pennylane_stock_price_hybrid_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QI4PhD_bi9dN"
   },
   "source": [
    "## **Importing Libraries**\n",
    "\n",
    "A library was built with the name tfqml.py to store all the dependencies, classes and methods that are used in this work, these classes are\n",
    "- ClassicalPreprocessing,\n",
    "\n",
    "together with the methods \n",
    "- visualization and \n",
    "- save_data\n",
    "\n",
    "\n",
    "the dependencies are:\n",
    "- numpy \n",
    "- pandas \n",
    "- matplotlib\n",
    "- sklearn\n",
    "- tensorflow\n",
    "- pennylane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-25 02:34:02.460452: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.490385: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.490617: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.491244: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-02-25 02:34:02.491868: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.492042: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.492198: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.884307: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.884511: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.884673: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-25 02:34:02.884817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5964 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3070, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "from tfqml import *\n",
    "import pennylane as qml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3J64HGEcjKho"
   },
   "source": [
    "## **Choose the 8 stocks**\n",
    "\n",
    "we select 8 stocks from 8 conglomerates based on the dataset of Xu et al. [[2]](https://aclanthology.org/P18-1183/): \n",
    "\n",
    "- Basic Materials: TOTAL S.A. \"TOT\"\n",
    "\n",
    "- Consumer Goods: Appel Inc. \"AAPL\"\n",
    "\n",
    "- Healthcare: AbbVie Inc. \"ABBV\"\n",
    "\n",
    "- Services: Wall-Mart Stores Inc. \"WMT\"\n",
    "\n",
    "- Utilites: Duke energy corporation \"DUK\"\n",
    "\n",
    "- Financial: HSBS Holding pcl \"HSBC\"\n",
    "\n",
    "- Industrial Goods: ABB Ltd. \"ABB\"\n",
    "\n",
    "- Technology: China Mobile Limited \"CHL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_stocks = ['AAPL','ABB','ABBV','TOT','WMT','DUK','CHL','HSBC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classical Preprocessing\n",
    "\n",
    "The methods of our library are used to preprocess the csv files of the 8 stocks,\n",
    "We consider 30 days por each instance and we obtain the shape of the train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train data: (823, 1)\n",
      "Shape of test data: (354, 1)\n"
     ]
    }
   ],
   "source": [
    "cp = ClassicalPreprocessing(filename=name_stocks[index])\n",
    "days = 30\n",
    "x_train,y_train,x_test,y_test = cp.preprocessing(days=days,flag_pca=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum Pre processing\n",
    "\n",
    "Convert the classical information fo the train and test dataset into a quantumcircuit this is possible using cirq, for this proposal we consider two encoding method, amplitude and angle, we keep the last one, for its result it was very well with respect the amplitude. Using as reference this the paper [Quantum Long Short-Term Memory](https://arxiv.org/pdf/2009.01783.pdf)  for the encoding, is important apply the arctang of the data and add a proposal. It was proposed for this encoding to reduce the number of qubits with the consideration that there are 30 data, one for each of the 3 coordinates, therefore the number of days divided by 3 will have to be used, it should be noted that data of multiples of 3 are needed to use this encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_qubits = days//3\n",
    "\n",
    "dev = qml.device(\"default.qubit.tf\", wires=n_qubits) # using backend tensorflow\n",
    "\n",
    "\n",
    "#quantum circuit for the encoding and ansayz\n",
    "\n",
    "@qml.qnode(dev)\n",
    "def qnode(inputs,weights_0,weights_1,weights_2,weights_3):#structure to use mennylane with tensorflow\n",
    "    for i in range(10):\n",
    "        qml.RY(np.arctan(inputs[i]),wires=i) #first 10 inputs apply in ry\n",
    "        qml.Hadamard(i)\n",
    "        qml.RX(np.arctan(inputs[10+i]),wires=i) #the 10-20 inputs apply in rx\n",
    "    \n",
    "    qml.CNOT(wires=[0, 1])\n",
    "    qml.CNOT(wires=[2, 3])\n",
    "    qml.CNOT(wires=[4, 5])\n",
    "    qml.CNOT(wires=[6, 7])\n",
    "    qml.CNOT(wires=[8, 9])\n",
    "    for i in range(10): \n",
    "        qml.RZ(np.arctan(inputs[20+i]),wires=i) #the 20-30 inputs apply in rz\n",
    "    \n",
    "    \n",
    "    #ansatz\n",
    "    for j,w in enumerate(weights_0): #weights\n",
    "        qml.RY(w, wires=j)   #apply a layer of ry\n",
    "        \n",
    "\n",
    "    # apply different cnots between the ansatz\n",
    "    qml.CNOT(wires=[0, 1])\n",
    "    \n",
    "    qml.CNOT(wires=[4, 3])\n",
    "    \n",
    "    qml.CNOT(wires=[3, 2])    \n",
    "    \n",
    "    qml.CNOT(wires=[2, 1])\n",
    "    \n",
    "    \n",
    "    #apply RY and Hadamard with the target output of each cx\n",
    "    list_index = [1,3,5,7,9]\n",
    "    for j,w in enumerate(weights_1):\n",
    "        qml.RY(w, wires=list_index[j])      \n",
    "        \n",
    "\n",
    "    qml.Hadamard(1)\n",
    "    qml.Hadamard(3)\n",
    "    qml.Hadamard(5)\n",
    "    qml.Hadamard(7)\n",
    "    qml.Hadamard(9)\n",
    "    \n",
    "    # repeat the same ansatz using RX in stead of RY\n",
    "    for j,w in enumerate(weights_2):\n",
    "        qml.RX(w, wires=j)\n",
    "    \n",
    "    qml.CNOT(wires=[0, 1])\n",
    "    \n",
    "    qml.CNOT(wires=[4, 3])\n",
    "    \n",
    "    qml.CNOT(wires=[3, 2])    \n",
    "    \n",
    "    qml.CNOT(wires=[2, 1])\n",
    "    \n",
    "    list_index = [1,3,5,7,9]\n",
    "    for j,w in enumerate(weights_3):\n",
    "        qml.RX(w, wires=list_index[j])     \n",
    "        \n",
    "    qml.Hadamard(1)\n",
    "    qml.Hadamard(3)\n",
    "    qml.Hadamard(5)\n",
    "    qml.Hadamard(7)\n",
    "    qml.Hadamard(9)\n",
    "    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2)), qml.expval(qml.PauliZ(3)), qml.expval(qml.PauliZ(4)),qml.expval(qml.PauliZ(5)), qml.expval(qml.PauliZ(6)), qml.expval(qml.PauliZ(7)), qml.expval(qml.PauliZ(8)), qml.expval(qml.PauliZ(9))\n",
    "        \n",
    "weight_shapes = {\"weights_0\": 10, \"weights_1\": 5, \"weights_2\": 10, \"weights_3\": 5}#,\"weights_5\": 1,\"weights_6\": 1,\"weights_7\": 1,\"weights_8\": 1,\"weights_9\": 1}\n",
    "qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating our LSTM hibryd model.The first layer is our proposal which input is 30 parameters per instance and output is 10, followed by LSTM and Dropout layers, at the end a Dense layer to have an output value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "\n",
    "model.add(tf.keras.layers.Input(shape=()))\n",
    "\n",
    "model.add(qlayer)\n",
    "model.add(tf.keras.layers.Reshape((1,10)))\n",
    "model.add(tf.keras.layers.LSTM(units=100, return_sequences=True))\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.LSTM(units=100, return_sequences=True))\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.LSTM(units=5))\n",
    "model.add(tf.keras.layers.Dense(units=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the as loss the MSE and optimizer ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " keras_layer (KerasLayer)    (None, 10)                0 (unused)\n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 1, 10)             0         \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 1, 100)            44400     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1, 100)            0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 1, 100)            80400     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 1, 100)            0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 5)                 2120      \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 126,926\n",
      "Trainable params: 126,926\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss='mean_squared_error',\n",
    "    optimizer=tf.keras.optimizers.Adam(),\n",
    "    metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "model.build()\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider only 60 epoch with a batch size of 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-25 02:34:20.894423: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8202\n",
      "2022-02-25 02:34:21.027682: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 133s 5s/step - loss: 0.3040 - root_mean_squared_error: 0.5513\n",
      "Epoch 2/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0987 - root_mean_squared_error: 0.3142\n",
      "Epoch 3/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0509 - root_mean_squared_error: 0.2257\n",
      "Epoch 4/60\n",
      "25/25 [==============================] - 129s 5s/step - loss: 0.0430 - root_mean_squared_error: 0.2074\n",
      "Epoch 5/60\n",
      "25/25 [==============================] - 129s 5s/step - loss: 0.0380 - root_mean_squared_error: 0.1949\n",
      "Epoch 6/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0283 - root_mean_squared_error: 0.1682\n",
      "Epoch 7/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0187 - root_mean_squared_error: 0.1366\n",
      "Epoch 8/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0095 - root_mean_squared_error: 0.0974\n",
      "Epoch 9/60\n",
      "25/25 [==============================] - 129s 5s/step - loss: 0.0063 - root_mean_squared_error: 0.0793\n",
      "Epoch 10/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0058 - root_mean_squared_error: 0.0759\n",
      "Epoch 11/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0060 - root_mean_squared_error: 0.0777\n",
      "Epoch 12/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0057 - root_mean_squared_error: 0.0758\n",
      "Epoch 13/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0051 - root_mean_squared_error: 0.0716\n",
      "Epoch 14/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0051 - root_mean_squared_error: 0.0713\n",
      "Epoch 15/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0048 - root_mean_squared_error: 0.0690\n",
      "Epoch 16/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0048 - root_mean_squared_error: 0.0694\n",
      "Epoch 17/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0051 - root_mean_squared_error: 0.0711\n",
      "Epoch 18/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0045 - root_mean_squared_error: 0.0670\n",
      "Epoch 19/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0048 - root_mean_squared_error: 0.0690\n",
      "Epoch 20/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0046 - root_mean_squared_error: 0.0680\n",
      "Epoch 21/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0043 - root_mean_squared_error: 0.0653\n",
      "Epoch 22/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0047 - root_mean_squared_error: 0.0689\n",
      "Epoch 23/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0044 - root_mean_squared_error: 0.0663\n",
      "Epoch 24/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0050 - root_mean_squared_error: 0.0709\n",
      "Epoch 25/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0042 - root_mean_squared_error: 0.0652\n",
      "Epoch 26/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0047 - root_mean_squared_error: 0.0683\n",
      "Epoch 27/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0040 - root_mean_squared_error: 0.0632\n",
      "Epoch 28/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0039 - root_mean_squared_error: 0.0623\n",
      "Epoch 29/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0040 - root_mean_squared_error: 0.0631\n",
      "Epoch 30/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0041 - root_mean_squared_error: 0.0644\n",
      "Epoch 31/60\n",
      "25/25 [==============================] - 134s 5s/step - loss: 0.0043 - root_mean_squared_error: 0.0653\n",
      "Epoch 32/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0039 - root_mean_squared_error: 0.0621\n",
      "Epoch 33/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0042 - root_mean_squared_error: 0.0647\n",
      "Epoch 34/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0039 - root_mean_squared_error: 0.0621\n",
      "Epoch 35/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0040 - root_mean_squared_error: 0.0635\n",
      "Epoch 36/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0039 - root_mean_squared_error: 0.0625\n",
      "Epoch 37/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0038 - root_mean_squared_error: 0.0618\n",
      "Epoch 38/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0039 - root_mean_squared_error: 0.0623\n",
      "Epoch 39/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0038 - root_mean_squared_error: 0.0614\n",
      "Epoch 40/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0042 - root_mean_squared_error: 0.0647\n",
      "Epoch 41/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0036 - root_mean_squared_error: 0.0604\n",
      "Epoch 42/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0037 - root_mean_squared_error: 0.0609\n",
      "Epoch 43/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0038 - root_mean_squared_error: 0.0620\n",
      "Epoch 44/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0038 - root_mean_squared_error: 0.0613\n",
      "Epoch 45/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0036 - root_mean_squared_error: 0.0602\n",
      "Epoch 46/60\n",
      "25/25 [==============================] - 131s 5s/step - loss: 0.0033 - root_mean_squared_error: 0.0573\n",
      "Epoch 47/60\n",
      "25/25 [==============================] - 130s 5s/step - loss: 0.0036 - root_mean_squared_error: 0.0597\n",
      "Epoch 48/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0034 - root_mean_squared_error: 0.0585\n",
      "Epoch 49/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0035 - root_mean_squared_error: 0.0594\n",
      "Epoch 50/60\n",
      "25/25 [==============================] - 134s 5s/step - loss: 0.0035 - root_mean_squared_error: 0.0593\n",
      "Epoch 51/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0033 - root_mean_squared_error: 0.0577\n",
      "Epoch 52/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0034 - root_mean_squared_error: 0.0581\n",
      "Epoch 53/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0034 - root_mean_squared_error: 0.0581\n",
      "Epoch 54/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0032 - root_mean_squared_error: 0.0566\n",
      "Epoch 55/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0032 - root_mean_squared_error: 0.0567\n",
      "Epoch 56/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0032 - root_mean_squared_error: 0.0568\n",
      "Epoch 57/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0033 - root_mean_squared_error: 0.0571\n",
      "Epoch 58/60\n",
      "25/25 [==============================] - 133s 5s/step - loss: 0.0032 - root_mean_squared_error: 0.0564\n",
      "Epoch 59/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0031 - root_mean_squared_error: 0.0558\n",
      "Epoch 60/60\n",
      "25/25 [==============================] - 132s 5s/step - loss: 0.0031 - root_mean_squared_error: 0.0552\n",
      "11/11 [==============================] - 32s 3s/step - loss: 0.0028 - root_mean_squared_error: 0.0526\n"
     ]
    }
   ],
   "source": [
    "qnn_history = model.fit(\n",
    "      tf.constant(x_train), y_train,\n",
    "      batch_size=32,\n",
    "      epochs=60)\n",
    "\n",
    "qnn_results = model.evaluate(tf.constant(x_test), y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(model,tf.constant(x_train),y_train)\n",
    "visualization(model,tf.constant(x_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the result between the  real and predict with both data set, train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 3.78%\n",
      "MAE 4.04%\n"
     ]
    }
   ],
   "source": [
    "iteration= 0\n",
    "save_data(model,x_train,y_train,\"pennylane_\"+name_stocks[index]+\"_\"+str(iteration)+\"_train\")\n",
    "save_data(model,x_test,y_test,\"pennylane_\"+name_stocks[index]+\"_\"+str(iteration)+\"_test\")\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPVe0xJHFIRM8xqWAthKDQp",
   "include_colab_link": true,
   "name": "stock price predection.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}