results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96963d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efc40d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33a08936",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyqpanda import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01c40c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4c74d7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font',family='Microsoft YaHei')\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d48811",
   "metadata": {},
   "source": [
    "# 1. Prepare Dadaset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "757bbfef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91a53ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = './data/DailyDelhiClimateTrain.csv'\n",
    "test_path = './data/DailyDelhiClimateTest.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef5a3d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [1,2,3,4]\n",
    "\n",
    "train = pd.read_csv(train_path, usecols=cols, engine=\"python\")\n",
    "test = pd.read_csv(test_path, usecols=cols, engine=\"python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c112f41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(train)=1462\n",
      "len(test)=114\n"
     ]
    }
   ],
   "source": [
    "print(f'len(train)={len(train)}')\n",
    "print(f'len(test)={len(test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ca701d",
   "metadata": {},
   "source": [
    "## 1.1 Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b3dc6f",
   "metadata": {},
   "source": [
    "### 1.1.1 outlier detection for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d076311",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cabfb85b80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(train)), train[\"meanpressure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6f7435",
   "metadata": {},
   "source": [
    "### - remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb83d8fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remove outliers num: 9\n"
     ]
    }
   ],
   "source": [
    "unnormal_num = 0\n",
    "for i in range(len(train)):\n",
    "    mp = train.iloc[i][3]\n",
    "    if mp > 1200 or mp < 950:\n",
    "        unnormal_num += 1\n",
    "        train.iloc[i][3] = train.iloc[i + 1][3]\n",
    "print(f'remove outliers num: {unnormal_num}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c182752f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cabfca8310>,\n",
       " <matplotlib.lines.Line2D at 0x1cabfca8490>,\n",
       " <matplotlib.lines.Line2D at 0x1cabfca8550>,\n",
       " <matplotlib.lines.Line2D at 0x1cabfca8610>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2a352d5",
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>meantemp</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>25.495521</td>\n",
       "      <td>7.348103</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.857143</td>\n",
       "      <td>27.714286</td>\n",
       "      <td>31.305804</td>\n",
       "      <td>38.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>60.771702</td>\n",
       "      <td>16.769652</td>\n",
       "      <td>13.428571</td>\n",
       "      <td>50.375000</td>\n",
       "      <td>62.625000</td>\n",
       "      <td>72.218750</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind_speed</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>6.802209</td>\n",
       "      <td>4.561602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.475000</td>\n",
       "      <td>6.221667</td>\n",
       "      <td>9.238235</td>\n",
       "      <td>42.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanpressure</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>1008.247674</td>\n",
       "      <td>7.437992</td>\n",
       "      <td>991.375000</td>\n",
       "      <td>1001.625000</td>\n",
       "      <td>1008.563492</td>\n",
       "      <td>1014.875000</td>\n",
       "      <td>1023.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count         mean        std         min          25%  \\\n",
       "meantemp      1462.0    25.495521   7.348103    6.000000    18.857143   \n",
       "humidity      1462.0    60.771702  16.769652   13.428571    50.375000   \n",
       "wind_speed    1462.0     6.802209   4.561602    0.000000     3.475000   \n",
       "meanpressure  1462.0  1008.247674   7.437992  991.375000  1001.625000   \n",
       "\n",
       "                      50%          75%          max  \n",
       "meantemp        27.714286    31.305804    38.714286  \n",
       "humidity        62.625000    72.218750   100.000000  \n",
       "wind_speed       6.221667     9.238235    42.220000  \n",
       "meanpressure  1008.563492  1014.875000  1023.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52776605",
   "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>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   meantemp  humidity  wind_speed  meanpressure\n",
       "0      10.0      84.5         0.0   1015.666667"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6e536f",
   "metadata": {},
   "source": [
    "### 1.1.2 outlier detection for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6fa0149b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cabfd55be0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(test)), test[\"meanpressure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09b1874",
   "metadata": {},
   "source": [
    "### - remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d62ae65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.iloc[0][3] = test.iloc[1][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cf7014f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cabfda9ee0>,\n",
       " <matplotlib.lines.Line2D at 0x1cabfda9fd0>,\n",
       " <matplotlib.lines.Line2D at 0x1cabfdb6100>,\n",
       " <matplotlib.lines.Line2D at 0x1cabfdb61c0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddd4f67",
   "metadata": {},
   "source": [
    "## 1.2 Transfer data to LSTM representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d1fe2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d28fd514",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_process(data, window_size, predict_size):\n",
    "    scaler = StandardScaler()\n",
    "    data = scaler.fit_transform(np.array(data).reshape(-1, 1))\n",
    "    \n",
    "    data_in = []\n",
    "    data_out = []\n",
    "    \n",
    "    for i in range(data.shape[0] - window_size - predict_size):\n",
    "        data_in.append(data[i:i + window_size].reshape(1, window_size)[0])\n",
    "        data_out.append(data[i + window_size:i + window_size + predict_size].reshape(1, predict_size)[0])\n",
    "        \n",
    "    data_in = np.array(data_in).reshape(-1, window_size)\n",
    "    data_out = np.array(data_out).reshape(-1, predict_size)\n",
    "    \n",
    "    data_process = {'datain': data_in, 'dataout': data_out}\n",
    "    \n",
    "    return data_process, scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b642d83",
   "metadata": {},
   "source": [
    "## 1.3 prepare train/test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b1b5ca0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_size = 4\n",
    "window_size = features_size * 3 # features num * time steps\n",
    "predict_size = features_size # features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da56bdd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_processed, train_scaler = data_process(train, window_size, predict_size)\n",
    "X_train, y_train = train_processed['datain'], train_processed['dataout']\n",
    "\n",
    "test_processed, test_scaler = data_process(test, window_size, predict_size)\n",
    "X_test, y_test = test_processed['datain'], test_processed['dataout']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89c5ab59",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.from_numpy(X_train.astype(np.float32))\n",
    "X_test = torch.from_numpy(X_test.astype(np.float32))\n",
    "\n",
    "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
    "y_test = torch.from_numpy(y_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81da8b10",
   "metadata": {},
   "source": [
    "### - data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "881c39bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as Data\n",
    "\n",
    "train_data = Data.TensorDataset(X_train, y_train)\n",
    "test_data = Data.TensorDataset(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "000e5717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of train_data: 5832\n",
      "size of test_data: 440\n"
     ]
    }
   ],
   "source": [
    "print(f'size of train_data: {len(train_data)}')\n",
    "print(f'size of test_data: {len(test_data)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fd5abd4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.6262, -0.4504, -0.6498,  1.7473, -0.6323, -0.4327, -0.6428,  1.7523,\n",
       "         -0.6329, -0.4445, -0.6389,  1.7543]),\n",
       " tensor([-0.6293, -0.4814, -0.6469,  1.7508]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f77a830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.6067, -0.4426, -0.6376,  1.7437, -0.6006, -0.4629, -0.6372,  1.7437,\n",
       "         -0.6039, -0.4520, -0.6346,  1.7438]),\n",
       " tensor([-0.6001, -0.4797, -0.6333,  1.7377]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99ad9fa",
   "metadata": {},
   "source": [
    "# 2. Quantum Enhanced LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee1c6bb",
   "metadata": {},
   "source": [
    "## 2.1 initiate quantum environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7f83f918",
   "metadata": {},
   "outputs": [],
   "source": [
    "class InitQMachine:\n",
    "    def __init__(self, qubitsCount, cbitsCount = 0, machineType = QMachineType.CPU):\n",
    "        self.machine = init_quantum_machine(machineType)\n",
    "        \n",
    "        self.qubits = self.machine.qAlloc_many(qubitsCount)\n",
    "        self.cbits = self.machine.cAlloc_many(cbitsCount)\n",
    "        \n",
    "        print(f'Init Quantum Machine with qubits:[{qubitsCount}] / cbits:[{cbitsCount}] Successfully')\n",
    "    \n",
    "    def __del__(self):\n",
    "        destroy_quantum_machine(self.machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1b73cfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Quantum Machine with qubits:[5] / cbits:[0] Successfully\n"
     ]
    }
   ],
   "source": [
    "# maximum qubits size\n",
    "ctx = InitQMachine(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fd176a",
   "metadata": {},
   "source": [
    "## 2.2 Quantum Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993c3c69",
   "metadata": {},
   "source": [
    "### - Tool Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "467e68dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(prog, filename=''):\n",
    "    dir_path = './images/'\n",
    "    \n",
    "    if filename != '':\n",
    "        draw_qprog(prog, 'pic', filename=f'{dir_path}{filename}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1db12c",
   "metadata": {},
   "source": [
    "### 2.2.1 Quantum Layer Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a9372635",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torch import Tensor\n",
    "from torch.nn import Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0ef35c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantumLayerBase(nn.Module):\n",
    "    def __init__(self, input_size, output_size, *, n_qubits, n_layers = 1, ctx = None):\n",
    "        super(QuantumLayerBase, self).__init__()\n",
    "        \n",
    "        self.data = None # need to input during forward\n",
    "    \n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size # hidden size, not n_qubits\n",
    "        \n",
    "        # quantum infos\n",
    "        self.n_qubits = n_qubits\n",
    "        self.n_layers = n_layers\n",
    "        \n",
    "        self.ctx = ctx\n",
    "        self.qubits = ctx.qubits\n",
    "        self.machine = ctx.machine\n",
    "        \n",
    "        # convert quantum input/output to match classical computation\n",
    "        self.qin = nn.Linear(self.input_size, self.n_qubits)\n",
    "        self.qout = nn.Linear(self.n_qubits, self.output_size)\n",
    "        \n",
    "    @property\n",
    "    def circuit(self):\n",
    "        raise NotImplementedError('Should init circuit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21d0b1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure(self):\n",
    "    HamiZ = [ PauliOperator({f'Z{i}': 1}) for i in range(len(self.qubits)) ]\n",
    "    res = [ eval(qop(self.circuit, Hami, self.machine, self.qubits))[0,0] for Hami in HamiZ ]\n",
    "    \n",
    "    return Parameter(Tensor(res[:self.n_qubits]))\n",
    "\n",
    "QuantumLayerBase.measure = measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8dd09ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs):\n",
    "    y_t = self.qin(Parameter(inputs))\n",
    "    self.data = y_t[0]\n",
    "    \n",
    "    return self.qout(self.measure())\n",
    "\n",
    "QuantumLayerBase.forward = forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73293db3",
   "metadata": {},
   "source": [
    "### 2.2.2 Quantum Layer Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3f45cef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantumLayer(QuantumLayerBase):\n",
    "    def __init__(self, input_size, output_size, *, n_qubits, degree = 1, n_layers = 1, ctx = None):\n",
    "        super(QuantumLayer, self).__init__(input_size, output_size, \n",
    "                                         n_qubits = n_qubits, n_layers = n_layers, ctx = ctx)\n",
    "        \n",
    "        self.degree = degree\n",
    "        self.angles = Parameter(torch.rand(n_layers + 1, degree, self.n_qubits))\n",
    "        \n",
    "    @property\n",
    "    def qparameters_size(self):\n",
    "        return self.angles.flatten().size()[0]\n",
    "        \n",
    "    @property\n",
    "    def circuit(self):\n",
    "        if self.data == None:\n",
    "            raise ValueError('Need to feed a input data!')\n",
    "        \n",
    "        n = self.n_qubits\n",
    "        q = self.qubits\n",
    "        x = self.data\n",
    "        p = self.angles\n",
    "        degree = self.degree\n",
    "        \n",
    "        # quantum gates - must use small case!\n",
    "        identity = VariationalQuantumGate_I\n",
    "        h = VariationalQuantumGate_H\n",
    "        ry = VariationalQuantumGate_RY\n",
    "        cz = VariationalQuantumGate_CZ\n",
    "        u = [\n",
    "            None,\n",
    "            VariationalQuantumGate_U1,\n",
    "            VariationalQuantumGate_U2,\n",
    "            VariationalQuantumGate_U3\n",
    "        ]\n",
    "        \n",
    "        # init variational quantum circuit\n",
    "        vqc = VariationalQuantumCircuit()\n",
    "\n",
    "        # in order to use each qubits => when n_qubits < len(ctx.qubits)\n",
    "        [ vqc.insert(identity(q[i])) for i in range(len(q)) ]\n",
    "        \n",
    "        [ vqc.insert( h(q[i]) ) for i in range(n) ]\n",
    "        [ vqc.insert( ry(q[i], var(x[i] * torch.pi/2)) ) for i in range(n) ]\n",
    "        [ vqc.insert( u[degree](q[i], *[ var(p[0][d][i]) for d in range(degree) ]) ) \n",
    "                 for i in range(n) ]\n",
    "        \n",
    "        for layer in range(self.n_layers):\n",
    "            for i in range(n - 1):\n",
    "                vqc.insert(cz(q[i], q[i + 1]))\n",
    "            vqc.insert(cz(q[n - 1], q[0]))\n",
    "            \n",
    "            [ vqc.insert( u[degree](q[i], *[ var(p[layer + 1][d][i]) for d in range(degree) ]) ) \n",
    "                 for i in range(n) ]\n",
    "        \n",
    "        return vqc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d956e7",
   "metadata": {},
   "source": [
    "## 2.3 Quantum-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d437f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLSTMBase(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.ctx = ctx\n",
    "        \n",
    "    @property\n",
    "    def qparameters_size(self):\n",
    "        num = 0\n",
    "        for attr in dir(self):\n",
    "            if attr.endswith('_circuit'):\n",
    "                num += getattr(self, attr).qparameters_size\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "be3c3cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs, init_states = None):\n",
    "    sequence_size, batch_size, _ = inputs.size()\n",
    "    hidden_sequence = []\n",
    "    \n",
    "    if init_states == None:\n",
    "        h_t, c_t = (\n",
    "            torch.zeros(1, batch_size, self.hidden_size).to(inputs.device),\n",
    "            torch.zeros(1, batch_size, self.hidden_size).to(inputs.device),\n",
    "        )\n",
    "    else:\n",
    "        h_t, c_t = init_states\n",
    "    \n",
    "    return hidden_sequence, (h_t, c_t)\n",
    "\n",
    "QLSTMBase.forward = forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce36bcc1",
   "metadata": {},
   "source": [
    "### 2.3.1 classical Quantum-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1539d83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # Parameters: angles\n",
    "        #  => Q * (n + 1) * degree\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     5       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      5       2       3\n",
    "        # output gate:    5       2       3\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(QLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "\n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "\n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        # reshape hidden_seq p/ retornar\n",
    "        #\n",
    "        # [tensor([[[0.0444, ...]]] => tensor([[[0.0444, ...]]]\n",
    "        # \n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6caa3435",
   "metadata": {},
   "source": [
    "### 2.3.2 adjusted classical QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d152d2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdjustedQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     4       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      4       1       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 4, n_layers = 2, degree = 3, ctx = ctx) # 36\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 4, n_layers = 1, degree = 3, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 3, n_layers = 2, degree = 2, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(AdjustedQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "\n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "\n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bda938",
   "metadata": {},
   "source": [
    "### 2.3.3 peephole QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "66ed785c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PeepholeQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     4       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      4       1       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 4, n_layers = 2, degree = 3, ctx = ctx) # 36\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 4, n_layers = 1, degree = 2, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 3, n_layers = 2, degree = 2, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(PeepholeQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((c_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "            \n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e631aeb",
   "metadata": {},
   "source": [
    "### 2.3.4 Coupled Input and Forget gates QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f77c5ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CIFGQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # coupled IF:     5       2       3\n",
    "        # candidate:      4       2       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # Coupled Input and Forget gate\n",
    "        self.coupled_IF_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 1, degree = 3, ctx = ctx) # 30\n",
    "        # candidate for cell state update\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 3, n_layers = 1, degree = 2, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 4, n_layers = 1, degree = 3, ctx = ctx) # 12\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(CIFGQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # coupled input and forget gate\n",
    "            f_t = torch.sigmoid(self.coupled_IF_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + ((1 - f_t) * g_t)\n",
    "            \n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fded5be6",
   "metadata": {},
   "source": [
    "### 2.3.5 Recurrent Gate Units - QGRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "62d3eb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QGRU(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # update gate:     5       1       3\n",
    "        # candidate:       4       1       3\n",
    "        # reset gate:      3       1       2\n",
    "        \n",
    "        # update gates\n",
    "        self.update_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 1, degree = 1, ctx = ctx) # 45\n",
    "        # candidate for hidden state update\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 3, n_layers = 1, degree = 1, ctx = ctx) # 36\n",
    "        # reset gates\n",
    "        self.reset_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 4, n_layers = 1, degree = 1, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(QGRU, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # update gates\n",
    "            z_t = torch.sigmoid(self.update_circuit(v_t))\n",
    "            # reset gates\n",
    "            r_t = torch.sigmoid(self.reset_circuit(v_t))\n",
    "        \n",
    "            v_hat_t = torch.cat(((r_t * h_t)[0], x_t), dim = 1)\n",
    "            # candidate for hidden state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_hat_t))\n",
    "            h_t = (z_t * g_t) + (1 - z_t) * h_t \n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63514807",
   "metadata": {},
   "source": [
    "### - QLSTMs Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5ab09833",
   "metadata": {},
   "outputs": [],
   "source": [
    "QLSTMMap = {\n",
    "    'classical': ('QLSTM', QLSTM),\n",
    "    'adjusted': ('QLSTM(adjusted)', AdjustedQLSTM),\n",
    "    'peephole': ('peephole QLSTM', PeepholeQLSTM),\n",
    "    'CIFG': ('CIFG-QLSTM', CIFGQLSTM),\n",
    "    'GRU': ('QGRU', QGRU)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6497332",
   "metadata": {},
   "source": [
    "## 2.4 Stacked QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2c6cb0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "class StackedQLSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, *, num_layers = 1, ctx = None, mode = 'classical'):\n",
    "        super().__init__()\n",
    "        \n",
    "        label, qlstm = QLSTMMap.get(mode)\n",
    "        self.qlstms = nn.Sequential(OrderedDict([\n",
    "            (f'{label} {i + 1}', qlstm(input_size if i == 0 else hidden_size , hidden_size, ctx = ctx)) \n",
    "                for i in range(num_layers)\n",
    "        ]))\n",
    "\n",
    "    def forward(self, inputs, parameters = None):\n",
    "        outputs = None\n",
    "        \n",
    "        for i, qlstm in enumerate(self.qlstms):\n",
    "            if i != 0:\n",
    "                inputs = outputs\n",
    "            \n",
    "            outputs, parameters = qlstm(inputs, parameters)\n",
    "        \n",
    "        return outputs, parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55dbbb2f",
   "metadata": {},
   "source": [
    "# 3. Quantum Model and Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8313baa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_output, *, num_layers = 1, ctx = None, mode = 'classical'):\n",
    "        super(QModel, self).__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.mode = mode\n",
    "        \n",
    "        self.qlstm = StackedQLSTM(input_size, hidden_size, \n",
    "                                  num_layers = num_layers, ctx = ctx, mode = mode)\n",
    "        self.predict = nn.Linear(hidden_size, num_output)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.unsqueeze(0)\n",
    "        \n",
    "        # sequence lenth , batch_size, features length\n",
    "        # \n",
    "        h0 = torch.zeros(1, x.size(1), self.hidden_size)\n",
    "        c0 = torch.zeros(1, x.size(1), self.hidden_size)\n",
    "        \n",
    "        out, _ = self.qlstm(x, (h0, c0))\n",
    "        out = self.predict(out[0])\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf519363",
   "metadata": {},
   "source": [
    "## 3.1 train QModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1abf2ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import RandomSampler\n",
    "\n",
    "def train_model(model, datas, batch_size, *, loss_func, optimizer, epoch = 50):\n",
    "    losses = []\n",
    "    sampler = RandomSampler(datas, num_samples = batch_size)\n",
    "    \n",
    "    for step in range(epoch):\n",
    "        train_loss = 0.0\n",
    "        \n",
    "        for index in sampler:\n",
    "            batch_x, batch_y = datas[index][0], datas[index][1]\n",
    "            b_x = batch_x.unsqueeze(0)\n",
    "            b_y = batch_y.unsqueeze(0)\n",
    "            \n",
    "            output = model(b_x)\n",
    "\n",
    "            loss = loss_func(output, b_y)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        print(f'Epoch {step + 1}/{epoch}: Loss: {train_loss / batch_size}')\n",
    "        losses.append(train_loss / batch_size)\n",
    "    \n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cdd42a",
   "metadata": {},
   "source": [
    "## 3.2 Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d7ba3d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "84475602",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAE_naive(actuals, predicteds):\n",
    "    n = len(actuals)\n",
    "    err = 0.0\n",
    "    \n",
    "    for i in range(1, n):\n",
    "        err += np.abs(actuals[i] - actuals[i - 1])\n",
    "    return err / (n - 1)\n",
    "\n",
    "def calculate_accuarcy(model, X_test, y_test, scaler=train_scaler):\n",
    "    n = len(X_test)\n",
    "    \n",
    "    actuals = []\n",
    "    predicteds = []\n",
    "    \n",
    "    for i in range(0, n, predict_size):\n",
    "        actual = scaler.inverse_transform(y_test[i:i+1].data)\n",
    "        actuals.append(np.array(actual[0]))\n",
    "        predicted = scaler.inverse_transform(model(X_test[i:i+1]).data)\n",
    "        predicteds.append(np.array(predicted[0]))\n",
    "    \n",
    "    actuals = np.array(actuals)\n",
    "    predicteds = np.array(predicteds)\n",
    "    \n",
    "    mae = mean_absolute_error(actuals, predicteds)\n",
    "    mase = mae / MAE_naive(actuals.flatten(), predicteds.flatten())\n",
    "    mape = mean_absolute_percentage_error(actuals, predicteds)\n",
    "    mse = mean_squared_error(actuals, predicteds)\n",
    "    rmse = mse ** 0.5\n",
    "    \n",
    "    return np.array([(1 - mase) * 100, rmse, mse, mae, mape])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cdcdb169",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuarcies(model, X_test, y_test, scaler=test_scaler):\n",
    "    n = len(X_test)\n",
    "    \n",
    "    actuals = []\n",
    "    predicteds = []\n",
    "    \n",
    "    for i in range(0, n, predict_size):\n",
    "        actual = scaler.inverse_transform(y_test[i:i+1].data)\n",
    "        actuals.append(np.array(actual[0]))\n",
    "        predicted = scaler.inverse_transform(model(X_test[i:i+1]).data)\n",
    "        predicteds.append(np.array(predicted[0]))\n",
    "    \n",
    "    accuarcies = []\n",
    "    for i in range(predict_size):\n",
    "        a = [row[i] for row in actuals]\n",
    "        p = [row[i] for row in predicteds]\n",
    "    \n",
    "        mae = mean_absolute_error(a, p)\n",
    "        mase = mae / MAE_naive(a, p)\n",
    "        mape = mean_absolute_percentage_error(a, p)\n",
    "        mse = mean_squared_error(a, p)\n",
    "        rmse = mse ** 0.5\n",
    "        \n",
    "        accuarcies.append([rmse, mse, mae, mape, mase])\n",
    "    \n",
    "    return np.array(accuarcies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec9e103",
   "metadata": {},
   "source": [
    "## 3.3 Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "38531f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_size = 4\n",
    "window_size = features_size * 3 # \n",
    "predict_size = features_size # features\n",
    "\n",
    "input_size = window_size\n",
    "num_output = predict_size\n",
    "\n",
    "hidden_size = 32\n",
    "num_layers = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8060d12f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'classical'\n",
    "qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                    num_layers = num_layers, ctx = ctx, mode=mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0b0a20eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel.load_state_dict(torch.load(f\"./model/{mode}/model_{mode}_6.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "94b59d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([98.84762115,  7.73534096, 59.83549978,  5.93476099,  0.29862687])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcy(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "09d3f586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([98.84762115,  7.78554785, 60.61475538,  5.97328106,  0.4204885 ])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcy(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3e2d0e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.85687814, 23.58926528,  4.0263041 ,  0.20484892,  3.08078494],\n",
       "       [ 9.92983245, 98.6015725 ,  7.89079557,  0.19749369,  1.34324395],\n",
       "       [ 9.49464805, 90.14834155,  7.62023141,  1.27529353,  2.47091174],\n",
       "       [ 5.48815472, 30.11984219,  4.35579316,  0.00431786,  2.91710985]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcies(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2afd6ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLosses(losses, color=\"red\", fill_color=\"#CCCCFF\", title=None, fontsize=14):\n",
    "    losses_mean = np.mean(losses, axis=0)\n",
    "    losses_std = np.std(losses, axis=0)\n",
    "    \n",
    "    x = range(1, len(losses_mean) + 1)\n",
    "    plt.grid()\n",
    "    plt.plot(x, losses_mean, color=color, linewidth=2.0, linestyle='--', label=\"平均值\")\n",
    "    plt.fill_between(x, losses_mean + losses_std, losses_mean - losses_std, \n",
    "                     color=fill_color, label=\"标准差\")\n",
    "    plt.legend([\"平均值\", \"标准差\"], loc=\"upper right\")\n",
    "    \n",
    "    if title != None:\n",
    "        plt.title(title, fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5820cf0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_losses = []\n",
    "for i in range(10):\n",
    "    with open(f'loss/{mode}/loss_{mode}_{i + 1}.pkl', 'rb') as pkl_file:\n",
    "        losses = pickle.load(pkl_file)\n",
    "    all_losses.append(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f7f1dfb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLosses(all_losses, color=\"#F5C340\", fill_color=\"#5B72E0\", title=f'损失函数 - QLSTM')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f6c1ec",
   "metadata": {},
   "source": [
    "### - describe trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "29c8cdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QModel(\n",
       "  (qlstm): StackedQLSTM(\n",
       "    (qlstms): Sequential(\n",
       "      (QLSTM 1): QLSTM(\n",
       "        (input_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (forget_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (candidate_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (output_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "      )\n",
       "      (QLSTM 2): QLSTM(\n",
       "        (input_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (forget_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (candidate_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (output_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (predict): Linear(in_features=32, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3497e649",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total parameters: 4228\n",
      "quantum paramers: 180\n"
     ]
    }
   ],
   "source": [
    "trainable = 0\n",
    "for p in qmodel.parameters():\n",
    "    if p.requires_grad:\n",
    "        trainable += p.numel()\n",
    "print(f'total parameters: {trainable}')\n",
    "\n",
    "qlstm = QLSTMMap.get(qmodel.mode)[1]\n",
    "print(f'quantum paramers: {qlstm(1, 1, ctx = ctx).qparameters_size}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbfe49c2",
   "metadata": {},
   "source": [
    "## 3.4 Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "785b303c",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "for i in range(0, len(X_test), features_size):\n",
    "    input_data = X_test[i:i+1]\n",
    "    result = qmodel(input_data)\n",
    "    result = test_scaler.inverse_transform(result.data)\n",
    "    \n",
    "    results.append(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "21f631e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = pd.DataFrame(test, columns=('meantemp', 'humidity', 'wind_speed'))\n",
    "test2 = pd.DataFrame(test, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ec7cdfd7",
   "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>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.433262</td>\n",
       "      <td>67.986304</td>\n",
       "      <td>12.390009</td>\n",
       "      <td>1011.641839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.422735</td>\n",
       "      <td>65.429704</td>\n",
       "      <td>12.960063</td>\n",
       "      <td>1012.886420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.991511</td>\n",
       "      <td>64.440130</td>\n",
       "      <td>14.080064</td>\n",
       "      <td>1009.895073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.479808</td>\n",
       "      <td>65.682538</td>\n",
       "      <td>14.061177</td>\n",
       "      <td>1013.699025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.969849</td>\n",
       "      <td>75.054734</td>\n",
       "      <td>12.020615</td>\n",
       "      <td>1015.360331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>45.835824</td>\n",
       "      <td>32.757781</td>\n",
       "      <td>23.189005</td>\n",
       "      <td>1005.709679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>50.518395</td>\n",
       "      <td>29.435729</td>\n",
       "      <td>24.903844</td>\n",
       "      <td>1002.224536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>48.996311</td>\n",
       "      <td>29.410894</td>\n",
       "      <td>25.328550</td>\n",
       "      <td>1003.903025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>45.138495</td>\n",
       "      <td>32.242023</td>\n",
       "      <td>24.528070</td>\n",
       "      <td>1006.058483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>41.434517</td>\n",
       "      <td>36.707640</td>\n",
       "      <td>22.471875</td>\n",
       "      <td>1007.482626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      meantemp   humidity  wind_speed  meanpressure\n",
       "0    15.433262  67.986304   12.390009   1011.641839\n",
       "1    16.422735  65.429704   12.960063   1012.886420\n",
       "2    18.991511  64.440130   14.080064   1009.895073\n",
       "3    14.479808  65.682538   14.061177   1013.699025\n",
       "4     5.969849  75.054734   12.020615   1015.360331\n",
       "..         ...        ...         ...           ...\n",
       "105  45.835824  32.757781   23.189005   1005.709679\n",
       "106  50.518395  29.435729   24.903844   1002.224536\n",
       "107  48.996311  29.410894   25.328550   1003.903025\n",
       "108  45.138495  32.242023   24.528070   1006.058483\n",
       "109  41.434517  36.707640   22.471875   1007.482626\n",
       "\n",
       "[110 rows x 4 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(results)\n",
    "results.columns = ('meantemp', 'humidity', 'wind_speed', 'meanpressure')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5aa79a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = pd.DataFrame(results, columns=('meantemp', 'humidity', 'wind_speed'))\n",
    "result2 = pd.DataFrame(results, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5cfd68e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cac25b1640>,\n",
       " <matplotlib.lines.Line2D at 0x1cac25b1760>,\n",
       " <matplotlib.lines.Line2D at 0x1cac25b1820>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(test1)\n",
    "plt.subplot(1,2,2)\n",
    "plt.ylim(0, 100)\n",
    "plt.plot(result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0e0cac4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cac2616df0>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(test2)\n",
    "plt.subplot(1,2,2)\n",
    "plt.ylim(1000, 1030)\n",
    "plt.plot(result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "157c6a98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f33f79f3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}