CPU_circuit_cut_SPSA_CE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:19.320003Z",
     "start_time": "2024-02-15T18:09:19.308526Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3j/3p_dzlvs1nsdfryf9hm7gjnr0000gn/T/ipykernel_8385/2279893312.py:9: DeprecationWarning: ``qiskit.algorithms`` has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. The ``qiskit.algorithms`` import path is deprecated as of qiskit-terra 0.25.0 and will be removed in Qiskit 1.0. Please run ``pip install qiskit_algorithms`` and use ``import qiskit_algorithms`` instead.\n",
      "  from qiskit.algorithms.optimizers import ADAM, SPSA\n"
     ]
    }
   ],
   "source": [
    "from circuit_knitting.cutting import partition_problem\n",
    "from qiskit.circuit import QuantumCircuit, ParameterVector\n",
    "from qiskit.quantum_info import SparsePauliOp, PauliList\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from qiskit_algorithms.optimizers.cobyla import COBYLA\n",
    "from qiskit.algorithms.optimizers import ADAM, SPSA\n",
    "from qiskit_algorithms.utils import algorithm_globals\n",
    "from IPython.display import clear_output\n",
    "from loss_optimization.accuracy_score import encode_y\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b94d0bd-445c-48c7-949c-11d240fce1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c00f7c-def5-4106-bd98-522927f57892",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84e0ff14-514d-440f-851a-63c136d80243",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:20.482075Z",
     "start_time": "2024-02-15T18:09:20.472994Z"
    }
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.923998</td>\n",
       "      <td>1.531329</td>\n",
       "      <td>0.566517</td>\n",
       "      <td>-0.920265</td>\n",
       "      <td>-3.141593</td>\n",
       "      <td>0.004682</td>\n",
       "      <td>-1.66872</td>\n",
       "      <td>-0.104720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.771994</td>\n",
       "      <td>-0.457820</td>\n",
       "      <td>0.257508</td>\n",
       "      <td>-1.301064</td>\n",
       "      <td>-3.141593</td>\n",
       "      <td>-0.650792</td>\n",
       "      <td>-2.40918</td>\n",
       "      <td>-2.094395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0    -0.923998  1.531329       0.566517      -0.920265 -3.141593  0.004682   \n",
       "1    -2.771994 -0.457820       0.257508      -1.301064 -3.141593 -0.650792   \n",
       "\n",
       "   DiabetesPedigreeFunction       Age  Outcome  \n",
       "0                  -1.66872 -0.104720        1  \n",
       "1                  -2.40918 -2.094395        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "data = pd.read_csv(\"diabetes_normalized.csv\")\n",
    "data = data.drop([\"Unnamed: 0\"], axis=1)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92a101c1-41f3-4fbe-b1c4-78eee1e9d524",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:20.926480Z",
     "start_time": "2024-02-15T18:09:20.920864Z"
    }
   },
   "outputs": [],
   "source": [
    "y = data[\"Outcome\"]\n",
    "x = data.drop([\"Outcome\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8ef2ba7-dda4-41ea-944f-c710c9fc9528",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:21.304727Z",
     "start_time": "2024-02-15T18:09:21.299733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(537, 8) (537,) (231, 8) (231,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "505092d4-3e8d-4853-9f59-3391739bae30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:21.800966Z",
     "start_time": "2024-02-15T18:09:21.796485Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train_A = x_train.iloc[:, :4]\n",
    "x_train_B = x_train.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea2b1465-28bb-4db5-9541-4ba60e770021",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:22.250366Z",
     "start_time": "2024-02-15T18:09:22.235445Z"
    }
   },
   "outputs": [],
   "source": [
    "x_test_A = x_test.iloc[:, :4]\n",
    "x_test_B = x_test.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "878cdeed-18a8-4f22-bea4-2031a7be0a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_y_train = encode_y(y_train)\n",
    "new_y_test = encode_y(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202a0b22-ad7d-468f-878a-e4569cc6d0fa",
   "metadata": {},
   "source": [
    "## Data Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23a08b2f-5a0b-4aa5-96e7-5cd2a4655aaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:23.793347Z",
     "start_time": "2024-02-15T18:09:23.786288Z"
    }
   },
   "outputs": [],
   "source": [
    "# Data Embedding - Angle Encoding\n",
    "def angle_encoding(feature_dims: int):\n",
    "    embedding = QuantumCircuit(feature_dims)\n",
    "    feature_param = ParameterVector(\"Theta\", feature_dims)\n",
    "    for qubit in range(feature_dims):\n",
    "        embedding.ry(feature_param[qubit], qubit)\n",
    "    return embedding, feature_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cbfc86a-2a27-4381-84ce-2a469fb57661",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:24.329495Z",
     "start_time": "2024-02-15T18:09:24.321044Z"
    }
   },
   "outputs": [],
   "source": [
    "embedding, feature_params = angle_encoding(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09640b63-6b10-40b0-837f-7acbc1f88c47",
   "metadata": {},
   "source": [
    "## Tensor Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06ed2511-a0bb-4f5b-85b9-85940a954de3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:25.102164Z",
     "start_time": "2024-02-15T18:09:25.070178Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensor_network.ttn import TTN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c2d9089-9292-4580-98e4-6f6287a59473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.552x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttn = TTN(num_qubits=8).ttn_simple(complex_structure=False)\n",
    "ttn.draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebafa996-adaf-4060-9f32-21cdb706a932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ttn.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f81b56c-c727-47cb-94a6-e2b5359d8a19",
   "metadata": {},
   "source": [
    "## Circuit Cutting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6cda3f8fc3c6b2d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:34:50.371027Z",
     "start_time": "2024-02-15T03:34:50.309487Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "observables = PauliList([\"ZIIIIIII\"])\n",
    "partitioned_problem = partition_problem(circuit=ttn, partition_labels=\"AAAABBBB\", observables=observables)\n",
    "sub_circuits = partitioned_problem.subcircuits\n",
    "sub_observables = partitioned_problem.subobservables\n",
    "bases = partitioned_problem.bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a43241fdcd1decfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:34:50.869863Z",
     "start_time": "2024-02-15T03:34:50.865500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': PauliList(['IIII']), 'B': PauliList(['ZIII'])}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_observables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81331ea7-8123-4e9b-867d-54fd786e3fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 723.581x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_circuits[\"A\"].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2af67465-17d0-4791-8e1c-279835483dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 807.394x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_circuits[\"B\"].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77655d0a6a0d1c81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:00.944952Z",
     "start_time": "2024-02-15T03:35:00.935773Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling overhead: 9.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df0c32d-bfd9-407f-b867-d08632f53764",
   "metadata": {},
   "source": [
    "### Sub Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3327e421fea3182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:05.890185Z",
     "start_time": "2024-02-15T03:35:05.693811Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from circuit_knitting.cutting import generate_cutting_experiments\n",
    "\n",
    "subexperiments, coefficients = generate_cutting_experiments(\n",
    "    circuits=sub_circuits, observables=sub_observables, num_samples=np.inf\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "560e4327-900d-4758-b747-7ae311fe2902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subexperiments\n",
    "len(subexperiments[\"A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cb2b2aa32d6d16b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:14.153045Z",
     "start_time": "2024-02-15T03:35:14.062981Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1060.9x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[\"A\"][0].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a0e854a-f742-4b19-8367-8155a54ab2b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1311.74x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[\"B\"][3].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce6334-c721-40fc-b46e-f720eae969c0",
   "metadata": {},
   "source": [
    "## Neural Network Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2504dedb-a5f4-4d4e-b75d-1b0bed7615e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.neural_networks import SamplerQNN, NeuralNetwork\n",
    "from qiskit_aer.primitives import Sampler\n",
    "from qiskit_aer import AerSimulator\n",
    "from typing import Callable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0873fe1f-7f6c-43d0-b661-5b29ca82d167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from qiskit_ibm_provider import IBMProvider\n",
    "# provider = IBMProvider()\n",
    "# qasm_backend = provider.get_backend(\"ibmq_qasm_simulator\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b81d4ba-7d75-4a89-b721-394b7c0dc82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = AerSimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "874404ef-4077-42c4-9db0-159fb621b542",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = Sampler(backend_options={\"simulator\": sim}, transpile_options={\"optimization_level\": 3})\n",
    "# sampler = Sampler(backend_options={\"simulator\": sim})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c252b4-15d2-44e7-a577-329c684c5c78",
   "metadata": {},
   "source": [
    "### Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0e55d30c-e279-4f58-bfe6-e9a6d9c7d446",
   "metadata": {},
   "outputs": [],
   "source": [
    "from primitives.custom_sampler_qnn import CustomSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5926c303-49fe-4052-b910-9e0763014904",
   "metadata": {},
   "source": [
    "#### For subexperiments[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5aefce4e-f3fb-4694-91a3-a5161d74aee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]\n",
    "# final_circuits[0].draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8853b9e4-a866-43f5-a908-e1f0d968c765",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler_qcnn = CustomSampler(\n",
    "    circuits=final_circuits, \n",
    "    sampler=sampler,\n",
    "    input_params=feature_params.params,\n",
    "    weight_params=sub_circuits[\"A\"].parameters,\n",
    "    input_gradients=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f60076a5-2776-4eb9-b316-5e0b71ace948",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saashajoshi/anaconda3/envs/qiskit_x86/lib/python3.8/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: invalid value encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n",
      "/Users/saashajoshi/anaconda3/envs/qiskit_x86/lib/python3.8/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: divide by zero encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 1.6968419551849365\n"
     ]
    }
   ],
   "source": [
    "weights_A = algorithm_globals.random.random(7)\n",
    "forward_output = sampler_qcnn.forward(\n",
    "    input_data=x_train_A,\n",
    "    weights=weights_A,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f534f8b7-9104-4101-9646-8e0760fea432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16)\n",
      "537\n",
      "Output of the forward pass for first sample: \n",
      "[[0.         1.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         1.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.41699219 0.         0.58300781 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.38574219 0.         0.61425781 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         1.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         1.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# forward output is a dictionary of 6 subex_circ items.\n",
    "print(f\"Output shape for {len(x_train_A)} samples: {forward_output[0].shape}\")\n",
    "print(len(forward_output[0]))\n",
    "print(f\"Output of the forward pass for first sample: \\n{np.array([forward_output[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d59acee2-9e90-4105-92bd-ee4801a2db86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Backward Pass: %s seconds 35.82046985626221\n"
     ]
    }
   ],
   "source": [
    "input_grad, weights_grad = sampler_qcnn.backward(\n",
    "    input_data=x_train_A,\n",
    "    weights=weights_A\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9b081ccf-6d7b-4d8b-bece-61518575b4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16, 7)\n",
      "Output of the backward pass for first sample for first subexperiment circuit: \n",
      "[[[ 0.02539062  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.02539062  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[-0.00732422  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.00732422  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[-0.00097656  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.05175781  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.00097656  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.05175781  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[ 0.0078125   0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.09667969  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.01953125  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.06933594  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[ 0.00390625  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.00390625  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[ 0.0078125   0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.0078125   0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Output shape for {len(x_train_A)} samples: {weights_grad[0].shape}\")\n",
    "print(f\"Output of the backward pass for first sample for first subexperiment circuit: \\n{np.array([weights_grad[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7a3765-0f3f-434b-bbad-adb88c863fc7",
   "metadata": {},
   "source": [
    "#### For subexperiments[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "476fd0f1-1156-4a1a-96a8-17a2c1812c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c4cb1766-d548-4c35-b0c5-c612372e0d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler_qcnn2 = CustomSampler(\n",
    "    circuits=final_circuits2, \n",
    "    sampler=sampler,\n",
    "    input_params=feature_params.params,\n",
    "    weight_params=sub_circuits[\"B\"].parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "35b3ceb5-5e16-4f30-b89d-56a5651c7f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 2.9668989181518555\n"
     ]
    }
   ],
   "source": [
    "weights_B = algorithm_globals.random.random(8)\n",
    "forward_output2 = sampler_qcnn2._forward(\n",
    "    input_data=x_train_B,\n",
    "    weights=weights_B,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fc2aef81-e8c6-4b1b-a563-2dd41b3e27d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16)\n",
      "537\n",
      "Output of the forward pass for first sample: \n",
      "[[0.62207031 0.37792969 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.62597656 0.37402344 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.61132812 0.38867188 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.63964844 0.36035156 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.07714844 0.21582031 0.53808594 0.16894531 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.05566406 0.23242188 0.53613281 0.17578125 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Output shape for {len(x_train_A)} samples: {forward_output2[0].shape}\")\n",
    "print(len(forward_output2[0]))\n",
    "print(f\"Output of the forward pass for first sample: \\n{np.array([forward_output2[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "734f3787-0de1-44f5-8c66-c7f1425d9f11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Backward Pass: %s seconds 73.23601412773132\n"
     ]
    }
   ],
   "source": [
    "input_grad2, weights_grad2 = sampler_qcnn2._backward(\n",
    "    input_data=x_train_B,\n",
    "    weights=weights_B\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e91322c9-7a6e-4b7c-8c5d-b298dba19b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16, 8)\n",
      "Output of the backward pass for first sample for first subexperiment circuit: \n",
      "[[[ 0.52001953  0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [-0.52001953  0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
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     ]
    }
   ],
   "source": [
    "print(f\"Output shape for {len(x_train_B)} samples: {weights_grad2[0].shape}\")\n",
    "print(f\"Output of the backward pass for first sample for first subexperiment circuit: \\n{np.array([weights_grad2[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fdb89d-3094-44a5-8aae-078836e29602",
   "metadata": {},
   "source": [
    "## Loss and Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "73d6684f-d62d-452e-a503-5676541f9a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.utils.loss_functions import L2Loss\n",
    "from loss_optimization.custom_cross_entropy_loss import CustomCrossEntropyLoss\n",
    "from qiskit_algorithms.optimizers import COBYLA, SPSA, GradientDescent\n",
    "from loss_optimization.objective_func import CustomMultiClassObjectiveFunction\n",
    "# from loss_optimization.callback import callback\n",
    "from loss_optimization.optimization import create_objective, minimizer, print_optimizer_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a795c0c0-88f0-44f3-9de9-dc333d2d6466",
   "metadata": {},
   "outputs": [],
   "source": [
    "def callback_spsa(nfev=None, params=None, fval=None, stepsize=None, accepted=None):\n",
    "    \"\"\"\n",
    "    nfev: the number of function evals\n",
    "    params: the current parameters\n",
    "    fval: the current function value\n",
    "    stepsize: size of the update step\n",
    "    accepted: whether the step was accepted (not used for )\n",
    "    \"\"\"\n",
    "    # objective_func_vals = loss\n",
    "    global objective_func_vals\n",
    "\n",
    "    if (nfev % 3) == 0:\n",
    "        objective_func_vals.append(fval)\n",
    "        print(f\"SPSA Epoch {len(objective_func_vals)}: {fval:.5f}\")\n",
    "\n",
    "def callback_cobyla(weights, obj_func_eval):\n",
    "    # clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    print(f\"COBYLA Epoch {len(objective_func_vals)}: {obj_func_eval:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "00fcbea5-d74a-4227-aa21-ae9fe3892fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals = []\n",
    "# loss = L2Loss()\n",
    "loss = CustomCrossEntropyLoss()\n",
    "# optimizer = COBYLA(maxiter=5)\n",
    "optimizer = SPSA(maxiter=50, callback=callback_spsa)\n",
    "# optimizer = GradientDescent(maxiter=2) # This doesn't work yet. The gradient shape doesn't match."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba6ba3e-791d-4f6a-92d8-116d7e03d4d6",
   "metadata": {},
   "source": [
    "#### Optimizer Result for sub-circuits[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc3ab8f7-1902-4c28-bcd3-13035cd4c0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.random.random((7,))\n",
    "function = CustomMultiClassObjectiveFunction(x_train_A, y_train, sampler_qcnn, loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8269a9e8-beae-4ee3-b9da-2f727af3b1f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saashajoshi/anaconda3/envs/qiskit_x86/lib/python3.8/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: invalid value encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n",
      "/Users/saashajoshi/anaconda3/envs/qiskit_x86/lib/python3.8/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: divide by zero encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 1.8229498863220215\n",
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      "Running Time for Forward Pass: %s seconds 1.4675822257995605\n",
      "Running Time for Forward Pass: %s seconds 1.3974111080169678\n",
      "SPSA Epoch 1: 4.52984\n",
      "Running Time for Forward Pass: %s seconds 1.4863109588623047\n",
      "Running Time for Forward Pass: %s seconds 1.4164810180664062\n",
      "Running Time for Forward Pass: %s seconds 1.4632749557495117\n",
      "SPSA Epoch 2: 3.66654\n",
      "Running Time for Forward Pass: %s seconds 1.387760877609253\n",
      "Running Time for Forward Pass: %s seconds 1.4566431045532227\n",
      "Running Time for Forward Pass: %s seconds 1.463125228881836\n",
      "SPSA Epoch 3: 3.28394\n",
      "Running Time for Forward Pass: %s seconds 1.3913919925689697\n",
      "Running Time for Forward Pass: %s seconds 1.4698779582977295\n",
      "Running Time for Forward Pass: %s seconds 1.3995311260223389\n",
      "SPSA Epoch 4: 3.10047\n",
      "Running Time for Forward Pass: %s seconds 1.4719150066375732\n",
      "Running Time for Forward Pass: %s seconds 1.4142990112304688\n",
      "Running Time for Forward Pass: %s seconds 1.469527006149292\n",
      "SPSA Epoch 5: 3.01589\n",
      "Running Time for Forward Pass: %s seconds 1.403404951095581\n",
      "Running Time for Forward Pass: %s seconds 1.4664220809936523\n",
      "Running Time for Forward Pass: %s seconds 1.3907370567321777\n",
      "SPSA Epoch 6: 2.96158\n",
      "Running Time for Forward Pass: %s seconds 1.4710631370544434\n",
      "Running Time for Forward Pass: %s seconds 1.4014348983764648\n",
      "Running Time for Forward Pass: %s seconds 1.4772469997406006\n",
      "SPSA Epoch 7: 2.94215\n",
      "Running Time for Forward Pass: %s seconds 1.3944199085235596\n",
      "Running Time for Forward Pass: %s seconds 1.4689710140228271\n",
      "Running Time for Forward Pass: %s seconds 1.4707226753234863\n",
      "SPSA Epoch 8: 2.92144\n",
      "Running Time for Forward Pass: %s seconds 1.4128661155700684\n",
      "Running Time for Forward Pass: %s seconds 1.4915635585784912\n",
      "Running Time for Forward Pass: %s seconds 1.417051076889038\n",
      "SPSA Epoch 9: 2.90740\n",
      "Running Time for Forward Pass: %s seconds 1.4711008071899414\n",
      "Running Time for Forward Pass: %s seconds 1.410682201385498\n",
      "Running Time for Forward Pass: %s seconds 1.463536262512207\n",
      "SPSA Epoch 10: 2.90084\n",
      "Running Time for Forward Pass: %s seconds 1.3756048679351807\n",
      "Running Time for Forward Pass: %s seconds 1.4455230236053467\n",
      "Running Time for Forward Pass: %s seconds 1.3778657913208008\n",
      "SPSA Epoch 11: 2.90116\n",
      "Running Time for Forward Pass: %s seconds 1.441192865371704\n",
      "Running Time for Forward Pass: %s seconds 1.3755130767822266\n",
      "Running Time for Forward Pass: %s seconds 1.4415292739868164\n",
      "SPSA Epoch 12: 2.89183\n",
      "Running Time for Forward Pass: %s seconds 1.4037210941314697\n",
      "Running Time for Forward Pass: %s seconds 1.4714949131011963\n",
      "Running Time for Forward Pass: %s seconds 1.4757659435272217\n",
      "SPSA Epoch 13: 2.89242\n",
      "Running Time for Forward Pass: %s seconds 1.4061360359191895\n",
      "Running Time for Forward Pass: %s seconds 1.4723918437957764\n",
      "Running Time for Forward Pass: %s seconds 1.4046778678894043\n",
      "SPSA Epoch 14: 2.88403\n",
      "Running Time for Forward Pass: %s seconds 1.4747660160064697\n",
      "Running Time for Forward Pass: %s seconds 1.4074349403381348\n",
      "Running Time for Forward Pass: %s seconds 1.472214937210083\n",
      "SPSA Epoch 15: 2.89754\n",
      "Running Time for Forward Pass: %s seconds 1.4085400104522705\n",
      "Running Time for Forward Pass: %s seconds 1.4668190479278564\n",
      "Running Time for Forward Pass: %s seconds 1.4054112434387207\n",
      "SPSA Epoch 16: 2.88189\n",
      "Running Time for Forward Pass: %s seconds 1.4884772300720215\n",
      "Running Time for Forward Pass: %s seconds 1.408186912536621\n",
      "Running Time for Forward Pass: %s seconds 1.4733660221099854\n",
      "SPSA Epoch 17: 2.89667\n",
      "Running Time for Forward Pass: %s seconds 1.4056439399719238\n",
      "Running Time for Forward Pass: %s seconds 1.4983069896697998\n",
      "Running Time for Forward Pass: %s seconds 1.399151086807251\n",
      "SPSA Epoch 18: 2.89198\n",
      "Running Time for Forward Pass: %s seconds 1.4683728218078613\n",
      "Running Time for Forward Pass: %s seconds 1.406123161315918\n",
      "Running Time for Forward Pass: %s seconds 1.4667272567749023\n",
      "SPSA Epoch 19: 2.89680\n",
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      "SPSA Epoch 50: 2.89122\n",
      "{   'fun': 2.8912175485396525,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.14140348,  0.71451792,  1.51738345,  1.46617832,  1.28437779,\n",
      "        0.04761344, -0.17326146])}\n",
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      "SPSA Epoch 100: 2.88555\n",
      "{   'fun': 2.8855494897922234,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.14312509,  0.21938955,  1.13892567,  1.43655847,  1.63655782,\n",
      "        0.62061583,  1.30246146])}\n",
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      "SPSA Epoch 150: 0.40036\n",
      "{   'fun': 0.400355476435428,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.13357454,  0.62828233,  0.16852707,  0.30004134,  1.35669265,\n",
      "        0.37734118, -0.91340949])}\n",
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      "{   'fun': 0.3913122368360614,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.20813145,  0.89513296,  2.4872956 ,  1.3152346 , -0.25949703,\n",
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      "SPSA Epoch 250: 2.89841\n",
      "{   'fun': 2.8984083693386307,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.14341401, -0.52262021,  1.57092957, -0.66165622, -0.31650961,\n",
      "        1.19333458,  1.93345493])}\n",
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      "Running Time for Forward Pass: %s seconds 1.3958337306976318\n",
      "Running Time for Forward Pass: %s seconds 1.4544718265533447\n",
      "Running Time for Forward Pass: %s seconds 1.3963451385498047\n",
      "SPSA Epoch 297: 2.89096\n",
      "Running Time for Forward Pass: %s seconds 1.458329677581787\n",
      "Running Time for Forward Pass: %s seconds 1.3969249725341797\n",
      "Running Time for Forward Pass: %s seconds 1.4560561180114746\n",
      "SPSA Epoch 298: 2.89653\n",
      "Running Time for Forward Pass: %s seconds 1.3900237083435059\n",
      "Running Time for Forward Pass: %s seconds 1.4634130001068115\n",
      "Running Time for Forward Pass: %s seconds 1.5086581707000732\n",
      "SPSA Epoch 299: 2.89156\n",
      "Running Time for Forward Pass: %s seconds 1.3968591690063477\n",
      "Running Time for Forward Pass: %s seconds 1.5081050395965576\n",
      "Running Time for Forward Pass: %s seconds 1.394083023071289\n",
      "SPSA Epoch 300: 2.89418\n",
      "{   'fun': 2.8941784747509964,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.14049349e+00,  4.96052800e-04,  4.56841861e-01, -4.32138507e-01,\n",
      "        3.49425315e-01,  4.39178850e-01,  4.62204821e-01])}\n",
      "Running Time for optimizing Subex-A6: %s seconds 287.4765782356262\n"
     ]
    }
   ],
   "source": [
    "# Optimizer result for 0th circuit for sub-circuit-A\n",
    "start_time = time.time()\n",
    "optimizer_result_A0 = minimizer(function, function.objective0, function.gradient0, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A1 = minimizer(function, function.objective1, function.gradient1, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A2: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A2 = minimizer(function, function.objective2, function.gradient2, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A3: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A3 = minimizer(function, function.objective3, function.gradient3, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A4: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A4 = minimizer(function, function.objective4, function.gradient4, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A5: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A5 = minimizer(function, function.objective5, function.gradient5, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A6: %s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d06fd73-6d5f-4251-b91f-7803a4eb8098",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_results_A = [optimizer_result_A0, optimizer_result_A1, optimizer_result_A2, optimizer_result_A3, optimizer_result_A4, optimizer_result_A5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "62002702-1c92-4bec-942b-7a6cfa2ee79d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New params (The final point of the minimization): [-1.14140348  0.71451792  1.51738345  1.46617832  1.28437779  0.04761344\n",
      " -0.17326146]\n",
      "The final value of the minimization: 2.8912175485396525\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.14312509  0.21938955  1.13892567  1.43655847  1.63655782  0.62061583\n",
      "  1.30246146]\n",
      "The final value of the minimization: 2.8855494897922234\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.13357454  0.62828233  0.16852707  0.30004134  1.35669265  0.37734118\n",
      " -0.91340949]\n",
      "The final value of the minimization: 0.400355476435428\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.20813145  0.89513296  2.4872956   1.3152346  -0.25949703  1.1159287\n",
      " -1.58754194]\n",
      "The final value of the minimization: 0.3913122368360614\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.14341401 -0.52262021  1.57092957 -0.66165622 -0.31650961  1.19333458\n",
      "  1.93345493]\n",
      "The final value of the minimization: 2.8984083693386307\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.14049349e+00  4.96052800e-04  4.56841861e-01 -4.32138507e-01\n",
      "  3.49425315e-01  4.39178850e-01  4.62204821e-01]\n",
      "The final value of the minimization: 2.8941784747509964\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n"
     ]
    }
   ],
   "source": [
    "# Print results from 6 sub-experiments of sub-circuit-A\n",
    "for opt_result in optimizer_results_A:\n",
    "    print_optimizer_results(opt_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8be438f3-e234-4217-bd1c-7e313b393e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 42,
     "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": [
    "plt.plot(objective_func_vals[:50], label=\"A Subex-1\")\n",
    "plt.plot(objective_func_vals[50:100], label=\"A Subex-2\")\n",
    "plt.plot(objective_func_vals[100:150], label=\"A Subex-3\")\n",
    "plt.plot(objective_func_vals[150:200], label=\"A Subex-4\")\n",
    "plt.plot(objective_func_vals[200:250], label=\"A Subex-5\")\n",
    "plt.plot(objective_func_vals[250:300], label=\"A Subex-6\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "621adb10-e0d3-4016-ad2b-615a998ada42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective_func_vals.clear()\n",
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8310166a-4dc2-4ffd-b1cf-2d8f83aa3548",
   "metadata": {},
   "source": [
    "#### Optimizer Result for sub-circuits[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2d4424fc-7ce6-41f0-b177-543f3708c0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point2 = np.random.random((8,))\n",
    "function2 = CustomMultiClassObjectiveFunction(x_train_B, y_train, sampler_qcnn2, loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "60edb6f0-937d-4faf-b4f2-24fc1cd5c731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 3.051767110824585\n",
      "Running Time for Forward Pass: %s seconds 2.92987322807312\n",
      "Running Time for Forward Pass: %s seconds 2.86759090423584\n",
      "Running Time for Forward Pass: %s seconds 2.915104627609253\n",
      "Running Time for Forward Pass: %s seconds 2.796194076538086\n",
      "Running Time for Forward Pass: %s seconds 2.869007110595703\n",
      "Running Time for Forward Pass: %s seconds 2.7848219871520996\n",
      "Running Time for Forward Pass: %s seconds 2.8639800548553467\n",
      "Running Time for Forward Pass: %s seconds 2.772935152053833\n",
      "Running Time for Forward Pass: %s seconds 2.8676090240478516\n",
      "Running Time for Forward Pass: %s seconds 2.820146083831787\n",
      "Running Time for Forward Pass: %s seconds 2.850147008895874\n",
      "Running Time for Forward Pass: %s seconds 2.804391860961914\n",
      "Running Time for Forward Pass: %s seconds 2.884356737136841\n",
      "Running Time for Forward Pass: %s seconds 2.8291709423065186\n",
      "Running Time for Forward Pass: %s seconds 2.922947883605957\n",
      "Running Time for Forward Pass: %s seconds 3.0303149223327637\n",
      "Running Time for Forward Pass: %s seconds 3.025515079498291\n",
      "Running Time for Forward Pass: %s seconds 2.9312102794647217\n",
      "Running Time for Forward Pass: %s seconds 2.901219129562378\n",
      "Running Time for Forward Pass: %s seconds 3.1611549854278564\n",
      "Running Time for Forward Pass: %s seconds 3.1184229850769043\n",
      "Running Time for Forward Pass: %s seconds 2.948880910873413\n",
      "Running Time for Forward Pass: %s seconds 2.9763922691345215\n",
      "Running Time for Forward Pass: %s seconds 2.844249963760376\n",
      "Running Time for Forward Pass: %s seconds 3.231339693069458\n",
      "Running Time for Forward Pass: %s seconds 2.9131181240081787\n",
      "Running Time for Forward Pass: %s seconds 3.0019478797912598\n",
      "Running Time for Forward Pass: %s seconds 2.954099178314209\n",
      "Running Time for Forward Pass: %s seconds 2.8484601974487305\n",
      "Running Time for Forward Pass: %s seconds 2.978458881378174\n",
      "Running Time for Forward Pass: %s seconds 3.0434229373931885\n",
      "Running Time for Forward Pass: %s seconds 3.0528929233551025\n",
      "Running Time for Forward Pass: %s seconds 2.9133851528167725\n",
      "Running Time for Forward Pass: %s seconds 3.261096954345703\n",
      "Running Time for Forward Pass: %s seconds 3.063778877258301\n",
      "Running Time for Forward Pass: %s seconds 3.2568929195404053\n",
      "Running Time for Forward Pass: %s seconds 3.105670928955078\n",
      "Running Time for Forward Pass: %s seconds 3.0472848415374756\n",
      "Running Time for Forward Pass: %s seconds 2.8752050399780273\n",
      "Running Time for Forward Pass: %s seconds 2.907343864440918\n",
      "Running Time for Forward Pass: %s seconds 3.135648012161255\n",
      "Running Time for Forward Pass: %s seconds 2.9073140621185303\n",
      "Running Time for Forward Pass: %s seconds 3.048259973526001\n",
      "Running Time for Forward Pass: %s seconds 2.9767229557037354\n",
      "Running Time for Forward Pass: %s seconds 2.905271053314209\n",
      "Running Time for Forward Pass: %s seconds 3.09224009513855\n",
      "Running Time for Forward Pass: %s seconds 3.1019577980041504\n",
      "Running Time for Forward Pass: %s seconds 3.127346992492676\n",
      "Running Time for Forward Pass: %s seconds 2.9218859672546387\n",
      "Running Time for Forward Pass: %s seconds 3.217377185821533\n",
      "Running Time for Forward Pass: %s seconds 2.885171890258789\n",
      "Running Time for Forward Pass: %s seconds 2.991481065750122\n",
      "SPSA Epoch 1: 4.12235\n",
      "Running Time for Forward Pass: %s seconds 3.0191612243652344\n",
      "Running Time for Forward Pass: %s seconds 2.9981298446655273\n",
      "Running Time for Forward Pass: %s seconds 3.158975839614868\n",
      "SPSA Epoch 2: 4.51866\n",
      "Running Time for Forward Pass: %s seconds 3.40252685546875\n",
      "Running Time for Forward Pass: %s seconds 3.044419765472412\n",
      "Running Time for Forward Pass: %s seconds 3.057865858078003\n",
      "SPSA Epoch 3: 3.67919\n",
      "Running Time for Forward Pass: %s seconds 2.9550440311431885\n",
      "Running Time for Forward Pass: %s seconds 3.035546064376831\n",
      "Running Time for Forward Pass: %s seconds 3.1404600143432617\n",
      "SPSA Epoch 4: 3.40268\n",
      "Running Time for Forward Pass: %s seconds 3.191451072692871\n",
      "Running Time for Forward Pass: %s seconds 3.1075809001922607\n",
      "Running Time for Forward Pass: %s seconds 4.047506809234619\n",
      "SPSA Epoch 5: 3.40946\n",
      "Running Time for Forward Pass: %s seconds 3.1892690658569336\n",
      "Running Time for Forward Pass: %s seconds 3.3626177310943604\n",
      "Running Time for Forward Pass: %s seconds 3.492655038833618\n",
      "SPSA Epoch 6: 3.41218\n",
      "Running Time for Forward Pass: %s seconds 3.4614899158477783\n",
      "Running Time for Forward Pass: %s seconds 3.338736057281494\n",
      "Running Time for Forward Pass: %s seconds 3.757035970687866\n",
      "SPSA Epoch 7: 3.39864\n",
      "Running Time for Forward Pass: %s seconds 3.3947629928588867\n",
      "Running Time for Forward Pass: %s seconds 3.635061025619507\n",
      "Running Time for Forward Pass: %s seconds 3.432297945022583\n",
      "SPSA Epoch 8: 3.10634\n",
      "Running Time for Forward Pass: %s seconds 3.5452637672424316\n",
      "Running Time for Forward Pass: %s seconds 3.4457638263702393\n",
      "Running Time for Forward Pass: %s seconds 3.581038236618042\n",
      "SPSA Epoch 9: 3.11312\n",
      "Running Time for Forward Pass: %s seconds 3.636941909790039\n",
      "Running Time for Forward Pass: %s seconds 3.6237621307373047\n",
      "Running Time for Forward Pass: %s seconds 3.500836133956909\n",
      "SPSA Epoch 10: 3.10923\n",
      "Running Time for Forward Pass: %s seconds 3.7635247707366943\n",
      "Running Time for Forward Pass: %s seconds 3.5614588260650635\n",
      "Running Time for Forward Pass: %s seconds 3.68188214302063\n",
      "SPSA Epoch 11: 3.11603\n",
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      "SPSA Epoch 50: 2.71399\n",
      "{   'fun': 2.7139885652280715,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-2.51114365e-01,  7.06722311e-01,  4.46730456e-01,  6.53160531e-01,\n",
      "       -1.64479033e-01,  2.84203253e-04,  6.75209126e-02, -1.56896044e+00])}\n",
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      "SPSA Epoch 100: 2.70704\n",
      "{   'fun': 2.7070416383510616,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.87011078,  0.10230073,  2.18832823,  0.50894278,  0.14398486,\n",
      "        0.25858764,  0.05897474, -1.59120401])}\n",
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      "SPSA Epoch 101: 6.00855\n",
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      "SPSA Epoch 150: 2.98722\n",
      "{   'fun': 2.9872204060714393,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.69684256,  1.42072531,  1.60347079,  0.82707356,  1.04408667,\n",
      "        2.49860318,  1.60651006, -0.62371355])}\n",
      "Running Time for optimizing Subex-B3: %s seconds 688.0677289962769\n",
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      "SPSA Epoch 200: 2.71211\n",
      "{   'fun': 2.712105362159003,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.58379333, -1.00104714, -1.47784382,  3.76188553, -2.64388801,\n",
      "       -0.06955919,  2.29327882, -2.42673447])}\n",
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      "SPSA Epoch 250: -0.26126\n",
      "{   'fun': -0.26125866646770285,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.0967462 , -0.96764343, -0.08463251,  0.70431114, -0.90236289,\n",
      "       -0.16288288,  0.17585649,  1.56969655])}\n",
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      "SPSA Epoch 251: 1.48642\n",
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      "SPSA Epoch 287: -0.23400\n",
      "Running Time for Forward Pass: %s seconds 3.3228633403778076\n",
      "Running Time for Forward Pass: %s seconds 3.474302291870117\n",
      "Running Time for Forward Pass: %s seconds 3.383786916732788\n",
      "SPSA Epoch 288: -0.23430\n",
      "Running Time for Forward Pass: %s seconds 3.5287551879882812\n",
      "Running Time for Forward Pass: %s seconds 3.5297818183898926\n",
      "Running Time for Forward Pass: %s seconds 3.634006977081299\n",
      "SPSA Epoch 289: -0.23439\n",
      "Running Time for Forward Pass: %s seconds 3.514191150665283\n",
      "Running Time for Forward Pass: %s seconds 3.6706631183624268\n",
      "Running Time for Forward Pass: %s seconds 3.3393101692199707\n",
      "SPSA Epoch 290: -0.23453\n",
      "Running Time for Forward Pass: %s seconds 3.409389019012451\n",
      "Running Time for Forward Pass: %s seconds 3.367223024368286\n",
      "Running Time for Forward Pass: %s seconds 3.3820669651031494\n",
      "SPSA Epoch 291: -0.23456\n",
      "Running Time for Forward Pass: %s seconds 3.3105311393737793\n",
      "Running Time for Forward Pass: %s seconds 3.4187541007995605\n",
      "Running Time for Forward Pass: %s seconds 3.4539148807525635\n",
      "SPSA Epoch 292: -0.23523\n",
      "Running Time for Forward Pass: %s seconds 3.3648171424865723\n",
      "Running Time for Forward Pass: %s seconds 3.368335008621216\n",
      "Running Time for Forward Pass: %s seconds 3.3823890686035156\n",
      "SPSA Epoch 293: -0.23491\n",
      "Running Time for Forward Pass: %s seconds 3.3155877590179443\n",
      "Running Time for Forward Pass: %s seconds 3.3761239051818848\n",
      "Running Time for Forward Pass: %s seconds 3.2525758743286133\n",
      "SPSA Epoch 294: -0.23475\n",
      "Running Time for Forward Pass: %s seconds 3.410203218460083\n",
      "Running Time for Forward Pass: %s seconds 3.274653196334839\n",
      "Running Time for Forward Pass: %s seconds 3.3282558917999268\n",
      "SPSA Epoch 295: -0.23475\n",
      "Running Time for Forward Pass: %s seconds 3.2698440551757812\n",
      "Running Time for Forward Pass: %s seconds 3.342303991317749\n",
      "Running Time for Forward Pass: %s seconds 3.2978649139404297\n",
      "SPSA Epoch 296: -0.23510\n",
      "Running Time for Forward Pass: %s seconds 3.3815698623657227\n",
      "Running Time for Forward Pass: %s seconds 3.2883152961730957\n",
      "Running Time for Forward Pass: %s seconds 3.402287006378174\n",
      "SPSA Epoch 297: -0.23505\n",
      "Running Time for Forward Pass: %s seconds 3.397933006286621\n",
      "Running Time for Forward Pass: %s seconds 3.39548397064209\n",
      "Running Time for Forward Pass: %s seconds 3.3777332305908203\n",
      "SPSA Epoch 298: -0.23524\n",
      "Running Time for Forward Pass: %s seconds 3.428288221359253\n",
      "Running Time for Forward Pass: %s seconds 3.2712879180908203\n",
      "Running Time for Forward Pass: %s seconds 3.4225239753723145\n",
      "SPSA Epoch 299: -0.23545\n",
      "Running Time for Forward Pass: %s seconds 3.49727201461792\n",
      "Running Time for Forward Pass: %s seconds 3.4519240856170654\n",
      "Running Time for Forward Pass: %s seconds 3.349057912826538\n",
      "SPSA Epoch 300: -0.23504\n",
      "{   'fun': -0.23503588718204424,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([ 1.03369905,  0.02131792, -0.43808993,  1.25086367,  0.80716828,\n",
      "        0.91607134,  0.34826731,  1.56362386])}\n",
      "Running Time for optimizing Subex-B6: %s seconds 715.8237099647522\n"
     ]
    }
   ],
   "source": [
    "# Optimizer result for 0th circuit for sub-circuit-B\n",
    "start_time = time.time()\n",
    "optimizer_result_B0 = minimizer(function2, function2.objective0, function2.gradient0, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B1 = minimizer(function2, function2.objective1, function2.gradient1, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B2: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B2 = minimizer(function2, function2.objective2, function2.gradient2, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B3: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B3 = minimizer(function2, function2.objective3, function2.gradient3, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B4: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B4 = minimizer(function2, function2.objective4, function2.gradient4, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B5: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B5 = minimizer(function2, function2.objective5, function2.gradient5, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B6: %s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d6607356-2e2a-4e70-85ff-f258c2f5cbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_results_B = [optimizer_result_B0, optimizer_result_B1, optimizer_result_B2, optimizer_result_B3, optimizer_result_B4, optimizer_result_B5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0896372c-0930-4451-b75b-5edb6733efc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New params (The final point of the minimization): [-2.51114365e-01  7.06722311e-01  4.46730456e-01  6.53160531e-01\n",
      " -1.64479033e-01  2.84203253e-04  6.75209126e-02 -1.56896044e+00]\n",
      "The final value of the minimization: 2.7139885652280715\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.87011078  0.10230073  2.18832823  0.50894278  0.14398486  0.25858764\n",
      "  0.05897474 -1.59120401]\n",
      "The final value of the minimization: 2.7070416383510616\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.69684256  1.42072531  1.60347079  0.82707356  1.04408667  2.49860318\n",
      "  1.60651006 -0.62371355]\n",
      "The final value of the minimization: 2.9872204060714393\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.58379333 -1.00104714 -1.47784382  3.76188553 -2.64388801 -0.06955919\n",
      "  2.29327882 -2.42673447]\n",
      "The final value of the minimization: 2.712105362159003\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.0967462  -0.96764343 -0.08463251  0.70431114 -0.90236289 -0.16288288\n",
      "  0.17585649  1.56969655]\n",
      "The final value of the minimization: -0.26125866646770285\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [ 1.03369905  0.02131792 -0.43808993  1.25086367  0.80716828  0.91607134\n",
      "  0.34826731  1.56362386]\n",
      "The final value of the minimization: -0.23503588718204424\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n"
     ]
    }
   ],
   "source": [
    "# Print results from 6 sub-experiments of sub-circuit-B\n",
    "for opt_result in optimizer_results_B:\n",
    "    print_optimizer_results(opt_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d38f5b9e-b92e-429d-b715-f734eae3a76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 72,
     "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": [
    "plt.plot(objective_func_vals[:50], label=\"B Subex1\")\n",
    "plt.plot(objective_func_vals[50:100], label=\"B Subex2\")\n",
    "plt.plot(objective_func_vals[100:150], label=\"B Subex3\")\n",
    "plt.plot(objective_func_vals[150:200], label=\"B Subex4\")\n",
    "plt.plot(objective_func_vals[200:250], label=\"B Subex5\")\n",
    "plt.plot(objective_func_vals[250:300], label=\"B Subex6\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ed7eacba-18a0-476e-97ba-53acde85b048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective_func_vals.clear()\n",
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8068f82d-6639-4886-a96f-f605d23f34bb",
   "metadata": {},
   "source": [
    "### At this point, the training is complete. \n",
    "We have 12 lists of 7 or 8 parameter values that will now be used to make predictions on the test cases. \n",
    "New subcircuits will be built using these parameter values and then their results will be used to reconstruct expectation values. This process will occur for all the inputs in the test set.\n",
    "\n",
    "\n",
    "Let us think of the training process as a box that we can tune/edit while training on the train set. However, once the training process is complete, the box is locked and provided to the user. The user can now send test data to the box and get its desired output.\n",
    "\n",
    "However, in the case of circuit cutting, instead of having one box, we have multiple boxes. In our case, we have a total of 12 boxes (6 boxes per cut). These 12 boxes are trained independently of each other and locked after the training process is complete. The user, now, instead of sending the test data to one box, splits it into 2 parts that are sent to these 12 boxes. The 12 boxes produce quasi-probability distributions respective to the circuits they implement which are then reconstructed to produce the expectation value according to the test input. This process is repeated for every data point in the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1fc3ef-8cef-4326-8ff9-bbe594b32a23",
   "metadata": {},
   "source": [
    "## Reconstruct Expectation Values and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8824ed8a-0db9-4133-adf5-41a31171b8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from circuit_knitting.cutting import reconstruct_expectation_values\n",
    "from qiskit_aer.primitives import Sampler\n",
    "from qiskit.primitives import SamplerResult\n",
    "from loss_optimization.accuracy_score import get_accuracy_score, predict\n",
    "from circuit_cut.reconstruct_exp_val import get_subcircuit_results, get_dict_sampler_results, get_reconstructed_expvals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3f4e61-4039-40d7-8604-bfbad8adbc6a",
   "metadata": {},
   "source": [
    "### Reconstructing for train accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2331db8c-82ab-43a1-b9c7-2f401fc78493",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed0d1504-3faa-483c-b78e-54ef28d13de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "22d2c629-840b-4d59-af33-90cdeb74649b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_results_A = get_subcircuit_results(x_train_A, final_circuits, optimizer_results_A, sampler)\n",
    "train_results_B = get_subcircuit_results(x_train_B, final_circuits2, optimizer_results_B, sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6d812aa1-58ec-4db9-98c5-102d5a7a2611",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of SamplerResults of 6 sub-circuits A and B for each 537 train data inputs\n",
    "A_dict = get_dict_sampler_results(x_train_A, subexperiments[\"A\"], train_results_A)\n",
    "B_dict = get_dict_sampler_results(x_train_B, subexperiments[\"B\"], train_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17f6e961-c44f-472c-bbd9-a8989db338ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_expvals = get_reconstructed_expvals(A_dict, B_dict, coefficients, sub_observables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5a7d686f-8dbb-4f07-9ba5-b7dabd1f8eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = predict(reconstructed_expvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "307e1bed-0b68-4578-a52c-78974b35a50c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Train Accuracy of the model is: (0.6536312849162011, 0.6536312849162011)\n"
     ]
    }
   ],
   "source": [
    "# accuracy_score function encodes y_train inside the function. Therefore, pass original y_train here.\n",
    "train_score1 = get_accuracy_score(new_y_train, y_predicted)\n",
    "train_score2 = get_accuracy_score(y_train, y_predicted)\n",
    "print(f\"The Train Accuracy of the model is: {train_score1, train_score2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4897db-5983-4c81-8fdc-7d2fddb947fa",
   "metadata": {},
   "source": [
    "### Reconstructing for test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c74ec028-ab75-4414-8430-c587149f33b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1830f0a-ea9b-4d25-9a29-afefe51e1e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ff3c0935-3633-4ce9-af6a-99b4e7715944",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results_A = get_subcircuit_results(x_test_A, final_circuits, optimizer_results_A, sampler)\n",
    "test_results_B = get_subcircuit_results(x_test_B, final_circuits2, optimizer_results_B, sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ea59d60c-3e63-4210-b67e-3078362bd26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of SamplerResults of 6 sub-circuits A and B for each 231 test data inputs\n",
    "A_dict = get_dict_sampler_results(x_test_A, subexperiments[\"A\"], test_results_A)\n",
    "B_dict = get_dict_sampler_results(x_test_B, subexperiments[\"B\"], test_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d7c9624b-ab21-4aee-99b7-4749885197dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_expvals = get_reconstructed_expvals(A_dict, B_dict, coefficients, sub_observables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "69dded00-c876-44e4-a143-5f5bb4032dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = predict(reconstructed_expvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f5fd5b7d-5afc-45bc-831b-8e715f7f99af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Test Accuracy of the model is: (0.645021645021645, 0.645021645021645)\n"
     ]
    }
   ],
   "source": [
    "# Pass original y_test here.\n",
    "test_score1 = get_accuracy_score(new_y_test, y_predicted)\n",
    "test_score2 = get_accuracy_score(y_test, y_predicted)\n",
    "print(f\"The Test Accuracy of the model is: {test_score1, test_score2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687229a8-a1bc-45cd-a063-4b19c46e4ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# brand_new_y_train = new_y_train.replace({-1: 1, 1: -1})\n",
    "# brand_new_y_test = new_y_test.replace({-1: 1, 1: -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5854fa-04cb-4991-8a8a-b17af63d2c5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059bba06-e0d9-44f7-8a59-0bbdf3eb9dfe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba9466da-de3c-41ae-8656-320b7804ea79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "40b0e231-939a-489f-8567-6b973a87fdb2",
   "metadata": {},
   "source": [
    "## Test original circuit for x_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "aec16e6c-9909-4fbb-84bb-19daa47e7354",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.neural_networks import EstimatorQNN\n",
    "from qiskit_machine_learning.algorithms import NeuralNetworkClassifier\n",
    "from qiskit.primitives import BackendEstimator\n",
    "from qiskit_aer.primitives import Estimator\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_machine_learning.utils.loss_functions import CrossEntropyLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cfb7a3be-ddf5-4d96-9bd0-76d02eac5dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding, features = angle_encoding(8)\n",
    "new_ttn = TTN(num_qubits=8).ttn_simple(complex_structure=False)\n",
    "org_circuit = embedding.compose(new_ttn)\n",
    "# final_circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dfed65af-ebb2-495b-8258-cb0b5af6c914",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gpu_simulator = AerSimulator(device='GPU')\n",
    "simulator = AerSimulator()\n",
    "backend = Estimator(backend_options={\"simulator\": simulator}, transpile_options={\"optimization_level\": 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "510fdd70-ba73-4838-874b-5546a15433d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss = L2Loss()\n",
    "loss = CrossEntropyLoss()\n",
    "# optimizer = COBYLA(maxiter=5)\n",
    "# optimizer = SPSA(maxiter=5, callback=callback_spsa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f67c702c-de14-48ee-a04a-e5ae82092c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable = SparsePauliOp([\"ZIIIIIII\"])\n",
    "estimator_qnn = EstimatorQNN(\n",
    "    estimator=backend,\n",
    "    circuit=org_circuit,\n",
    "    observables=observable,\n",
    "    input_params=features.params,\n",
    "    weight_params=new_ttn.parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d0b6b000-2d96-4d31-8b4e-525712575671",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = np.random.random(len(new_ttn.parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "268bba60-b00f-4cda-8217-f5ced8f8d27a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forward pass\n",
    "output = estimator_qnn.forward(x_train, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "723e885f-c12c-44a6-8224-f1e80f457434",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backward pass\n",
    "_, weights_grad = estimator_qnn.backward(x_train, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c36d86e1-a036-4137-b27d-90ab4849f897",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.random.random((len(new_ttn.parameters),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d77f89c6-a938-4068-bfff-08398099255f",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals = []\n",
    "classifier = NeuralNetworkClassifier(\n",
    "    estimator_qnn,\n",
    "    optimizer=SPSA(maxiter=50, callback=callback_spsa),\n",
    "    loss=loss,\n",
    "    # optimizer=COBYLA(maxiter=10),\n",
    "    initial_point=initial_point,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "370ae6ca-1f60-417a-a7b7-5514280c2bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "231 231\n"
     ]
    }
   ],
   "source": [
    "print(len(x_test.values), len(new_y_test.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "be3c666f-dbde-4091-871b-a0afdf836e39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saashajoshi/anaconda3/envs/qiskit_x86/lib/python3.8/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: divide by zero encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n",
      "/Users/saashajoshi/anaconda3/envs/qiskit_x86/lib/python3.8/site-packages/numpy/linalg/linalg.py:2154: RuntimeWarning: invalid value encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPSA Epoch 1: -9.00314\n",
      "SPSA Epoch 2: -9.19728\n",
      "SPSA Epoch 3: -9.91617\n",
      "SPSA Epoch 4: -9.73278\n",
      "SPSA Epoch 5: -9.81750\n",
      "SPSA Epoch 6: -10.09641\n",
      "SPSA Epoch 7: -10.09362\n",
      "SPSA Epoch 8: -10.52323\n",
      "SPSA Epoch 9: -10.52412\n",
      "SPSA Epoch 10: -10.42063\n",
      "SPSA Epoch 11: -10.42690\n",
      "SPSA Epoch 12: -10.39509\n",
      "SPSA Epoch 13: -10.54550\n",
      "SPSA Epoch 14: -10.25579\n",
      "SPSA Epoch 15: -10.21417\n",
      "SPSA Epoch 16: -10.02173\n",
      "SPSA Epoch 17: -10.17903\n",
      "SPSA Epoch 18: -10.43317\n",
      "SPSA Epoch 19: -10.02028\n",
      "SPSA Epoch 20: -10.35346\n",
      "SPSA Epoch 21: -10.60921\n",
      "SPSA Epoch 22: -10.43796\n",
      "SPSA Epoch 23: -10.55991\n",
      "SPSA Epoch 24: -10.48873\n",
      "SPSA Epoch 25: -10.55645\n",
      "SPSA Epoch 26: -10.77438\n",
      "SPSA Epoch 27: -10.51864\n",
      "SPSA Epoch 28: -10.42836\n",
      "SPSA Epoch 29: -10.31098\n",
      "SPSA Epoch 30: -10.31430\n",
      "SPSA Epoch 31: -10.48607\n",
      "SPSA Epoch 32: -10.53681\n",
      "SPSA Epoch 33: -10.56251\n",
      "SPSA Epoch 34: -10.46696\n",
      "SPSA Epoch 35: -10.45181\n",
      "SPSA Epoch 36: -10.60377\n",
      "SPSA Epoch 37: -10.49732\n",
      "SPSA Epoch 38: -10.50980\n",
      "SPSA Epoch 39: -10.26305\n",
      "SPSA Epoch 40: -10.46407\n",
      "SPSA Epoch 41: -10.23289\n",
      "SPSA Epoch 42: -10.47608\n",
      "SPSA Epoch 43: -10.43427\n",
      "SPSA Epoch 44: -10.18811\n",
      "SPSA Epoch 45: -10.55133\n",
      "SPSA Epoch 46: -10.48539\n",
      "SPSA Epoch 47: -10.54776\n",
      "SPSA Epoch 48: -10.70327\n",
      "SPSA Epoch 49: -10.76283\n",
      "SPSA Epoch 50: -10.50990\n",
      "Running Time%s seconds 585.9843897819519\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "classifier.fit(x_train, new_y_train.values)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time%s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "065c196d-eab9-4924-85d1-2ca43c50ea58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 85,
     "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": [
    "plt.plot(objective_func_vals)\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0b24d698-69c9-4243-8386-c6414619981d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict(x_test)\n",
    "# y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "55fc154f-aa3f-4051-8968-bf5181a6dbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6871508379888268"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_train, new_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "058ccbbf-3b51-45fc-9149-7b196c685b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.696969696969697"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_test, new_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ecde04-79db-49d3-9710-421cfef35b7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2cff8e7-8f11-4d34-bf80-31ec886461d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6fc64f-fd49-4220-9642-8b1f3018718d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52b89a65-71e2-4880-bfb1-83fb8d6ca645",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ab51f72-0540-40a6-8b3e-02c723be785f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e11ca7af-c7c2-4cfa-8903-326f48a56a80",
   "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": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}