filippo_1912_1.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Import libraries"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:24:35.009948Z","iopub.status.busy":"2023-12-13T13:24:35.009471Z","iopub.status.idle":"2023-12-13T13:24:35.016357Z","shell.execute_reply":"2023-12-13T13:24:35.015200Z","shell.execute_reply.started":"2023-12-13T13:24:35.009922Z"},"trusted":true},"outputs":[],"source":["# Fix randomness and hide warnings\n","seed = 42\n","\n","import os\n","os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n","os.environ['PYTHONHASHSEED'] = str(seed)\n","os.environ['MPLCONFIGDIR'] = os.getcwd()+'/configs/'\n","\n","import warnings\n","warnings.simplefilter(action='ignore', category=FutureWarning)\n","warnings.simplefilter(action='ignore', category=Warning)\n","\n","import numpy as np\n","np.random.seed(seed)\n","\n","import logging\n","\n","import random\n","random.seed(seed)"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:24:35.200514Z","iopub.status.busy":"2023-12-13T13:24:35.200098Z","iopub.status.idle":"2023-12-13T13:24:47.070873Z","shell.execute_reply":"2023-12-13T13:24:47.069876Z","shell.execute_reply.started":"2023-12-13T13:24:35.200485Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["2.14.0\n"]}],"source":["# Import tensorflow\n","import tensorflow as tf\n","from tensorflow import keras as tfk\n","from tensorflow.keras import layers as tfkl\n","tf.autograph.set_verbosity(0)\n","tf.get_logger().setLevel(logging.ERROR)\n","tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n","tf.random.set_seed(seed)\n","tf.compat.v1.set_random_seed(seed)\n","print(tf.__version__)"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:24:47.073441Z","iopub.status.busy":"2023-12-13T13:24:47.072388Z","iopub.status.idle":"2023-12-13T13:24:48.677751Z","shell.execute_reply":"2023-12-13T13:24:48.676800Z","shell.execute_reply.started":"2023-12-13T13:24:47.073412Z"},"trusted":true},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","plt.rc('font', size=16)\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n","from sklearn.metrics import confusion_matrix\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import train_test_split"]},{"cell_type":"markdown","metadata":{},"source":["# Load and process data"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:24:48.679060Z","iopub.status.busy":"2023-12-13T13:24:48.678799Z","iopub.status.idle":"2023-12-13T13:24:56.763089Z","shell.execute_reply":"2023-12-13T13:24:56.762032Z","shell.execute_reply.started":"2023-12-13T13:24:48.679039Z"},"trusted":true},"outputs":[{"data":{"text/plain":["((48000, 2776), (48000, 2), (48000,))"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# Load the data from the files\n","training_data = np.load('training/processed_data.npy')\n","valid_periods = np.load('training/valid_periods.npy')\n","categories = np.load('training/categories.npy')\n","\n","training_data.shape, valid_periods.shape, categories.shape"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:24:56.765520Z","iopub.status.busy":"2023-12-13T13:24:56.765278Z","iopub.status.idle":"2023-12-13T13:25:03.750007Z","shell.execute_reply":"2023-12-13T13:25:03.748855Z","shell.execute_reply.started":"2023-12-13T13:24:56.765499Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 48000 entries, 0 to 47999\n","Columns: 2777 entries, Timestep_1 to Category\n","dtypes: float64(2776), object(1)\n","memory usage: 1017.0+ MB\n"]},{"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>Timestep_1</th>\n","      <th>Timestep_2</th>\n","      <th>Timestep_3</th>\n","      <th>Timestep_4</th>\n","      <th>Timestep_5</th>\n","      <th>Timestep_6</th>\n","      <th>Timestep_7</th>\n","      <th>Timestep_8</th>\n","      <th>Timestep_9</th>\n","      <th>Timestep_10</th>\n","      <th>...</th>\n","      <th>Timestep_2767</th>\n","      <th>Timestep_2768</th>\n","      <th>Timestep_2769</th>\n","      <th>Timestep_2770</th>\n","      <th>Timestep_2771</th>\n","      <th>Timestep_2772</th>\n","      <th>Timestep_2773</th>\n","      <th>Timestep_2774</th>\n","      <th>Timestep_2775</th>\n","      <th>Timestep_2776</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>...</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","      <td>48000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.294617</td>\n","      <td>0.296570</td>\n","      <td>0.311277</td>\n","      <td>0.312676</td>\n","      <td>0.315905</td>\n","      <td>0.327528</td>\n","      <td>0.324596</td>\n","      <td>0.323519</td>\n","      <td>0.326198</td>\n","      <td>0.324225</td>\n","      <td>...</td>\n","      <td>0.000005</td>\n","      <td>0.000005</td>\n","      <td>0.000005</td>\n","      <td>0.000005</td>\n","      <td>0.000005</td>\n","      <td>0.000005</td>\n","      <td>0.000006</td>\n","      <td>0.000006</td>\n","      <td>0.000006</td>\n","      <td>0.000005</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.313868</td>\n","      <td>0.309717</td>\n","      <td>0.316246</td>\n","      <td>0.316806</td>\n","      <td>0.308752</td>\n","      <td>0.312038</td>\n","      <td>0.312138</td>\n","      <td>0.310601</td>\n","      <td>0.308453</td>\n","      <td>0.306235</td>\n","      <td>...</td>\n","      <td>0.001105</td>\n","      <td>0.001194</td>\n","      <td>0.001158</td>\n","      <td>0.001136</td>\n","      <td>0.001151</td>\n","      <td>0.001191</td>\n","      <td>0.001223</td>\n","      <td>0.001242</td>\n","      <td>0.001232</td>\n","      <td>0.001197</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.017053</td>\n","      <td>0.026143</td>\n","      <td>0.036552</td>\n","      <td>0.041840</td>\n","      <td>0.048021</td>\n","      <td>0.054732</td>\n","      <td>0.053503</td>\n","      <td>0.053497</td>\n","      <td>0.059964</td>\n","      <td>0.062067</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.164482</td>\n","      <td>0.166667</td>\n","      <td>0.179424</td>\n","      <td>0.177778</td>\n","      <td>0.189224</td>\n","      <td>0.204628</td>\n","      <td>0.198634</td>\n","      <td>0.199197</td>\n","      <td>0.202721</td>\n","      <td>0.200518</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.530799</td>\n","      <td>0.531936</td>\n","      <td>0.558958</td>\n","      <td>0.557046</td>\n","      <td>0.563917</td>\n","      <td>0.585731</td>\n","      <td>0.580660</td>\n","      <td>0.577783</td>\n","      <td>0.579325</td>\n","      <td>0.570832</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>...</td>\n","      <td>0.242176</td>\n","      <td>0.261673</td>\n","      <td>0.253678</td>\n","      <td>0.248855</td>\n","      <td>0.252103</td>\n","      <td>0.260920</td>\n","      <td>0.267974</td>\n","      <td>0.272139</td>\n","      <td>0.269904</td>\n","      <td>0.262239</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 2776 columns</p>\n","</div>"],"text/plain":["         Timestep_1    Timestep_2    Timestep_3    Timestep_4    Timestep_5  \\\n","count  48000.000000  48000.000000  48000.000000  48000.000000  48000.000000   \n","mean       0.294617      0.296570      0.311277      0.312676      0.315905   \n","std        0.313868      0.309717      0.316246      0.316806      0.308752   \n","min        0.000000      0.000000      0.000000      0.000000      0.000000   \n","25%        0.017053      0.026143      0.036552      0.041840      0.048021   \n","50%        0.164482      0.166667      0.179424      0.177778      0.189224   \n","75%        0.530799      0.531936      0.558958      0.557046      0.563917   \n","max        1.000000      1.000000      1.000000      1.000000      1.000000   \n","\n","         Timestep_6    Timestep_7    Timestep_8    Timestep_9   Timestep_10  \\\n","count  48000.000000  48000.000000  48000.000000  48000.000000  48000.000000   \n","mean       0.327528      0.324596      0.323519      0.326198      0.324225   \n","std        0.312038      0.312138      0.310601      0.308453      0.306235   \n","min        0.000000      0.000000      0.000000      0.000000      0.000000   \n","25%        0.054732      0.053503      0.053497      0.059964      0.062067   \n","50%        0.204628      0.198634      0.199197      0.202721      0.200518   \n","75%        0.585731      0.580660      0.577783      0.579325      0.570832   \n","max        1.000000      1.000000      1.000000      1.000000      1.000000   \n","\n","       ...  Timestep_2767  Timestep_2768  Timestep_2769  Timestep_2770  \\\n","count  ...   48000.000000   48000.000000   48000.000000   48000.000000   \n","mean   ...       0.000005       0.000005       0.000005       0.000005   \n","std    ...       0.001105       0.001194       0.001158       0.001136   \n","min    ...       0.000000       0.000000       0.000000       0.000000   \n","25%    ...       0.000000       0.000000       0.000000       0.000000   \n","50%    ...       0.000000       0.000000       0.000000       0.000000   \n","75%    ...       0.000000       0.000000       0.000000       0.000000   \n","max    ...       0.242176       0.261673       0.253678       0.248855   \n","\n","       Timestep_2771  Timestep_2772  Timestep_2773  Timestep_2774  \\\n","count   48000.000000   48000.000000   48000.000000   48000.000000   \n","mean        0.000005       0.000005       0.000006       0.000006   \n","std         0.001151       0.001191       0.001223       0.001242   \n","min         0.000000       0.000000       0.000000       0.000000   \n","25%         0.000000       0.000000       0.000000       0.000000   \n","50%         0.000000       0.000000       0.000000       0.000000   \n","75%         0.000000       0.000000       0.000000       0.000000   \n","max         0.252103       0.260920       0.267974       0.272139   \n","\n","       Timestep_2775  Timestep_2776  \n","count   48000.000000   48000.000000  \n","mean        0.000006       0.000005  \n","std         0.001232       0.001197  \n","min         0.000000       0.000000  \n","25%         0.000000       0.000000  \n","50%         0.000000       0.000000  \n","75%         0.000000       0.000000  \n","max         0.269904       0.262239  \n","\n","[8 rows x 2776 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Convert training_data and categories to DataFrame\n","data_df = pd.DataFrame(training_data, columns=[f'Timestep_{i+1}' for i in range(training_data.shape[1])])\n","categories_df = pd.DataFrame(categories, columns=['Category'])\n","\n","# Concatenate DataFrames along the columns\n","df = pd.concat([data_df, categories_df], axis=1)\n","\n","# Display the resulting DataFrame\n","df.info()\n","df.describe()"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:25:03.752153Z","iopub.status.busy":"2023-12-13T13:25:03.751752Z","iopub.status.idle":"2023-12-13T13:25:04.003680Z","shell.execute_reply":"2023-12-13T13:25:04.002627Z","shell.execute_reply.started":"2023-12-13T13:25:03.752120Z"},"trusted":true},"outputs":[{"data":{"image/png":"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size 1700x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Inspect activities timestamps\n","plt.figure(figsize=(17,5))\n","sns.countplot(x = 'Category', data = df, order = sorted(df['Category'].unique()))\n","plt.title('Activity Timestamps')\n","plt.show()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:25:04.005464Z","iopub.status.busy":"2023-12-13T13:25:04.005122Z","iopub.status.idle":"2023-12-13T13:25:07.181172Z","shell.execute_reply":"2023-12-13T13:25:07.180076Z","shell.execute_reply.started":"2023-12-13T13:25:04.005438Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABVkAAAEiCAYAAADjztJbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB/qklEQVR4nO3dd3iTVf8G8DvdpRPaAi0tLUNAhkBlCFRAhkxFQZYilKX+ZCkoUwSUVxBBGS+vKMoSFdmCLAFpKcgeStmjQNkttLR0tzm/P2oem2avZt2f68plkmedpOH2yfc5OUcmhBAgIiIiIiIiIiIiIqO4WLsBRERERERERERERPaMRVYiIiIiIiIiIiIiE7DISkRERERERERERGQCFlmJiIiIiIiIiIiITMAiKxEREREREREREZEJWGQlIiIiIiIiIiIiMgGLrEREREREREREREQmYJGViIiIiIiIiIiIyAQsshIRERERERERERGZgEVWInJo9+/fx9y5c9GpUydERkaiXLlyKFeuHCIiItC1a1d88cUXuH37trWbSWXg119/hUwmg0wmg7e3Nx4/fmztJuH69etSm4y9xcXFKe0nLi7O2i+rTGzbtg39+/dHzZo14ePjAy8vL0RERODZZ5/FG2+8gf/+97+4evWqtZuJ2NhYyGQytG3b1tpNMcrVq1cxYcIENGvWDOXLl4e7uztCQkJQr149dO7cGVOnTkV8fDyKioqs3VSrmj59OmQyGaKioqzdFCIiIiKyEjdrN4CIyBLkcjk++eQTfPHFF8jOzlZZfuvWLdy6dQs7duzA5MmTMWzYMHz55Zfw9vY2WxtiY2OxcuVKtGnTxmkKX7Zs5cqV0v3c3FysXbsWw4cPt2KLyBgZGRno3bs3fv/9d5Vlin/XJ0+exE8//YR69eohMTHRCq10DAsXLsSHH36I/Px8pedTU1ORmpqKc+fOYdeuXZg5cyaOHTuGJk2aWKmlRERERETWx56sRORw8vLy0KNHD8yYMQPZ2dkIDw/HrFmzcPToUdy5cwcPHjzA6dOnsWDBAjRr1gyFhYVYsmQJ7t+/b+2mk4U8fPgQ27ZtU3quZNHVWiIjI5GZman2tn37dmm9JUuWaFzv+eeft+IrKHslC6wdOnTAxo0bcf78eTx69AhJSUnYsmUL3nvvPYSHh1u5pfbtp59+wpgxY5Cfn4+IiAjMnTsXJ06cwP3793H37l38+eefmDt3LmJiYqzdVCIiIiIim8CerETkcN5//3389ttvAIDXX38d3333nUoP1ZCQEDRs2BCjR4/G5s2b8fbbb1ujqVRGfv75Z6k3Xo8ePfDrr7/i4MGDuHLlCmrWrGm1dslkMvj6+qpdVvIz6+npqXE9AIiKioIQwuztszVxcXFSgXX06NFYsGCB0vLy5csjKioKL730EubOnYs///zTGs1UsmLFCqxYscLazTDYlClTABR/tk6ePIny5csrLa9cuTJatGiBcePG4ezZswgJCbFGM4mIiIiIbAZ7shKRQ0lISMDXX38NAGjfvj1Wr16tcwiAV155BSdOnECFChXKoolkBYpeqw0aNMC8efOk51etWmWtJpERdu/eLd2fMGGC1nVdXV2drpevuVy+fBnXr18HAAwfPlylwFpavXr1ULFixTJoGRERERGR7WKRlYgcypw5cwAU9xD8+uuvIZPJ9NouPDwc/v7+Ss9lZmZiw4YNiI2NxdNPPw0fHx94eHggNDQU3bt3x9q1a9X2HlyxYgVkMplU2IuPj1eZrEjTJDinT5/GW2+9hVq1asHX1xc+Pj6oW7cuxo4dq9cEXdu2bUOnTp0QFBSEcuXKoU6dOpg8eTLS09P1nhzp0KFDePPNNxEVFQUvLy8EBgYiOjoa06ZNw6NHjzRuV3qCnyNHjuCNN95A1apV4eHhgaioKGRlZcHf3x8ymQwTJ07U+Xrat28PmUyGZs2a6VxXk3PnzuH48eMAgDfffBM1atRAq1atAAA//PCDQ/QA1fW3jYqKgkwmw/Tp0wEAGzduRIcOHRASEgJfX19ER0fj22+/VXov7t27hwkTJqBOnTooV64cQkJC0KdPH1y8eFFnex4/foxZs2ahZcuWCA4OhqenJ8LCwtCzZ0+1Y6nqKyUlRbrv5+dn9H4Url69ivfeew/169eHv78/vL29UbNmTbz11ltaX2fbtm0hk8kQGxsLANi1axdeeeUVVKlSBW5ubkr/vvWd+CouLg5vvvkmqlWrBm9vb/j7+6NRo0b4+OOPkZaWpnE7IQR+/vlndO3aFaGhofDw8EBAQABq1qyJjh07Ys6cObh586Yhb4vZ3ufCwkLExcVh3LhxaNKkiTRxVlBQEGJiYjBv3jxkZWVp3L70ZFJXrlzBW2+9haioKHh7e6NatWoYMWIE7t69K20jhMDKlSsRExODoKAg+Pj4oFmzZli9erXex0lMTMSgQYMQEREBT09PVKlSBQMHDsT58+eNfi8UTMn47du3o2fPnlK7/Pz8UL16dbRp0wYzZswwS/uIiIiIyASCiMhBPHnyRLi5uQkA4oUXXjB5f6+88ooAoPX28ssvi7y8PKXtli9frnO7Nm3aKG0jl8vF+PHjhUwm07iNr6+v2L59u8b2jh07VuO2UVFRIi4uTnq8b98+le3lcrn44IMPtLY7KChIHDx4UO3xBw0aJL22JUuWCFdXV6VtIyMjhRBCDB8+XAAQoaGhorCwUOPrSUpKkt6Pr7/+WuN6uowfP14AEC4uLuL27dtCCCG++eYbqV1xcXFG79uS9u3bJ7Vx+fLlWtdNSkrS+reNjIwUAMS0adPE//3f/2n8+w4fPlwIIcSpU6dEaGio2nUCAgLE33//rbEt8fHxIjg4WOvn6K233tL6t9dE8bcEIDZu3Gjw9iUtWLBAuLu7a2yjm5ubWLZsmdpt27RpIwCIQYMGiSlTpmj9913y34U6ubm54s0339T6flWqVEkcO3ZMZdvCwkLRo0cPnXnzxRdfGPTenDt3TinjjDV//nydbatdu7a4fv262u2nTZsmZceePXuEn5+f2n1Uq1ZN3Lp1S+Tm5mrN7U8++UTncbZt2ya8vb3Vbu/h4SE2bNigcx/qmJrxI0eO1PlejhgxQvcfhYiIiIgshkVWInIYu3fvlr5sTps2zeT9vfXWW2LUqFFi48aN4uTJk+Lu3bvi9u3b4vDhw+L999+XvohPnDhRabuCggKRmZkp3njjDQFAxMTEiMzMTKVbdna20jYTJkwQAIRMJhMDBw4U8fHx4sGDB+LBgwdi+/btonnz5gKAKFeunEhMTFRp69KlS6XX3qRJE7Fz507x4MEDcf36dbFgwQIREBAgatSoobUQN3v2bGl58+bNxa5du8SDBw9EUlKSmD9/vvD39xcAhL+/v7h27ZrK9opiUqVKlYSbm5to0aKF2LFjh7h//764efOm2LJlixBCiCNHjkjH2bp1q8b3X1G08Pb2Funp6fr8yVQUFhaKsLAwAUB07NhRej4tLU14eXkJAGLw4MFG7dvSLFFkrV69ugAg3nnnHXHq1Cnx8OFDcfr0adG9e3el4mVkZKSoUaOG+Pnnn8WdO3fEvXv3xLJly4Svr68AIFq1aqW2HadOnZLe1wYNGojVq1eLpKQk8ejRI/HXX3+J9957Tyoyffzxxwa/J7t27ZLaGRQUJL799lujPhtff/21tJ+XXnpJ7Ny5U9y5c0ekpqaKuLg40aVLFwEUF+b37Nmjsr2iyFqlShUBQHTr1k3Ex8eLlJQUce3aNbFz505pXV1F1r59+0pF3TFjxogjR46I1NRUcefOHbF27VpRp04d6d/V3bt3lbb97rvvpNfRr18/sX//fnHr1i3x6NEjceHCBfHzzz+L/v37i4ULFxr0/sjlcum1KYriFy5cMGgfQhRfzOjfv79YuXKlOHz4sLh+/bpISUkRf/31l5g7d650jOeee07t9ooMCAgIEBUqVBCNGjUSW7duFffv3xfJycli3rx50oW1N998U7z33nvC3d1dfPTRR+LcuXPi4cOH4tChQ1J+urq6ivPnz2s8TmBgoChfvryoXr26WLt2rbh3755ITk4WS5cuFSEhIVKh9ezZsxr3oanIakrG79mzR/pbdOzYUezatUvcvHlTpKWliStXrohNmzaJYcOGiQkTJhj8NyIiIiIi82GRlYgcxrfffit9EV2zZo3Fj7d9+3ap91FGRobKcl3FFYUTJ05Ihadvv/1W7Tr5+fkiJiZGABDdu3dXWpaTkyOCgoIEANGwYUORlZWlsv2BAweEi4uLxkLc/fv3haenpwAgWrRoIXJyclT2cfjwYannX69evTS+XkVhuXQP35IaNGggAIiePXuqXS6Xy0VUVJQAIAYMGKBxP7rs3LlTatOqVauUlvXp00cAEH5+fmrfM2uzRJEVgPjPf/6jsjwvL09Uq1ZNKvZFRESIlJQUlfVK9gC+ePGiyvJGjRoJoLgneW5urtq2/u9//5OKVXfu3NH6utTp3LmzUu89Nzc30bhxYzFs2DDx7bffikuXLmnd/u7du1IhePLkyWrXkcvlUvGzQYMGKssVRVYAom/fvkIul2s8nrYc2LRpk1TM3bFjh9rt09PTxVNPPSUAiJEjRyot69mzpwAgoqOjtbxi4/z0008qPSXDw8PFq6++KmbOnCni4+NFfn6+Sce4ffu2CAwMFADEH3/8obJcUbgEIBo1aiSePHmiso6iJ7Grq6uQyWRqsz81NVXqBTtp0iStxwkNDVUpZgshRGJiovS56dq1q8Z9qCuymprxil8pVKpUyeT3nIiIiIgsh2OyEpHDKDleaGBgoMWP16VLF4SEhODJkyc4dOiQ0ftZuHAhhBBo1aoVhg8frnYdd3d3zJw5E0DxuHzp6enSsq1bt+Lhw4cAgFmzZqFcuXIq27dq1Qp9+/bV2IYffvgBeXl5Unu8vLxU1mnevDmGDh0KANi8ebPSuI2lzZ07Fx4eHhqXK17n1q1bkZqaqrJ837590sQ7imMaQzEuro+PD3r27Km0bODAgQCKx97duHGj0cewJxEREWonjPLw8JDen8LCQnz88ccIDg5WWa9fv37SOMdHjhxRWhYXF4fTp09DJpPhm2++gaenp9o2vPPOO6hevTry8/Oxbt06g1/Dxo0bMXLkSLi5uUntPXXqFL777jtprMsmTZpg69atardfsmQJcnNzERkZiRkzZqhdRyaT4fPPPwcAnDlzBn///bfa9VxdXfHll1/qPfZzafPnzwcA9O/fH507d1a7TkBAAKZMmQIA+Omnn5TGzS0sLAQAhIaGGnV8bfr374/169ejSpUq0nO3bt3Cpk2b8NFHH6FNmzYICwvD1KlTtY6rqk1YWBg6duwIQHlSM3U+//xz+Pj4qDzfr18/AEBRURFatmypNueCgoKk45T+3Jb20UcfoXLlyirP16tXD++++y4AYOfOnUrjwOpiasYr/s7BwcFwd3fX+7hEREREVLZYZCUi0uLWrVuYOnUqWrZsiaCgILi7uytNYKUoNOozGZAme/bsAQB07NgRT5480XirW7cuAEAul+PEiRPS9gcPHgQAeHt7S4UEdXr06KFxWUJCAgCgWrVqaNKkicb1FAWMoqIijYXloKAgNG/eXOM+AGDAgAHw8vJCQUEBfvjhB5Xly5YtAwDUqFEDbdq00bovTTIyMrB582YAQM+ePVUKNJ06dUKlSpUA/FuMdXQdO3aEq6ur2mU1atSQ7nfq1EntOv7+/ggJCQEAlSKT4nNco0YNhIaGavwcZ2VloWHDhgCAY8eOGfwavL29sWjRIly/fh3z5s1D165dERQUpLTOiRMn8PLLL6stKCva2a5dO+Tm5mpsZ1BQkFRo1tTORo0aISwszODXAADZ2dn4888/ARRP8Kbt3369evUAFF9IunbtmtLxAWDHjh1YuHCh0cVOTXr16oVr165hw4YNGDx4MGrVqqVUUE5NTcXMmTPRvHlzjRddsrOzsXjxYnTq1AlhYWHw8vJSylBFoV1bhnp6euKFF15Qu0yfzy0A1KxZE4Dq57a00hdjSurVqxeA4gw25MKaqRmv+DufPXsWU6ZM0ToBIRERERFZD4usROQwKlSoIN0v2QvIWL/++iuefvppzJw5E4cOHcKjR4+kHkWlPX782KhjPHnyRJpRevr06fDz89N4q1ixorRdyYKGosdnjRo1pN596tSpU0fjshs3bgCA9CVfE0Wxp+RxS6tevbrWfQBA+fLlpWLG8uXLlZZlZGRIPUsVM7MbY+3atcjJyQHwb6/Vktzc3NC/f38AwB9//KFzZm91cnJyNBZMioqKjGq3JWkrCHp7exu0nuK9Vbhw4QKA4hngtX2O/fz8sGnTJgDQ2htalypVqmDs2LHYtm0bUlNTce3aNXz33Xd47rnnpHXmzJmDX3/9VW07ly9frrOdil7Wmtqpz2ddk2vXrqGgoAAAMGTIEK3taNq0qbRdyba8//77iIiIgFwux5gxYxAcHIx27dph2rRp2Ldvn8a8MoSil/OyZctw8eJFpKenY+fOnRg6dKjUW/3s2bN46623VLa9fPky6tevj5EjR+L333/H3bt3pR7zpWnL0JCQEI09OE393JYUGBiotherQsl81JR/pZkj4wcMGIDo6GgAwGeffYZKlSqhZcuWmDBhArZv347c3Fy92kJERERElsUiKxE5jGrVqkn3FYUUY12/fh39+vXDkydPEBUVhYULF+L48eO4e/cuHj9+jMzMTGRmZiI8PBwAjC5mGFucLfml+smTJwAAX19frdtoW56ZmQkA8PPz07qPkssV25SmbrgCdYYNGwag+OfYx48fl57/+eefkZOTAxcXF8TGxuq1L3UUvVP9/f3h7++P48ePq9waNGgAoLjnmLoetbp06dJFY8FE0TvYlmjqxWrMeiV/tg4Y91k2Z3GoWrVqGDp0KA4dOoSxY8dKzy9atEhpPXO2U9/Pujrm+LcfGBiIY8eOYcSIEQgMDERubi727duHTz75BO3atUOVKlUwb948yOVyo9tZmr+/Pzp16oTvvvsOCQkJ0rAQmzdvRnJysrReUVERevbsiaSkJPj4+GDKlCmIj4/HzZs3kZaWJmWo4kKHtgy15Oe2JEMyVFP+lWaOv7O7uzvi4uIwefJkVK5cGYWFhTh06BDmzJmDbt26oVKlSpg8ebLGAjYRERERlQ3NXZ6IiOzMc889Bzc3NxQWFiI+Pt6kfS1btgy5ubnw9/fH4cOHpZ+Vl5aRkWHScUp+aV+4cCFGjRpl9D50/VRYUYxVR1E81bZO6eW6CrK6tG3bFjVr1sSVK1ewbNkyaZgCRc/WF198USpiG+rq1as4cOAAgOK/ka7hC4DiouzEiRONOh79+zmMjo5W+qmzNcyePRvLli1Denq6Slt8fX2Rnp6OsWPHYt68eVZqofK//S1btuCll14yaj+VKlXCf//7XyxYsACnTp3C4cOH8ccff2Dnzp148OABPvjgA9y4cQMLFy40V9MlzZo1w7Bhw7B48WIAwMmTJxEREQEAiI+PR2JiIgBg/fr1GsecNfcQB6awRP6ZI+MVx/vPf/6DmTNn4uzZszh06BDi4uKwbds2PH78GLNmzUJiYiK2bNli1P6JiIiIyHTsyUpEDsPX1xcvvvgigOJJeK5cuWL0vk6fPg2geNxGTQXWmzdvmlxkDQgIkMaTvHr1qlH7iIyMBFD8M21tP1HXNuZhVFQUgOKf/WqjKJqU3MZYMplMmtTq559/Rm5uLs6fPy9NTGPKhFerVq0yeJsLFy7g6NGjBm0TFxcHIYTaW9u2bQ1ugz1T/HQ+KSnJrD0njeHu7o5atWoBKB4TtCRFO43992YuUVFRcHFxMVtbXF1d0aRJE4wcORIbN25EcnIyWrRoAQBYvHix2gnmzKHkECIl32tFhpYvX15jgRUo7sluK9LT03Hv3j2Ny8+dOyfd1zf/zJHxJclkMtSvXx/Dhw/Hjz/+iNu3b+PVV18FUDyR4KlTp0w+BhEREREZh0VWInIoioluhBD4v//7P60/DS3p9u3bSgVTxc8utRUtdf28XDGGoK6xORWF4Y0bNyI/P1+v9pbUqlUrAMVjDWqbobv02JQlPf/88wCKC2QnT57UuJ5ikhpXV1e0bNnS4LaWFhsbCzc3N6Snp2PTpk3ShFfBwcF4+eWXjdqnEEIqsjZo0EBjEVRxu3//vvQzY2eZAMsSFJMOpaWl4ffff7dqW4QQ0jiYpcfpVLRzz549ePjwYZm3TSEgIEDqYb1mzRqz7z8oKAjvv/8+gOLhMC5fvmz2YwBQGiKg5HutT4YePHgQSUlJFmmXsRTjQauzYcMGAICLi4tUwNaHqRmvjY+PDyZNmiQ9NnWoHCIiIiIyHousRORQWrduLU3AsmfPHrz55ps6x3387bffEB0drTRjs6K328GDB9UWYhITEzF79myt+1XMTH7nzh2t6ynGj0xOTsaYMWN09gIs/SX65Zdflib9mjRpktqJXQ4fPoxffvlF4z4HDBggja04ZswYtWP7HTt2DEuXLgUAvPrqq9LrM0XlypXRrVs3AMC3336L1atXAwDeeOMNaVIdQ+3fv1+alOb111/XuX7FihXRvn17AMXFLnMXQZxFx44d8cwzzwAA3n33XZ0Tid2/fx9paWkGHWPp0qVYu3atzn8jX3/9tXR8RYFLYcSIEfDy8kJWVhYGDx6scxxLSxatxo0bBwA4cuQIZs2apXVdIYRKb3RdbSvZc1LRm1IfV69exeTJk3UWoW/evCllgr+/v9KkY4oMzcjIwL59+1S2zcjIwLvvvqt3m8rKzJkz1fZmPXv2LP73v/8BADp37ozQ0FC992lqxmv7FQJg/N+ZiIiIiMyLRVYicjgLFixAly5dAAA//vgjatWqhdmzZ+P48eO4d+8eUlJS8Pfff2Px4sVo1aoVXnrpJTx48EBpH3379gUAPHr0CJ06dcLu3bvx4MEDJCUlYcGCBWjdujV8fX2l4qY6ijFGr127hm+//RZpaWkoLCxEYWGhUu+uJk2aYPLkyQCAJUuWICYmBr/88guuX7+O9PR03L59GwkJCfj888/x7LPP4rXXXlM6jpeXFz777DMAxT/RbdOmDX7//Xekpqbi5s2b+O9//4suXbpo/XlrxYoVMWPGDADAgQMH8MILL2D37t1ITU3FjRs3sGjRInTs2BEFBQXw9/fHF198oc+fQi+KCbDi4uKk4oYpQwUoeqPKZDK9iqxAcVEXKP57//bbb0Yf25nJZDKsXLkS5cqVQ1JSEho1aoRZs2bhr7/+wqNHj5CamorExESsWrUKffr0QWRkpME/n7548SL69u2LqKgoTJgwATt27JD+ndy5cwe7d+/GwIEDMXLkSADF41gq/m0pVKlSRRqfdOvWrXj22WexbNkyXLlyRfq5+JEjR7Bw4UK0adMGTZs2Nc8bpEavXr2kz97kyZPRrVs3bNmyBbdu3cLjx4+RnJyMvXv3Ytq0aXj66aeloqxCly5d8Nxzz2HevHk4dOgQ7t27h4cPHyIxMRGffvoppk2bBqB4nFzF8An6yMnJwaxZs1ClShX07dsXK1euxNmzZ5GamopHjx7h9OnTUh4phiGYNm2adKEGKC5EBgQEAAD69++PVatW4ebNm7h37x7Wr1+P5557DomJiahdu7ZJ76E5BQYGIicnBzExMVi/fj3u37+P27dv4/vvv8cLL7yA3NxceHh4GJx/pmb822+/jfr162PmzJmIj4/HnTt3kJaWhgsXLmDBggV45513ABT3JG7durV53gwiIiIiMpwgInJAhYWFYsqUKcLb21sA0Hrz8PAQY8aMETk5OUr7ePfddzVuU758eZGQkCAiIyMFADFt2jSVNuTm5oratWur3b5NmzZK68rlcjFz5kzh6uqqs72NGzdW+5rHjBmjcZvIyEixb98+6XFCQoLK9nK5XHzwwQdajx0UFCQOHDig9viDBg1S+9p0KSwsFFWqVJGO0aRJE4O2LykrK0v4+fkJACImJkbv7TIyMqTPyssvv2z08c2p5N9r+fLlWtdNSkqS1t23b5/Kcm2fU4Xly5dL+9BG176OHDkiqlatqvNzDECcPn1a67FKmzFjhl77BSAiIiLEn3/+qXFf33//vV75UL58eZVt27RpIwCIQYMG6Wyzrn8X+fn5YuTIkXq9pldffVVpW8XfQtstKipKXLx4UWc7S7p06ZLw8PDQq01ubm4aPws///yzxkxzcXERCxcu1Pr+TJs2TcovbfT5d6JtXyWX/fbbb8LLy0vj/ys2bNhg8P6FMC3jFZ83Xdms7fNORERERJbHnqxE5JBcXV0xc+ZMXLt2DXPmzEGHDh0QHh4Ob29veHl5ITw8HF26dMG8efNw48YNzJ8/H15eXkr7WLx4MVasWIHnnnsOPj4+8Pb2Rs2aNTFq1CicOnUKMTExWtvg6emJ+Ph4jBo1CrVr11bZf0kymQxTpkzB5cuX8eGHHyI6OhqBgYFwdXWFv78/6tevj4EDB+LHH39EQkKC2n3Mnz8fW7ZsQceOHVG+fHl4eXmhVq1aGD9+PE6ePKn0M1J/f3+1bfjiiy/w559/4o033kDVqlXh6ekJf39/NG7cGFOnTsWlS5ekMWDNxdXVFYMHD5Yem9KLdePGjcjMzASg31ABCn5+ftLs7jt27EBKSorRbXB2zZo1w8WLF7FkyRJ06dIFoaGh8PDwgJeXF6pWrYouXbrg888/x5UrV9CwYUOD9v3xxx/j0qVLWLBgAfr06YN69erB398frq6u8PHxQbVq1dCjRw8sXboUFy5c0Dpu5pAhQ5CUlITp06ejRYsWCAoKkvZTu3Zt9O3bF0uXLjVpAj19uLu7Y9GiRfjrr78wYsQI1K9fX3pNgYGBaNy4Md566y1s3rxZZezWXbt2YeHChXjllVdQt25dlC9fHm5ubggODkbr1q0xb948JCYmGtSLFQCeeuoppKSkYO3atRgxYgRatmyJihUrwt3dHR4eHqhYsSJiYmIwefJknDt3DtOnT1e7n379+iE+Ph7du3dH+fLl4eHhgfDwcPTp0wf79+/HqFGjjH3bLKZbt244cuQI3njjDVSpUgUeHh4IDQ3FgAEDcPr0afTs2dOo/ZqS8StXrsTSpUvRr18/NGjQAMHBwdLno3nz5pgxYwYuXrxo0DixRERERGR+MiH0nBWGiIjs2qZNm6QCQWpqqk2N3ff5559j4sSJ8Pb2xt27d6WfGRMRWdr06dMxY8YMREZGSuM5ExEREREZij1ZiYicxK+//goAiIqKsqkCKwCsWLECQPEYlSywEhERERERkb1hkZWIyEE8evRI47L9+/dj9erVAP6d1MtW7NixQ5pNWzGBCxEREREREZE9cbN2A4iIyDzq1auH3r17S+Mzenh44ObNm9i4cSPmzp2LoqIiBAUF4f3337d2UwEARUVFOHHihDQTfMuWLc0+3isRERERERFRWWCRlYjIQWRmZmLRokVYtGiR2uXly5fH5s2bUalSpTJumaqoqCjcuHFDeuzp6amx3URERERERES2jsMFEBE5iFWrVmHIkCGoW7cugoOD4ebmhsDAQDRp0gRTp07FpUuXEBMTY+1mKgkMDET79u0RFxeH6OhoazeHiIiIiIiIyCgyIYSwdiOIiIiIiIiIiIiI7BV7shIRERERERERERGZgEVWIiIiIiIiIiIiIhOwyEpERERERERERERkAhZZiYiIiIiIiIiIiEzAIisRERERERERERGRCVhkJSIiIiIiIiIiIjIBi6xEREREREREREREJmCRlYiIiIiIiIiIiMgELLISERERERERERERmYBFViIiIiIiIiIiIiITsMhKREREREREREREZAIWWYmIiIiIiIiIiIhMwCIrERERERERERERkQlYZCUiIiIiIiIiIiIyAYusRERERERERERERCZgkZWIiIiIiIiIiIjIBCyyEhEREREREREREZmARVYiIiIiIiIiIiIiE7DISkRERERERERERGQCFlmJiIiIiIiIiIiITMAiKxEREREREREREZEJWGQlIiIiIiIiIiIiMgGLrEREREREREREREQmYJGViIiIiIiIiIiIyAQsshIRERERERERERGZgEVWIiIiIiIiIiIiIhOwyEpERERERERERERkAhZZiYiIiIiIiIiIiEzAIisRERERERERERGRCVhkJSIiIiIiIiIiIjIBi6xEREREREREREREJmCRlYiIiIiIiIiIiMgELLISERERERERERERmYBFViIiIiIiIiIiIiITsMhKREREREREREREZAIWWYmIiIiIiIiIiIhMwCIrERERERERERERkQlYZCUiIiIiIiIiIiIyAYusRERERERERERERCZgkZWIiIiIiIiIiIjIBCyyEhEREREREREREZmARdYycPXqVUyYMAHNmjVD+fLl4e7ujpCQENSrVw+dO3fG1KlTER8fj6KiIms31aqmT58OmUyGqKgoq7Xh1KlT+M9//oNOnTohPDwcnp6e8PX1Ra1atTBkyBAcOXJE732dP38eb731FqpVqwYvLy9UqlQJL774In755Re9tk9LS8Mnn3yCJk2aIDAwEN7e3qhRowb+7//+D+fOndNrH7dv38aHH36IBg0awM/PDz4+Pnj66afxwQcfIDk5Weu2ubm5+PXXXzFy5Eg0a9YMFSpUgLu7OypUqICWLVti5syZSElJ0asdRNbC/NUP81eZtfP36tWrWLBgAQYMGICGDRsiLCxMej9q166NwYMH4+DBg3q1g8hamL/6Yf4qs3b+antdXl5ekMlkkMlkmD59ulH7ISoLzF/9MH+VWTt/4+LipIzVdUtNTdWrPVYhyKIWLFggPDw8BACdt2PHjlm7uVY1bdo0AUBERkZa5fijRo3S6+80cuRIUVRUpHVfP/74o/Dy8tK4j169eomCggKN2x86dEhUqlRJ4/aenp5i2bJlWtuwZcsW4evrq3EfAQEBYvv27Rq39/Pz0/leBAUFiR07dmh/Y4mshPmrP+bvv2whfz/99FO93o+33npLFBYWan9ziayA+as/5u+/bCF/1ZHL5SImJkZpP9OmTTNoH0RlhfmrP+bvv2whf/ft26fX+wFApKSkaH9zrYhFVgv68ccfpQ9BRESEmDt3rjhx4oS4f/++uHv3rvjzzz/F3Llzpf9pM+SsG3KDBg0SAET9+vXF7NmzxZEjR6S/1caNG0WDBg2kv+eECRM07ufQoUPC3d1dABB16tQR27ZtEw8ePBB///23dAwAYtSoUWq3v379uqhQoYIAIHx9fcX8+fNFUlKSSElJEbt27RJNmjQRAISrq6vYvXu32n0cO3ZMeHp6CgCiUqVKYtmyZeLWrVvi/v37YsOGDeKpp54SAISPj484c+aM2n0AEG5ubuLVV18Vq1atEhcvXhSPHj0SFy9eFLNmzRLe3t4CgPDy8hKnTp0y+P0msiTmr2GYv8VsJX+//PJL8dJLL4l58+aJvXv3ivPnz4vU1FRx/vx58eOPP4qGDRtKr2XSpEmGv+FEFsT8NQzzt5it5K86X3/9tQAgqlevziIr2TTmr2GYv8VsJX9LFlnPnj0rMjMzNd5sGYusFhQVFSUAiKioKPHo0SOt6yYmJor79++XUctsk7VDbuHCheL333/XuPzJkyfimWeeEQCEu7u7uHPnjtr1WrRoIQCIkJAQtX/TgQMHSiF1/vx5jctdXFzEvn37VJZnZ2dL7Xj66afV9mJq3bq1ACDKlSsnzp07p7L8wYMHIiwsTAAQHTt2VPs63nnnHZGcnKx2mRBCxMXFCRcXFwFAdOvWTeN6RNbA/DUM81d5ubXzV5fc3Fyp0FquXDmRlZVl1H6ILIH5axjmr/JyW8vf27dvi4CAAOHm5ia2bt3KIivZNOavYZi/ysutnb8li6xJSUlq17EHLLJayKVLl6QPyH/+8x9rN8cuWDvk9LF+/Xrp77py5UqV5SdOnJCWz5kzR+0+bt++LVxdXQUAMWbMGKVleXl5oly5cgKA6Nq1q8Z2bNiwQTpO6S73t27dkpa9++67Gvcxb948aT11YauP7t27CwDCw8ND5OfnG7UPInNj/hqO+Wt/+VtyH8ePHzdqH0Tmxvw1HPPXtvP3lVdekXqRJSUlschKNov5azjmr23lr6MUWTnxlYWUnBDIz8/P6P0UFhYiLi4O48aNQ5MmTaSBq4OCghATE4N58+YhKytL4/alB3O+cuUK3nrrLURFRcHb2xvVqlXDiBEjcPfuXWkbIQRWrlyJmJgYBAUFwcfHB82aNcPq1av1Pk5iYiIGDRqEiIgIeHp6okqVKhg4cCDOnz9v9HuhcPr0abz11luoVasWfH194ePjg7p162Ls2LG4ffu2yfvXpn79+tJ9dcfasmWLdL9v375q9xEWFobnn38eAPDrr78qLbt8+TKys7MBAG3bttXYjpLLNm3apLTs9OnTatfTto+NGzdqXE8bxfuRn59v24NPk1Nh/jJ/nSF/PTw8pPuenp5G7YPI3Ji/zF9Hyt+NGzdi8+bNiIqKwscff6x1XSJrY/4yfx0pf+2adWu8juvcuXNSFf7ll182ej/z58+X9qPpVrt2bXH9+nW125e8OrNnzx6NkxlVq1ZN3Lp1S+Tm5kpXbNXdPvnkE53H2bZtmzReZ+mbh4eH2LBhg859qCOXy8X48eOFTCbT2D5fX1+DB7M3xP79+6Vj/fe//1VZrujZGRYWpnU/kyZNkvZT8qccBw4ckJ5funSpxu3lcrm0XqNGjZSWrV69WlqmacwUIYTS1fhXXnlFa3s1GTJkiLSPJ0+eGLUPInNj/jJ/tXGE/C0qKhLPP/+8ACACAwNFbm6uwfsgsgTmL/NXG3vK3/T0dOmnrdu2bVPZlj1ZydYwf5m/2thD/qrryZqXl6f1ddki9mS1kDp16qBKlSoAiq8uvP3227h48aLB+/H29kb//v2xcuVKHD58GNevX0dKSgr++usvzJ07F1WqVMHFixfRr18/rftJT09Hnz59UKNGDWzduhX3799HcnIy5s2bBzc3NyQlJWHSpEmYOHEitm3bho8++gjnzp3Dw4cPcejQITRv3hwAMGPGDFy4cEHjcR4/fowBAwYgNDQUa9euxb1795CcnIylS5ciJCQE+fn56N+/P86dO2fwezFp0iTMmTMHADBw4EDEx8fjwYMHePDgAbZv347mzZvjyZMneO2113D27FmD96+P9evXS/dbtGihslzx3lSvXl3rfqpVq6ayDQD4+/tL99PS0jRuX3LZxYsXIYQweB+PHj2S7htzhS8vLw+//fYbAKBBgwbw8fExeB9ElsD8Zf5qY6/5K4TAvXv38Pvvv6Njx45ISEgAAHz66afsyUo2g/nL/NXGnvJ3/PjxuHPnDnr37o2uXbtqXI/IVjB/mb/a2FP+AsArr7wCb29veHp6wtvbG9HR0ZgyZYpSD2ibZdUSr4P76aefVK50hIeHi1dffVXMnDlTxMfHmzyO5e3bt0VgYKAAIP744w+V5YqrM/jnioO63oZTpkwRQPFAyDKZTKxZs0ZlndTUVOkqlLqZjEseJzQ0VNy9e1dlncTEROHl5SUA9eN9aLuSdOLECekK0rfffqv2vcjPz5dmSuzevbvadUxx4cIF4eHhIQCI5s2bq11H8bfo1auX1n39+uuv0vu1ZcsW6fns7GzpGNomk9q4caPS56rkDHtXrlyRnh8xYoTGfXz55ZfSekFBQVrbq87HH38sbf/1118bvD2RJTF/lTF//2Vv+dupUye1PScqV66s8e9BZE3MX2XM33/ZS/7u379fyGQy4e/vL27fvi09z56sZOuYv8qYv/+yh/wt2ZNV083Pz0+sW7dO62u1NhZZLWz9+vWiSpUqGj8kwcHB4qOPPjLpp9a9e/fWK3x27dqldvszZ85I67Rq1UrjcXr27CkAiHbt2mk9zuLFizXuY+zYsQIonrmu9Ox42kJu0KBBOtsnRPGM94r9p6WlaV3XEE+ePBENGjQQQPHMfkePHlW7nru7uwAg3njjDa37+/3336X366efflJa9vLLL0uvYf/+/Srb5uTkiEaNGil9jkq/l4rZ/3x8fMTFixdV9pGamqr0ufTw8ND1FijZu3evNHh2kyZNREFBgUHbE5UF5q8y5m8xe8tfdUVWLy8vMWPGDKUv/0S2hPmrjPlbzB7yNzc3V9SpU0cAEIsWLVJaxiIr2QPmrzLmbzF7yN8DBw6Inj17ip9++kkkJiaKx48fi+zsbHH69GkxceJEqRjs5uamdVgCa+NwARbWq1cvXLt2DRs2bMDgwYNRq1YtyGQyaXlqaipmzpyJ5s2bKw1WXVJ2djYWL16MTp06ISwsDF5eXpDJZNJt3bp1AKD15wCenp544YUX1C6rUaOGdL9Tp04a91GzZk0A0NlFu2fPnhqX9erVCwAgl8tx6NAhrfspac+ePQCAjh074smTJxpvdevWlfZ/4sQJvfevjVwux4ABA3DmzBkAwKxZs9C0aVOt25T8Gxu6fMaMGfD09IRcLke3bt2waNEi3Lx5Ew8fPsTu3bvRtm1bnD59Gt7e3hr3N3v2bMhkMmRlZaFNmzZYuXIl7ty5gwcPHmDz5s1o1aoVbt++Le1DV3tLunTpEvr06YOioiIEBgbi559/hpubm97bE5UV5q8y5q/u5baYv5s3b0ZmZiYyMjJw/fp1rFmzBvXr18e0adPQoEED/PHHH1q3J7IG5q8y5q/u5baSvzNnzsSFCxfQtGlTvPvuu1pfD5EtYv4qY/7qXm4r+duqVSts2LAB/fv3R7169eDv7w9vb280bNgQs2bNwt69e+Hl5YXCwkKMGDECRUVFWl+z1Vi7yuuMHj9+LHbu3CmGDh0qVeMB9YP/Xrp0SVSrVk3jlaiSt/bt26tsr7g6Ex4errVNin189913GtdR7CsqKkrjssDAQK3HSUtLk441b948tfsofSUpMzNTr9df+vbzzz9rbYu+hg0bJu1z1KhRWtfVt7v+li1bpH2W7K6vsGHDBo2DdwMQ/fv3F7169ZIeq5v0ZNGiRVJvU3W3CRMmiKZNmwqg+Gen+khOThaRkZECgChXrpxISEjQazsiW8H8Zf7aa/6WVFBQILp16yb1GLhx44bB+yAqa8xf5q+t5++ZM2eEu7u7cHV1FSdPnlTZN3uykr1i/jJ/bT1/9fXBBx9I+zt06JBR+7A09mS1An9/f3Tq1AnfffcdEhISpAkrNm/ejOTkZGm9oqIi9OzZE0lJSfDx8cGUKVMQHx+PmzdvIi0tDZmZmcjMzET//v0BAIWFhRqP6erqqlfb9FlPlBjkuDRfX1+t25ZcnpmZqVebHj9+rNd6peXm5hq1XUnjxo3Dd999BwCIjY3FggULtK4fHBwMAHjw4IHW9e7fvy/dDwoKUlnes2dPnD17FiNHjkStWrXg7e0Nf39/xMTEYPXq1fjpp5+Qnp4OAAgJCVE76cnIkSNx8uRJDB48GFFRUfD09ESFChXw4osvYtu2bZg9e7Y0+HRERITW9gJASkoKOnbsiBs3bsDDwwMbN25ETEyMzu2IbAnztxjzt5i95G9pbm5uWLhwIQAgKysLS5cuNXgfRGWN+VuM+VvMFvN39OjRKCgowKhRo9C4cWOtr4XInjB/izF/i9li/urrlVdeke6fPHnSqH1Ymll+45ufn4+HDx9KbyLpr1mzZhg2bBgWL14MoPiDovjAxcfHIzExEUDxrHKdO3dWu4+srKyyaawenjx5ovdyPz8/vfZZMhgXLlyIUaNGGdc4A3388cf48ssvAQB9+vTB999/r7Mbfu3atXHlyhVcvXpV63pJSUnS/Tp16qhdp1q1ali0aJHGfShmMNT204FnnnkGy5YtU7ssJydHaoeunx+kpaWhY8eOuHDhAtzc3PDLL79o/WkHlR3mr/GYv7oxf9Ury/zVpHr16ggJCUFKSgpOnTpl1D7INMxf4zF/dWP+qmfJ/L127RoAYP78+Zg/f77G/QPFP6+dMWMGAGDfvn1o27at1vXJ/JjBxmH+6sb8Vc8Wzn8rVqwo3VcUfW2NST1ZV69ejWbNmsHHxwfh4eH44IMPpGWbNm3C66+/rvTHJPXq1asn3c/Ozpbunz59GgBQvnx5jQEHQBqrwxakp6fj3r17GpefO3dOuh8VFaXXPgMCAqSrLbrCw1w+//xzfPrppwCA7t27Y/Xq1XBx0f3PpUmTJgCAO3fuKF0VLE0xHkxUVJRRJwV///239D536dLF4O0BYPfu3ZDL5Tr3kZGRgc6dO+Ovv/6Ci4sLVqxYoXQFiayD+WsezF/tmL+qyjJ/dbHZsagcHPPXPJi/2jF/VdlS/pL1MINNx/zVjvmrylbyt+Tfunz58kbtw9KMLrIOGzYMgwYNwvHjx+Ht7a3ShbtWrVpYs2YNNmzYYHIjHV3JfwxhYWHS/by8PADav0QdPHjQ5v4nsnHjRo3LFJ8HFxcXtGjRQu99vvjii9K+8/PzTWugDgsXLsTEiRMBAO3bt8e6devg7u6u17YvvfSSdH/t2rVq17l79y4SEhIAAC+//LJRbVT8bKBcuXIYMGCAUftQ/NQ0IiJCY8hlZ2ejW7duOHr0KGQyGZYsWYI33njDqOOR+TB/zYf5qxvzV1lZ5a8uiYmJ0k+uSk4gQZbF/DUf5q9uzF9lls7f7du349SpUxpv27Ztk9Z9++23pecVRQ6yPGaweTB/dWP+KrOV89+Sf+vo6Gij9mFxxgzkunr1aiGTycQzzzwjjh8/LuRyuZDJZGLw4MFK60VERIh27dqZNGisvbpy5YqYNGmSSE1N1brejRs3RHBwsAAg/P39lQYQXrNmjTSo7x9//KGy7ePHj8UzzzwjrdOmTRuVdTQN5lyaYh/Lly/XuI62fSmWARChoaHi7t27KuskJiYKLy8vAUB07drVoP0fO3ZM2v8777wjioqKtL6e8+fPa12uyffffy9kMpkAIFq1aiWysrIM3sdzzz0nAIiKFSuKlJQUleWxsbECgHB1dRXnzp0zeP8///yz1MYvvvjC4O2FEOLzzz+X3s9169apXSc3N1d07NhRWm/+/PlGHYvMi/mrG/OX+WvP+ZuXlyeuXr2qdR85OTmiTZs20n72799vVFvIMMxf3Zi/zF97zl9dOPGVdTGDtWP+Mn/tOX/z8/PFvXv3tO5j79690sRttWrV0vk3sRajiqytW7cWfn5+4ubNm9Jz6gKua9euolq1aqa10E6dOXNGABCenp6iT58+YsWKFSIxMVGkpKSIhw8filOnTonZs2dLAQeoznaXnp4uAgICBABRqVIlsXLlSnHjxg1x9+5dsW7dOvH0008LFxcXUbt2bZsJucDAQBEYGChq1Kgh1q1bJ+7duydu3bolvvvuOxESEiIACA8PD3H27FmD2zp58mSpnS1atBBr1qwRSUlJIi0tTdy6dUvs379fzJ49W0RHR4t69eppfb3qrF27Vri4uAgAon79+uL27dsiMzNT403djHpCCHHo0CHh7u4uAIinn35abN++XTx48EAkJiZKAQdonymwZs2a4tNPPxXHjh0TKSkp4t69eyI+Pl7ExsZKAde1a1dRWFiodvukpCTRsGFD8dVXX4m//vpLPHz4UNy5c0fs3LlT9OjRQ2rD22+/rXb7wsJC8corr0jrTZo0Set7kZmZqbEtZF7MX92Yv8xfe87ftLQ04e7uLnr16iVWrVolfXYfPXokzp49K5YsWSJ97gCIN954w+D3m4zD/NWN+cv8tef81YVFVutiBmvH/GX+2nP+pqWlCS8vLzFgwACxZs0acf78eZGamipSUlLEn3/+KUaPHi3c3NwEAOHm5ib27t1r8PtdVowqsgYEBIgOHTooPacu4AYMGCDKlStnfOvs2KVLl6Qqu66bm5ubxv9R//zzz8LV1VXtdi4uLmLhwoVi0KBBNhNykZGR4rfffpOuGJW+eXh4iA0bNhi8fyGEkMvlYubMmRrfj5K3xo0ba3296pTsFaTPbdCgQRr39eOPP2p8DwCIXr16iYKCAo3b6zr2wIEDNYasEMongepurq6u4sMPPxRyudyo7dXd9u3bp+9bTSZg/urG/GX+2nP+pqWl6fUeyGQy8fbbb4v8/Hy932cyDfNXN+Yv89ee81cXFlmtixmsHfOX+WvP+avv+W9QUJD49ddf9X6PrcENRsjLy0NAQIDO9VJSUuDq6mrMIezeU089hZSUFOzatQvx8fE4deoUrly5grS0NMhkMgQGBqJWrVpo3bo1YmNj8dRTT6ndT79+/RAREYHZs2fj4MGDyMrKQsWKFdGyZUuMHj0arVq1QmxsbNm+OB26deuGI0eOYM6cOYiLi0NKSgqCgoLQvn17TJ48GU8//bRR+5XJZJgyZQpef/11fP3119i7dy+uXbuGzMxM+Pj4oGrVqoiOjkanTp3Qo0cPM78qw7z++uto3LgxvvzyS+zZswd3796Fv78/GjZsiKFDh6Jfv35at//f//6HvXv34vTp07h//z7kcjlCQ0PRunVrDBkyBDExMVq3r1SpEubNm4e9e/fi7NmzSElJgYuLC6pUqYIOHTpg+PDhaNiwoTlfMpUR5q9uzF/mrz3nb0BAAPbt24e4uDgkJCTgxo0bePDgAXJzc+Hv74+aNWsiJiYGgwYNQoMGDYx6j8g4zF/dmL/MX3vOX7JtzGDtmL/MX3vOXz8/P/zwww84fPgwjh07hrt37yI1NRVyuRzly5dHgwYN0KVLF8TGxtrshFcKMiFKjRath5o1a8LT0xNnz56VnnNxcUFsbCyWLVsGABBCIDIyEuXLl8dff/1lvhaTTZo+fTpmzJiByMhIXL9+3drNIXJYzF8qjflLVDaYv1Qa85eo7DCDqSTmL9kqF2M2at++PS5cuIBff/1V4zo//PADbt26hY4dOxrdOCIiUsb8JSKyDuYvEZH1MIOJyB4YVWT94IMP4Onpiddffx3z58/HnTt3pGWPHj3CkiVL8O6778LHxwejR482W2OJiJwd85eIyDqYv0RE1lMWGXzx4kUsWrQIsbGxaNCgAdzc3CCTyTBz5kxzvQwicnBGFVmfeuoprFy5EnK5HOPGjUNERARkMhlWrlyJkJAQjBgxAoWFhVixYgWqVq1q7jYTETkt5i8RkXUwf4mIrKcsMvjrr7/G6NGjsXLlSiQmJqKoqMjMr4KIHJ1RRVYA6N27N44dO4bevXvDz88PQggIIeDl5YWXXnoJhw4dQq9evczZViIiAvOXiMhamL9ERNZj6QyuX78+PvjgA/z44484f/483nzzTTO2noicgZspG9evXx9r1qyBEAIPHz6EXC5HcHAwXFyMrt0SEZEemL9ERNbB/CUish5LZvCwYcOUHjPXichQMiGEsHYj1Ll48SJ+//13nDhxAidOnMD58+dRVFSETz/9FB999JG1m0dE5LCYv0RE1sMMJiKyDbGxsVi5ciXzl4j0ZlJPVkv6+uuvsWDBArPuUy6X486dO/Dz84NMJjPrvomo7AghkJmZibCwMF5htgDmLxFpwvy1PHNnMPOXyDEwf+0P85fIMRiSv0YVWYcMGaL3ujKZDN9//73Bx1CMh9K4cWNER0fjs88+ww8//GDwfkq6c+cOIiIiTNoHEdmO5ORkhIeHW7sZZYr5S0S2gPmrnbH5C5g/g5m/RI7FGfMXKLsMNifmL5Fj0Sd/jSqyrlixQutyxVUaIYTRAWeJ8VD8/PwAFL8x/v7+Ju+PiKwjIyMDERER0r9pZ8L8JSJrYv5qZo78BcyfwcxfIsfgzPkLlF0GmyIvLw95eXnSY8XIjMxfIvtmSP4aVWRdvny52uflcjlu3LiB7du34/jx43jvvffQsGFDYw5hEYrg9ff3Z8gROQBn/NkN85eIbAHz91/MXyIqS86Yv4B9ZPCsWbMwY8YMleeZv0SOQZ/8NarIOmjQIK3Lp0+fjvHjx2Pp0qU4efKkMYcgIiI1mL9ERNbB/CUish57yOBJkyZh7Nix0mNF7zcich4WGzH7s88+g5+fHz7++GNLHcKm/X0rHY9zCqzdDCJyQs6Yv4m3HyM9O9/azSAiJ+eM+Xs9NQvJj7Kt3QwiIqtnsKenp9Rr1dK9V3MLinDiRhqK5MJixyAiwxnVk1WvHbu5ITo6Gnv27LHUIXQqPSZKRkZGmRw34XIK3vz+KEL8PHFsSocyOSYRkYKz5e/haw/R79vDCPB2x1/TXrTYcYiIdHG2/M3OL0TbuXEAgCv/6QI3V854TkTWYwsZXFaGrTyOA1dSAQA7xjyPp0M5HAGRLbDomVBOTg7S0tIseQitZs2ahYCAAOlWVl31t5+5BwBIyczTsSYRkWU4U/7uOXcfAPjrASKyCc6Uv2nZ/+ZubqHcYschItKXtTO4rCgKrADw0eZEK7aEiEqyWJH1/PnzOHDggFXHIJk0aRIeP34s3ZKTk8vkuAVFPMkkIutxtvzlj6SIyFY4W/66u/w7AUQhz3+JyMpsIYOtoZBDBhDZDKOGC1i1apXGZZmZmTh//jx++OEH5Obm4vXXXze6caby9PSEp6dnmR+XJ5lEZCnMX1VywRNLIrI85q8q15JFVn7JJyILKosMPnnyJN59913p8dWrVwEA33zzDX777Tfp+U2bNiE0NNSoY1gC6w9EtsOoImtsbCxkMpnG5eKfL7w9evTARx99ZFzL7BhPMonIUpi/qlhjJaKywPzVjpOvEJEllUUGZ2Rk4MiRIyrP37p1C7du3ZIelxz32hacvVM2c88QkW5GFVkHDhyoMeA8PDxQpUoVdOjQAS1btjSpcfaKvaqIyFKYv6qYuURUFpi/qkrWVZnFRGRJZZHBbdu2lYq1RETGMKrIumLFCjM3w7HwSj4RWQrzV9X1h9nWbgIROQHmrypRYlRsnv4SkSUxgzV7oXaItZtARP8wqshaFux1PBQA4JAoRGTP7Cl/s/MLsf9SilXbQERkTvaUwSVnHpSzykpEZBUuWoZRIKKyZbNFVnsdDwXgz6WIyL7ZU/4+fJJv7SYQEZmVPWVwyTNenv8SERGRs9OryKptJj99DBw40OBt7Hk8FA4XQETmwvzVzsWFV+6JyDKskb+AfWWwUBqT1XrtICLHY60MtgcPn1j/IhsRqadXkVXXTH66OHLAqRPPn64SkZkwf7Vz5c+jiMhCmL+6lRyTlZ0MiMicmMGafbn7krWbQEQa6FVk1TaTHynLyC2wdhOIyIEwf7Ur3ZFVCMH3i4jMgvmrW8m6qr30viUi+8AM1uzHIzet3QQi0kCvIitn8tPf7bQcazeBiBwI81e70l/pi+QCbq48ISci0zF/dStZWGVHViIyJ2aw/liMJrIdLtZugKPhRXwiorJTOnML+S2fiKjMFBZxuAAiorKk7lcDnKKAyHawyGpmQqVfFRERWUrp2az5JZ+IqOyM+eW0dL90HhMRkfnFqZn/xYU9WYlshl7DBWiTlZWFK1euICMjQ+NYTK1btzb1MHaD55dEVFaYv6pf6tmTlYjKAvO32F/J6dJ9ngMTUVlx5gwumbsKruzKSmQzjC6yXrt2DWPGjMHOnTshl8s1rieTyVBYWGjsYewOTzCJyNKYv/8q/fLlLLISkQUxfzUr4kkwEVkYM1h9r1V2ZCWyHUYVWe/evYsWLVogJSUFYWFhKCwsxIMHD9CiRQtcvnwZqampkMlkaNGiBdzd3c3dZpvG4QKIyJKYv8qm/pqo9Jg9WYnIUpi/yo5ce6j0mMMFEJElMYOLqeu0yomviGyHUWOyzp49GykpKZg8eTJu3bqFLl26QCaT4eDBg3jw4AF27NiByMhIeHt7Y/fu3eZus03j+SURWRLzV1l8qXGpOCYrEVkK81dZ328PKz3mLwmIyJKYwcXUFVSZv0S2w6gi665du1ClShXMmDFD7fJOnTphx44d2L9/P+bNm2dSA+1N6Xj7+1a6NZpBRA6K+asdf65KRJbC/NWO3/GJyJKYwcXUdVplJwMi22FUkfXmzZto1KgRXF1di3fiUrybkuOe1K5dG88//zx++uknMzTTfpQeeHvBnstWagkROSLm77/yC1XH4ioq4kkmEVkG81c7DhdARJbEDC6mbkxWF6OqOkRkCUb9c3R3d4ePj4/0WHE/NTVVab2KFSvi2rVrJjTP/pQ+vdx74YFV2kFEjon5+6+CIs0THhARmRvzVzv+XJWILIkZXEzdmKyV/b3LviFEpJZRRdawsDAkJydLj6tVqwYAOH78uNJ6Z8+eRbly5Uxonv3hRXwisiTm77/U9Zri5INEZCnMX+1YYyUiS3L2DH6UlY8ZW89izs6L1m4KEWlhVJH12Wefxfnz56Wu+e3bt4cQAhMnTsTZs2eRmZmJzz77DGfOnEHDhg3N2mDbp3qGySv7RGQuzN9/cfwpIipLzF/tOFwAEVmSs2fwuLWnsfzgdRSqOf9lJwMi22FUkbVz585IT0/Hzp07AQDPPPMMXnnlFZw7dw7PPPMMAgMDMXXqVLi4uGDatGlmbbCtU/edf/SaU2XfECJySMzfYuuOJ+PHIzdVnud3fCKyFOavdpx4kIgsydkz+EjSI2s3gYj0oFeRNT8/X+lxv379kJycjLZt20rPrV69GiNHjkTFihXh5uaGBg0aYN26dWjVqpVZG2zr1J1f/vb33bJvCBE5BOavqtPJ6fhw/d/4Yhd/LkVElsP8NUzpyV+JiEzBDFambsIrBcYvke3Qq8gaFhaG9957D3///TcAwM3NDVWqVIGvr6+0Trly5bBw4ULcvXsXeXl5OH36NF599VXLtNqG8QSTiMyJ+avqxsMsjcuYwERkLsxfw3AuQiIyJ2awMnUTXhGR7dGryPro0SMsWrQIjRs3RtOmTbFkyRJkZGRYum12iV/wicicmL+qtF3JJyIyF+avYTgmKxGZEzNYmSurrER2Qa8i64YNG9C1a1e4urrixIkTGDFiBEJDQzFw4EDs27fP0m20K5rOL2+n55RtQ4jIITB/VbHGSkRlgflrGP6ai4jMiRmsTF2RtUGVACu0hIi00avI+uqrr2Lr1q1ITk7G7NmzUbt2beTk5GD16tXo0KEDatasic8++wy3b9+2dHttnqaZ/Q5eSS3jlhCRI2D+qpJB9STTz8sNAL/kE5H5MH/VS8/Ox4V7qr3JOFwAEZkTM7g01fNf9m4lsj16FVkVKlWqhPHjx+PcuXM4ePAghg4dCl9fX1y7dg1Tp05FVFQUunXrho0bN6KwsNBSbbZtGr7fFxbxiz8RGY/5+y9XNf/n4ikmEVkK81fZ60uPoPP8BJXnOVwAEVkCM7iYtl9ysZMBke0wqMhaUosWLbB06VLcu3cPy5cvR0xMDORyOXbs2IHevXsjLCwMH3zwAc6ePWvO9to8TfFWJOflfSIyD2fPX5m22VXLsB1E5HycPX8B4Nxd5x0TkYisixlMRLbO6CKrgre3NwYNGoT4+HhcvnwZkydPRpUqVZCamoqvvvoKDRs2NEc77Yami0gF7MlKRGbmrPmrbuIrbYVXIiJzc9b81YZnukRUVpwxg7Wd6TJ/iWyHyUXWkqpXr46pU6di5syZCAkJgRDC6bquaxqTlT+hIiJLcqb81XqS6ZgvmYhsmDPlLxGRrXGWDH6SpzoUguO9SiL752auHR09ehTLly/HmjVrkJGRASEEXF1d0a1bN3Mdwi7INSSdA+Y8EdkIZ8vfIjWByo6sRGQNzpa/49b+pXGZIxY1iMi2OVMGZ+cXqT75T+4yfolsh0lF1pSUFPzwww9Yvnw5zp07B6D4BOupp57CkCFDMGjQIFSuXNksDbUXmnqssicrEZmTM+fv9wlJWpYya4nIspw5fzecvGXtJhCRk3PmDCYi22dwkVUul2Pbtm1YtmwZtm/fjsLCQggh4O3tjddeew1Dhw5F69atLdFWu6DpKr66nldERIZg/gIX72Xi6PVHKs+zIysRWRLzl4jIepjBRGQv9C6yXrhwAcuWLcPq1atx//59qZj47LPPYtiwYejfvz/8/f0t1lB7IZdrep5FViIyDvP3X4+y8rUu5/UsIjIn5i8RkfUwg/WjaV4YIip7ehVZW7ZsiSNHjgAo7qlZoUIFvPHGGxg6dCieeeYZizbQ3mgaFuDi/Sdl3BIicgTMX2WuLqp9VmtX8kPKkzwrtIaIHBnz1zC8yEVE5sQMJiJ7pFeR9fDhw5DJZGjXrh2GDh2Knj17wsPDw9Jts0uaiqxb/7qDRf0bl3FriMjeMX+VubqoPvfj8OZ48av9ADgiKxGZD/PXMOxJRUTmxAzWHy9yEdkOvYqsU6dOxeDBgxEVFWXh5tg/jgpARObE/FXmIlPtyRrs62mFlhCRo2P+EhFZDzOYiOyRXkXWGTNmWLodDkNTT1YiImMwf5WpK7ICnPiKiMyP+WsYngITkTkxg/XH+CWyHWp+eEmmYE9WIiLL0VRkVeCXfCIiy7iWwvkFiIhsSVSwj7WbQESlsMhqZoLf8ImILEbTrwV01F6JiMhE7ebFa13OU2AiorLTpX5l1AjxtXYziKgUFlnNjMMFEBFZTqGOnwtw4hUiIiIicnR9mkRI91mCILIdLLKamVxu7RYQETmu/ELlkO0ZXeWfe+zKSkRkTfyOT0RUdgQEz36JbBCLrGbGnqxERJZTUKRcZJ3T6xmlx4xgIiIiInJ0yp27eAJMZCtYZDUzFlmJiCynZJH10KR2cHMt/t8Yx2QlIrKcnPwinetwXgIiorJTLcSH579ENohFVjPTMVwgERGZQFFkbRJZHqEB3irL+R2fiMj8zt55bO0mEBHRP6Z2r6s06RXPf4lsh0lF1qtXr2L8+PGIiYlB7dq1MX78eGnZkSNH8O233+LxY+c6KVP0ZPVwY/2aiCzHWfN33fFbAAB3V+WM5YV8Iiorzpi/f9/S/Xr4HZ+IyoIzZnBpfZqEW7sJRKSBm7Ebrly5Eu+88w7y8vIAADKZDKmpqdLy7Oxs/N///R88PDwQGxtrckPtxa+n7gAAQgO8cONhtpVbQ0SOyFnzNz07H3svPAAAHE56qHYdwa/5RGRBzpq/n/x2ztpNICJy2gwuzcvdFUDx6yci22JUd8vDhw9j2LBh8PDwwJw5c3DkyBGVcZjatGmDgIAAbN261SwNtRdHrz8CANx4mI2DE9tZuTVE5GicOX/vZ+RJ9/mzKCIqa86cv3phLhORBTlzBm8/c1fpcelfdPG8mMh2GNWTdc6cORBCYNu2bYiJiVG7jouLCxo1aoRz55z3yneVQNXxAomITOHM+euq5bKg4kI+TzKJyFKcOX+1aVkjCH9eVf/rAiIic3HmDH73x5PWbgIR6cmonqwHDx5Es2bNNIabQuXKlXH37l2t6xARkf6cOX/5kygisiZnzd/L9zO1Lvf8Zx4CDtdCRJbkrBmsD+Yvke0wqsianp6OqlWr6lwvJycH+fn5xhyCiIjUcOb81dZLVcapr4jIwpw1fzt+tV/rchdeACOiMuCsGawN45fI9hhVZA0KCsKNGzd0rnflyhVUrlzZmEM4jBkv1wMANKtWwcotISJH4Mz5221hgrWbQEROzJnzV5PqIT7SfQ7XQkSWxAzWjPlLZDuMKrI+99xzOH78OM6ePatxnYMHD+Ls2bM6u/M7kiK5arp5uRe/xb6eRg1/S0SkxJnzN69QrnEZr+QTkaU5U/5m5xfijwv3kVtQpHU9D1cX5i8RlQlnymAisl9GFVlHjBiBoqIi9OrVC6dPn1ZZfv78eQwZMgQymQzvvvuuqW20G0mpWSrP8SesRGROzpq/pWeP1byehRtCRE7LmfJ39M+nMGTFcXzymz6TxxSf6zJ+iciSnCmD9cVaA5HtMarI2r59e4wdOxaXLl3Cs88+i1q1akEmk2HXrl145pln0KBBA1y+fBkffvghnnvuOXO32WapLQLItCwjIjKQs+Zv6V8KRAWVU3rMU0wisjRnyt895x8AAH46clPrejy9JaKy4kwZbChGMZHtMKrICgBz587FN998g8qVK+PKlSsQQuDu3btITExEhQoVsGjRIsyePducbbV52n4uxeAjInNxxvy9cE95duuXG4apXY+zqxKRJTlj/uoikzoUWLcdROT4mMHAF689I93ncC1EtsekgUKHDx+OYcOG4dSpU7h27RrkcjkiIiLQtGlTuLk53xikJU8uP+xUGwB7VxGRZThb/o76+ZTS4wEtIpUey3iWSURlxJnyVybTXjzlhS0iKmvOlMEAkPwoW+mxXE0o8yIXke0wKoXatWuH8PBwrFq1CjKZDNHR0YiOjjZ32+xOUYl0a1kjSGkZg4+IzMFZ8zc1M0/pcUU/L7XrMWuJyFKcMX9dZDKl89vS5OLfDgUsuBKRJTljBgPAtC3KE33xXJfIthk1XMCff/6J/Px8c7fF7pUcM7BRRCAA9q4iIvNy1vwtlPOMkoisyxnzV9dZrLoeVUREluCMGSyEwB8XHig/V+I+L3IR2R6jiqzh4eHIy8vTvaKTkcuL/xsa4CUVV/8NPiIi0zlr/ur7RZ5ZS0SW4oz5q6uvgBAck5WIyoYzZnBmXqHKc8xaIttmVJG1e/fuSEhIQFZWlrnbY9cUP6dyUXNGKpiGRGQGzpq/7C1FRNbmjPkr09GXtWQ2M6WJyJKcMYN1nf7yR7NEtseoIuu0adMQEBCAnj174saNG+Zuk91SDBfg6vJv2jH4iMicnDV/i3QMF8CsJSJLc8r81ZGtRXKhsxBLRGQOzpjB6jpqqR0agFe5iGyGURNfjRs3DvXq1cNvv/2G2rVro3HjxoiKioK3t7fKujKZDN9//73JDbUHiqv5JYusRETm5Kz5q++QrPzVABFZijPmr64zWqXIZf4SkQU5Ywb/feuxynMlo5YXuYhsj1FF1hUrVkhjjubn5+PIkSM4cuSI2nUdJeD0oehpVbLGyt5VRGROzpi/uQVFOtdh1hKRpTlL/qY++XfMw7xCudZ15UIwf4moTDhLBpd04V6GynPBvh4qz/ESF5HtMKrIunz5cnO3w6YUyUXxz59kgLur/iMqyNUMF6DAi/tEZA6Onr/qfPrbOaXHFf08Na7LqCUiS3GW/E28rdpzShOOyUpEZcVZMrik6sG+Ks+9WLeyFVpCRPoyqsg6aNAgc7fDpnyz/yrm7LyI3s+G44veDfXeTt3EV4ou/GrHTiEiMpCj5686Px65Kd1vGB6A72ObqqzDn0sRkaU5S/4a0jGgSM5fEhBR2XCWDC7Jz0u5XPP1G9FwUTP/C4fLIrIdRk185egURVJ9xwBU4MRXRETm5+H27/+qrqVmIdhXS09WnmMSEZlEbkCQlvxiz/wlIjKv0hO/srZAZPtYZFVDUSM1tPeptomveOJJRGSckBJF1VcaVVG7Dk86iYjMw5BzVrkQ/CUBEZGFFJQqshraCYyIyp5RwwUMGTJE73XtcdBpRU9WQwujRXLl7YmIzM3R81eXN1tE6liDZ59EZBnOkr+GpGjJL/z8uSoRWZKzZHBJRXLtkw8qMH2JbIdRRdYVK1ZoXS6TipTCLgNOJg0XYFhcqRsuQIHnnURkDo6ev6XdSsvG7fQc6bG3u6sVW0NEzsxZ8jc9O1/vdeVyAXZkJaKy4CwZXFJBkXIRoXRNQcbOXUQ2x6giq6aZ/eRyOW7cuIHt27fj+PHjeO+999Cwof4TR9kKRY30WNIjg7aThgsoOfEVg4+IzMjR87e0mM/3KT32dFc/yo0iaXlBi4gsxRnyVy4X+HD931rXef6pYCRcTlV5nvFLRJbkDBlcWukxWTXh+S+R7TCqyKprZr/p06dj/PjxWLp0KU6ePGlUw6xJkWV3HuciJTMPIX7qJ1nJyS/C/L2X0KleZURXLS+FoEuJGoD0xZ+nnkRkBo6ev7p4sScrEVmJM+RvoaED/snYkZWIyoYzZHBpBUXKwwWwpkBk+yw28dVnn30GPz8/fPzxx5Y6hMWsO54s3b/3OFfjegv/uIxv4q+h5//+BMCJr4jINthz/uri5aa+yCr9RKwsG0NEVIq95++e8/cN20BpTFbztoWIyFD2nsGl6erJ+m+HLiKyFRYrsrq5uSE6Ohp79uyx1CEs5sK9TOn+gSuqP4dS+C7hmnRfCPFvT1al4QIs0EAiIi3sOX91cXdlqBKR7bL3/F2497LB23BoLCKyFfaewaUV6hiTlYhsj8WKrACQk5ODtLQ0Sx7C4g5fe6j2+fxCudJA1IeuPdQ+8ZVlmkdEpJYj5K86mr7M8ys+EdkKe87fkh0N9CJjTyoisi32nMGllR7CpXTO8hoXke2xWJH1/PnzOHDgACIiIix1iDKhKbiWHUxSejzix5PqJ77iV38iKmOOkr/G4BV+IrImZ8hfHw+jpnQgIrI4R8vgQrnymKyPs/PVrid4AkxkM4w6S1q1apXGZZmZmTh//jx++OEH5Obm4vXXXze6cdYS4O2OxzkFAAB/L3e16yRcTlF6nJZdgLTs4m1cXNQMF8DcIyIzcPT8LSkzt0Dp8WevNtC8Mq9nEZGFOXr+6vslfepLdbHz7D3pseJcl1/yiciSHD2DS7ufkYv/bDuv9NyNh9lKj3n6S2R7jCqyxsbGah1/SXGS1aNHD3z00UfGtcyKPur2ND5c/zcAoG3tELXrHLyiOozA7B0XACj3ZFXgTIBEZA6Onr8lnU5OV3pcOcBT5zb8kk9EluLo+Vv6Z6maVAn0tnBLiIhUOXoGlySEQMcv45FXqNyTtbyPh/r1y6JRRKQXo4qsAwcO1BhwHh4eqFKlCjp06ICWLVua1DhrCfb994t8fqlg00d6zr/d+Hl1iYjMydHzt6T3fzmt9NjbXfP/spi1RGRpjp6/umaxBoCXGoapPMf8JaKy4OgZXNLShGvIyC1Ueb5NLfUdwIjIdhhVZF2xYoWZm2G7Jm48A5kM6Nu0qt7bHL72SOU5dq4iInNwlvx9nFOA1CfK406V83DVuR2jlogsxdHzt6BIe8eClUOaIaZmcBm1hohImaNncEmfbb+g8lxlfy/UrxKg9JxUdOYJMJHNsNjEV45kwoYzRm/LGf+IiAx35JrqkCz6FFmJiMg4Hm7avxbUCPGBq4vqia3iSz47FBARWU61YB9rN4GI9GBUkdXV1RVDhw7Vud7w4cPh5mZ/M5B66jjJNMw/J55m3CMROS9Hz1+FSv5eKs95uWsusvJLPhFZmqPnr6eb9gtZ6n6my74ERFRWHD2DjcEOXUS2x6hqohBC78lF7HESkubVg1SeM/V12OP7QES2x9HzV6FITdvdXHkmSUTW4yz5q4maTqwA/i20cpJXIrIkZ89gbRnL/CWyHRYdLiA7Oxvu7u6WPIRFuLrIMK93Q6XnSg48HXfxgd774tUlIrIGe81fBXmpCVgq+HggpMSkhKXxSz4R2Qp7z19NZGr6rTJxicjW2HMGFxbJNU5CGNsyqmwbQ0RGsVg/+vT0dBw4cAChoaGWOkSZSn2ShwDv4rCOXX7M4O15EkpEZcUR8vdRlvKkV4cmtYObK4cRJyLb5gj5q4nGjgOKeVd4sktEVmbPGSyXC3RfdACPcwrULu9cX/U1SZ0MmL9ENkPvImv16tWVHq9fvx5xcXFq1y0sLMS9e/dQVFSEt99+26QGWsvJm2lKjydtPIO1b7cweD/syEpEpnK2/AWAt344Id3fM7a1HmMF/nOHJ5lEZEbOmL+aqCuylnyK8UtE5uZMGZyRW4AL9zKt3QwiMpHeRdbr169L92UyGZ48eYInT55oXN/DwwOvvPIKPvvsM5MaaC0pmXlKj48mPUJuQZHWiVfU4WQsRGQqZ8vf0mpW9LN2E4jISTl7/mrSMDwAf916jJcbhSEnX27t5hCRg3KmDNZWL2hcNVD9AtYaiGyO3kXWpKQkAMWDSFevXh2vvfYavvjiC7Xrenh4ICQkxK5n9RvwXCR+P3df6bk6U3fi+uxuSs+FBXjhzuNcpefe71DL4u0jIufhbPkLAB3rVsLuc/dRN9Rfr/XVjRVIRGQqZ8xffawa0hz7L6egY91KmLIpEQC/5BOR+TlTBhdqGIsVAAK97XOMWSJnpHcCRUZGSvcHDRqE559/Xuk5R1PBx0Pt87kFRUqPW9YMxvoTt5SeG92+pnSfv2AlIlM5W/7mFhRh9z8XuTo8XdGgbZm1RGROzpa/WpUI2IBy7nipYRgATvJKRJbjTBmsacIrAHDRELSMXyLbY9RlnuXLl5u7HTanXpj63lNj155Wevx+x1oqRVaZuhDk5X0iMgNnyN8Dl1Ol+0V6Zie/5BORpTlD/ppC8DIXEVmQo2ewtnNeXee5zF8i28GpmjVQWygFsP3MPaXHVQK9dezHbE0iInIKw1Ydl+5ruaivFq9nERFZhqZ45akuEZHpCos0j2+tqTZBRLbHpAFLjh8/jvXr1+PixYvIyMiAUPPtViaTYe/evaYcxmp6PxuOdaV6qRpKkYf83k9E5uTo+asgZ9WUiGyMs+SvoRjXRFQWHDWDN568rXGZphKrVGtg/hLZDKOLrB988AG++uorKdRkMplSwCke2/NVly96N0TDiEB8tDnR2k0hIpI4ev66u8pQUFT8euQGdmXlz6WIyJIcPX+18XBV/wM4B3ypRGSjHDmDjyQ91LisYURg2TWEiExi1HAB69atw5dffokqVargm2++wYsvvggA2LVrF/773/+iRYsWEEJg4sSJ+OOPP8za4LLWqmawxmVLBjwLABgWU03jOooZr3l1iYjMwRnyt2WNf3NXyy+niIjKlDPkryYv1A5BeQ2Twso4YAARlQFHz2BFBwN1hj2vvt4g1Ros0iIiMoZRRdZvv/0Wrq6u2Lt3L4YPH47Q0FAAQMeOHfHuu+/i4MGDmDJlCr788ksEBASYtcFlrWqFchqXda5fGQDQoW4lnfth7yoiMgdnyN/HOQXS/cBy7npto+ixwAtaRGQpzpC/ChVKFVRjW2nuUEBEVBYcPYMLNPQsaFE9CJ5urmXcGiIyllFF1lOnTqF58+Z46qmnNK4zY8YMhIaGYubMmUY3zha4uui+Oq91DV7cJyIzcob8TcnMA1Ccv4NbRVm3MURE/3CG/FVwKfVTW22ns/+OCcirXERkOY6ewU9X9lf7/KCWkRq3scNREYgcnlFF1szMTFStWlV67OFRfLX7yZMn/+7YxQXNmzfHwYMHTWrgunXr0LZtW5QvXx4+Pj5o2LAh5syZg4KCAt0bm0njqoFal2sb80WxhOedRGQOzpC/2fmFAIAdY56Hn5eePVn/+S+jlogsxRnyV6H08Kuli65ERGXN0TO4bphqkbWinyc61ausc1vWGohsh1FF1pCQEKSnp0uPg4OLx8+7fv260npZWVnIyMgwunHvvfce+vTpg4MHD6JZs2bo3Lkzbt68iQkTJqBdu3bIyckxet+GiFQzZMCUrk9L90MDvKT7HJSaiCzJGfI3O78IAODtzp9GEZHtcIb8VSg9zqq2GitntyaisuDoGZxfqDpcwLLYpnY5iReRMzOqyBoVFYUbN25Ijxs3bgwhBH766SfpuXv37iE+Ph6RkZq7t2uzefNmLFiwAL6+vjhy5Ah27dqFDRs24PLly2jQoAEOHDiAqVOnGrVvQ41sV1PluejI8tL9iArlsPj1aHzYqTZ+Ht5caT2OE0hE5uTo+VskF8j75ySznIf+RVb+XJWILM3R87ek0t/p2ZOViKzN0TM4X82YrBHlNc8PA5QcyoXnv0S2wqgia/v27XHhwgXpqlGXLl1QoUIFfP755+jduzfGjRuH5s2bIysrC7169TKqYZ999hkAYOLEiYiOjpaeDw4Oxv/+9z8AwH//+188fvzYqP0bomZFP5Xn3F2VTza7PROKES/URDkPN7X7YOwRkTk4ev7mFBRJ9zXlKRGRNTh6/pZUuqTq4abtKwNntyYiy3P0DM4r0ZN1bMdaSBj/AgL0nACWiGyHUUXWfv36YciQIUhOTgYA+Pj4YPny5fDy8sKGDRvw1VdfITk5GdHR0Zg0aZLB+799+zaOHTsGAHj99ddVlsfExCAiIgJ5eXnYvn27MS/BZPp2luJ1fyIyJ0fPX8V4rDIZ4OWu//+i2MmKiCzN0fO3pNI/T60S6G3R4xER6eLoGXzoaioAoFuDUIxu/xQi1AxZWBqHayGyPUZ1E3r66aexdOlSpedeeuklXL58GVu3bsWjR4/w9NNP46WXXoKrq+Fj6p06dQoAUKFCBVSrVk3tOk2aNEFycjJOnTqF/v37G/4iDPRG86r48chN6XEFHw+9tuNPWInInBw9f3P+GY+1nLurUWNQMWmJyFIcPX81iW0Zhcol5h8ojV/yiagsOHoGH7ueBgDYduYuFpt1z0RUlsz6W8ywsDC8/fbbJu8nKSkJAJRmDywtIiJCaV1LCy81Hoo+V5aIiMqKo+SvNOkVhwogIjvhKPlbUslrXG1qh1j0WEREpnDEDNZX6UkKicj6bPJbbGZmJoDinwBo4uvrCwBaZw7My8tDXl6e9NiUWQYHt4rCoj8uSwUAfSmCL/lRNoauOGb08YmcQcOIQIxu/5S1m+HUrJ2/iow1ZNIr4N+snb/7ElYfuqFjbSIqLaCcO77s08jazXBq1s7fkmQywM/TDZl5hWgUHqh93X/+u/n0bfx9K93gYxERMLd3Q5TX85eSZBnmyGBz1h8M8detdNYaiIw0ol1NRFctr3tFPZlUZE1ISMCiRYvw559/IiUlBQMGDMD3338PANi9ezf27duH0aNHo3LlymZprKFmzZqFGTNmmGVfXu6u+G1UDNrNizdou2C/4v9ZZuUXYe+FB2ZpC5Gj4ria+nPU/M0xssga7FuctX/dsvxkiESOqKKfp7WbYDccNX8BoFFEIE4np6P3sxEY/nx15BYU6Sz8BPsWf3aSUrOQlJpl1HGJnF3JSY9IO1vOYHPUHyZ1qaP3uopaQ+qTfNYaiIzUt2mEWfdndJF15syZmDZtmtJYoyXvBwQE4PPPP0d4eDjeffddg/bt5+cHAMjK0nyi9uTJEwCAv7+/xnUmTZqEsWPHSo8zMjKkLv7GqB7ii43vtkSIr/5fROpU9sdPw5rjVlqO0cclchahgZrHfKN/OXL+PlXJF3N7N4SPgUXWOa81RPylFMjlHBSQyBieBkw058wcOX8B4IehzXDqZjpa1giCm6sLvPXI4rfbVEfNir7SRTIiMpy/t03+wNTm2HoGm5K/SwZE40FmHrrUD9W3yWhTqyKWxTZBama+3tsQkbK6YZrPqYxhVJrv2LEDH3/8McLDw/Hll1+iTZs2qFSpktI6zZo1Q0hICH777TeDAy4qKgoApJkD1VEsU6yrjqenJzw9zdszw5huxC1rBpu1DUTkvBw9fyv5e+G1Z8MN3i7Ez9Oo7YiI9OXo+QsAfl7uaF3LsDFYy3m44aWGYUYdj4hIX/aQwabkb2cDiqsKri4ytKtTSfeKRFRmjCqyLliwAJ6entixYwfq1auncb2GDRvi8uXLBu+/cePGAICHDx8iKSlJ7ex+x48fBwBER0cbvH8iInvF/CUisg7mLxGR9TCDicgeGPXbsGPHjqFZs2Zaww0AQkJCcO/ePYP3Hx4ejqZNmwIAfvrpJ5XlBw4cQHJyMjw9PdG1a1eD909EZK+Yv0RE1sH8JSKyHmYwEdkDo3qyZmVl6TWQ9OPHjyGXGzeI9+TJk/Hqq69i9uzZ6NKli3S16OHDh1LX/5EjRyIgIEDvfSrGaymrWf6IyDIU/4ZLjsHkLJi/RGRNzF/mLxFZhzPnL2CfGcz8JXIMBuWvMELVqlVFdHS00nMymUwMHjxY6bmnnnpK1K5d25hDCCGEGD16tAAg3N3dRefOnUWvXr1EYGCgACBatWolsrOzDdpfcnKyAMAbb7w5yC05OdnofLFXzF/eeOPNFm7M32LMX954462sb86Yv0LYZwYzf3njzbFu+uSvUT1ZY2JisGbNGhw8eBCtWrVSu85vv/2GK1eu4O233zbmEACKx11p1aoVFi9ejD///BMFBQWoUaMGJk6ciPfffx8eHh4G7S8sLAzJycnw8/ODTCbTuq5iJsDk5GStM7gSmRs/e7oJIZCZmYmwMOebaIP5S2Q5/Ozpxvxl/hJZAj97ujlz/gL2mcHMX7IX/PxpZ0j+yoQw/PcGJ0+eRLNmzRAUFIRvvvkG3bt3h4eHB2JjY7Fs2TLs3LkTsbGxSEtLw6lTp1C3bl2jXog1ZWRkICAgAI8fP+aHjMoUP3ukDfOXyHL42SNtmL9ElsPPHuni6BnMfwNkTfz8mY9RRVag+ArP2LFjAQDe3t7IycmBr68vZDIZMjMzIYTAwoULMXLkSLM2uKzwQ0bWws8e6cL8JbIMfvZIF+YvkWXws0f6cOQM5r8BsiZ+/szHxdgNx4wZg+3bt6Np06bIycmRus9mZGSgQYMG2LJli12GGxGRrWP+EhFZB/OXiMh6mMFEZOuMGpNVoVOnTujUqRMePnyIpKQkyOVyREREIDQ01FztsxpPT09MmzYNnp6e1m4KORl+9kgfzF8i8+Nnj/TB/CUyP372SF+OmsH8N0DWxM+f+eg1XMDChQtRt25ddOjQoSzaRERE/2D+EhFZB/OXiMh6mMFEZI/0Gi7gvffew08//aR2Wbt27TBnzhyzNoqIiIoxf4mIrIP5S0RkPcxgIrJHJg0XAABxcXGIiooyQ1OIiMgQzF8iIutg/hIRWQ8zmIhsldETXzmqdevWoW3btihfvjx8fHzQsGFDzJkzBwUFBdZuGllZQUEB9u7diw8//BBNmzZFYGAg3N3dUblyZbz88svYtm2b1u337NmDrl27Ijg4GN7e3qhTpw6mTJmCJ0+eaN3uypUriI2NRXh4ODw9PREeHo7Y2Fhcu3ZN63aZmZmYPHkyateuDW9vbwQHB6Nbt274448/DH7tRGWB+UuaMH+JLIv5S9owg4ksixlMmjB/7ZDQg0wmE4MHDzZ4mb0ZM2aMACDc3NzEiy++KHr27CkCAwMFABETEyOys7Ot3USyot27dwsAAoCoXLmy6Natm+jTp4+oX7++9Pxbb70l5HK5yrZffvmlACBkMplo3bq16N27t6hcubIAIGrXri1SUlLUHvPAgQOiXLlyAoCoV6+e6Nu3r6hXr54AIHx8fMShQ4fUbnf//n1Rq1YtAUCEhoaK3r17i9atWwuZTCZkMplYuHChWd8bshzmL/OXmL9kHcxf5i8VYwaTNTCDmcHE/LVHLLL+Y9OmTQKA8PX1FSdOnJCeT0lJEQ0aNBAAxLhx46zYQrK2vXv3il69eon9+/erLFuzZo1wdXUVAMTKlSuVlp08eVLIZDLh6uoqtm/fLj2flZUl2rdvLwCIXr16qewzKytLhIWFCQBi0qRJSssmTZokAIiIiAi1/+Pt0aOHACDat28vsrKypOe3bdsmXF1dhYuLi/jrr78Mfg+o7DF/mb/E/CXrYP4yf6kYM5isgRnMDCbmrz1ikfUfTZs2FQDEzJkzVZYlJCQIAMLT01Okp6dboXVkD4YOHSqFSkm9e/cWAMSwYcNUtrl+/bpwcXERAMT58+eVli1evFgAELVq1RJFRUVKy4qKiqSrREuWLFFadvbsWQFAuLq6iuvXr2tsZ79+/Yx9qVSGmL/MX9KN+UuWwPxl/pJ+mMFkCcxgZjDpxvy1PXpPfHXlyhWsWrXK4GUAMHDgQH0PYxW3b9/GsWPHAACvv/66yvKYmBhEREQgOTkZ27dvR//+/cu6iWQHGjduDABITk6WnsvPz5fGSVH32YqMjESrVq2QkJCATZs2YdKkSdKyTZs2AQD69esHFxfl4ZNdXFzQt29ffPrpp9i4cSPefvttle1atWqFyMhIlWO+/vrr+P7777F161YUFBTA3d3d2JdMZYT5y/wl7Zi/ZCnMX+Yv6cYMJkthBjODSTvmr+3Ru8h68OBBHDx4UOV5mUymcZliua0H3KlTpwAAFSpUQLVq1dSu06RJEyQnJ+PUqVMMOFLr8uXLAIDQ0FDpuUuXLiE7OxtA8WdInSZNmiAhIUH6HCooHmvbruR6hm6XlZWFy5cvo27duppfFNkE5i/zl7Rj/pKlMH+Zv6QbM5gshRnMDCbtmL+2R68ia9WqVSGTySzdFqtJSkoCUPw6NYmIiFBal6ike/fuYcWKFQCAXr16Sc8rPi+BgYHw8/NTu626z1ZmZiYePnwIQPPnUrFdSkoKsrKy4OPjo7QfTdv5+/vD398fGRkZSEpKcuiAcwTMX+Yvacf8JUth/jJ/STdmMFkKM5gZTNoxf22TXkXW69evW7gZ1pWZmQkA0gdEHV9fXwBARkZGmbSJ7EdhYSEGDBiAx48fo0GDBkrd5o39bCm207atYjvFtor19D1mRkYGP892gPnL/CXNmL9kScxf5i9pxwwmS2IGM4NJM+av7XLRvQoRafPOO+9g7969CAoKwvr16+Hh4WHtJhEROQXmLxGR9TCDiYisg/lru1hkBaQu1FlZWRrXefLkCYDibs5ECmPGjMH333+P8uXLY/fu3ahVq5bScmM/WyW79WvaVrGdpm35eSZ7wM8rGYv5S2Qafl7JFMxgItPw80rGYv7aNhZZAURFRQFQnpGtNMUyxbpE48aNw8KFCxEYGIjff/9dmtmvJMXnJT09Xan7fUnqPlt+fn6oUKECAODmzZtatwsODlbqlq/Yj6btSnbR5+eZrI35S8Zg/hKZjvlLxmIGE5mOGUzGYP7aPhZZAemD+fDhQ42DSh8/fhwAEB0dXWbtIts1fvx4fPnllwgICMDvv/+ucRa92rVro1y5cgD+/QyVpumzpXhsqe18fHxUrnoRlTXmLxmK+UtkHsxfMgYzmMg8mMFkKOavnRAkhBCiadOmAoCYOXOmyrKEhAQBQHh6eor09HQrtI5syYQJEwQAERAQII4ePapz/d69ewsAYtiwYSrLrl+/LlxdXQUAcf78eaVlixcvFgBErVq1RFFRkdKyoqIiUatWLQFALFmyRGlZYmKiACBcXV3FjRs3VI45dOhQAUD069dPn5dLZHHMX9IX85fIvJi/ZAhmMJF5MYNJX8xf+8Ei6z82bdokAAhfX19x4sQJ6fnU1FTRoEEDAUCMGzfOii0kWzBlyhQBQAQGBuoVbkIIceLECSGTyYSrq6vYsWOH9HxWVpZo3769ACB69eqlsl1WVpYICwsTAMTkyZOVlk2ePFkAEOHh4SI7O1tl2x49eggAokOHDkrLt2/fLlxdXYWLi4v466+/9H3ZRBbF/CV9MH+JzI/5S/piBhOZHzOY9MH8tS8yIYQwvT+sYxgzZgwWLlwId3d3tG/fHj4+Pti7dy/S09PRqlUr7N69G97e3tZuJlnJli1b0KNHDwBAkyZNUK9ePbXrBQcHY+7cuUrPffXVVxg7dixkMhnatGmDihUrIiEhAXfv3kXt2rVx4MABBAcHq+zr4MGDePHFF5GdnY369eujfv36SExMRGJiInx8fLBnzx4899xzKts9ePAAMTExuHz5MkJDQ/H888/jwYMHiI+PhxACCxYswOjRo83wrhCZB/OXtGH+ElkO85d0YQYTWQ4zmLRh/toha1Z4bdEvv/wiWrduLfz9/YW3t7eoX7++mD17tsjLy7N208jKli9fLgDovEVGRqrdfvfu3aJz586iQoUKwtPTUzz11FNi0qRJIiMjQ+txL1++LAYOHCjCwsKEu7u7CAsLEwMHDhRXrlzRut3jx4/FxIkTxVNPPSU8PT1FhQoVROfOncWePXuMfQuILIr5S5owf4ksi/lL2jCDiSyLGUyaMH/tD3uyEhEREREREREREZnAxdoNICIiIiIiIiIiIrJnLLISERERERERERERmYBFViIiIiIiIiIiIiITsMhKREREREREREREZAIWWYmIiIiIiIiIiIhMwCIrERERERERERERkQlYZCUiIiIiIiIiIiIyAYusRERERERERERERCZgkZWIiIiIiIiIiIjIBCyyOjCZTGbwrW3btgCAtm3bQiaTIS4uzqqvwVT79u1Dv379EBkZCS8vL/j5+aFatWp44YUXMGXKFBw+fNjaTSQiB8T8Zf4SkfUwg5nBRGQdzF/mr7Nzs3YDyHIGDRqk8ty9e/ewa9cujcvr1Klj8XaVlfHjx+OLL74AAFSvXh0dO3aEn58f7t69i5MnTyIuLg4XL17E+vXrlbaTyWQAACFEmbeZiBwD85f5S0TWwwxmBhORdTB/mb/OTib4V3QqcXFxeOGFFwBo/wd88+ZNZGdno2rVqihXrlxZNc9stm3bhu7du8PNzQ0//PAD+vXrp7S8oKAAu3fvRlJSEkaMGKG0jAFHRJbA/C3G/CUia2AGF2MGE1FZY/4WY/46B/ZkJbWqVq1q7SaYZM2aNQCA3r17q4QbALi7u6Nr165l3SwiIp2Yv0RE1sMMJiKyDuYvOQKOyUpqaRoPJTY2FjKZDCtWrMDFixfRt29fVKxYET4+PmjatCl+/fVXad0jR47g5ZdfRkhICLy9vdGiRQvs3btX4zFzcnIwb948PPfccwgMDISXlxdq166N8ePH4+HDhwa1//79+wCAihUr6r3N9OnTpStIgOp4MtevX1da/9KlS3j77bdRo0YNeHl5ISAgAK1bt8bq1avV7r/kexofH48XX3wRFSpUQLly5dCsWTP88MMParfLy8vDF198gWeffRZ+fn7w8PBA5cqV0bRpU4wfPx6PHj3S+zUSke1j/jJ/ich6mMHMYCKyDuYv89chCHIq+/btEwCErj99mzZtBACxb98+pecHDRokAIhRo0YJHx8fUbt2bdGvXz/RokULAUDIZDKxbt06sWnTJuHu7i4aN24s+vbtKxo2bCgACDc3N5GQkKByvNu3b4sGDRoIAKJChQqiQ4cO4tVXXxWRkZECgIiKihLXr1/X+3UOHTpUABBVqlQRt27d0mubTZs2Sa8PgBg0aJDSLSUlRVp37dq1wsvLSwAQderUEa+++qpo166d8PHxEQDE4MGDNb6no0ePFi4uLqJu3bqiX79+onXr1sLFxUUAEGPHjlXapqioSLRv314AEP7+/qJLly6if//+okOHDtJ7c+rUKb3fFyKyHuavZsxfIrI0ZrBmzGAisiTmr2bMX8fDIquTMVfAARAzZ84UcrlcWrZw4UIBQISHh4vy5cuLVatWKW373nvvCQCiQ4cOSs/L5XLRqlUrAUAMHTpUZGRkSMsKCgrEuHHjBADxwgsv6P06jx49Ktzc3AQA4e3tLV577TUxf/58sX//fpGVlaV1W13vz99//y08PT2Fl5eX2LBhg9Ky69evS0G9cuVKpWWK9xSA+Oyzz5SWxcXFCW9vbwFA7Ny5U3o+Pj5eABCNGzdWel8Ujh07JlJTU7W+HiKyDcxf5i8RWQ8zmBlMRNbB/GX+OhMWWZ2MuQKuWbNmSuEmRHEYVahQQQAQvXv3VtlnamqqACA8PDxEfn6+9PyOHTsEANGoUSNRUFCgsl1RUZGoX7++ACDOnDmj92vdunWrCA8Pl16v4ubu7i46duwofv/9d7Xb6Xp/+vbtKwCIuXPnql1+9OhRAUA8++yzSs8r3tPGjRur3U4R5B07dpSeW7t2rXTliYjsG/OX+UtE1sMMZgYTkXUwf5m/zoRjspJRunTpojR2CAC4ubmhWrVqAKB2QOegoCBUqFAB+fn5SuObbNu2DQDQq1cvuLmpzsXm4uKC1q1bAwD+/PNPvdvYvXt3XLt2DVu3bsWYMWPQsmVLlCtXTprV78UXX8S0adP03h8AyOVy7NixAwDQt29ftes0adIEvr6+OHXqFHJzc1WWDxw4UO12gwYNAgAcOHAARUVFAIDo6Gi4urpi2bJlWLx4Me7evWtQe4nI8TB/mb9EZD3MYGYwEVkH85f5aw9YZCWjaJr5z9fXV+tyPz8/AFD6h3/t2jUAwNSpU1UGelbc/ve//wEAUlJSDGqnu7s7unfvjvnz5+PgwYN49OgRdu7ciSZNmgAAPvnkExw9elTv/T18+BAZGRkAgIiICLVtdXFxwZMnTyCXy9UOlq34n4Cm53NycqTtatSoga+++goFBQUYOXIkwsLCEBUVhf79++PHH39Efn6+Qe8HEdk/5i/zl4ishxnMDCYi62D+Mn/tgWrJnkgPLi7a6/O6lpckl8sBADExMahRo4bWdevVq6f3ftXx9PREp06d0KpVK9SpUwe3b9/Gr7/+imbNmhnUVuDfqz66jmcMIYR0f9SoUejTpw+2bNmCAwcO4MCBA1izZg3WrFmDadOmISEhAaGhoUYdh4jsD/OX+UtE1sMMZgYTkXUwf5m/9oBFVrK6iIgIAECPHj3wwQcflMkxfX190aJFC6xfvx6pqal6bxccHAxvb2/k5ORg7ty5CA4ONvjYSUlJap+/fv06AMDLywtBQUFKyypVqoThw4dj+PDhAIALFy5gyJAhOHToECZOnIiVK1ca3A4iIuZvMeYvEVkDM7gYM5iIyhrztxjz1/w4XABZXZcuXQAA69atU7p6Ygp99nPz5k0AQHh4uNLz7u7uAIDCwkKVbVxdXdGxY0cAwNq1a41q2+rVq9U+v2rVKgDFV9PUjQtTUp06dTBhwgQAwOnTp41qBxER87cY85eIrIEZXIwZTERljflbjPlrfiyyktX16NEDTZs2xdGjRzF48GC1Y56kpaVhyZIlakNHnaFDh+Kjjz7ClStXVJbl5ORg+vTpOHr0KNzc3PDaa68pLVcE3tmzZ9Xue9q0afDw8MCHH36IlStXKnXfV0hMTMTGjRvVbn/ixAnMmTNH6bkDBw5g8eLFAID3339fev6PP/7A9u3bUVBQoLS+EAK//fYbACAyMlLtcYiIdGH+Mn+JyHqYwcxgIrIO5i/z11I4XABZnYuLCzZv3oxu3bph5cqVWL9+PRo2bIiqVasiPz8f165dw5kzZ1BUVITY2FidV1gA4NGjR1i+fDn+85//oHr16qhXrx78/Pzw4MEDnDhxAmlpaXB1dcXChQvx9NNPK23bq1cvzJ07Fx06dEC7du2kgbI///xzBAUFITo6GqtXr0ZsbCxiY2Px0UcfoW7duggJCcGjR49w5swZ3Lp1C3379kXPnj1V2jZ69GhMmjQJq1atwjPPPIM7d+4gISEBcrkcY8aMUZoV8e+//8b7778Pf39/REdHIywsDDk5OTh58iRu3LiBgIAAfPLJJyb+BYjIWTF/mb9EZD3MYGYwEVkH85f5ayksspJNCAsLw+HDh7FixQr88ssv+Pvvv3H06FFUqFABYWFheOedd/Dyyy/Dy8tLr/0tXrwYr7zyCvbu3YszZ87gyJEjePToEby9vVGtWjW88cYbeOedd9QOYv3pp5/CxcUFGzduxObNm6XZ8z766CNpnJLevXujadOmWLhwIXbv3o2DBw+iqKgIlSpVQs2aNTFy5EiVq1MKr776Knr06IHPPvsM27dvR35+PqKjozFy5EiVgaxfeuklPH78GAkJCbh8+TIOHz4Mb29vREREYOLEiRgxYoTKTw2IiAzB/GX+EpH1MIOZwURkHcxf5q8lyIS5BqAgIq3atm2L+Ph47Nu3D23btrV2c4iInAbzl4jIepjBRETWwfwtexyTlYiIiIiIiIiIiMgELLISERERERERERERmYBFViIiIiIiIiIiIiITcExWIiIiIiIiIiIiIhOwJysRERERERERERGRCVhkJSIiIiIiIiIiIjIBi6xEREREREREREREJmCRlYiIiIiIiIiIiMgELLISERERERERERERmYBFViIiIiIiIiIiIiITsMhKREREREREREREZAIWWYmIiIiIiIiIiIhMwCIrERERERERERERkQn+H4N9xF4IWnsaAAAAAElFTkSuQmCC","text/plain":["<Figure size 1700x300 with 4 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","text/plain":["<Figure size 1700x300 with 4 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","text/plain":["<Figure size 1700x300 with 4 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","text/plain":["<Figure size 1700x300 with 4 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","text/plain":["<Figure size 1700x300 with 4 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","text/plain":["<Figure size 1700x300 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot some samples\n","for category in \"ABCDEF\":\n","    to_be_inspected = 4\n","\n","    plt.figure(figsize=(17, 3))  # Adjust the figure size as needed\n","    plt.suptitle(f'Category {category} - Time Series Samples')\n","\n","    for idx in range(len(training_data)):\n","        if to_be_inspected == 0:\n","            break\n","\n","        if categories[idx] != category:\n","            continue\n","\n","        plt.subplot(1, 5, 6 - to_be_inspected)  # 1 row, 5 columns, and the current plot position\n","        plt.plot(training_data[idx])\n","        plt.title(f'Sample {idx + 1}')\n","        plt.xlabel('Time Steps')\n","        plt.ylabel('Feature Value')\n","\n","        to_be_inspected -= 1\n","\n","    plt.tight_layout()  # Adjust layout for better appearance\n","    plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["## Split, Encode and Normalize"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:25:07.182693Z","iopub.status.busy":"2023-12-13T13:25:07.182424Z","iopub.status.idle":"2023-12-13T13:25:08.046501Z","shell.execute_reply":"2023-12-13T13:25:08.045399Z","shell.execute_reply.started":"2023-12-13T13:25:07.182671Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Training set shape: (38400, 2776) (38400,)\n","Testing set shape: (9600, 2776) (9600,)\n"]}],"source":["features = df.drop('Category', axis=1)\n","target = df['Category']\n","\n","X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, stratify=target, random_state=42)\n","\n","# Display the shapes of the resulting sets\n","print(\"Training set shape:\", X_train.shape, y_train.shape)\n","print(\"Testing set shape:\", X_test.shape, y_test.shape)"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["# Map categories to integers\n","category_mapping = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5}\n","\n","y_train = y_train.map(category_mapping)\n","y_test = y_test.map(category_mapping)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(38400, 2776) (9600, 2776)\n"]}],"source":["print(X_train.shape, X_test.shape)\n","\n","# Normalize both features and labels\n","X_min = X_train.min()\n","X_max = X_train.max()\n","\n","X_train_raw = (X_train-X_min)/(X_max-X_min)\n","X_test_raw = (X_test-X_min)/(X_max-X_min)\n","\n","# the test part is also important so it might be a good idea to retrain the model including the test set, after having assessed that the model behaves properly"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:25:08.383434Z","iopub.status.busy":"2023-12-13T13:25:08.383118Z","iopub.status.idle":"2023-12-13T13:25:08.392578Z","shell.execute_reply":"2023-12-13T13:25:08.391466Z","shell.execute_reply.started":"2023-12-13T13:25:08.383406Z"},"trusted":true},"outputs":[],"source":["# Map categories to integers\n","category_mapping = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5}\n","\n","# y_train = y_train.map(category_mapping)\n","# y_test = y_test.map(category_mapping)"]},{"cell_type":"markdown","metadata":{},"source":["Have you thought about further normalising your time series? In the end, they are only scaled between 0 and 1. Speaking of which, ever heard of robust scalers? (PS: remember to de-normalise in the prediction function)."]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Class Weights: [ 1.39664804  0.72813325  0.79864231  0.79872204  0.72892938 28.88086643]\n"]}],"source":["from sklearn.utils.class_weight import compute_class_weight\n","\n","# Compute class weights using 'balanced' mode\n","class_labels = np.unique(categories)\n","class_weights = compute_class_weight('balanced', classes=class_labels, y=categories)\n","\n","# Print or use the computed class weights\n","print(\"Class Weights:\", class_weights)"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:25:08.395364Z","iopub.status.busy":"2023-12-13T13:25:08.395041Z","iopub.status.idle":"2023-12-13T13:25:10.507841Z","shell.execute_reply":"2023-12-13T13:25:10.506798Z","shell.execute_reply.started":"2023-12-13T13:25:08.395337Z"},"trusted":true},"outputs":[],"source":["from sklearn.preprocessing import RobustScaler\n","\n","# Initialize the RobustScaler\n","scaler = RobustScaler()\n","\n","# Fit the scaler on the training data and transform both training and testing data\n","X_train = scaler.fit_transform(X_train)\n","X_test = scaler.transform(X_test)"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:25:10.509588Z","iopub.status.busy":"2023-12-13T13:25:10.509277Z","iopub.status.idle":"2023-12-13T13:25:10.516963Z","shell.execute_reply":"2023-12-13T13:25:10.515868Z","shell.execute_reply.started":"2023-12-13T13:25:10.509560Z"},"trusted":true},"outputs":[],"source":["from tensorflow.keras.utils import to_categorical\n","\n","forecasting_period_phase1 = 9\n","forecasting_period_phase2 = 18\n","\n","# y_train = to_categorical(y_train, num_classes=forecasting_period_phase1)\n","# y_test = to_categorical(y_test, num_classes=forecasting_period_phase1)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["window = 100\n","stride = 5"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["def build_sequences(df, target_labels, window=window, stride=stride, telescope=forecasting_period_phase2):\n","    # Sanity check to avoid runtime errors\n","    assert window % stride == 0\n","    dataset = []\n","    labels = []\n","    temp_df = df.copy().values\n","    temp_label = df[target_labels].copy().values\n","    padding_check = len(df)%window\n","\n","    if(padding_check != 0):\n","        # Compute padding length\n","        padding_len = window - len(df)%window\n","        padding = np.zeros((padding_len,temp_df.shape[1]), dtype='float32')\n","        temp_df = np.concatenate((padding,df))\n","        padding = np.zeros((padding_len,temp_label.shape[1]), dtype='float32')\n","        temp_label = np.concatenate((padding,temp_label))\n","        assert len(temp_df) % window == 0\n","\n","    for idx in np.arange(0,len(temp_df)-window-telescope,stride):\n","        dataset.append(temp_df[idx:idx+window])\n","        labels.append(temp_label[idx+window:idx+window+telescope])\n","\n","    dataset = np.array(dataset)\n","    labels = np.array(labels)\n","    return dataset, labels"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[],"source":["target_labels = categories_df\n","telescope = 50\n","\n","# telescope -> how many points I want to predict\n","# (should be no longer than how many points I want to predict)"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"text/plain":["((7657, 100, 2776), (7657, 18, 2776), (1897, 100, 2776), (1897, 18, 2776))"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["X_train, y_train = build_sequences(X_train_raw, target_labels, window, stride)\n","X_test, y_test = build_sequences(X_test_raw, target_labels, window, stride)\n","X_train.shape, y_train.shape, X_test.shape, y_test.shape"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["# sanity check: input shape is greater or equal than the output shape in terms of time domain\n","def inspect_multivariate(X, y, columns, telescope, idx=None):\n","    if(idx==None):\n","        idx=np.random.randint(0,len(X))\n","\n","    figs, axs = plt.subplots(len(columns), 1, sharex=True, figsize=(17,17))\n","    for i, col in enumerate(columns):\n","        axs[i].plot(np.arange(len(X[0,:,i])), X[idx,:,i])\n","        axs[i].scatter(np.arange(len(X[0,:,i]), len(X_train[0,:,i])+telescope), y[idx,:,i], color='orange')\n","        axs[i].set_title(col)\n","        axs[i].set_ylim(0,1)\n","    plt.show()"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[20], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m direct_telescope \u001b[38;5;241m=\u001b[39m telescope\n\u001b[0;32m----> 2\u001b[0m \u001b[43minspect_multivariate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirect_telescope\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn[19], line 6\u001b[0m, in \u001b[0;36minspect_multivariate\u001b[0;34m(X, y, columns, telescope, idx)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(idx\u001b[38;5;241m==\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m      4\u001b[0m     idx\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;28mlen\u001b[39m(X))\n\u001b[0;32m----> 6\u001b[0m figs, axs \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubplots\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m17\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m17\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, col \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(columns):\n\u001b[1;32m      8\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mplot(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mlen\u001b[39m(X[\u001b[38;5;241m0\u001b[39m,:,i])), X[idx,:,i])\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/pyplot.py:1599\u001b[0m, in \u001b[0;36msubplots\u001b[0;34m(nrows, ncols, sharex, sharey, squeeze, width_ratios, height_ratios, subplot_kw, gridspec_kw, **fig_kw)\u001b[0m\n\u001b[1;32m   1455\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1456\u001b[0m \u001b[38;5;124;03mCreate a figure and a set of subplots.\u001b[39;00m\n\u001b[1;32m   1457\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1596\u001b[0m \n\u001b[1;32m   1597\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1598\u001b[0m fig \u001b[38;5;241m=\u001b[39m figure(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfig_kw)\n\u001b[0;32m-> 1599\u001b[0m axs \u001b[38;5;241m=\u001b[39m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubplots\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mncols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mncols\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1600\u001b[0m \u001b[43m                   \u001b[49m\u001b[43msqueeze\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubplot_kw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubplot_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1601\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mgridspec_kw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgridspec_kw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight_ratios\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheight_ratios\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1602\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mwidth_ratios\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidth_ratios\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1603\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig, axs\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/figure.py:931\u001b[0m, in \u001b[0;36mFigureBase.subplots\u001b[0;34m(self, nrows, ncols, sharex, sharey, squeeze, width_ratios, height_ratios, subplot_kw, gridspec_kw)\u001b[0m\n\u001b[1;32m    928\u001b[0m     gridspec_kw[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth_ratios\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m width_ratios\n\u001b[1;32m    930\u001b[0m gs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_gridspec(nrows, ncols, figure\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mgridspec_kw)\n\u001b[0;32m--> 931\u001b[0m axs \u001b[38;5;241m=\u001b[39m \u001b[43mgs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubplots\u001b[49m\u001b[43m(\u001b[49m\u001b[43msharex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msqueeze\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    932\u001b[0m \u001b[43m                  \u001b[49m\u001b[43msubplot_kw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubplot_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    933\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m axs\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/gridspec.py:298\u001b[0m, in \u001b[0;36mGridSpecBase.subplots\u001b[0;34m(self, sharex, sharey, squeeze, subplot_kw)\u001b[0m\n\u001b[1;32m    296\u001b[0m         subplot_kw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msharex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m shared_with[sharex]\n\u001b[1;32m    297\u001b[0m         subplot_kw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msharey\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m shared_with[sharey]\n\u001b[0;32m--> 298\u001b[0m         axarr[row, col] \u001b[38;5;241m=\u001b[39m \u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_subplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubplot_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    301\u001b[0m \u001b[38;5;66;03m# turn off redundant tick labeling\u001b[39;00m\n\u001b[1;32m    302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sharex \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcol\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/figure.py:782\u001b[0m, in \u001b[0;36mFigureBase.add_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    780\u001b[0m         args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mstr\u001b[39m(args[\u001b[38;5;241m0\u001b[39m])))\n\u001b[1;32m    781\u001b[0m     projection_class, pkw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_projection_requirements(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 782\u001b[0m     ax \u001b[38;5;241m=\u001b[39m \u001b[43mprojection_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    783\u001b[0m     key \u001b[38;5;241m=\u001b[39m (projection_class, pkw)\n\u001b[1;32m    784\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_axes_internal(ax, key)\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:678\u001b[0m, in \u001b[0;36m_AxesBase.__init__\u001b[0;34m(self, fig, facecolor, frameon, sharex, sharey, label, xscale, yscale, box_aspect, *args, **kwargs)\u001b[0m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_axisbelow(mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes.axisbelow\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m    677\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rasterization_zorder \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 678\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclear\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    680\u001b[0m \u001b[38;5;66;03m# funcs used to format x and y - fall back on major formatters\u001b[39;00m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt_xdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:1388\u001b[0m, in \u001b[0;36m_AxesBase.clear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1386\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcla()\n\u001b[1;32m   1387\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1388\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__clear\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:1370\u001b[0m, in \u001b[0;36m_AxesBase.__clear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1368\u001b[0m share \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_share\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1369\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m share \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1370\u001b[0m     \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshare\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshare\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1371\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1372\u001b[0m     \u001b[38;5;66;03m# Although the scale was set to linear as part of clear,\u001b[39;00m\n\u001b[1;32m   1373\u001b[0m     \u001b[38;5;66;03m# polar requires that _set_scale is called again\u001b[39;00m\n\u001b[1;32m   1374\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpolar\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:1231\u001b[0m, in \u001b[0;36m_AxesBase.sharex\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1229\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mmajor \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mmajor  \u001b[38;5;66;03m# Ticker instances holding\u001b[39;00m\n\u001b[1;32m   1230\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mminor \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mminor  \u001b[38;5;66;03m# locator and formatter.\u001b[39;00m\n\u001b[0;32m-> 1231\u001b[0m x0, x1 \u001b[38;5;241m=\u001b[39m \u001b[43mother\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_xlim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1232\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_xlim(x0, x1, emit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, auto\u001b[38;5;241m=\u001b[39mother\u001b[38;5;241m.\u001b[39mget_autoscalex_on())\n\u001b[1;32m   1233\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39m_scale \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39m_scale\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:3567\u001b[0m, in \u001b[0;36m_AxesBase.get_xlim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3547\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_xlim\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   3548\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3549\u001b[0m \u001b[38;5;124;03m    Return the x-axis view limits.\u001b[39;00m\n\u001b[1;32m   3550\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3565\u001b[0m \u001b[38;5;124;03m    be greater than the *right* value.\u001b[39;00m\n\u001b[1;32m   3566\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3567\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviewLim\u001b[49m\u001b[38;5;241m.\u001b[39mintervalx)\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:849\u001b[0m, in \u001b[0;36m_AxesBase.viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    847\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    848\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mviewLim\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 849\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_unstale_viewLim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    850\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_viewLim\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:836\u001b[0m, in \u001b[0;36m_AxesBase._unstale_viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_unstale_viewLim\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    834\u001b[0m     \u001b[38;5;66;03m# We should arrange to store this information once per share-group\u001b[39;00m\n\u001b[1;32m    835\u001b[0m     \u001b[38;5;66;03m# instead of on every axis.\u001b[39;00m\n\u001b[0;32m--> 836\u001b[0m     need_scale \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    837\u001b[0m         name: \u001b[38;5;28many\u001b[39m(ax\u001b[38;5;241m.\u001b[39m_stale_viewlims[name]\n\u001b[1;32m    838\u001b[0m                   \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[name]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m    839\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_names}\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(need_scale\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m    841\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m need_scale:\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:837\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_unstale_viewLim\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    834\u001b[0m     \u001b[38;5;66;03m# We should arrange to store this information once per share-group\u001b[39;00m\n\u001b[1;32m    835\u001b[0m     \u001b[38;5;66;03m# instead of on every axis.\u001b[39;00m\n\u001b[1;32m    836\u001b[0m     need_scale \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m--> 837\u001b[0m         name: \u001b[38;5;28;43many\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stale_viewlims\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    838\u001b[0m \u001b[43m                  \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_shared_axes\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_siblings\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    839\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_names}\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(need_scale\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m    841\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m need_scale:\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/matplotlib/axes/_base.py:837\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_unstale_viewLim\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    834\u001b[0m     \u001b[38;5;66;03m# We should arrange to store this information once per share-group\u001b[39;00m\n\u001b[1;32m    835\u001b[0m     \u001b[38;5;66;03m# instead of on every axis.\u001b[39;00m\n\u001b[1;32m    836\u001b[0m     need_scale \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m--> 837\u001b[0m         name: \u001b[38;5;28many\u001b[39m(ax\u001b[38;5;241m.\u001b[39m_stale_viewlims[name]\n\u001b[1;32m    838\u001b[0m                   \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[name]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m    839\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_names}\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(need_scale\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m    841\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m need_scale:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["direct_telescope = telescope\n","# inspect_multivariate(X_train_test, y_train_test, target_labels, direct_telescope)"]},{"cell_type":"markdown","metadata":{},"source":["# Build and train models"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T14:19:16.152749Z","iopub.status.busy":"2023-12-13T14:19:16.152374Z","iopub.status.idle":"2023-12-13T14:19:16.160385Z","shell.execute_reply":"2023-12-13T14:19:16.159255Z","shell.execute_reply.started":"2023-12-13T14:19:16.152706Z"},"trusted":true},"outputs":[{"data":{"text/plain":["((100, 2776), (18, 2776), (7657, 100, 2776))"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["input_shape = X_train_test.shape[1:]\n","output_shape = y_train_test.shape[1:]\n","batch_size = 128\n","epochs = 200\n","\n","input_shape, output_shape, X_train_test.shape"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[],"source":["from datetime import datetime\n","\n","def save_model(model, description):\n","    current_datetime = datetime.now()\n","    formatted_datetime = current_datetime.strftime(\"%d%m_%H%M\")\n","\n","    model.save('models/' + formatted_datetime + '/' + description)"]},{"cell_type":"markdown","metadata":{},"source":["# LSTM"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["input_shape = X_train.shape[1:]\n","output_shape = y_train.shape[1:]\n","batch_size = 64\n","epochs = 200"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[],"source":["from tensorflow import keras\n","from keras.models import Model, Sequential\n","from keras.layers import Input, Conv1D, BatchNormalization, Activation, Add, Flatten, Dense, SimpleRNN, Masking, LSTM\n","from keras.preprocessing.sequence import pad_sequences\n","\n","def build_CONV_LSTM_model(input_shape, output_shape):\n","    # Ensure the input time steps are at least as many as the output time steps\n","    assert input_shape[0] >= output_shape[0], \"For this exercise we want input time steps to be >= of output time steps\"\n","\n","    # Define the input layer with the specified shape\n","    input_layer = tfkl.Input(shape=input_shape, name='input_layer')\n","\n","    # Add a Bidirectional LSTM layer with 64 units\n","    x = tfkl.Bidirectional(tfkl.LSTM(64, return_sequences=True, name='lstm'), name='bidirectional_lstm')(input_layer)\n","\n","    # Add a 1D Convolution layer with 128 filters and a kernel size of 3\n","    x = tfkl.Conv1D(128, 3, padding='same', activation='relu', name='conv')(x)\n","\n","    # Add a final Convolution layer to match the desired output shape -> this is a sequence.to.sequence problem, so two 1DCNN are wanted(?)\n","    output_layer = tfkl.Conv1D(output_shape[1], 3, padding='same', name='output_layer')(x)\n","\n","    # Calculate the size to crop from the output to match the output shape\n","    crop_size = output_layer.shape[1] - output_shape[0]\n","\n","    # Crop the output to the desired length\n","    output_layer = tfkl.Cropping1D((0, crop_size), name='cropping')(output_layer)\n","\n","    # Construct the model by connecting input and output layers\n","    model = tf.keras.Model(inputs=input_layer, outputs=output_layer, name='CONV_LSTM_model')\n","\n","    # Compile the model with Mean Squared Error loss and Adam optimizer\n","    model.compile(loss=tf.keras.losses.MeanSquaredError(), optimizer=tf.keras.optimizers.legacy.Adam())\n","\n","    return model"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2023-12-12T15:34:22.346623Z","iopub.status.busy":"2023-12-12T15:34:22.346215Z","iopub.status.idle":"2023-12-12T15:34:22.979842Z","shell.execute_reply":"2023-12-12T15:34:22.978364Z","shell.execute_reply.started":"2023-12-12T15:34:22.346594Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"CONV_LSTM_model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_layer (InputLayer)    [(None, 100, 2776)]       0         \n","                                                                 \n"," bidirectional_lstm (Bidire  (None, 100, 128)          1454592   \n"," ctional)                                                        \n","                                                                 \n"," conv (Conv1D)               (None, 100, 128)          49280     \n","                                                                 \n"," output_layer (Conv1D)       (None, 100, 2776)         1068760   \n","                                                                 \n"," cropping (Cropping1D)       (None, 18, 2776)          0         \n","                                                                 \n","=================================================================\n","Total params: 2572632 (9.81 MB)\n","Trainable params: 2572632 (9.81 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n","Model: \"CONV_LSTM_model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_layer (InputLayer)    [(None, 100, 2776)]       0         \n","                                                                 \n"," bidirectional_lstm (Bidire  (None, 100, 128)          1454592   \n"," ctional)                                                        \n","                                                                 \n"," conv (Conv1D)               (None, 100, 128)          49280     \n","                                                                 \n"," output_layer (Conv1D)       (None, 100, 2776)         1068760   \n","                                                                 \n"," cropping (Cropping1D)       (None, 18, 2776)          0         \n","                                                                 \n","=================================================================\n","Total params: 2572632 (9.81 MB)\n","Trainable params: 2572632 (9.81 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n"]},{"data":{"image/png":"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","text/plain":["<IPython.core.display.Image object>"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["model = build_CONV_LSTM_model(input_shape, output_shape)\n","model.summary()\n","tfk.utils.plot_model(model, expand_nested=True, show_shapes=True)\n","\n","# Display the model summary\n","model.summary()\n","\n","# Plot the model\n","tfk.utils.plot_model(model)\n"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"data":{"text/plain":["((38400, 2776), (9600, 2776))"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["X_train.shape, X_test.shape"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2023-12-12T15:27:39.839146Z","iopub.status.busy":"2023-12-12T15:27:39.838121Z","iopub.status.idle":"2023-12-12T15:27:40.354765Z","shell.execute_reply":"2023-12-12T15:27:40.353022Z","shell.execute_reply.started":"2023-12-12T15:27:39.839096Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/200\n","60/60 [==============================] - 22s 289ms/step - loss: nan - val_loss: nan - lr: 0.0010\n","Epoch 2/200\n","60/60 [==============================] - 11s 190ms/step - loss: nan - val_loss: nan - lr: 0.0010\n","Epoch 3/200\n","60/60 [==============================] - 12s 197ms/step - loss: nan - val_loss: nan - lr: 0.0010\n","Epoch 4/200\n","60/60 [==============================] - 12s 192ms/step - loss: nan - val_loss: nan - lr: 0.0010\n","Epoch 5/200\n","60/60 [==============================] - 12s 196ms/step - loss: nan - val_loss: nan - lr: 0.0010\n","Epoch 6/200\n","60/60 [==============================] - 12s 199ms/step - loss: nan - val_loss: nan - lr: 5.0000e-04\n","Epoch 7/200\n","18/60 [========>.....................] - ETA: 7s - loss: nan"]},{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[30], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mX_train_test\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my_train_test\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtfk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmin\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrestore_best_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtfk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mReduceLROnPlateau\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmin\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfactor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_lr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mhistory\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/keras/src/engine/training.py:1783\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1775\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1776\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1777\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1780\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1781\u001b[0m ):\n\u001b[1;32m   1782\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1783\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1784\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1785\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:831\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    828\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    830\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 831\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    833\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    834\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:867\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    864\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    865\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    866\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 867\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    868\u001b[0m \u001b[43m      \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_config\u001b[49m\n\u001b[1;32m    869\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    870\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    871\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    872\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    873\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1264\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1260\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1262\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1263\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1264\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1265\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1266\u001b[0m     args,\n\u001b[1;32m   1267\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1268\u001b[0m     executing_eagerly)\n\u001b[1;32m   1269\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:217\u001b[0m, in \u001b[0;36mAtomicFunction.flat_call\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mflat_call\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    216\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 217\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:252\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    251\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 252\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    257\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    258\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    261\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    262\u001b[0m     )\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/context.py:1479\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1477\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1479\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1480\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1481\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1482\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1483\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1484\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1485\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1487\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1488\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1489\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1493\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1494\u001b[0m   )\n","File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ANN/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:60\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m   \u001b[38;5;66;03m# Convert any objects of type core_types.Tensor to Tensor.\u001b[39;00m\n\u001b[1;32m     54\u001b[0m   inputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     55\u001b[0m       tensor_conversion_registry\u001b[38;5;241m.\u001b[39mconvert(t)\n\u001b[1;32m     56\u001b[0m       \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, core_types\u001b[38;5;241m.\u001b[39mTensor)\n\u001b[1;32m     57\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m t\n\u001b[1;32m     58\u001b[0m       \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m inputs\n\u001b[1;32m     59\u001b[0m   ]\n\u001b[0;32m---> 60\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     61\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     63\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["# Train the model\n","\n","history = model.fit(\n","    x = X_train_test,\n","    y = y_train_test,\n","    batch_size = batch_size,\n","    epochs = epochs,\n","    validation_data = (X_test_test, y_test_test),\n","    callbacks = [\n","        tfk.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience=10, restore_best_weights=True),\n","        tfk.callbacks.ReduceLROnPlateau(monitor='val_loss', mode='min', patience=5, factor=0.5, min_lr=1e-5)\n","    ]\n",").history"]},{"cell_type":"markdown","metadata":{},"source":["If you want to give a more robust interpretation of the results, given the individual normalisation of the series, you can consider the Mean Absolute Error (MAE) as the average percentage error (scaled between 0 and 1) obtained on the predicted points compared to the ground truth."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["save_model(model, 'resnetplusconv1d')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = tfk.models.load_model('models/1812_2339/resnetplusconv1d')"]},{"cell_type":"markdown","metadata":{},"source":["# Plot results"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:31:24.033039Z","iopub.status.busy":"2023-12-13T13:31:24.032581Z","iopub.status.idle":"2023-12-13T13:31:24.759157Z","shell.execute_reply":"2023-12-13T13:31:24.757183Z","shell.execute_reply.started":"2023-12-13T13:31:24.033007Z"},"trusted":true},"outputs":[],"source":["# Plot results\n","\n","best_epoch = np.argmin(history['val_loss'])\n","plt.figure(figsize=(17,4))\n","plt.plot(history['loss'], label='Training loss', alpha=.8, color='#ff7f0e')\n","plt.plot(history['val_loss'], label='Validation loss', alpha=.9, color='#5a9aa5')\n","plt.axvline(x=best_epoch, label='Best epoch', alpha=.3, ls='--', color='#5a9aa5')\n","plt.title('Mean Squared Error (Loss)')\n","plt.legend()\n","plt.grid(alpha=.3)\n","plt.show()\n","\n","plt.figure(figsize=(17,4))\n","plt.plot(history['mae'], label='Training accuracy', alpha=.8, color='#ff7f0e')\n","plt.plot(history['val_mae'], label='Validation accuracy', alpha=.9, color='#5a9aa5')\n","plt.axvline(x=best_epoch, label='Best epoch', alpha=.3, ls='--', color='#5a9aa5')\n","plt.title('Mean Absolute Error')\n","plt.legend()\n","plt.grid(alpha=.3)\n","plt.show()\n","\n","plt.figure(figsize=(18,3))\n","plt.plot(history['lr'], label='Learning Rate', alpha=.8, color='#ff7f0e')\n","plt.axvline(x=best_epoch, label='Best epoch', alpha=.3, ls='--', color='#5a9aa5')\n","plt.legend()\n","plt.grid(alpha=.3)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:31:41.654071Z","iopub.status.busy":"2023-12-13T13:31:41.653279Z","iopub.status.idle":"2023-12-13T13:31:42.736900Z","shell.execute_reply":"2023-12-13T13:31:42.736043Z","shell.execute_reply.started":"2023-12-13T13:31:41.654029Z"},"trusted":true},"outputs":[],"source":["# Predict the test set \n","predictions = model.predict(X_test)\n","print(predictions.shape)\n","\n","mean_squared_error = tfk.metrics.mse(y_test.flatten(),predictions.flatten())\n","mean_absolute_error = tfk.metrics.mae(y_test.flatten(),predictions.flatten())\n","mean_squared_error, mean_absolute_error"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:56:45.501914Z","iopub.status.busy":"2023-12-13T13:56:45.501597Z","iopub.status.idle":"2023-12-13T13:56:45.509163Z","shell.execute_reply":"2023-12-13T13:56:45.507907Z","shell.execute_reply.started":"2023-12-13T13:56:45.501889Z"},"trusted":true},"outputs":[],"source":["predictions.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:32:51.395615Z","iopub.status.busy":"2023-12-13T13:32:51.395144Z","iopub.status.idle":"2023-12-13T13:32:51.439254Z","shell.execute_reply":"2023-12-13T13:32:51.437854Z","shell.execute_reply.started":"2023-12-13T13:32:51.395587Z"},"trusted":true},"outputs":[],"source":["def inspect_multivariate_prediction(X, y, pred, columns, telescope, idx=None):\n","    if(idx==None):\n","        idx=np.random.randint(0,len(X))\n","\n","    figs, axs = plt.subplots(len(columns), 1, sharex=True, figsize=(17,17))\n","    for i, col in enumerate(columns):\n","        axs[i].plot(np.arange(len(X[0,:,i])), X[idx,:,i])\n","        axs[i].plot(np.arange(len(X[0,:,i]), len(X_train[0,:,i])+telescope), y[idx,:,i], color='orange')\n","        axs[i].plot(np.arange(len(X[0,:,i]), len(X_train[0,:,i])+telescope), pred[idx,:,i], color='green') \n","        # le linee verdi hanno dei segmenti che sono sovrascitti, poiché i samples in X_test sono sovrappost (visto che stride != window)\n","        axs[i].set_title(col)\n","        axs[i].set_ylim(0,1)\n","    plt.show()\n","    \n","# DOESN'T END EXECUTION\n","# inspect_multivariate_prediction(X_test, y_test, predictions, categories, forecasting_period_phase1)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T13:33:35.411888Z","iopub.status.busy":"2023-12-13T13:33:35.411539Z","iopub.status.idle":"2023-12-13T13:33:35.749314Z","shell.execute_reply":"2023-12-13T13:33:35.748381Z","shell.execute_reply.started":"2023-12-13T13:33:35.411861Z"},"trusted":true},"outputs":[],"source":["# Compute the confusion matrix\n","\n","cm = confusion_matrix(np.argmax(y_test, axis=-1), np.argmax(predictions, axis=-1))\n","\n","cm = cm /cm.astype(float).sum(axis=1)\n","\n","# Compute the classification metrics\n","accuracy = accuracy_score(np.argmax(y_test, axis=-1), np.argmax(predictions, axis=-1))\n","precision = precision_score(np.argmax(y_test, axis=-1), np.argmax(predictions, axis=-1), average='macro')\n","recall = recall_score(np.argmax(y_test, axis=-1), np.argmax(predictions, axis=-1), average='macro')\n","f1 = f1_score(np.argmax(y_test, axis=-1), np.argmax(predictions, axis=-1), average='macro')\n","print('Accuracy:',accuracy.round(4))\n","print('Precision:',precision.round(4))\n","print('Recall:',recall.round(4))\n","print('F1:',f1.round(4))\n","\n","# Plot the confusion matrix\n","plt.figure(figsize=(10,8))\n","sns.heatmap(cm.T, cmap='Blues', annot=True, annot_kws={'fontsize':9,'alpha':0.6})#, xticklabels=list(label_mapping.keys()), yticklabels=list(label_mapping.keys()))\n","plt.xlabel('True labels')\n","plt.ylabel('Predicted labels')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Save the model"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-12-13T14:30:38.221169Z","iopub.status.busy":"2023-12-13T14:30:38.216999Z","iopub.status.idle":"2023-12-13T14:30:38.960835Z","shell.execute_reply":"2023-12-13T14:30:38.959619Z","shell.execute_reply.started":"2023-12-13T14:30:38.220995Z"},"trusted":true},"outputs":[],"source":["import os\n","import zipfile\n","from datetime import datetime\n","import tensorflow as tf\n","from os.path import basename\n","\n","# Specify the name of the submission folder\n","submission_folder = \"models/SubmissionModel\"\n","\n","# Save best epoch model\n","model.save(os.path.join(submission_folder, \"SubmissionModel\"))\n","\n","# Save the model.py file in the main directory\n","with open(\"model.py\", \"w\") as model_file:\n","    model_file.write(\"\"\"\n","\n","import os\n","import tensorflow as tf\n","import numpy as np\n","import joblib\n","from scipy.special import softmax\n","\n","class model:\n","    def __init__(self, path):\n","        # Load the models\n","        self.loaded_encoder = tf.keras.models.load_model(os.path.join(path, 'encoder_model.h5'))\n","        self.loaded_kmeans = joblib.load(os.path.join(path, 'kmeans_model.pkl'))\n","\n","    def predict(self, X, categories):\n","        num_zeros_to_add = 2776 - X.shape[1]\n","        X_padded = np.pad(X, ((0, 0), (num_zeros_to_add, 0)), mode='constant')\n","\n","\n","        # Process new data (X is your new dataset)\n","        # Make sure to preprocess X as you did with the training data\n","        encoded_X = self.loaded_encoder.predict(X_padded)\n","\n","        # Calculate distances to cluster centroids\n","        distances = self.loaded_kmeans.transform(encoded_X)\n","\n","        # Convert distances to probabilities\n","        # The shape of probabilities will be (len(X), 9)\n","        probabilities = softmax(-distances, axis=1)\n","\n","        return probabilities\n","\"\"\")\n","\n","# Create an empty metadata file in the main directory\n","open(\"metadata\", \"w\").close()\n","\n","# Get the current date and time\n","current_datetime = datetime.now()\n","formatted_datetime = current_datetime.strftime(\"%Y%m%d_%H%M%S\")\n","\n","# Create the zipfile with date and time in the name\n","zipfile_name = f\"{submission_folder}_{formatted_datetime}.zip\"\n","\n","with zipfile.ZipFile(zipfile_name, 'w') as zip_file:\n","    # Add the entire \"SubmissionModel\" folder and its contents to the archive\n","    for foldername, subfolders, filenames in os.walk(submission_folder):\n","        for filename in filenames:\n","            file_path = os.path.join(foldername, filename)\n","            arcname = os.path.relpath(file_path, submission_folder)\n","\n","            if (foldername in [\"SubmissionModel\", \"SubmissionModel/model\", \"SubmissionModel/model/variables\", \"SubmissionModel/variables\"]):\n","                continue\n","\n","            # print(foldername)\n","            print(file_path)\n","\n","            zip_file.write(file_path, arcname)\n","\n","    # Add other files to the archive (model.py and metadata)\n","    zip_file.write(\"model.py\", arcname=\"model.py\")\n","    zip_file.write(\"metadata\", arcname=\"metadata\")\n","\n","print(zipfile_name)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":4133116,"sourceId":7156698,"sourceType":"datasetVersion"}],"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","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":"3.10.13"}},"nbformat":4,"nbformat_minor":4}