New model.ipynb

{
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   "id": "607786c3-a247-443c-85d3-015997f22fbc",
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    {
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     ]
    }
   ],
   "source": [
    "!pip install pandas\n",
    "!pip install torch\n",
    "!pip install torchvision\n",
    "!pip install qiskit\n",
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6b570d7-3aba-472b-ba12-094475228313",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from time import time\n",
    "import torch\n",
    "from torch.autograd import Function\n",
    "from torchvision import datasets, transforms\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import qiskit\n",
    "from qiskit import transpile, assemble\n",
    "from qiskit.visualization import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf5ea927-ff6b-4a5d-9c01-b7ed6909f849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected value for rotation pi 0.49\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐ ░ ┌───────────┐ ░ ┌─┐\n",
       "     q: ┤ H ├─░─┤ Ry(theta) ├─░─┤M├\n",
       "        └───┘ ░ └───────────┘ ░ └╥┘\n",
       "meas: 1/═════════════════════════╩═\n",
       "                                 0 </pre>"
      ],
      "text/plain": [
       "        ┌───┐ ░ ┌───────────┐ ░ ┌─┐\n",
       "     q: ┤ H ├─░─┤ Ry(theta) ├─░─┤M├\n",
       "        └───┘ ░ └───────────┘ ░ └╥┘\n",
       "meas: 1/═════════════════════════╩═\n",
       "                                 0 "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class QuantumCircuit:\n",
    "    \"\"\" \n",
    "    This class provides a simple interface for interaction \n",
    "    with the quantum circuit \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, n_qubits, backend, shots):\n",
    "        # --- Circuit definition ---\n",
    "        self._circuit = qiskit.QuantumCircuit(n_qubits)\n",
    "        \n",
    "        all_qubits = [i for i in range(n_qubits)]\n",
    "        self.theta = qiskit.circuit.Parameter('theta')\n",
    "        \n",
    "        self._circuit.h(all_qubits)\n",
    "        self._circuit.barrier()\n",
    "        self._circuit.ry(self.theta, all_qubits)\n",
    "        \n",
    "        self._circuit.measure_all()\n",
    "        # ---------------------------\n",
    "\n",
    "        self.backend = backend\n",
    "        self.shots = shots\n",
    "    \n",
    "    def run(self, thetas):\n",
    "        t_qc = transpile(self._circuit,\n",
    "                         self.backend)\n",
    "        qobj = assemble(t_qc,\n",
    "                        shots=self.shots,\n",
    "                        parameter_binds = [{self.theta: theta} for theta in thetas])\n",
    "        job = self.backend.run(qobj)\n",
    "        result = job.result().get_counts()\n",
    "        \n",
    "        counts = np.array(list(result.values()))\n",
    "        states = np.array(list(result.keys())).astype(float)\n",
    "        \n",
    "        # Compute probabilities for each state\n",
    "        probabilities = counts / self.shots\n",
    "        # Get state expectation\n",
    "        expectation = np.sum(states * probabilities)\n",
    "        \n",
    "        return np.array([expectation])\n",
    "    \n",
    "simulator = qiskit.Aer.get_backend('qasm_simulator')\n",
    "circuit = QuantumCircuit(1, simulator, 100)\n",
    "print('Expected value for rotation pi {}'.format(circuit.run([np.pi])[0]))\n",
    "circuit._circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2281e3a-b294-47a5-a017-c662f16c2ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class HybridFunction(Function):\n",
    "    \"\"\" Hybrid quantum - classical function definition \"\"\"\n",
    "    \n",
    "    @staticmethod\n",
    "    def forward(ctx, input, quantum_circuit, shift):\n",
    "        \"\"\" Forward pass computation \"\"\"\n",
    "        ctx.shift = shift\n",
    "        ctx.quantum_circuit = quantum_circuit\n",
    "\n",
    "        expectation_z = ctx.quantum_circuit.run(input[0].tolist())\n",
    "        result = torch.tensor([expectation_z])\n",
    "        ctx.save_for_backward(input, result)\n",
    "\n",
    "        return result\n",
    "        \n",
    "    @staticmethod\n",
    "    def backward(ctx, grad_output):\n",
    "        \"\"\" Backward pass computation \"\"\"\n",
    "        input, expectation_z = ctx.saved_tensors\n",
    "        input_list = np.array(input.tolist())\n",
    "        \n",
    "        shift_right = input_list + np.ones(input_list.shape) * ctx.shift\n",
    "        shift_left = input_list - np.ones(input_list.shape) * ctx.shift\n",
    "        \n",
    "        gradients = []\n",
    "        for i in range(len(input_list)):\n",
    "            expectation_right = ctx.quantum_circuit.run(shift_right[i])\n",
    "            expectation_left  = ctx.quantum_circuit.run(shift_left[i])\n",
    "            \n",
    "            gradient = torch.tensor([expectation_right]) - torch.tensor([expectation_left])\n",
    "            gradients.append(gradient)\n",
    "        gradients = np.array([gradients]).T\n",
    "        return torch.tensor([gradients]).float() * grad_output.float(), None, None\n",
    "\n",
    "class Hybrid(nn.Module):\n",
    "    \"\"\" Hybrid quantum - classical layer definition \"\"\"\n",
    "    \n",
    "    def __init__(self, backend, shots, shift):\n",
    "        super(Hybrid, self).__init__()\n",
    "        self.quantum_circuit = QuantumCircuit(1, backend, shots)\n",
    "        self.shift = shift\n",
    "        \n",
    "    def forward(self, input):\n",
    "        return HybridFunction.apply(input, self.quantum_circuit, self.shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef79605f-760a-47a2-a4d7-754a3d088447",
   "metadata": {},
   "outputs": [
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       "   q1  q2  q3  q4  q5  q6  q7  q8  q9  q10  gender  jundice  Class/ASD  autism\n",
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   "source": [
    "children = pd.read_csv('encoded.csv')\n",
    "children = children.drop(axis=1, columns=['Unnamed: 0'])\n",
    "children.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adf06ed9-a3dc-4750-a784-c91631fb6f2c",
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    {
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      "text/plain": [
       "   q1  q2  q3  q4  q5  q6  q7  q8  q9  q10  gender  jundice  Class/ASD  autism\n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescent = pd.read_csv('EncodedAdolescent.csv')\n",
    "adolescent = adolescent.drop(axis=1, columns=['id'])\n",
    "adolescent.rename(columns={\"austim\": \"autism\"}, inplace=True)\n",
    "adolescent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1de00081-3c45-4534-8614-6573680f0dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(396, 14)\n"
     ]
    },
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       "</table>\n",
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      ],
      "text/plain": [
       "     q1  q2  q3  q4  q5  q6  q7  q8  q9  q10  gender  jundice  Class/ASD  \\\n",
       "391   0   1   1   1   0   1   1   0   1    0       1        0          0   \n",
       "392   1   0   1   0   0   0   0   1   0    1       0        0          0   \n",
       "393   1   0   0   1   1   1   1   1   1    1       1        0          1   \n",
       "394   1   1   0   1   1   0   0   0   1    1       1        0          0   \n",
       "395   1   0   1   0   0   1   1   1   1    0       0        0          0   \n",
       "\n",
       "     autism  \n",
       "391       0  \n",
       "392       0  \n",
       "393       0  \n",
       "394       0  \n",
       "395       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([children, adolescent])\n",
    "data.reset_index(inplace=True)\n",
    "data = data.drop(axis=1, columns=['index'])\n",
    "print(data.shape)\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62c3c304-4d10-42c9-ae8a-5f3f88034942",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     q1  q2  q3  q4  q5  q6  q7  q8  q9  q10  gender  jundice  Class/ASD  \\\n",
       "0     0   1   0   0   1   0   1   0   0    1       0        1          0   \n",
       "1     1   1   0   0   0   0   1   0   1    0       0        0          0   \n",
       "2     1   1   1   1   1   1   1   1   1    1       1        0          1   \n",
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       "4     0   1   1   1   1   1   0   1   0    1       0        0          1   \n",
       "..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...     ...      ...        ...   \n",
       "125   1   1   0   0   0   1   0   1   1    1       1        0          0   \n",
       "126   1   1   1   1   0   1   0   0   1    1       1        0          1   \n",
       "127   1   1   1   0   1   0   1   0   1    0       0        0          0   \n",
       "128   1   1   0   1   1   1   1   1   1    1       0        1          1   \n",
       "129   1   1   1   0   1   1   1   1   1    1       1        0          1   \n",
       "\n",
       "     autism  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "125       1  \n",
       "126       1  \n",
       "127       1  \n",
       "128       1  \n",
       "129       1  \n",
       "\n",
       "[130 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data[data.autism == 0].sample(frac=1/5, random_state=10)\n",
    "data2 = pd.concat([sample, data[data.autism ==1]])\n",
    "data2.reset_index(inplace=True)\n",
    "data2 = data2.drop(axis=1, columns=['index'])\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e0a5cc5-3d74-4feb-bbfe-2b23d0a03f3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(609, 95)\n"
     ]
    },
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      ],
      "text/plain": [
       "        age  result  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  \\\n",
       "0  0.024590     0.6         1         1         1         1         0   \n",
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       "   relation_Health care professional  relation_Others  relation_Parent  \\\n",
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       "\n",
       "   relation_Relative  relation_Self  Class/ASD  \n",
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       "3                  0              1          0  \n",
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       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults = pd.read_csv('adult_encoded.csv')\n",
    "adults.drop(axis=1, columns=['Unnamed: 0'], inplace=True)\n",
    "print(adults.shape)\n",
    "adults.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd3197f7-308c-4261-9be9-e6c1c67a29aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(609, 93)\n"
     ]
    },
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      ],
      "text/plain": [
       "    age  result  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
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       "2         1         1  ...                             0   \n",
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       "\n",
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       "3                                  0                0                0   \n",
       "4                                  0                0                0   \n",
       "\n",
       "   relation_Relative  relation_Self  Class/ASD  \n",
       "0                  0              1          0  \n",
       "1                  0              1          0  \n",
       "2                  0              0          1  \n",
       "3                  0              1          0  \n",
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       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults2 = pd.read_csv('adult_encoded2.csv')\n",
    "adults2.drop(axis=1, columns=['Unnamed: 0'], inplace=True)\n",
    "print(adults2.shape)\n",
    "adults2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66b4789d-b0fa-42cd-8708-9835dbfdb0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    429\n",
       "1    180\n",
       "Name: Class/ASD, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults2['Class/ASD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a7a1f20e-7721-46b0-885d-6ef59e62004d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(adults2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0c5e06f-df30-4dba-857e-385ea4fbabba",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>24.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  result  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "0  26.0       6         1         1         1         1         0         0   \n",
       "1  24.0       5         1         1         0         1         0         0   \n",
       "2  27.0       8         1         1         0         1         1         0   \n",
       "3  35.0       6         1         1         0         1         0         0   \n",
       "4  36.0       9         1         1         1         1         1         0   \n",
       "\n",
       "   A7_Score  A8_Score  ...  contry_of_res_Iraq  contry_of_res_Isle of Man  \\\n",
       "0         1         1  ...                 0.0                        0.0   \n",
       "1         0         1  ...                 0.0                        0.0   \n",
       "2         1         1  ...                 0.0                        0.0   \n",
       "3         1         1  ...                 0.0                        0.0   \n",
       "4         1         1  ...                 0.0                        0.0   \n",
       "\n",
       "   contry_of_res_Japan  contry_of_res_Libya  contry_of_res_Malta  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   contry_of_res_Nigeria  contry_of_res_South Korea  contry_of_res_Syria  \\\n",
       "0                    0.0                        0.0                  0.0   \n",
       "1                    0.0                        0.0                  0.0   \n",
       "2                    0.0                        0.0                  0.0   \n",
       "3                    0.0                        0.0                  0.0   \n",
       "4                    0.0                        0.0                  0.0   \n",
       "\n",
       "   contry_of_res_U.S. Outlying Islands  relation_self  \n",
       "0                                  0.0            0.0  \n",
       "1                                  0.0            0.0  \n",
       "2                                  0.0            0.0  \n",
       "3                                  0.0            0.0  \n",
       "4                                  0.0            0.0  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ages = pd.read_csv('all_ages.csv')\n",
    "all_ages.drop(axis=1, columns=['Unnamed: 0'], inplace=True)\n",
    "print(all_ages.shape)\n",
    "all_ages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "212654c1-22d2-423b-ad3f-ee1c6a5bb2f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    587\n",
       "1    368\n",
       "Name: Class/ASD, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(all_ages.columns))\n",
    "all_ages['Class/ASD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16c6bb4a-acd0-41f1-a63a-4af8a3cb2dd0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5137a2fb-d16f-4a60-809c-fe1b01ddb270",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def partition(data, n):\n",
    "#     \"\"\"Randomly partitions the data into n non-overlapping parts\n",
    "#     Args:\n",
    "#         data: dataframe object, the dataset to partition\n",
    "#         n: number of equal parts to split the data\n",
    "#     Returns:\n",
    "#         splits: list of n parts of the data, each one is a dataframe\n",
    "#     \"\"\"\n",
    "#     splits = []\n",
    "#     remaining = data.copy(deep=True)\n",
    "#     for i in range(n):\n",
    "#         split = remaining.sample(frac=1/(n-i), random_state=10)\n",
    "#         splits.append(split)\n",
    "#         remaining = remaining.drop(split.index)\n",
    "#     return splits\n",
    "\n",
    "# determine the supported device\n",
    "def get_device():\n",
    "    if torch.cuda.is_available():\n",
    "        device = torch.device('cuda:0')\n",
    "    else:\n",
    "        device = torch.device('cpu') # don't have GPU \n",
    "    return device\n",
    "\n",
    "# convert a df to tensor to be used in pytorch\n",
    "def df_to_tensor(df):\n",
    "    device = get_device()\n",
    "    return torch.from_numpy(df.values).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3673ba51-1f12-48f0-aa03-7af3024be352",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, data, targets, transform=None, target_transform=None):\n",
    "        self.data = data\n",
    "        self.targets = targets\n",
    "        self.transform = transform\n",
    "        self.target_transform = target_transform\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.targets[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4bd1057-e89c-4897-ad24-80280f336d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        # self.conv1 = nn.Conv2d(1, 6, kernel_size=5)\n",
    "        # self.conv2 = nn.Conv2d(6, 16, kernel_size=5)\n",
    "        # self.dropout = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(116, 116)\n",
    "        self.fc2 = nn.Linear(116, 1)\n",
    "        self.hybrid1 = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)\n",
    "        # self.hybrid2 = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x = F.relu(self.conv1(x))\n",
    "        # x = F.max_pool2d(x, 2)\n",
    "        # x = F.relu(self.conv2(x))\n",
    "        # x = F.max_pool2d(x, 2)\n",
    "        # x = self.dropout(x)\n",
    "        # x = x.view(1, -1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        # x = F.relu(self.fc2(x))\n",
    "        x = self.fc2(x)\n",
    "        x = self.hybrid1(x)\n",
    "        # x = self.hybrid2(x)\n",
    "        return torch.cat((x, 1 - x), -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "855a86bb-0154-4a83-9814-b1e52ddd823a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (fc1): Linear(in_features=116, out_features=116, bias=True)\n",
       "  (fc2): Linear(in_features=116, out_features=1, bias=True)\n",
       "  (hybrid1): Hybrid()\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "90cc1aa2-61cf-40d3-ad68-cbdc3bc4c2d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, optimizer, epochs, train_loader, test_loader):\n",
    "    train_loss_list = []\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        losses = []\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            optimizer.zero_grad()\n",
    "            # Forward pass\n",
    "            output = model(data)\n",
    "            outputs_train.append(output)\n",
    "            # Calculate loss\n",
    "            loss = loss_func(output, target)\n",
    "            # Backward pass\n",
    "            loss.backward()\n",
    "            # Optimize weights\n",
    "            optimizer.step()\n",
    "            losses.append(loss.item())\n",
    "        train_loss_list.append(sum(losses)/len(losses))\n",
    "        print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(100. * (epoch + 1) / epochs, train_loss_list[-1]))\n",
    "        \n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    accuracy = 0\n",
    "    with torch.no_grad():\n",
    "        losses = []\n",
    "        correct = 0\n",
    "        for batch_idx, (data, target) in enumerate(test_loader):\n",
    "            output = model(data)\n",
    "            outputs_test.append(output)\n",
    "            pred = output.argmax(dim=1, keepdim=True)\n",
    "            preds_test.append(pred)\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "            loss = loss_func(output, target)\n",
    "            losses.append(loss.item())\n",
    "\n",
    "        print('Performance on test data:\\n\\tLoss: {:.4f}\\n\\tAccuracy: {:.1f}%'.format(\n",
    "            sum(losses) / len(losses),\n",
    "            correct / len(test_loader) * 100)\n",
    "            )\n",
    "        test_loss = sum(losses) / len(losses)\n",
    "        accuracy = correct / len(test_loader) * 100\n",
    "    return train_loss_list, test_loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6887813b-e931-44e9-a350-4f3fde875284",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ff59f83-655b-4d33-89ab-459af80d747b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = all_ages.drop(axis=1, columns=['Class/ASD'])\n",
    "y = all_ages['Class/ASD']\n",
    "# y = data2.autism.astype('int64')\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.3, random_state=10)\n",
    "# X_train, X_test, Y_train, Y_test = X, X, y, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2a493384-1baf-4fde-8ce7-cd6b64048a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_times = []\n",
    "all_accuracy = []\n",
    "all_train_loss = []\n",
    "all_test_loss = []\n",
    "\n",
    "outputs_train = []\n",
    "outputs_test = []\n",
    "preds_test = []\n",
    "\n",
    "X_train_series_list = []\n",
    "for _, row in X_train.iterrows():\n",
    "    tensor_from_series = df_to_tensor(row)\n",
    "    X_train_series_list.append(tensor_from_series)\n",
    "X_train_tensor = torch.stack(X_train_series_list)\n",
    "\n",
    "Y_train_tensor = df_to_tensor(Y_train)\n",
    "Y_train_tensor = Y_train_tensor.type(torch.int64)\n",
    "\n",
    "train_data = CustomDataset(data=X_train_tensor, targets=Y_train_tensor)\n",
    "\n",
    "X_test_series_list = []\n",
    "for _, row in X_test.iterrows():\n",
    "    tensor_from_series = df_to_tensor(row)\n",
    "    X_test_series_list.append(tensor_from_series)\n",
    "X_test_tensor = torch.stack(X_test_series_list)\n",
    "\n",
    "Y_test_tensor = df_to_tensor(Y_test)\n",
    "Y_test_tensor = Y_test_tensor.type(torch.int64)\n",
    "\n",
    "test_data = CustomDataset(data=X_test_tensor, targets=Y_test_tensor)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=1, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "54c5794e-fc33-498a-9244-0eb77416e8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 668 samples, test: 287 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_114/2571825612.py:32: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
      "  gradients = np.array([gradients]).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training [5%]\tLoss: -0.7433\n",
      "Training [10%]\tLoss: -0.8460\n",
      "Training [15%]\tLoss: -0.8830\n",
      "Training [20%]\tLoss: -0.8869\n",
      "Training [25%]\tLoss: -0.8943\n",
      "Training [30%]\tLoss: -0.9075\n",
      "Training [35%]\tLoss: -0.9099\n",
      "Training [40%]\tLoss: -0.9064\n",
      "Training [45%]\tLoss: -0.9105\n",
      "Training [50%]\tLoss: -0.9181\n",
      "Training [55%]\tLoss: -0.9235\n",
      "Training [60%]\tLoss: -0.9262\n",
      "Training [65%]\tLoss: -0.9247\n",
      "Training [70%]\tLoss: -0.9259\n",
      "Training [75%]\tLoss: -0.9344\n",
      "Training [80%]\tLoss: -0.9298\n",
      "Training [85%]\tLoss: -0.9318\n",
      "Training [90%]\tLoss: -0.9321\n",
      "Training [95%]\tLoss: -0.9366\n",
      "Training [100%]\tLoss: -0.9429\n",
      "Performance on test data:\n",
      "\tLoss: -0.8905\n",
      "\tAccuracy: 93.0%\n",
      "Training time: 149.50s\n"
     ]
    }
   ],
   "source": [
    "model = Net()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "loss_func = nn.NLLLoss()\n",
    "epochs = 20\n",
    "model.train()\n",
    "\n",
    "print(f\"Train: {len(X_train)} samples, test: {len(X_test)} samples\")\n",
    "t = time()\n",
    "train_loss_list, test_loss, accuracy = train_model(model, optimizer, epochs, train_loader, test_loader)\n",
    "training_times.append(time() - t)\n",
    "print(\"Training time: %.2fs\" % (time() - t))\n",
    "all_accuracy.append(accuracy)\n",
    "all_train_loss.append(train_loss_list)\n",
    "all_test_loss.append(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f23308f7-6784-4b1b-8fd1-ec3161c3a28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[1., 0.]], dtype=torch.float64), tensor([[0]]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 5\n",
    "outputs_test[i], preds_test[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "986d3071-6626-47b3-96f7-fbb5d4632b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(outputs_test[i][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e9ea2414-f4a3-401e-8710-399a70a069c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(955, 955)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(outputs_test), len(preds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c503b9d0-e8dc-4b51-8588-96de9bb018c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    587\n",
       "1    368\n",
       "Name: Class/ASD, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2ae69e6-2a69-41a3-9a42-fb51c4697298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_int = [int(pred) for pred in preds_test]\n",
    "sum(preds_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2164f57c-70c3-4db2-954c-5e6153a20757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "        1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "        1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
       "        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "        0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "        0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "        0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "        0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "        1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "        1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "        1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "        0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d59ba7ad-65fd-4973-bbf0-46c67fe27043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(1),\n",
       " tensor([4., 9., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.targets[8], train_data.data[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b2056d9e-9bb3-42f4-a072-fcf88b14eb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(1),\n",
       " tensor([20.,  9.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,\n",
       "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "          0.,  0.,  0.,  0.]))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.targets[10], train_data.data[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f7b8cf61-f67c-4365-8a5c-6f2effb1e423",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "10d5aaed-641c-492a-8cac-ef4e9e1e8855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.81881533101046"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avg accuracy\n",
    "# Old: 83.78954423449514\n",
    "# Old: 82.68787456241874\n",
    "# New: 84.82051282051282\n",
    "# 87.39495798319328\n",
    "sum(all_accuracy) / len(all_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "84452c81-486f-4df9-afb1-c5d6583319f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9014285714285718"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avg test loss\n",
    "sum(all_test_loss) / len(all_test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "53db0079-62e8-43a5-995f-35d321b84545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164.1439561843872"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avg training time\n",
    "sum(training_times) / len(training_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb7e7bc1-d649-4dcb-bfd0-1bfc8acac4a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '%')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_xlim([0, 9])\n",
    "ax.set_ylim([0, 100])\n",
    "\n",
    "plt.plot(all_accuracy)\n",
    "plt.title('Accuracies')\n",
    "plt.xlabel('Cross validation fold')\n",
    "plt.ylabel('%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6e98ce77-f237-4686-ab21-0dcafb58a826",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = {\n",
    "    \"age\": \"21\",\n",
    "    \"country\": \"American Samoa\",\n",
    "    \"ethnicity\": \"Others\",\n",
    "    \"gender\": \"1\",\n",
    "    \"jaundice\": \"0\",\n",
    "    \"q1\": \"0\",\n",
    "    \"q10\": \"0\",\n",
    "    \"q2\": \"0\",\n",
    "    \"q3\": \"1\",\n",
    "    \"q4\": \"0\",\n",
    "    \"q5\": \"1\",\n",
    "    \"q6\": \"0\",\n",
    "    \"q7\": \"0\",\n",
    "    \"q8\": \"0\",\n",
    "    \"q9\": \"0\",\n",
    "    \"relation\": \"Parent\"\n",
    "}\n",
    "\n",
    "columns = ['age', 'result', 'A1_Score', 'A2_Score', 'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score', 'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score', 'gender_f', 'gender_m', 'ethnicity_Asian', 'ethnicity_Black', 'ethnicity_Hispanic', 'ethnicity_Latino', 'ethnicity_Middle Eastern ', 'ethnicity_Others', 'ethnicity_Pasifika', 'ethnicity_South Asian', 'ethnicity_Turkish', 'ethnicity_White-European', 'ethnicity_others', 'jundice_no', 'jundice_yes', 'contry_of_res_Afghanistan', 'contry_of_res_AmericanSamoa', 'contry_of_res_Angola', 'contry_of_res_Armenia', 'contry_of_res_Aruba', 'contry_of_res_Australia', 'contry_of_res_Austria', 'contry_of_res_Bahamas', 'contry_of_res_Bangladesh', 'contry_of_res_Belgium', 'contry_of_res_Bolivia', 'contry_of_res_Brazil', 'contry_of_res_Burundi', 'contry_of_res_Canada', 'contry_of_res_Chile', 'contry_of_res_China', 'contry_of_res_Costa Rica', 'contry_of_res_Cyprus', 'contry_of_res_Czech Republic', 'contry_of_res_Ecuador', 'contry_of_res_Egypt', 'contry_of_res_Ethiopia', 'contry_of_res_Finland', 'contry_of_res_France', 'contry_of_res_Germany', 'contry_of_res_Iceland', 'contry_of_res_India', 'contry_of_res_Indonesia', 'contry_of_res_Iran', 'contry_of_res_Ireland', 'contry_of_res_Italy', 'contry_of_res_Jordan', 'contry_of_res_Malaysia', 'contry_of_res_Mexico', 'contry_of_res_Nepal', 'contry_of_res_Netherlands', 'contry_of_res_New Zealand', 'contry_of_res_Nicaragua', 'contry_of_res_Niger', 'contry_of_res_Oman', 'contry_of_res_Pakistan', 'contry_of_res_Philippines', 'contry_of_res_Portugal', 'contry_of_res_Romania', 'contry_of_res_Russia', 'contry_of_res_Saudi Arabia', 'contry_of_res_Serbia', 'contry_of_res_Sierra Leone', 'contry_of_res_South Africa', 'contry_of_res_Spain', 'contry_of_res_Sri Lanka', 'contry_of_res_Sweden', 'contry_of_res_Tonga', 'contry_of_res_Turkey', 'contry_of_res_Ukraine', 'contry_of_res_United Arab Emirates', 'contry_of_res_United Kingdom', 'contry_of_res_United States', 'contry_of_res_Uruguay', 'contry_of_res_Viet Nam', 'relation_Health care professional', 'relation_Others', 'relation_Parent', 'relation_Relative', 'relation_Self', 'Class/ASD', 'contry_of_res_Albania', 'contry_of_res_Anguilla', 'contry_of_res_Argentina', 'contry_of_res_Azerbaijan', 'contry_of_res_Bahrain', 'contry_of_res_Comoros', 'contry_of_res_Croatia', 'contry_of_res_Greenland', 'contry_of_res_Norway', 'contry_of_res_Bhutan', 'contry_of_res_Bulgaria', 'contry_of_res_Europe', 'contry_of_res_Georgia', 'contry_of_res_Ghana', 'contry_of_res_Iraq', 'contry_of_res_Isle of Man', 'contry_of_res_Japan', 'contry_of_res_Libya', 'contry_of_res_Malta', 'contry_of_res_Nigeria', 'contry_of_res_South Korea', 'contry_of_res_Syria', 'contry_of_res_U.S. Outlying Islands', 'relation_self']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "26cfc845-209c-4d95-a74a-c123065f4073",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_raw = dict()\n",
    "for col in columns:\n",
    "    if col == 'age':\n",
    "        res_raw['age'] = int(res['age'])\n",
    "    elif col.startswith('ethnicity_'):\n",
    "        ethn = col[len(\"ethnicity_\"):]\n",
    "        if ethn == res['ethnicity']:\n",
    "            res_raw['ethnicity_' + ethn] = 1\n",
    "        else:\n",
    "            res_raw['ethnicity_' + ethn] = 0\n",
    "    elif col.endswith('_Score'):\n",
    "        q_num = col[1:len(col)-len(\"_Score\")]\n",
    "        res_raw[col] = int(res[\"q\" + q_num])\n",
    "    elif col == 'result':\n",
    "        res_raw['result'] = 0\n",
    "    elif col.startswith(\"contry_of_res_\"):\n",
    "        if res['country'] == \"American Samoa\":\n",
    "            res_raw['contry_of_res_AmericanSamoa'] = 1\n",
    "        country = col[len(\"contry_of_res_\"):]\n",
    "        if country != \"AmericanSamoa\" and country == res['country']:\n",
    "            res_raw['contry_of_res_' + country] = 1\n",
    "        else:\n",
    "            res_raw['contry_of_res_' + country] = 0\n",
    "    elif col.startswith('relation_'):\n",
    "        rela = col[len('relation_'):]\n",
    "        if rela == res['relation']:\n",
    "            res_raw[\"relation_\" + rela] = 1\n",
    "        else:\n",
    "            res_raw[\"relation_\" + rela] = 0\n",
    "    elif col == \"gender_f\":\n",
    "        if int(res['gender']) == 1:\n",
    "            res_raw['gender_f'] = 1\n",
    "        else:\n",
    "            res_raw['gender_f'] = 0\n",
    "    elif col == \"gender_m\":\n",
    "        if int(res['gender']) == 0:\n",
    "            res_raw['gender_m'] = 1\n",
    "        else:\n",
    "            res_raw['gender_m'] = 0\n",
    "    elif col == \"jundice_yes\":\n",
    "        if int(res['jaundice']) == 1:\n",
    "            res_raw['jundice_yes'] = 1\n",
    "        else:\n",
    "            res_raw['jundice_yes'] = 0\n",
    "    elif col == \"jundice_no\":\n",
    "        if int(res['jaundice']) == 0:\n",
    "            res_raw['jundice_no'] = 1\n",
    "        else:\n",
    "            res_raw['jundice_no'] = 0\n",
    "    elif col != 'Class/ASD':\n",
    "        res_raw[col] = 0\n",
    "for i in range(1, 11):\n",
    "    res_raw['result'] += res_raw[f'A{i}_Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2269f3e2-2a2b-4c4b-8cc6-91b3bfefb96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, 116, 117)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(res_raw) == len(columns), len(res_raw), len(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "085cc5bd-4c69-4ebe-a5a3-440e10ffa1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_in_order = []\n",
    "for col in columns:\n",
    "    if col in res_raw:\n",
    "        vals_in_order.append(res_raw[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "45f18375-34ce-4243-abf7-c06b27c0fc2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>result</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>...</th>\n",
       "      <th>contry_of_res_Iraq</th>\n",
       "      <th>contry_of_res_Isle of Man</th>\n",
       "      <th>contry_of_res_Japan</th>\n",
       "      <th>contry_of_res_Libya</th>\n",
       "      <th>contry_of_res_Malta</th>\n",
       "      <th>contry_of_res_Nigeria</th>\n",
       "      <th>contry_of_res_South Korea</th>\n",
       "      <th>contry_of_res_Syria</th>\n",
       "      <th>contry_of_res_U.S. Outlying Islands</th>\n",
       "      <th>relation_self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  result  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "0   21       2         0         0         1         0         1         0   \n",
       "\n",
       "   A7_Score  A8_Score  ...  contry_of_res_Iraq  contry_of_res_Isle of Man  \\\n",
       "0         0         0  ...                   0                          0   \n",
       "\n",
       "   contry_of_res_Japan  contry_of_res_Libya  contry_of_res_Malta  \\\n",
       "0                    0                    0                    0   \n",
       "\n",
       "   contry_of_res_Nigeria  contry_of_res_South Korea  contry_of_res_Syria  \\\n",
       "0                      0                          0                    0   \n",
       "\n",
       "   contry_of_res_U.S. Outlying Islands  relation_self  \n",
       "0                                    0              0  \n",
       "\n",
       "[1 rows x 116 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data2 = {0: vals_in_order}\n",
    "X_new = pd.DataFrame.from_dict(new_data2, orient='index', columns=[a for a in columns if a in list(res_raw.keys())])\n",
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "56c47b8d-ed86-4634-8be1-30e1c9faf871",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewData(Dataset):\n",
    "    def __init__(self, data, transform=None, target_transform=None):\n",
    "        self.data = data\n",
    "        self.transform = transform\n",
    "        self.target_transform = target_transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2485d0cf-1fac-426b-85d0-e2eff04d914e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.9900, dtype=torch.float64) tensor(0.0100, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "X_series_list = []\n",
    "for _, row in X_new.iterrows():\n",
    "    tensor_from_series = df_to_tensor(row)\n",
    "    X_series_list.append(tensor_from_series)\n",
    "X_new_tensor = torch.stack(X_series_list)\n",
    "new_data = NewData(data=X_new_tensor)\n",
    "new_loader = torch.utils.data.DataLoader(\n",
    "    new_data, batch_size=1, shuffle=True)\n",
    "with torch.no_grad():\n",
    "    for batch_idx, (data) in enumerate(new_loader):\n",
    "        output = model(data)\n",
    "        print(output[0][0], output[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eea70337-9fe9-4a49-9c2a-435404bfbde5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4., 7., 0., 1., 1., 1., 1., 1., 0., 1., 0., 1., 0., 1., 1., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.tensor([4., 7., 0., 1., 1., 1., 1., 1., 0., 1., 0., 1., 0., 1., 1., 0., 0., 0.,\n",
    "         0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
    "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
    "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
    "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
    "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
    "         0., 0., 0., 0., 0., 0., 0., 0.])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "737f3022-c5f0-4197-b9c4-f512a17bc599",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 [Braket]",
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    "name": "ipython",
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   "file_extension": ".py",
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