stock_price_hibryd_algorithm.ipynb

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   "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",
    "- QuantumPreprocessing,\n",
    "- CircuitLayer,\n",
    "- QuantumModel-\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",
    "- cirq\n",
    "- tensorflow_quantum\n",
    "- tensorflow\n",
    "- sympy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "2022-02-25 08:05:23.884557: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-02-25 08:05:23.884577: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-02-25 08:05:25.243971: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-02-25 08:05:25.243994: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (toto): /proc/driver/nvidia/version does not exist\n",
      "2022-02-25 08:05:25.244517: 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"
     ]
    }
   ],
   "source": [
    "from tfqml import *"
   ]
  },
  {
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train data: (880, 1)\n",
      "Shape of test data: (378, 1)\n"
     ]
    }
   ],
   "source": [
    "cp = ClassicalPreprocessing(filename=name_stocks[0])\n",
    "days = 30\n",
    "x_train,y_train,x_test,y_test = cp.preprocessing(days=days,flag_pca=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we can check the dataset for trai nand test and confirm is the same dimention of the days and using a normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50210573, 0.5198373 , 0.5154993 , 0.52431991, 0.52865792,\n",
       "       0.50093085, 0.50407582, 0.52309078, 0.54584719, 0.56281965,\n",
       "       0.56377762, 0.56446444, 0.56247619, 0.56835058, 0.54024395,\n",
       "       0.54277451, 0.50749205, 0.49944871, 0.52558515, 0.51186616,\n",
       "       0.49496611, 0.50047898, 0.51202891, 0.5010935 , 0.46798009,\n",
       "       0.45310439, 0.45507448, 0.46729319, 0.43667413, 0.44171711])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "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": [
    "qp = QuantumPreprocessing(x_train,x_test)\n",
    "x_trainq,x_testq = qp.data2qubits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print a quantum circuit of our encoding, apart of the angle we add a Hadamard prevous the X and Z rotation and  do an entenglament for each pair of qubits. You can see in the next cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7f22599cbb20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qp.print_circuit(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "following with the idea that they must be multiples of 3, the ansatz is used and this is designed with a layer of RY followed by CX with the qubit target of each CX applying an RY and then an H and the same is done with the RX gate, the number of parameters for each layer is indicated. these are 15 per layer. We use reference the tutorial from [tensorflow Quantum](https://www.tensorflow.org/quantum/tutorials/mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7f2254512a60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubits_required = days//3\n",
    "qm = QuantumModel(qubits_required)\n",
    "model_circuit, model_readout =qm.quantum_circuit([cirq.Y,cirq.X])\n",
    "SVGCircuit(model_circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eyHweGaykp-o"
   },
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the Keras model.\n",
    "model = tf.keras.Sequential([\n",
    "    # The input is the data-circuit, encoded as a tf.string\n",
    "    tf.keras.layers.Input(shape=(), dtype=tf.string),\n",
    "    # The PQC layer returns the expected value of the readout gate.\n",
    "    tfq.layers.PQC(model_circuit, model_readout),\n",
    "    tf.keras.layers.Reshape((1,10)),\n",
    "    tf.keras.layers.LSTM(units=100, return_sequences=True),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.LSTM(units=100, return_sequences=True),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.LSTM(units=5),\n",
    "    tf.keras.layers.Dense(units=1),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the as loss the MSE and optimizer ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " pqc (PQC)                   (None, 10)                30        \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: 126,956\n",
      "Trainable params: 126,956\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",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider only 60 epoch with a batch size of 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "27/27 [==============================] - 5s 98ms/step - loss: 0.2290 - root_mean_squared_error: 0.4785\n",
      "Epoch 2/60\n",
      "27/27 [==============================] - 3s 100ms/step - loss: 0.0369 - root_mean_squared_error: 0.1921\n",
      "Epoch 3/60\n",
      "27/27 [==============================] - 3s 96ms/step - loss: 0.0103 - root_mean_squared_error: 0.1014\n",
      "Epoch 4/60\n",
      "27/27 [==============================] - 3s 98ms/step - loss: 0.0076 - root_mean_squared_error: 0.0874\n",
      "Epoch 5/60\n",
      "27/27 [==============================] - 3s 103ms/step - loss: 0.0069 - root_mean_squared_error: 0.0830\n",
      "Epoch 6/60\n",
      "27/27 [==============================] - 3s 108ms/step - loss: 0.0065 - root_mean_squared_error: 0.0807\n",
      "Epoch 7/60\n",
      "27/27 [==============================] - 3s 103ms/step - loss: 0.0060 - root_mean_squared_error: 0.0775\n",
      "Epoch 8/60\n",
      "27/27 [==============================] - 3s 102ms/step - loss: 0.0055 - root_mean_squared_error: 0.0741\n",
      "Epoch 9/60\n",
      "27/27 [==============================] - 3s 100ms/step - loss: 0.0049 - root_mean_squared_error: 0.0702\n",
      "Epoch 10/60\n",
      "27/27 [==============================] - 3s 106ms/step - loss: 0.0043 - root_mean_squared_error: 0.0657\n",
      "Epoch 11/60\n",
      "27/27 [==============================] - 3s 102ms/step - loss: 0.0042 - root_mean_squared_error: 0.0651\n",
      "Epoch 12/60\n",
      "27/27 [==============================] - 3s 109ms/step - loss: 0.0042 - root_mean_squared_error: 0.0645\n",
      "Epoch 13/60\n",
      "27/27 [==============================] - 3s 103ms/step - loss: 0.0037 - root_mean_squared_error: 0.0606\n",
      "Epoch 14/60\n",
      "27/27 [==============================] - 3s 107ms/step - loss: 0.0030 - root_mean_squared_error: 0.0548\n",
      "Epoch 15/60\n",
      "27/27 [==============================] - 3s 103ms/step - loss: 0.0030 - root_mean_squared_error: 0.0550\n",
      "Epoch 16/60\n",
      "27/27 [==============================] - 3s 96ms/step - loss: 0.0025 - root_mean_squared_error: 0.0505\n",
      "Epoch 17/60\n",
      "27/27 [==============================] - 3s 99ms/step - loss: 0.0027 - root_mean_squared_error: 0.0518\n",
      "Epoch 18/60\n",
      "27/27 [==============================] - 3s 103ms/step - loss: 0.0024 - root_mean_squared_error: 0.0486\n",
      "Epoch 19/60\n",
      "27/27 [==============================] - 3s 96ms/step - loss: 0.0023 - root_mean_squared_error: 0.0481\n",
      "Epoch 20/60\n",
      "27/27 [==============================] - 3s 96ms/step - loss: 0.0023 - root_mean_squared_error: 0.0476\n",
      "Epoch 21/60\n",
      "27/27 [==============================] - 3s 100ms/step - loss: 0.0021 - root_mean_squared_error: 0.0459\n",
      "Epoch 22/60\n",
      "27/27 [==============================] - 3s 102ms/step - loss: 0.0020 - root_mean_squared_error: 0.0447\n",
      "Epoch 23/60\n",
      "27/27 [==============================] - 3s 96ms/step - loss: 0.0021 - root_mean_squared_error: 0.0459\n",
      "Epoch 24/60\n",
      "27/27 [==============================] - 3s 94ms/step - loss: 0.0021 - root_mean_squared_error: 0.0459\n",
      "Epoch 25/60\n",
      "27/27 [==============================] - 2s 89ms/step - loss: 0.0019 - root_mean_squared_error: 0.0437\n",
      "Epoch 26/60\n",
      "27/27 [==============================] - 3s 101ms/step - loss: 0.0018 - root_mean_squared_error: 0.0423\n",
      "Epoch 27/60\n",
      "27/27 [==============================] - 3s 108ms/step - loss: 0.0018 - root_mean_squared_error: 0.0429\n",
      "Epoch 28/60\n",
      "27/27 [==============================] - 3s 102ms/step - loss: 0.0018 - root_mean_squared_error: 0.0424\n",
      "Epoch 29/60\n",
      "27/27 [==============================] - 3s 97ms/step - loss: 0.0018 - root_mean_squared_error: 0.0419\n",
      "Epoch 30/60\n",
      "27/27 [==============================] - 3s 93ms/step - loss: 0.0018 - root_mean_squared_error: 0.0420\n",
      "Epoch 31/60\n",
      "27/27 [==============================] - 3s 95ms/step - loss: 0.0018 - root_mean_squared_error: 0.0427\n",
      "Epoch 32/60\n",
      "27/27 [==============================] - 2s 92ms/step - loss: 0.0017 - root_mean_squared_error: 0.0413\n",
      "Epoch 33/60\n",
      "27/27 [==============================] - 3s 97ms/step - loss: 0.0017 - root_mean_squared_error: 0.0413\n",
      "Epoch 34/60\n",
      "27/27 [==============================] - 2s 91ms/step - loss: 0.0016 - root_mean_squared_error: 0.0395\n",
      "Epoch 35/60\n",
      "27/27 [==============================] - 2s 92ms/step - loss: 0.0016 - root_mean_squared_error: 0.0405\n",
      "Epoch 36/60\n",
      "27/27 [==============================] - 3s 93ms/step - loss: 0.0016 - root_mean_squared_error: 0.0400\n",
      "Epoch 37/60\n",
      "27/27 [==============================] - 2s 90ms/step - loss: 0.0016 - root_mean_squared_error: 0.0397\n",
      "Epoch 38/60\n",
      "27/27 [==============================] - 2s 89ms/step - loss: 0.0015 - root_mean_squared_error: 0.0384\n",
      "Epoch 39/60\n",
      "27/27 [==============================] - 3s 101ms/step - loss: 0.0015 - root_mean_squared_error: 0.0393\n",
      "Epoch 40/60\n",
      "27/27 [==============================] - 3s 99ms/step - loss: 0.0017 - root_mean_squared_error: 0.0416\n",
      "Epoch 41/60\n",
      "27/27 [==============================] - 3s 94ms/step - loss: 0.0015 - root_mean_squared_error: 0.0394\n",
      "Epoch 42/60\n",
      "27/27 [==============================] - 3s 98ms/step - loss: 0.0015 - root_mean_squared_error: 0.0393\n",
      "Epoch 43/60\n",
      "27/27 [==============================] - 3s 96ms/step - loss: 0.0015 - root_mean_squared_error: 0.0386\n",
      "Epoch 44/60\n",
      "27/27 [==============================] - 3s 99ms/step - loss: 0.0015 - root_mean_squared_error: 0.0389\n",
      "Epoch 45/60\n",
      "27/27 [==============================] - 2s 91ms/step - loss: 0.0015 - root_mean_squared_error: 0.0384\n",
      "Epoch 46/60\n",
      "27/27 [==============================] - 3s 92ms/step - loss: 0.0014 - root_mean_squared_error: 0.0379\n",
      "Epoch 47/60\n",
      "27/27 [==============================] - 3s 93ms/step - loss: 0.0014 - root_mean_squared_error: 0.0367\n",
      "Epoch 48/60\n",
      "27/27 [==============================] - 3s 97ms/step - loss: 0.0013 - root_mean_squared_error: 0.0366\n",
      "Epoch 49/60\n",
      "27/27 [==============================] - 3s 97ms/step - loss: 0.0014 - root_mean_squared_error: 0.0376\n",
      "Epoch 50/60\n",
      "27/27 [==============================] - 3s 95ms/step - loss: 0.0015 - root_mean_squared_error: 0.0382\n",
      "Epoch 51/60\n",
      "27/27 [==============================] - 3s 97ms/step - loss: 0.0014 - root_mean_squared_error: 0.0372\n",
      "Epoch 52/60\n",
      "27/27 [==============================] - 3s 98ms/step - loss: 0.0013 - root_mean_squared_error: 0.0360\n",
      "Epoch 53/60\n",
      "27/27 [==============================] - 3s 92ms/step - loss: 0.0014 - root_mean_squared_error: 0.0375\n",
      "Epoch 54/60\n",
      "27/27 [==============================] - 3s 99ms/step - loss: 0.0014 - root_mean_squared_error: 0.0374\n",
      "Epoch 55/60\n",
      "27/27 [==============================] - 3s 97ms/step - loss: 0.0013 - root_mean_squared_error: 0.0367\n",
      "Epoch 56/60\n",
      "27/27 [==============================] - 2s 91ms/step - loss: 0.0013 - root_mean_squared_error: 0.0359\n",
      "Epoch 57/60\n",
      "27/27 [==============================] - 3s 95ms/step - loss: 0.0014 - root_mean_squared_error: 0.0368\n",
      "Epoch 58/60\n",
      "27/27 [==============================] - 3s 94ms/step - loss: 0.0014 - root_mean_squared_error: 0.0368\n",
      "Epoch 59/60\n",
      "27/27 [==============================] - 2s 88ms/step - loss: 0.0012 - root_mean_squared_error: 0.0353\n",
      "Epoch 60/60\n",
      "27/27 [==============================] - 2s 91ms/step - loss: 0.0012 - root_mean_squared_error: 0.0349\n",
      "11/11 [==============================] - 1s 52ms/step - loss: 0.0124 - root_mean_squared_error: 0.1115\n"
     ]
    }
   ],
   "source": [
    "qnn_history = model.fit(\n",
    "      x_trainq, y_train,\n",
    "      batch_size=32,\n",
    "      epochs=60)\n",
    "\n",
    "qnn_results = model.evaluate(x_testq, y_test)\n"
   ]
  },
  {
   "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": [
    {
     "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,x_trainq,y_train)\n",
    "visualization(model,x_testq,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we save the model and outputs, the proposal save this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_data(model,x_trainq,y_train,name_stocks[1]+\"_train\")\n",
    "save_data(model,x_testq,y_test,name_stocks[1]+\"_test\")"
   ]
  }
 ],
 "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
}