----> 4 knn_result = neigh.predict_proba(test)\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/classification.py in predict_proba(self=KNeighborsClassifier(algorithm='auto', leaf_size..._neighbors=25, p=2,\n           weights='uniform'), X=array([[ 6.92660e+04,  4.20000e+01,  1.00000e+00....00000e+00,\n         2.00000e+00, -1.00000e+00]]))\n    185             The class probabilities of the input samples. Classes are ordered\n    186             by lexicographic order.\n    187         \"\"\"\n    188         X = check_array(X, accept_sparse='csr')\n    189 \n--> 190         neigh_dist, neigh_ind = self.kneighbors(X)\n        neigh_dist = undefined\n        neigh_ind = undefined\n        self.kneighbors = <bound method KNeighborsMixin.kneighbors of KNei...neighbors=25, p=2,\n           weights='uniform')>\n        X = array([[ 6.92660e+04,  4.20000e+01,  1.00000e+00....00000e+00,\n         2.00000e+00, -1.00000e+00]])\n    191 \n    192         classes_ = self.classes_\n    193         _y = self._y\n    194         if not self.outputs_2d_:\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self=KNeighborsClassifier(algorithm='auto', leaf_size..._neighbors=25, p=2,\n           weights='uniform'), X=array([[ 6.92660e+04,  4.20000e+01,  1.00000e+00....00000e+00,\n         2.00000e+00, -1.00000e+00]]), n_neighbors=25, return_distance=True)\n    380                     \"%s does not work with sparse matrices. Densify the data, \"\n    381                     \"or set algorithm='brute'\" % self._fit_method)\n    382             result = Parallel(n_jobs, backend='threading')(\n    383                 delayed(self._tree.query, check_pickle=False)(\n    384                     X[s], n_neighbors, return_distance)\n--> 385                 for s in gen_even_slices(X.shape[0], n_jobs)\n        X.shape = (100000, 14)\n        n_jobs = 8\n    386             )\n    387             if return_distance:\n    388                 dist, neigh_ind = tuple(zip(*result))\n    389                 result = np.vstack(dist), np.vstack(neigh_ind)\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=8), iterable=<generator object KNeighborsMixin.kneighbors.<locals>.<genexpr>>)\n    784             if pre_dispatch == \"all\" or n_jobs == 1:\n    785                 # The iterable was consumed all at once by the above for loop.\n    786                 # No need to wait for async callbacks to trigger to\n    787                 # consumption.\n    788                 self._iterating = False\n--> 789             self.retrieve()\n        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=8)>\n    790             # Make sure that we get a last message telling us we are done\n    791             elapsed_time = time.time() - self._start_time\n    792             self._print('Done %3i out of %3i | elapsed: %s finished',\n    793                         (len(self._output), len(self._output),\n\n---------------------------------------------------------------------------\nSub-process traceback:\n---------------------------------------------------------------------------\nValueError                                         Tue Jun 26 21:28:50 2018\nPID: 11063                              Python 3.6.4: /anaconda3/bin/python\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)\n    126     def __init__(self, iterator_slice):\n    127         self.items = list(iterator_slice)\n    128         self._size = len(self.items)\n    129 \n    130     def __call__(self):\n--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]\n        self.items = [(<built-in method query of sklearn.neighbors.kd_tree.KDTree object>, (array([[ 6.92660000e+04,  4.20000000e+01,  1.000...40000000e+01,  0.00000000e+00,  2.72928688e+01]]), 25, True), {})]\n    132 \n    133     def __len__(self):\n    134         return self._size\n    135 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)\n    126     def __init__(self, iterator_slice):\n    127         self.items = list(iterator_slice)\n    128         self._size = len(self.items)\n    129 \n    130     def __call__(self):\n--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]\n        func = <built-in method query of sklearn.neighbors.kd_tree.KDTree object>\n        args = (array([[ 6.92660000e+04,  4.20000000e+01,  1.000...40000000e+01,  0.00000000e+00,  2.72928688e+01]]), 25, True)\n        kwargs = {}\n    132 \n    133     def __len__(self):\n    134         return self._size\n    135 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/kd_tree.cpython-36m-darwin.so in sklearn.neighbors.kd_tree.BinaryTree.query()\n\nValueError: query data dimension must match training data dimension\n___________________________________________________________________________",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32msklearn/neighbors/binary_tree.pxi\u001b[0m in \u001b[0;36msklearn.neighbors.kd_tree.BinaryTree.query\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: query data dimension must match training data dimension",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTransportableException\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    698\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    645\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mworker\u001b[0;34m(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me_tb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTransportableException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTransportableException\u001b[0m: TransportableException\n___________________________________________________________________________\nValueError                                         Tue Jun 26 21:28:50 2018\nPID: 11063                              Python 3.6.4: /anaconda3/bin/python\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)\n    126     def __init__(self, iterator_slice):\n    127         self.items = list(iterator_slice)\n    128         self._size = len(self.items)\n    129 \n    130     def __call__(self):\n--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]\n        self.items = [(<built-in method query of sklearn.neighbors.kd_tree.KDTree object>, (array([[ 6.92660000e+04,  4.20000000e+01,  1.000...40000000e+01,  0.00000000e+00,  2.72928688e+01]]), 25, True), {})]\n    132 \n    133     def __len__(self):\n    134         return self._size\n    135 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)\n    126     def __init__(self, iterator_slice):\n    127         self.items = list(iterator_slice)\n    128         self._size = len(self.items)\n    129 \n    130     def __call__(self):\n--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]\n        func = <built-in method query of sklearn.neighbors.kd_tree.KDTree object>\n        args = (array([[ 6.92660000e+04,  4.20000000e+01,  1.000...40000000e+01,  0.00000000e+00,  2.72928688e+01]]), 25, True)\n        kwargs = {}\n    132 \n    133     def __len__(self):\n    134         return self._size\n    135 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/kd_tree.cpython-36m-darwin.so in sklearn.neighbors.kd_tree.BinaryTree.query()\n\nValueError: query data dimension must match training data dimension\n___________________________________________________________________________",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mJoblibValueError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-67c4373b57e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mneigh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mneigh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mknn_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneigh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/classification.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m         \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    383\u001b[0m                 delayed(self._tree.query, check_pickle=False)(\n\u001b[1;32m    384\u001b[0m                     X[s], n_neighbors, return_distance)\n\u001b[0;32m--> 385\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_even_slices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m             )\n\u001b[1;32m    387\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mreturn_distance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    787\u001b[0m                 \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    790\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    738\u001b[0m                     \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 740\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    742\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mJoblibValueError\u001b[0m: JoblibValueError\n___________________________________________________________________________\nMultiprocessing exception:\n...........................................................................\n/anaconda3/lib/python3.6/runpy.py in _run_module_as_main(mod_name='ipykernel_launcher', alter_argv=1)\n    188         sys.exit(msg)\n    189     main_globals = sys.modules[\"__main__\"].__dict__\n    190     if alter_argv:\n    191         sys.argv[0] = mod_spec.origin\n    192     return _run_code(code, main_globals, None,\n--> 193                      \"__main__\", mod_spec)\n        mod_spec = ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py')\n    194 \n    195 def run_module(mod_name, init_globals=None,\n    196                run_name=None, alter_sys=False):\n    197     \"\"\"Execute a module's code without importing it\n\n...........................................................................\n/anaconda3/lib/python3.6/runpy.py in _run_code(code=<code object <module> at 0x1037a2660, file \"/ana...3.6/site-packages/ipykernel_launcher.py\", line 5>, run_globals={'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': '/anaconda3/lib/python3.6/site-packages/__pycache__/ipykernel_launcher.cpython-36.pyc', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nTh...orts until\\nafter removing the cwd from sys.path.\\n', '__file__': '/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from '/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py'>, ...}, init_globals=None, mod_name='__main__', mod_spec=ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), pkg_name='', script_name=None)\n     80                        __cached__ = cached,\n     81                        __doc__ = None,\n     82                        __loader__ = loader,\n     83                        __package__ = pkg_name,\n     84                        __spec__ = mod_spec)\n---> 85     exec(code, run_globals)\n        code = <code object <module> at 0x1037a2660, file \"/ana...3.6/site-packages/ipykernel_launcher.py\", line 5>\n        run_globals = {'__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__cached__': '/anaconda3/lib/python3.6/site-packages/__pycache__/ipykernel_launcher.cpython-36.pyc', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nTh...orts until\\nafter removing the cwd from sys.path.\\n', '__file__': '/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': '', '__spec__': ModuleSpec(name='ipykernel_launcher', loader=<_f...b/python3.6/site-packages/ipykernel_launcher.py'), 'app': <module 'ipykernel.kernelapp' from '/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py'>, ...}\n     86     return run_globals\n     87 \n     88 def _run_module_code(code, init_globals=None,\n     89                     mod_name=None, mod_spec=None,\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py in <module>()\n     11     # This is added back by InteractiveShellApp.init_path()\n     12     if sys.path[0] == '':\n     13         del sys.path[0]\n     14 \n     15     from ipykernel import kernelapp as app\n---> 16     app.launch_new_instance()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py in launch_instance(cls=<class 'ipykernel.kernelapp.IPKernelApp'>, argv=None, **kwargs={})\n    653 \n    654         If a global instance already exists, this reinitializes and starts it\n    655         \"\"\"\n    656         app = cls.instance(**kwargs)\n    657         app.initialize(argv)\n--> 658         app.start()\n        app.start = <bound method IPKernelApp.start of <ipykernel.kernelapp.IPKernelApp object>>\n    659 \n    660 #-----------------------------------------------------------------------------\n    661 # utility functions, for convenience\n    662 #-----------------------------------------------------------------------------\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py in start(self=<ipykernel.kernelapp.IPKernelApp object>)\n    473         if self.poller is not None:\n    474             self.poller.start()\n    475         self.kernel.start()\n    476         self.io_loop = ioloop.IOLoop.current()\n    477         try:\n--> 478             self.io_loop.start()\n        self.io_loop.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>\n    479         except KeyboardInterrupt:\n    480             pass\n    481 \n    482 launch_new_instance = IPKernelApp.launch_instance\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)\n    172             )\n    173         return loop\n    174     \n    175     def start(self):\n    176         try:\n--> 177             super(ZMQIOLoop, self).start()\n        self.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>\n    178         except ZMQError as e:\n    179             if e.errno == ETERM:\n    180                 # quietly return on ETERM\n    181                 pass\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)\n    883                 self._events.update(event_pairs)\n    884                 while self._events:\n    885                     fd, events = self._events.popitem()\n    886                     try:\n    887                         fd_obj, handler_func = self._handlers[fd]\n--> 888                         handler_func(fd_obj, events)\n        handler_func = <function wrap.<locals>.null_wrapper>\n        fd_obj = <zmq.sugar.socket.Socket object>\n        events = 5\n    889                     except (OSError, IOError) as e:\n    890                         if errno_from_exception(e) == errno.EPIPE:\n    891                             # Happens when the client closes the connection\n    892                             pass\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 5), **kwargs={})\n    272         # Fast path when there are no active contexts.\n    273         def null_wrapper(*args, **kwargs):\n    274             try:\n    275                 current_state = _state.contexts\n    276                 _state.contexts = cap_contexts[0]\n--> 277                 return fn(*args, **kwargs)\n        args = (<zmq.sugar.socket.Socket object>, 5)\n        kwargs = {}\n    278             finally:\n    279                 _state.contexts = current_state\n    280         null_wrapper._wrapped = True\n    281         return null_wrapper\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=5)\n    435             # dispatch events:\n    436             if events & IOLoop.ERROR:\n    437                 gen_log.error(\"got POLLERR event on ZMQStream, which doesn't make sense\")\n    438                 return\n    439             if events & IOLoop.READ:\n--> 440                 self._handle_recv()\n        self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>\n    441                 if not self.socket:\n    442                     return\n    443             if events & IOLoop.WRITE:\n    444                 self._handle_send()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)\n    467                 gen_log.error(\"RECV Error: %s\"%zmq.strerror(e.errno))\n    468         else:\n    469             if self._recv_callback:\n    470                 callback = self._recv_callback\n    471                 # self._recv_callback = None\n--> 472                 self._run_callback(callback, msg)\n        self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>\n        callback = <function wrap.<locals>.null_wrapper>\n        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]\n    473                 \n    474         # self.update_state()\n    475         \n    476 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py in _run_callback(self=<zmq.eventloop.zmqstream.ZMQStream object>, callback=<function wrap.<locals>.null_wrapper>, *args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})\n    409         close our socket.\"\"\"\n    410         try:\n    411             # Use a NullContext to ensure that all StackContexts are run\n    412             # inside our blanket exception handler rather than outside.\n    413             with stack_context.NullContext():\n--> 414                 callback(*args, **kwargs)\n        callback = <function wrap.<locals>.null_wrapper>\n        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)\n        kwargs = {}\n    415         except:\n    416             gen_log.error(\"Uncaught exception, closing connection.\",\n    417                           exc_info=True)\n    418             # Close the socket on an uncaught exception from a user callback\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py in null_wrapper(*args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})\n    272         # Fast path when there are no active contexts.\n    273         def null_wrapper(*args, **kwargs):\n    274             try:\n    275                 current_state = _state.contexts\n    276                 _state.contexts = cap_contexts[0]\n--> 277                 return fn(*args, **kwargs)\n        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)\n        kwargs = {}\n    278             finally:\n    279                 _state.contexts = current_state\n    280         null_wrapper._wrapped = True\n    281         return null_wrapper\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in dispatcher(msg=[<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>])\n    278         if self.control_stream:\n    279             self.control_stream.on_recv(self.dispatch_control, copy=False)\n    280 \n    281         def make_dispatcher(stream):\n    282             def dispatcher(msg):\n--> 283                 return self.dispatch_shell(stream, msg)\n        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]\n    284             return dispatcher\n    285 \n    286         for s in self.shell_streams:\n    287             s.on_recv(make_dispatcher(s), copy=False)\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in dispatch_shell(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, msg={'buffers': [], 'content': {'allow_stdin': True, 'code': 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 27, 0, 22, 41, 893184, tzinfo=tzutc()), 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'session': '334d994fc64444e39c70701df189b9f3', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'parent_header': {}})\n    228             self.log.warn(\"Unknown message type: %r\", msg_type)\n    229         else:\n    230             self.log.debug(\"%s: %s\", msg_type, msg)\n    231             self.pre_handler_hook()\n    232             try:\n--> 233                 handler(stream, idents, msg)\n        handler = <bound method Kernel.execute_request of <ipykernel.ipkernel.IPythonKernel object>>\n        stream = <zmq.eventloop.zmqstream.ZMQStream object>\n        idents = [b'334d994fc64444e39c70701df189b9f3']\n        msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 27, 0, 22, 41, 893184, tzinfo=tzutc()), 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'session': '334d994fc64444e39c70701df189b9f3', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'parent_header': {}}\n    234             except Exception:\n    235                 self.log.error(\"Exception in message handler:\", exc_info=True)\n    236             finally:\n    237                 self.post_handler_hook()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'334d994fc64444e39c70701df189b9f3'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': datetime.datetime(2018, 6, 27, 0, 22, 41, 893184, tzinfo=tzutc()), 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'session': '334d994fc64444e39c70701df189b9f3', 'username': 'username', 'version': '5.2'}, 'metadata': {}, 'msg_id': '5e8d74eb8ab3408aa9ffc8fffb39035e', 'msg_type': 'execute_request', 'parent_header': {}})\n    394         if not silent:\n    395             self.execution_count += 1\n    396             self._publish_execute_input(code, parent, self.execution_count)\n    397 \n    398         reply_content = self.do_execute(code, silent, store_history,\n--> 399                                         user_expressions, allow_stdin)\n        user_expressions = {}\n        allow_stdin = True\n    400 \n    401         # Flush output before sending the reply.\n    402         sys.stdout.flush()\n    403         sys.stderr.flush()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code='from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', silent=False, store_history=True, user_expressions={}, allow_stdin=True)\n    203 \n    204         self._forward_input(allow_stdin)\n    205 \n    206         reply_content = {}\n    207         try:\n--> 208             res = shell.run_cell(code, store_history=store_history, silent=silent)\n        res = undefined\n        shell.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>\n        code = 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)'\n        store_history = True\n        silent = False\n    209         finally:\n    210             self._restore_input()\n    211 \n    212         if res.error_before_exec is not None:\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, *args=('from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)',), **kwargs={'silent': False, 'store_history': True})\n    532             )\n    533         self.payload_manager.write_payload(payload)\n    534 \n    535     def run_cell(self, *args, **kwargs):\n    536         self._last_traceback = None\n--> 537         return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n        self.run_cell = <bound method ZMQInteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>\n        args = ('from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)',)\n        kwargs = {'silent': False, 'store_history': True}\n    538 \n    539     def _showtraceback(self, etype, evalue, stb):\n    540         # try to preserve ordering of tracebacks and print statements\n    541         sys.stdout.flush()\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell='from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)', store_history=True, silent=False, shell_futures=True)\n   2723                 self.displayhook.exec_result = result\n   2724 \n   2725                 # Execute the user code\n   2726                 interactivity = \"none\" if silent else self.ast_node_interactivity\n   2727                 has_raised = self.run_ast_nodes(code_ast.body, cell_name,\n-> 2728                    interactivity=interactivity, compiler=compiler, result=result)\n        interactivity = 'last_expr'\n        compiler = <IPython.core.compilerop.CachingCompiler object>\n   2729                 \n   2730                 self.last_execution_succeeded = not has_raised\n   2731                 self.last_execution_result = result\n   2732 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.ImportFrom object>, <_ast.Assign object>, <_ast.Expr object>, <_ast.Assign object>], cell_name='<ipython-input-8-67c4373b57e3>', interactivity='none', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<ExecutionResult object at 104741fd0, execution_..._before_exec=None error_in_exec=None result=None>)\n   2845 \n   2846         try:\n   2847             for i, node in enumerate(to_run_exec):\n   2848                 mod = ast.Module([node])\n   2849                 code = compiler(mod, cell_name, \"exec\")\n-> 2850                 if self.run_code(code, result):\n        self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>\n        code = <code object <module> at 0x10c0f0d20, file \"<ipython-input-8-67c4373b57e3>\", line 4>\n        result = <ExecutionResult object at 104741fd0, execution_..._before_exec=None error_in_exec=None result=None>\n   2851                     return True\n   2852 \n   2853             for i, node in enumerate(to_run_interactive):\n   2854                 mod = ast.Interactive([node])\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x10c0f0d20, file \"<ipython-input-8-67c4373b57e3>\", line 4>, result=<ExecutionResult object at 104741fd0, execution_..._before_exec=None error_in_exec=None result=None>)\n   2905         outflag = True  # happens in more places, so it's easier as default\n   2906         try:\n   2907             try:\n   2908                 self.hooks.pre_run_code_hook()\n   2909                 #rprint('Running code', repr(code_obj)) # dbg\n-> 2910                 exec(code_obj, self.user_global_ns, self.user_ns)\n        code_obj = <code object <module> at 0x10c0f0d20, file \"<ipython-input-8-67c4373b57e3>\", line 4>\n        self.user_global_ns = {'In': ['', 'import pandas as pd\\nfrom sklearn import preproce...est.head()\\ntest = pd.concat([test,postulaciones])', 'from sklearn import preprocessing\\nle = preproces...est[col])\\n    test[col] = le.transform(test[col])', 'postulaciones = test[100000:].fillna(-1)\\ntest = test[:100000].fillna(-1)', \"postulaciones.drop('Unnamed: 0.1',axis=1,inplace=True)\", 'postulaciones.head()', 'partitions = []\\npartitions_res = []\\nn = 3\\nindex ...tions_res.append(resultados[index*i:index*(i+1)])', 'from sklearn.model_selection import train_test_s...artitions_res[0], test_size=0.2, random_state=43)', 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)'], 'KNeighborsClassifier': <class 'sklearn.neighbors.classification.KNeighborsClassifier'>, 'Out': {5:    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  }, 'X_test':          fechanacimiento  sexo  nivel  tipo_de_t...      1  30.407378  \n\n[1313644 rows x 13 columns], 'X_train':          fechanacimiento  sexo  nivel  tipo_de_t...      2  27.478095  \n\n[5254573 rows x 13 columns], '_':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '_5':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '__': '', '___': '', '__builtin__': <module 'builtins' (built-in)>, ...}\n        self.user_ns = {'In': ['', 'import pandas as pd\\nfrom sklearn import preproce...est.head()\\ntest = pd.concat([test,postulaciones])', 'from sklearn import preprocessing\\nle = preproces...est[col])\\n    test[col] = le.transform(test[col])', 'postulaciones = test[100000:].fillna(-1)\\ntest = test[:100000].fillna(-1)', \"postulaciones.drop('Unnamed: 0.1',axis=1,inplace=True)\", 'postulaciones.head()', 'partitions = []\\npartitions_res = []\\nn = 3\\nindex ...tions_res.append(resultados[index*i:index*(i+1)])', 'from sklearn.model_selection import train_test_s...artitions_res[0], test_size=0.2, random_state=43)', 'from sklearn.neighbors import KNeighborsClassifi..., y_train)\\nknn_result = neigh.predict_proba(test)'], 'KNeighborsClassifier': <class 'sklearn.neighbors.classification.KNeighborsClassifier'>, 'Out': {5:    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  }, 'X_test':          fechanacimiento  sexo  nivel  tipo_de_t...      1  30.407378  \n\n[1313644 rows x 13 columns], 'X_train':          fechanacimiento  sexo  nivel  tipo_de_t...      2  27.478095  \n\n[5254573 rows x 13 columns], '_':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '_5':    fechanacimiento  sexo  nivel  tipo_de_trabajo...88  \n2  26.095588  \n3  26.095588  \n4  26.095588  , '__': '', '___': '', '__builtin__': <module 'builtins' (built-in)>, ...}\n   2911             finally:\n   2912                 # Reset our crash handler in place\n   2913                 sys.excepthook = old_excepthook\n   2914         except SystemExit as e:\n\n...........................................................................\n/Users/federicocavazzoli/Documents/GitHub/DatosTP/<ipython-input-8-67c4373b57e3> in <module>()\n      1 from sklearn.neighbors import KNeighborsClassifier\n      2 neigh = KNeighborsClassifier(n_neighbors=25 ,n_jobs=-1, algorithm='auto')\n      3 neigh.fit(X_train, y_train)\n----> 4 knn_result = neigh.predict_proba(test)\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/classification.py in predict_proba(self=KNeighborsClassifier(algorithm='auto', leaf_size..._neighbors=25, p=2,\n           weights='uniform'), X=array([[ 6.92660e+04,  4.20000e+01,  1.00000e+00....00000e+00,\n         2.00000e+00, -1.00000e+00]]))\n    185             The class probabilities of the input samples. Classes are ordered\n    186             by lexicographic order.\n    187         \"\"\"\n    188         X = check_array(X, accept_sparse='csr')\n    189 \n--> 190         neigh_dist, neigh_ind = self.kneighbors(X)\n        neigh_dist = undefined\n        neigh_ind = undefined\n        self.kneighbors = <bound method KNeighborsMixin.kneighbors of KNei...neighbors=25, p=2,\n           weights='uniform')>\n        X = array([[ 6.92660e+04,  4.20000e+01,  1.00000e+00....00000e+00,\n         2.00000e+00, -1.00000e+00]])\n    191 \n    192         classes_ = self.classes_\n    193         _y = self._y\n    194         if not self.outputs_2d_:\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self=KNeighborsClassifier(algorithm='auto', leaf_size..._neighbors=25, p=2,\n           weights='uniform'), X=array([[ 6.92660e+04,  4.20000e+01,  1.00000e+00....00000e+00,\n         2.00000e+00, -1.00000e+00]]), n_neighbors=25, return_distance=True)\n    380                     \"%s does not work with sparse matrices. Densify the data, \"\n    381                     \"or set algorithm='brute'\" % self._fit_method)\n    382             result = Parallel(n_jobs, backend='threading')(\n    383                 delayed(self._tree.query, check_pickle=False)(\n    384                     X[s], n_neighbors, return_distance)\n--> 385                 for s in gen_even_slices(X.shape[0], n_jobs)\n        X.shape = (100000, 14)\n        n_jobs = 8\n    386             )\n    387             if return_distance:\n    388                 dist, neigh_ind = tuple(zip(*result))\n    389                 result = np.vstack(dist), np.vstack(neigh_ind)\n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=8), iterable=<generator object KNeighborsMixin.kneighbors.<locals>.<genexpr>>)\n    784             if pre_dispatch == \"all\" or n_jobs == 1:\n    785                 # The iterable was consumed all at once by the above for loop.\n    786                 # No need to wait for async callbacks to trigger to\n    787                 # consumption.\n    788                 self._iterating = False\n--> 789             self.retrieve()\n        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=8)>\n    790             # Make sure that we get a last message telling us we are done\n    791             elapsed_time = time.time() - self._start_time\n    792             self._print('Done %3i out of %3i | elapsed: %s finished',\n    793                         (len(self._output), len(self._output),\n\n---------------------------------------------------------------------------\nSub-process traceback:\n---------------------------------------------------------------------------\nValueError                                         Tue Jun 26 21:28:50 2018\nPID: 11063                              Python 3.6.4: /anaconda3/bin/python\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)\n    126     def __init__(self, iterator_slice):\n    127         self.items = list(iterator_slice)\n    128         self._size = len(self.items)\n    129 \n    130     def __call__(self):\n--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]\n        self.items = [(<built-in method query of sklearn.neighbors.kd_tree.KDTree object>, (array([[ 6.92660000e+04,  4.20000000e+01,  1.000...40000000e+01,  0.00000000e+00,  2.72928688e+01]]), 25, True), {})]\n    132 \n    133     def __len__(self):\n    134         return self._size\n    135 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)\n    126     def __init__(self, iterator_slice):\n    127         self.items = list(iterator_slice)\n    128         self._size = len(self.items)\n    129 \n    130     def __call__(self):\n--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]\n        func = <built-in method query of sklearn.neighbors.kd_tree.KDTree object>\n        args = (array([[ 6.92660000e+04,  4.20000000e+01,  1.000...40000000e+01,  0.00000000e+00,  2.72928688e+01]]), 25, True)\n        kwargs = {}\n    132 \n    133     def __len__(self):\n    134         return self._size\n    135 \n\n...........................................................................\n/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/kd_tree.cpython-36m-darwin.so in sklearn.neighbors.kd_tree.BinaryTree.query()\n\nValueError: query data dimension must match training data dimension\n___________________________________________________________________________"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=25 ,n_jobs=-1, algorithm='auto')\n",
    "neigh.fit(X_train, y_train)\n",
    "knn_result = neigh.predict_proba(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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