research/old/randomexp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qreservoirpy import QReservoir, Layers, utilities\n",
    "\n",
    "\n",
    "from qiskit.quantum_info import random_unitary\n",
    "from qiskit_aer import AerSimulator\n",
    "backend = AerSimulator(method=\"statevector\")\n",
    "\n",
    "from qiskit.circuit.library.standard_gates import HGate, SGate, CXGate\n",
    "\n",
    "import tikzplotlib\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_qubits = 4\n",
    "SHOTS = 10000\n",
    "timeseries = [0, 1, 1, 2, 1, 1, 2] * 5\n",
    "encoder = {\n",
    "    0: '00',\n",
    "    1: '01',\n",
    "    2: '10',\n",
    "    3: '11'\n",
    "} \n",
    "\n",
    "\n",
    "OP = random_unitary(2**n_qubits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "models = np.array([\n",
    "    SVC(kernel=ker, max_iter=1000) for ker in ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "] \n",
    "+ [\n",
    "    RidgeClassifier(alpha=alph, max_iter=1000) for alph in np.linspace(0, 1e-3, 9)\n",
    "] \n",
    "+ [\n",
    "    KNeighborsClassifier(n_neighbors=n) for n in range(1, 10)\n",
    "]\n",
    ", dtype=object)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def build_method(circuit, timestep, encoding, reservoir):\n",
    "    circuit.measure(range(2))\n",
    "    circuit.initialize(encoding[timestep], range(2))\n",
    "    circuit.unitary(reservoir, circuit.qubits)\n",
    "\n",
    "qres = QReservoir(qubits=n_qubits, layers=[\n",
    "    Layers.Reset(),\n",
    "    Layers.Timeseries(build_method=build_method, \n",
    "                      encoding = encoder,\n",
    "                      reservoir = OP)\n",
    "])\n",
    "fig = qres.circuit.draw('mpl')\n",
    "# tikzplotlib.save(\"data/circuit_random.tex\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Simulating: 100%|██████████| 1/1 [00:02<00:00,  2.30s/it]\n"
     ]
    }
   ],
   "source": [
    "states = qres.run(\n",
    "    timeseries=timeseries, \n",
    "    shots=SHOTS,\n",
    "\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qres.circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WARMUP = 0.1 # 10%\n",
    "warmup_idx = int(len(states) * WARMUP)\n",
    "\n",
    "xstates = states[:-1][warmup_idx:]\n",
    "target = timeseries[1:][warmup_idx:]\n",
    "fig, ax = utilities.result_plotter(xstates, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "cmap = plt.get_cmap('jet', len(np.unique(timeseries)))\n",
    "\n",
    "plt.scatter(xstates[:, 0], xstates[:, 1], cmap=cmap, c=target)\n",
    "# for i, x, y in zip(target, xstates[:, 2 * ii], xstates[:, 2 * ii + 1]):\n",
    "#     ax.scatter(x, y,  color=cmap(i))\n",
    "\n",
    "# ax.set_xlabel(f'state {2 * ii + 1}')\n",
    "# ax.set_ylabel(f'state {2*(ii+1)}')\n",
    "plt.xlabel(\"state 1\")\n",
    "plt.ylabel(\"state 2\")\n",
    "tikzplotlib.save(\"data/phase_random.tex\")\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "if xstates.shape[-1] >= 3:\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "    cmap = plt.get_cmap('jet', len(np.unique(timeseries)))\n",
    "    for i, x, y, z in zip(target, xstates[:, 0], xstates[:, 1], xstates[:, 2]):\n",
    "        ax.scatter(x, y, z, color=cmap(i))\n",
    "    ax.set_xlabel('state 1')\n",
    "    ax.set_ylabel('state 2')\n",
    "    ax.set_zlabel('state 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of KNeighborsClassifier(n_neighbors=1): 0.8918181818181807\n",
      "Score of KNeighborsClassifier(n_neighbors=3): 0.8199999999999998\n",
      "Score of KNeighborsClassifier(n_neighbors=2): 0.8090909090909086\n",
      "Score of KNeighborsClassifier(n_neighbors=4): 0.7281818181818178\n",
      "Score of RidgeClassifier(alpha=0.001, max_iter=1000): 0.6809090909090905\n",
      "Score of RidgeClassifier(alpha=0.000625, max_iter=1000): 0.6799999999999997\n",
      "Score of RidgeClassifier(alpha=0.0005, max_iter=1000): 0.6799999999999997\n",
      "Score of RidgeClassifier(alpha=0.000375, max_iter=1000): 0.6799999999999996\n",
      "Score of RidgeClassifier(alpha=0.00075, max_iter=1000): 0.6790909090909086\n",
      "Score of RidgeClassifier(alpha=0.00025, max_iter=1000): 0.6790909090909086\n",
      "Score of RidgeClassifier(alpha=0.000875, max_iter=1000): 0.6781818181818177\n",
      "Score of RidgeClassifier(alpha=0.000125, max_iter=1000): 0.676363636363636\n",
      "Score of RidgeClassifier(alpha=0.0, max_iter=1000): 0.6727272727272723\n",
      "Score of KNeighborsClassifier(): 0.6472727272727269\n",
      "Score of SVC(kernel='sigmoid', max_iter=1000): 0.5899999999999994\n",
      "Score of SVC(kernel='linear', max_iter=1000): 0.5899999999999994\n",
      "Score of KNeighborsClassifier(n_neighbors=6): 0.587272727272727\n",
      "Score of KNeighborsClassifier(n_neighbors=7): 0.5663636363636363\n",
      "Score of SVC(max_iter=1000): 0.557272727272727\n",
      "Score of SVC(kernel='poly', max_iter=1000): 0.557272727272727\n",
      "Score of KNeighborsClassifier(n_neighbors=8): 0.5554545454545451\n",
      "Score of KNeighborsClassifier(n_neighbors=9): 0.5536363636363638\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "results = utilities.stress_test_models(xstates, target, models, test_to_train_ratio=1/3)\n",
    "sorting = np.flip(np.argsort(results))\n",
    "for model, res in zip(models[sorting], results[sorting]):\n",
    "    print(f'Score of {model}: {res}')\n",
    "    \n",
    "\n",
    "model = models[sorting][0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predicting: 100%|██████████| 43/43 [00:02<00:00, 14.78it/s]\n"
     ]
    }
   ],
   "source": [
    "predict_from = timeseries[-20:]\n",
    "num_preds = 2\n",
    "\n",
    "\n",
    "pred_states, preds = qres.predict(\n",
    "    num_pred=num_preds,\n",
    "    model=model,\n",
    "    shots=SHOTS,\n",
    "    from_series=predict_from,\n",
    "    backend=backend\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e77f6d87c0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_values = timeseries\n",
    "\n",
    "pred_plot = np.append(predict_from[-num_preds:], preds)\n",
    "true_plot = np.append(predict_from[-num_preds:], true_values)\n",
    "\n",
    "minlen = min(len(pred_plot), len(true_plot))\n",
    "\n",
    "plt.plot(pred_plot[:minlen], label=\"Predictions\")\n",
    "plt.vlines(num_preds - 1, ymin=np.min(target), ymax=np.max(target), colors='k', linestyles='dashed')\n",
    "plt.plot(true_plot[:minlen], label=\"True timeseries\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Experiment: 100%|██████████| 1000/1000 [2:08:41<00:00,  7.72s/it] \n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "NUM_EXP = 1000\n",
    "exp_res = np.zeros((NUM_EXP, len(models)))\n",
    "\n",
    "for i in tqdm(range(NUM_EXP), desc=\"Experiment\"):\n",
    "\n",
    "\n",
    "    OP = random_unitary(2**n_qubits)\n",
    "    qres = QReservoir(qubits=n_qubits, layers=[\n",
    "    Layers.Reset(),\n",
    "    Layers.Timeseries(build_method=build_method, \n",
    "                      encoding = encoder,\n",
    "                      reservoir = OP)\n",
    "    ])\n",
    "\n",
    "    states = qres.run(\n",
    "        timeseries=timeseries, \n",
    "        shots=SHOTS,\n",
    "        disable_status_bar=True,\n",
    "        transpile=True,\n",
    "        backend=backend\n",
    "    )\n",
    "\n",
    "    xstates = states[:-1][warmup_idx:]\n",
    "    target = timeseries[1:][warmup_idx:]\n",
    "    exp_res[i] = utilities.stress_test_models(xstates, target, models, test_to_train_ratio=1/3, N=50)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(\"data/random_LONGSIM\", exp_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(ncols=3, nrows=3, sharex='col', sharey='row')\n",
    "fig.suptitle(\"KNeighborsClassifier\")\n",
    "ii = 0\n",
    "jj = 0\n",
    "\n",
    "mod = KNeighborsClassifier(n_neighbors=2)\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == KNeighborsClassifier:\n",
    "        axes[ii][jj].hist(exp_res[:, idx], density=True)\n",
    "        axes[ii][jj].legend([model.n_neighbors])\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "# temp_res = exp_res[:, 0]\n",
    "# _ = axes.hist(temp_res)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "plt.title(\"KNeighborsClassifier\")\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == KNeighborsClassifier and model.n_neighbors in range(1, 4):\n",
    "        plt.xlim([0.5, 1])\n",
    "        plt.hist(exp_res[:, idx], density=True, label=model.n_neighbors, alpha=0.7, bins=50)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "plt.legend()\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Probability density\")\n",
    "# tikzplotlib.save(\"data/random_KNeighbors.tex\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=4, nrows=3)\n",
    "fig.suptitle(\"RidgeClassifier\")\n",
    "ii = 0\n",
    "jj = 0\n",
    "\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == RidgeClassifier:\n",
    "        # axes[ii][jj].set_xlim([0, 1])\n",
    "        axes[ii][jj].hist(exp_res[:, idx], density=True)\n",
    "        axes[ii][jj].legend([round(model.alpha * 1e4, 1)])\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, axes = plt.subplots(ncols=2, nrows=2)\n",
    "# fig.suptitle(\"SVC\")\n",
    "# ii = 0\n",
    "# jj = 0\n",
    "plt.figure()\n",
    "plt.title(\"SVC\")\n",
    "# plt.xlim([0, 1])\n",
    "\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == SVC:\n",
    "        plt.xlim([0.3, 1])\n",
    "        plt.hist(exp_res[:, idx], density=True, label=model.kernel, alpha=0.7, bins=70)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "plt.legend()\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Probability density\")\n",
    "# tikzplotlib.save(\"data/random_SVC.tex\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=1, sharey=True)\n",
    "\n",
    "BINS = 30\n",
    "ax[0].set_title(\"KNeighborsClassifier\")\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == KNeighborsClassifier and model.n_neighbors in [1, 2, 3]:\n",
    "        ax[0].set_xlim([0.2, 1])\n",
    "        ax[0].hist(exp_res[:, idx], density=True, label=model.n_neighbors, alpha=0.7, bins=BINS)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"Score\")\n",
    "ax[0].set_ylabel(\"Probability density\")\n",
    "\n",
    "ax[1].set_title(\"SVC\")\n",
    "\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == SVC:\n",
    "        ax[1].set_xlim([0.2, 1])\n",
    "        ax[1].hist(exp_res[:, idx], density=True, label=model.kernel, alpha=0.7, bins=BINS)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "ax[1].legend()\n",
    "ax[1].set_xlabel(\"Score\")\n",
    "\n",
    "# tikzplotlib.save(\"data/random_BOTH.tex\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quantum",
   "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}