TRAINER_ONGC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = kite.generate_session(\"dohZhLUFSmhy0pKo955oQGZw86OiddRN\",\n",
    "                             api_secret=\"gsw8ps17ex7lf3cuji4prfnwb4vlyr4y\")\n",
    "kite.set_access_token(data[\"access_token\"])\n",
    "\n",
    "with open(homediraddr+'/kitefile'+'.p', 'wb') as fp:\n",
    "                pickle.dump(kite, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://kite.trade/connect/login?api_key=w19o0chuo929jxkp&v=3'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from stkdef import One_trian_stk_class\n",
    "import logging\n",
    "import pickle\n",
    "from kiteconnect import KiteConnect\n",
    "logging.basicConfig(level=logging.ERROR)\n",
    "import threading\n",
    "import pandas as pd\n",
    "homediraddr='/home/hemangjoshi37a_gmail_com'\n",
    "kite = KiteConnect(api_key=\"w19o0chuo929jxkp\")\n",
    "kite.login_url()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('kitefile.p', 'rb') as fp:\n",
    "    kite = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded : TATASTEEL_DF.p 1\n",
      "Loaded scale_dict\n",
      "Loaded scaler 2\n",
      "Checking Model Path : /home/hemangjoshi37a_gmail_com/stkwise_models/TATASTEEL/TATASTEEL_181.h5\n",
      "model loaded : /home/hemangjoshi37a_gmail_com/stkwise_models/TATASTEEL/TATASTEEL_181.h5\n",
      "TATASTEEL : x_test shape :  (2911, 60, 1)\n",
      "TATASTEEL : x_train shape :  (94070, 60, 1)\n",
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-4:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 862, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/stkdef.py\", line 180, in plot_predict\n",
      "    last_60_days_scaled = np.reshape(asdasdasd, (X_test.shape[0], X_test.shape[1], 1 ))[0]\n",
      "  File \"<__array_function__ internals>\", line 6, in reshape\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 301, in reshape\n",
      "    return _wrapfunc(a, 'reshape', newshape, order=order)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 61, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: cannot reshape array of size 119 into shape (1,60,1)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  500/18814 [..............................] - ETA: 1:01:54 - loss: 0.0052 - accuracy: 0.0000e+00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-5:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 862, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/stkdef.py\", line 180, in plot_predict\n",
      "    last_60_days_scaled = np.reshape(asdasdasd, (X_test.shape[0], X_test.shape[1], 1 ))[0]\n",
      "  File \"<__array_function__ internals>\", line 6, in reshape\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 301, in reshape\n",
      "    return _wrapfunc(a, 'reshape', newshape, order=order)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 61, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: cannot reshape array of size 119 into shape (1,60,1)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1000/18814 [>.............................] - ETA: 1:00:18 - loss: 0.0026 - accuracy: 0.0000e+00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-6:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 862, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/stkdef.py\", line 180, in plot_predict\n",
      "    last_60_days_scaled = np.reshape(asdasdasd, (X_test.shape[0], X_test.shape[1], 1 ))[0]\n",
      "  File \"<__array_function__ internals>\", line 6, in reshape\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 301, in reshape\n",
      "    return _wrapfunc(a, 'reshape', newshape, order=order)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 61, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: cannot reshape array of size 119 into shape (1,60,1)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1500/18814 [=>............................] - ETA: 58:39 - loss: 0.0018 - accuracy: 0.0000e+00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-7:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 862, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/stkdef.py\", line 180, in plot_predict\n",
      "    last_60_days_scaled = np.reshape(asdasdasd, (X_test.shape[0], X_test.shape[1], 1 ))[0]\n",
      "  File \"<__array_function__ internals>\", line 6, in reshape\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 301, in reshape\n",
      "    return _wrapfunc(a, 'reshape', newshape, order=order)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 61, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: cannot reshape array of size 119 into shape (1,60,1)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2005/18814 [==>...........................] - ETA: 56:43 - loss: 0.0013 - accuracy: 0.0000e+00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-8:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 862, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/stkdef.py\", line 180, in plot_predict\n",
      "    last_60_days_scaled = np.reshape(asdasdasd, (X_test.shape[0], X_test.shape[1], 1 ))[0]\n",
      "  File \"<__array_function__ internals>\", line 6, in reshape\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 301, in reshape\n",
      "    return _wrapfunc(a, 'reshape', newshape, order=order)\n",
      "  File \"/home/hemangjoshi37a_gmail_com/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\", line 61, in _wrapfunc\n",
      "    return bound(*args, **kwds)\n",
      "ValueError: cannot reshape array of size 119 into shape (1,60,1)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2052/18814 [==>...........................] - ETA: 56:32 - loss: 0.0013 - accuracy: 0.0000e+00"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-cf4209b970a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtrainit\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mOne_trian_stk_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'insttkn'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nbatch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nepochs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'kite'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mtrainit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/stkdef.py\u001b[0m in \u001b[0;36mloadfit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[0;31m#                                      PlotLossesKeras(),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m                                      \u001b[0mTrainingPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m                                      \u001b[0mMyCustomCallback\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[0m\u001b[1;32m    268\u001b[0m                                     ])\n\u001b[1;32m    269\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthreading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mThread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavemodelh5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\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[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\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[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\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[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "xx={'name':'TATASTEEL',\n",
    "    'insttkn':895745,\n",
    "    'nbatch':5,\n",
    "    'nepochs':5,\n",
    "    'kite':kite}\n",
    "\n",
    "trainit= One_trian_stk_class(xx['name'], xx['insttkn'],xx['nbatch'],xx['nepochs'],xx['kite'])\n",
    "trainit.loadfit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_param_List=[{'name':'SBIN',\n",
    "                   'insttkn':779521,\n",
    "                   'nbatch':1,\n",
    "                   'nepochs':1,\n",
    "                   'kite':kite\n",
    "                  },\n",
    "                 {'name':'ONGC',\n",
    "                   'insttkn':633601,\n",
    "                   'nbatch':1,\n",
    "                   'nepochs':1,\n",
    "                   'kite':kite\n",
    "                  },\n",
    "                 {'name':'GLENMARK',\n",
    "                   'insttkn':1895937,\n",
    "                   'nbatch':1,\n",
    "                   'nepochs':1,\n",
    "                   'kite':kite\n",
    "                  },\n",
    "                 {'name':'TATASTEEL',\n",
    "                   'insttkn':895745,\n",
    "                   'nbatch':1,\n",
    "                   'nepochs':1,\n",
    "                   'kite':kite\n",
    "                  },]\n",
    "\n",
    "# x = One_trian_stk_class('ONGC', 633601,1,2,kite)\n",
    "\n",
    "# print('Stock Name  : '+ x.name)\n",
    "# print('Stock insttkn  : '+ str(x.insttkn))\n",
    "# print('Stock nbatch  : '+ str(x.nbatch))\n",
    "# print('Stock nepochs  : '+ str(x.nepochs))\n",
    "# print('Stock kite  : '+ str(kite))\n",
    "\n",
    "# print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kite.holdings()\n",
    "# print(train_param_List)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# threading.activeCount() − Returns the number of thread objects that are active.\n",
    "# threading.currentThread() − Returns the number of thread objects in the caller's thread control.\n",
    "# threading.enumerate() − Returns a list of all thread objects that are currently active.\n",
    "# In addition to the methods, the threading module has the Thread class that implements threading. The methods provided by the Thread class are as follows −\n",
    "# run() − The run() method is the entry point for a thread.\n",
    "# start() − The start() method starts a thread by calling the run method.\n",
    "# join([time]) − The join() waits for threads to terminate.\n",
    "# isAlive() − The isAlive() method checks whether a thread is still executing.\n",
    "# getName() − The getName() method returns the name of a thread.\n",
    "# setName() − The setName() method sets the name of a thread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# threading.activeCount() \n",
    "threading.currentThread()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "process_tuple=()\n",
    "process_names=[]\n",
    "\n",
    "for onestkdatain in train_param_List:\n",
    "    \n",
    "    globals()[onestkdatain['name']]=One_trian_stk_class(onestkdatain['name'],\n",
    "                                                       onestkdatain['insttkn'],\n",
    "                                                       onestkdatain['nbatch'],\n",
    "                                                       onestkdatain['nepochs'],\n",
    "                                                       onestkdatain['kite'],\n",
    "                                                       ).fit\n",
    "    process_names.append(onestkdatain['name'])\n",
    "    L1=list(process_tuple)\n",
    "    L1.append(globals()[onestkdatain['name']])\n",
    "    process_tuple=tuple(L1)\n",
    "\n",
    "process_tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exitFlag = 0\n",
    "\n",
    "class myThread (threading.Thread):\n",
    "    \n",
    "    def __init__(self,fitfn):\n",
    "        threading.Thread.__init__(self)\n",
    "        self.fitfn=fitfn\n",
    "    def run(self):\n",
    "        self.fitfn()\n",
    "        print(self.name + ' Just Exited....!!')\n",
    "        \n",
    "n=0\n",
    "for oneprocess in list(process_tuple):\n",
    "    globals()['thread'+str(n)]=myThread(oneprocess)\n",
    "    n+=1\n",
    "    \n",
    "n=0\n",
    "for oneprocess in list(process_tuple):\n",
    "    globals()['thread'+str(n)].name =process_names[n]\n",
    "    n+=1\n",
    "\n",
    "n=0\n",
    "for oneprocess in list(process_tuple):\n",
    "    globals()['thread'+str(n)].start()\n",
    "    n+=1\n",
    "\n",
    "print (\"Exiting Main Thread\")\n",
    "\n",
    "n=0\n",
    "for oneprocess in list(process_tuple):\n",
    "    globals()['thread'+str(n)].join()\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "import os\n",
    "import zipfile\n",
    "\n",
    "def zipdir(path, ziph):\n",
    "    # ziph is zipfile handle\n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for file in files:\n",
    "            ziph.write(os.path.join(root, file))\n",
    "\n",
    "zipf = zipfile.ZipFile('fig19.zip', 'w', zipfile.ZIP_DEFLATED)\n",
    "zipdir('/home/hemangjosh37a_gmail_com/predict_plots2/TATASTEEL12/', zipf)\n",
    "zipf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from livelossplot.tf_keras import PlotLossesCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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