LSTM-anomaly-detection.ipynb

{
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   "source": [
    "## LSTM Autoencoders\n",
    "Autoencoders Neural Networks try to learn data representation of its input. So the input of the Autoencoder is the same as the output? Not quite. Usually, we want to learn an efficient encoding that uses fewer parameters/memory.\n",
    "\n",
    "The encoding should allow for output similar to the original input. In a sense, we’re forcing the model to learn the most important features of the data using as few parameters as possible.\n",
    "\n",
    "## Anomaly Detection with Autoencoders\n",
    "Here are the basic steps to Anomaly Detection using an Autoencoder:\n",
    "\n",
    "### Train an Autoencoder on normal data (no anomalies)\n",
    "### Take a new data point and try to reconstruct it using the Autoencoder\n",
    "### If the error (reconstruction error) for the new data point is above some threshold, we label the example as an anomaly\n",
    "Good, but is this useful for Time Series Data? Yes, we need to take into account the temporal properties of the data. Luckily, LSTMs can help us with that.\n",
    "\n",
    "S&P 500 Index Data\n",
    "Our data is the daily closing prices for the S&P 500 index from 1986 to 2018.\n",
    "\n",
    "The S&P 500, or just the S&P, is a stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United States. It is one of the most commonly followed equity indices, and many consider it to be one of the best representations of the U.S. stock market. -Wikipedia\n",
    "\n",
    "It is provided by Patrick David and hosted on Kaggle. The data contains only two columns/features - the date and the closing price. Let’s download and load into a Data Frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('spx.csv', parse_dates=['date'], index_col='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7782, 1) (410, 1)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-02</th>\n",
       "      <td>209.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>210.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>210.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>213.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>207.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close\n",
       "date              \n",
       "1986-01-02  209.59\n",
       "1986-01-03  210.88\n",
       "1986-01-06  210.65\n",
       "1986-01-07  213.80\n",
       "1986-01-08  207.97"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This training set takes 95% of earlier data and the test data is the last 5%\n",
    "train_size = int(len(df) * 0.95)\n",
    "test_size = len(df) - train_size\n",
    "train, test = df.iloc[0:train_size], df.iloc[train_size:len(df)]\n",
    "print(train.shape, test.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.475126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.472703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.473135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.467219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.478169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7777</th>\n",
       "      <td>2.047774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7778</th>\n",
       "      <td>2.134814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7779</th>\n",
       "      <td>2.149916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7780</th>\n",
       "      <td>2.194431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7781</th>\n",
       "      <td>2.202357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7782 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         close\n",
       "0    -1.475126\n",
       "1    -1.472703\n",
       "2    -1.473135\n",
       "3    -1.467219\n",
       "4    -1.478169\n",
       "...        ...\n",
       "7777  2.047774\n",
       "7778  2.134814\n",
       "7779  2.149916\n",
       "7780  2.194431\n",
       "7781  2.202357\n",
       "\n",
       "[7782 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "def scale(X_train, X_test):    \n",
    "    scaler = StandardScaler()\n",
    "    scaler = scaler.fit(X_train.values)\n",
    "    train = scaler.transform(X_train.values)    \n",
    "    test = scaler.transform(X_test.values)\n",
    "    df_X_train = pd.DataFrame(train, columns=X_train.columns)\n",
    "    df_X_test = pd.DataFrame(test, columns=X_test.columns)    \n",
    "    return df_X_train, df_X_test\n",
    "\n",
    "train, test = scale(train, test)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(X, y, time_steps=1):\n",
    "    Xs, ys = [], []\n",
    "    for i in range(len(X) - time_steps):\n",
    "        v = X.iloc[i:(i + time_steps)].values\n",
    "        Xs.append(v)\n",
    "        ys.append(y.iloc[i + time_steps])\n",
    "    return np.array(Xs), np.array(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7752, 30, 1)\n"
     ]
    }
   ],
   "source": [
    "TIME_STEPS = 30\n",
    "# reshape to [samples, time_steps, n_features]\n",
    "X_train, y_train = create_dataset(\n",
    "  train[['close']],\n",
    "  train.close,\n",
    "  TIME_STEPS\n",
    ")\n",
    "\n",
    "X_test, y_test = create_dataset(\n",
    "  test[['close']],\n",
    "  test.close,\n",
    "  TIME_STEPS\n",
    ")\n",
    "\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTM Autoencoder in Keras\n",
    "Our Autoencoder should take a sequence as input and outputs a sequence of the same shape. Here’s how to build such a simple model in Keras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n",
      "Metal device set to: Apple M1\n",
      "\n",
      "systemMemory: 16.00 GB\n",
      "maxCacheSize: 5.33 GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-20 20:47:03.419179: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-10-20 20:47:03.419266: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "model = keras.Sequential()\n",
    "model.add(keras.layers.LSTM(\n",
    "    units=64,\n",
    "    input_shape=(X_train.shape[1], X_train.shape[2])\n",
    "))\n",
    "model.add(keras.layers.Dropout(rate=0.2))\n",
    "model.add(keras.layers.RepeatVector(n=X_train.shape[1]))\n",
    "model.add(keras.layers.LSTM(units=64, return_sequences=True))\n",
    "model.add(keras.layers.Dropout(rate=0.2))\n",
    "model.add(\n",
    "  keras.layers.TimeDistributed(\n",
    "    keras.layers.Dense(units=X_train.shape[2])\n",
    "  )\n",
    ")\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of things that might be new to you in this model. The RepeatVector layer simply repeats the input n times. Adding return_sequences=True in LSTM layer makes it return the sequence.\n",
    "\n",
    "Finally, the TimeDistributed layer creates a vector with a length of the number of outputs from the previous layer. Your first LSTM Autoencoder is ready for training.\n",
    "\n",
    "Training the model is no different from a regular LSTM model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-20 20:47:03.648189: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2022-10-20 20:47:03.648374: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-20 20:47:04.198006: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:47:04.347523: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:47:04.440561: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:47:04.552415: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  3/218 [..............................] - ETA: 6s - loss: 1.2988  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-20 20:47:04.732022: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "217/218 [============================>.] - ETA: 0s - loss: 0.1662"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-20 20:47:10.910682: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:47:10.966817: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:47:11.032964: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218/218 [==============================] - 8s 30ms/step - loss: 0.1662 - val_loss: 0.1815\n",
      "Epoch 2/10\n",
      "218/218 [==============================] - 6s 29ms/step - loss: 0.0759 - val_loss: 0.2720\n",
      "Epoch 3/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0537 - val_loss: 0.2354\n",
      "Epoch 4/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0550 - val_loss: 0.2194\n",
      "Epoch 5/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0487 - val_loss: 0.1962\n",
      "Epoch 6/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0472 - val_loss: 0.1611\n",
      "Epoch 7/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0422 - val_loss: 0.1825\n",
      "Epoch 8/10\n",
      "218/218 [==============================] - 6s 29ms/step - loss: 0.0430 - val_loss: 0.1559\n",
      "Epoch 9/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0376 - val_loss: 0.1765\n",
      "Epoch 10/10\n",
      "218/218 [==============================] - 6s 28ms/step - loss: 0.0382 - val_loss: 0.1567\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=10,\n",
    "    batch_size=32,\n",
    "    validation_split=0.1,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1a1K9enX69evH1q1br3qeiRMnEhIS4ryFh4cX7M24u52LICMFKlwHNdqbXU3emg6CW4ymVH5+H3543dx6RERKsVmzZlGmTJk8b02aNDG7vCJhWgtQQkICWVlZhIWF5dofFhZGXFxcvs7x5ptvkpKSwpAhQ5z7GjZsyIwZM2jWrBlJSUlMnjyZTp06sW3btitOrDRhwgSioqKc95OSkjwrBG25ZOFTV25ViRhmzMa67AlY+2/wCYDrHze7KhGRUueWW26hXbt2eT5W3DM0lxTTJ0L867wBV5tL4FJz5szhpZdeYvHixVSqVMm5v3379rRvf7EVo1OnTrRq1Yp3332Xd955J89z+fr64uvre43vwM2d3At//goWW/EufFpU2t5vrI+z+kVY/ZJxOazdGLOrEpFSyMQusqYLCgoiKMg1J6Etqs/FtEtgoaGh2Gy2y1p74uPjL2sV+qt58+YxevRo5s+fT/fu3a96rNVqpU2bNuzfv7/QNZdKOTM/1+8FQVf/vbuM68dDl6eM7eVPGp23RUSKSE4Lx/nz502uRPKSM3GjzWYr1HlMawHy8fEhMjKSVatWceuttzr3r1q1igEDBlzxeXPmzOHee+9lzpw53HzzzX/7Og6HwznbpfxFVgZsm2tsu2Ln56u58Z/G5bCN78GSR8Db3+gnJCJSSDabjbJlyxIfHw8YQ7jzc2VCip/dbufkyZMEBATg5VW4CGPqJbCoqCiGDx9O69at6dChA9OmTSMmJoaxY8cCRt+cY8eOMXPmTMAIPyNGjGDy5Mm0b9/e2Xrk7+9PSEgIAC+//DLt27enXr16JCUl8c477xAdHc37779vzpt0Zfu+hZSTUCYM6vX8++NdicUCPV81Lodtng4Lx4CXPzTsa3ZlIlIK5IwkzglB4jqsVis1atQodCg1NQANHTqUU6dO8corrxAbG0vTpk1ZtmwZNWvWBCA2NjbXIm0ffvghmZmZjBs3jnHjxjn3jxw5khkzZgBw9uxZxowZQ1xcHCEhIURERLB27Vratm1bou/NLeR0fm5xJ9hM7w5WcBYL3PyWsVbY9rmwYCTcNQ/q3mR2ZSLi5iwWC1WqVKFSpUpkZGSYXY5cwsfHB6u18D14TJ0I0VV5xESIScfh7SbgsMPDv0Fo3iPk3EJWJnwxypgt2ssfhi+Emh3NrkpEREpYQb6/TV8KQ0yybY4Rfmp0cO/wA0br1aBP4LoekHkBZg2BY1vMrkpERFyYApAncjgujpxyt87PV+LlA0M/g1qdIf0cfH4bnNhpdlUiIuKiFIA80ZH1cPog+ARBk4FmV1N0vP3hzjlQvQ1cOAMzB0DCAbOrEhERF6QA5IlyOj83vQ18As2tpaj5BsGwL6ByM2OE28xb4MwRs6sSEREXowDkaVITYddiY7u0XP76K/+yMPwrCG0ASceMEJR03OyqRETEhSgAeZodXxgdhSs2hOqtza6m+ASGwojFUK42nDlsXA5LSTC7KhERcREKQJ7m0s7PpX1m0+AqMHIJBFeHhH0wc6DRN0hERDyeApAnObETjm8Bqze0uMPsakpG2RpGS1BgJTixAz6/HdLOmV2ViIiYTAHIk+R0fm7Qx7hE5ClCrzNCkH85OPYbzL4D0rXIoYiIJ1MA8hSZacZyEQCtRphbixnCGsPwReAbDEd+gvnDjd+JiIh4JAUgT7HnG6P/S1BVz10rq2oEDFsA3gFwYDV8ca+xjIaIiHgcBSBPsTX78lfLu8BqM7cWM9Vob0yWaPOFPV/DVw+CPcvsqkREpIQpAHmCs0fhjx+M7Yi7za3FFdTpCkNmgtULdsyHRWPhXJzZVYmISAlSAPIE0bMBh7FOVvnaZlfjGhr0hts+AovVCEGTmsHicXByr9mViYhICVAAKu3sdojOnvvHEzs/X03T2+DuhRDeHrLSjTmS3m9rjBI7stFYNFZEREolBaDS7tAaOBsDviHQqL/Z1bieujfC6BUwehU07AdYYN9ymN4bPu4Bu5aoj5CISCmkAFTa5XR+bna7sVq65C28LdwxCx7+DSJHGZ2k/9xkDJd/rw389glkXDC7ShERKSIKQKXZ+dOw+2tju1UpXfi0qIVeB/0nw+O/Q+cnwK8snP4Dvn4c3m4Ka/5t/F5FRMStKQCVZju+gKw0CGsGVVqaXY17KVMJuj0Pj++E3m9ASA04nwA/vAZvN4FlTxmLrIqIiFtSACrNts40frbygIVPi4tvGWg/Fh7dCoM+hsrNIeM8/PohvBMBC+6B41vNrlJERApIAai0Oh4NcTuMvizNBptdjfuzeRn9qB5YC8O/MmbTdthh50KY1hU+7W/MLq2RYyIibsHL7AKkmOR0fm7UDwLKm1tLaWKxGCPH6t5oBMz178DvX8KhtcYtrCl0fASaDgKbt9nViojIFagFqDTKuADbFxjbmvm5+FRuBoM+gse2Qftx4B0IJ36HRQ/A5Baw4T1IO2d2lSIikgcFoNJo91JISzQ67tbuanY1pV/ZcOj9OkTthG4vQJkwSDoGK5+Ft5rAqhe11IaIiItRACqNci5/RQwDqz7iEuNfDjr/A8bvgFvehQr1jCC6fpKW2hARcTH6dixtTh8y+qJggZbDzK7GM3n5GsuOjPsV7pgDNTrksdTGBnWYFhExkQJQaRM9y/hZ90bj0oyYx2qFhn3h3m/zWGqjD/yvu5baEBExiQJQaWLPyl75HYjQzM8uJa+lNo79lr3URmvY9LGW2hARKUEKQKXJH98bnW/9y0HDm82uRvJy6VIbXZ7MXmrjIHwTpaU2RERKkAJQabIle+bn5kONfijiuspUgpueu8pSG09qqQ0RkWKkAFRapCTA3uXGti5/uY8rLrUxTUttiIgUIwWg0mL7PLBnQNUIqNzU7GqkoC5damPEYqjb7fKlNvZrqQ0RkaKipTBKA4cDtuTM/aPWH7dmsUCdrsYtbgdseBd2fHFxqY1KTaDTo1pqQ0SkkNQCVBoc2wwnd4OXn9GKIKVD5WZw27SLS234lIH4nZcstfEupCaZXaWIiFtSACoNcjo/Nx4AfiHm1iJFL2epjcd/h24vXrLUxnPGyLFVL0JSrNlVioi4FQUgd5eeAr8vNLZ1+at08y8HnaMuLrURWj/3UhtLHtUQehGRfFIAcne7FkP6OShXG2pdb3Y1UhJyltp46JeLS23YM2DLp/B+O2MxXBERuSoFIHfn7Px8t9GBVjzHpUtt3LMcKjaElHiYdzd8cS+knDK7QhERl6UA5M4SDkDMBrBYoeVdZlcjZqrZEcasgeujjD8Pv38JU9oZLYQiInIZBSB3tjW79ee6HhBc1dxaxHzeftD9RbhvNVRsBCknYf4IWDDKmChTREScFIDcVVYmbJtjbEfcbW4t4lqqRcIDa6DzE2Cxwc5FRt+gnYvMrkxExGUoALmrA6sg+QQEhEL93mZXI67Gyxe6PQ/3fweVGhvrjC0YZbQIJZ80uzoREdMpALmrnM7PLe4ALx9zaxHXVTXC6BvU5SmjNWjXYqNv0O9falkNEfFoCkDu6NwJ2Petsd1qhLm1iOvz8oGbnoX7v4ewpnD+lDFKbP5wSI43uzoREVMoALmjbXPAkQXV20LFBmZXI+6iaku4/we44RmwehnzBb3fzlhrTK1BIuJhFIDcjcNxcfSXOj9LQXn5wI0TjCAU1gwunIYvRxtzB507YXZ1IiIlRgHI3cT8DKcOgHcgNL3N7GrEXVVpDmN+gK7/NFqD9nxt9A3aPl+tQSLiERSA3M3Wz42fTW4F3yBzaxH3ZvOGrk/DmB+hcnO4cAYW3g9z74JzcWZXJyJSrBSA3EnauYtzubTSwqdSRCo3MzpI3/gcWL1h7zKjb9C2uWoNEpFSSwHInfy+EDJSoEI9CG9ndjVSmti84YYnjQkUq7SA1LOw6AGYcwckxZpdnYhIkVMAcic5nZ9bDdfCp1I8wprAfd/BTc+DzceYbmFKO4ierdYgESlVFIDcRfwe+HOTMZld8zvMrkZKM5s3dHnCmECxagSkJsJXD8LsIZB03OzqRESKhAKQu8hp/anfG4LCzK1FPENYYxi9Grq9aLQG7V8J77c3OuKrNUhE3JwCkDvITDc6pII6P0vJsnlB5yh4YJ2xyGpaIiweB7Nuh8Q/za5OROSaKQC5g33fGotZlqkM1/UwuxrxRJUawr0rofvLYPOFA6thSgfYMlOtQSLilhSA3EHO5a+Wdxr/Ixcxg80Lrh8PY9dBtdaQlgRLHoHPb4OzR82uTkSkQBSAXF3SceN/2wARuvwlLqBiAxi9Enr8C7z84I/vjdagzTPUGiQibkMByNVFzwKHHWp0hAp1za5GxGC1QadHYexPxqK86edg6WPw2a1wNsbs6kRE/pYCkCuz2y8ufaHOz+KKQuvBvd9Cz9eM1qCDPxitQb99otYgEXFpCkCu7Mh6OHMYfIKg8QCzqxHJm9UGHR+GseshvD2kJ8PXj8PMAXDmiNnViYjkSQHIleV0fm42CHwCza1F5O+EXgf3LINeE8HLHw6tMVqDNv3PaM0UEXEhpgegKVOmULt2bfz8/IiMjGTdunVXPHbhwoX06NGDihUrEhwcTIcOHVixYsVlx3355Zc0btwYX19fGjduzKJFi4rzLRSPC2dh12JjO2KEqaWI5JvVBh0eggfXG/3WMlLgm3/AzFuM1kwRERdhagCaN28e48eP59lnn2Xr1q107tyZPn36EBOTdyfKtWvX0qNHD5YtW8bmzZu58cYb6d+/P1u3bnUes3HjRoYOHcrw4cPZtm0bw4cPZ8iQIfzyyy8l9baKxu9fQGYqVGoM1VqZXY1IwVSoC6O+gT7/Bu8AOLwOpnSEXz9Sa5CIuASLw2FeT8V27drRqlUrpk6d6tzXqFEjBg4cyMSJE/N1jiZNmjB06FBeeOEFAIYOHUpSUhLLly93HtO7d2/KlSvHnDlz8jxHWloaaWlpzvtJSUmEh4eTmJhIcHDwtby1wvvwBoiNhl6vQ4dx5tQgUhROH4TFj8CRn4z7Na+HAe9C+Trm1iUipU5SUhIhISH5+v42rQUoPT2dzZs307Nnz1z7e/bsyYYNG/J1Drvdzrlz5yhfvrxz38aNGy87Z69eva56zokTJxISEuK8hYeHF+CdFIO4HUb4sXpr4VNxf+XrwMil0Pe/RmvQkZ9gaif45UO1BomIaUwLQAkJCWRlZREWlnthz7CwMOLi4vJ1jjfffJOUlBSGDBni3BcXF1fgc06YMIHExETn7ehRk2e1zRn63rAvBFYwtxaRomC1Qtv74cENUKszZJyH5U/Bp/3g1B9mVyciHsj0TtAWiyXXfYfDcdm+vMyZM4eXXnqJefPmUalSpUKd09fXl+Dg4Fw302SmwfZ5xrY6P0tpU742jFgCN78J3oHGVA9TO8HPU9UaJCIlyrQAFBoais1mu6xlJj4+/rIWnL+aN28eo0ePZv78+XTv3j3XY5UrV76mc7qMPV/DhTMQXB3q3mh2NSJFz2qFNvfBQxugdhfIvADfPgMz+qo1SERKjGkByMfHh8jISFatWpVr/6pVq+jYseMVnzdnzhxGjRrF7Nmzufnmmy97vEOHDpedc+XKlVc9p0vZkrPw6V3GkGKR0qpcLaM1qN/b4FMGYjbC1I6w8X3NIi0ixc7UpcWjoqIYPnw4rVu3pkOHDkybNo2YmBjGjh0LGH1zjh07xsyZMwEj/IwYMYLJkyfTvn17Z0uPv78/ISEhADz22GN06dKFN954gwEDBrB48WJWr17NTz/9ZM6bLIizMXDwR2O75V2mliJSIiwWaH0vXNfdWFn+4I+w4p/G34Xe/2c8LiJSDEztAzR06FAmTZrEK6+8QsuWLVm7di3Lli2jZs2aAMTGxuaaE+jDDz8kMzOTcePGUaVKFeftsccecx7TsWNH5s6dy/Tp02nevDkzZsxg3rx5tGvXrsTfX4FtnQU4jMsC5WubXY1IySlbA4Z/Bb3fMO7/8gF8O0EtQSJSbEydB8hVFWQegSJjt8Pk5pB4FG77HzQfXDKvK+Jqtsw0WoMA2j9kzIWlliARyQe3mAdI/uLQj0b48QuBRv3MrkbEPK1GQP/JxvbPU2DFs2oJEpEipwDkKnI6PzcbAt7+5tYiYrbIUdBvkrH98/uw8jmFIBEpUgpAruD8aWP4O0Cr4ebWIuIqWt9jjBAD2PgerHpeIUhEiowCkCvYPh+y0qFyM6jSwuxqRFxH63vh5reM7Q3vwqoXFIJEpEgoAJnN4YCt2Ze/NPOzyOXajDZmjgbY8A6sflEhSEQKTQHIbLHRcOJ3sPlq5JfIlbS5z1hMFWD9ZFj9kkKQiBSKApDZcjo/N+oP/uXMrUXElbW9/5IQNAm+e0UhSESumQKQmTIuwI4vjG11fhb5e23vhz7/MbZ/egu+/5dCkIhcEwUgM+1aAmmJxiy4tbqYXY2Ie2g3Bvr829he9yZ8/6pCkIgUmAKQmXI6P7e821ghW0Typ90DxlphAOv+Cz+8phAkIgWib12znD4Ih9cBFi18KnIt2j8IvSYa22v/Az9ONLceEXErCkBm2TrL+Fn3Jigbbm4tIu6qQ/ZaYQBr3oAfFIJEJH8UgMxgz4Lo2ca2Oj+LFE6HcdDzNWN7zf/Bj/9nbj0i4hYUgMxw4Ds4dxz8y0ODvmZXI+L+Oj4MPV81tn+cCD++YW49IuLyFIDMsHWm8bPFHeDla24tIqVFx0egx7+M7R9fhzX/NrceEXFpCkAlLfkk7F1ubEfcbW4tIqVNp0eh+8vG9g+vGZ2jRUTyoABU0rbPBXsmVG0FYU3Mrkak9Ll+PHR/ydj+/lVY+18zq3FfDgdsXwDzR8Kfv5ldjUiR8zK7AI/icMDWz41tdX4WKT7XP278ffvuZWO2aIsFOv/D7Krcx8l9sOwfcGitcX/PN9DzX9BurPG7FCkF1AJUkv78DU7uAS9/aDrI7GpESrfOUdDtBWP7u1dg3Vvm1uMO0s8bv6upHY3w4+UHNTqCPQO+fQYWjITURLOrFCkSagEqSTZvqN8HAiuAX4jZ1YiUfp3/YbQEff8vozXIYjUukcnl9q2AZU/A2Rjjfr2expIj5WrBr9NgxbOwazHE7YAhM6FyM1PLFSksi8Oh+eP/KikpiZCQEBITEwkODi76F3A41IwsUpLW/Ad+yB4m3+MV6PSYufW4krNHjdadPV8b94OrQZ83oGG/3P9O/fkbLBgFiUeNlqG+/4GI4fq3TFxKQb6/dQnMDPoHQ6Rk3fAk3Pissb3qBVj/jrn1uIKsDFg/Gd5va4Qfqxd0fBTG/QqN+l/+71T11vDAWqjXCzJTYckj8NWDkJ5iTv0ihaQAJCKe4YanoOs/je1Vz8OGd82tx0xHNsAHnY0wmHEeanSAB9YZHZ19y1z5eQHl4c650O1F43LitjnwUTej07SImyl0AMrKyiI6OpozZ84URT0iIsWn69PQdYKxvfI52Pi+ufWUtJQE+OohmN4HTu6GgAowYAqMWgZhjfN3DqvV6GA+cimUCTPOM60r7PiiWEsXKWoFDkDjx4/n448/Bozwc8MNN9CqVSvCw8P58ccfi7o+EZGi1fUZuOFpY3vFPz0jBNnt8Nt0eDcSorMXYm41Eh7+DSKGGaGmoGpdb7Qa1eoMGSnw5Wj4Ogoy04q2drnI4TA6oWdcMLuSUqHAf+q/+OILWrRoAcDSpUs5dOgQe/bsYfz48Tz77LNFXqCISJHrOgG6PGVsr/gnbJxibj3FKXY7fNITvh4PqWchrBmMXg23vGNc0iqMoDAYsRi6PGnc/+1j+LgnnDlcyKLlMnG/w4yb4YPr4d3WRoubxjAVSoFHgfn5+XHgwAGqV6/OmDFjCAgIYNKkSRw6dIgWLVqQlJRUXLWWmGIfBSYi5nM4ci+X0fv/oP2D5tZUlFKT4IfX4dcPwWEHnzJGR/C2Y8BWDDOg7F8NC++HC6eNaT4GfgANtdhzoV04Y3yOm/5nfI6Xqt4Gek2E8Dbm1OaCinUUWFhYGLt27SIrK4tvv/2W7t27A3D+/HlsNtu1VSwiUtIsFiMQdH7CuP/tM/DLh+bWVBQcDvh9oTG665epxpdmk1vh4U3Q4aHiCT8A9brD2HXGl3JqIsy90+hnlZVRPK9X2tntsPlT47Llr9OMz7HxQOOy5U3PgXcg/LkJPu4OX94HiX+aXbHbKXAAuueeexgyZAhNmzbFYrHQo0cPAH755RcaNmxY5AWKiBQbi8X4MslZJmP5U/DLNHNrKoxTf8Dnt8EX98C5WChfB+5eCINnQHDV4n/9kOpGh+r244z7G96FT/tD0vHif+3S5M/f4H83wdJH4fwpqNgQRiyBIZ9CaD3jkuMjm6Hl3YAFdiwwLov98LqmJSiAa5oI8YsvvuDo0aMMHjyY6tWrA/Dpp59StmxZBgwYUORFljRdAhPxMDnrhv30tnG/73+h7f3m1lQQGalG7T+9DVlpYPM1Rmp1Gg/efubUtGsJLB4HaUkQEAqDPoK6N5lTi7tIjofVL0N09pqRvsFGf7W29xsrCeTl+Fb49p8Qs8G4H1TFWAy42ZBr69zu5gry/V0kM0GfPXuWsmXLFvY0LkMBSMQDORyw+iVYP8m47y4h6MBq+OYJOHPIuF/3JqP2CnXNrQuMFqkFI42RS1iMEXhdngSrukvkkpVh9PH54XUjMAK0HGbMtxQU9vfPdzhg9xLjkmPOUiZVI4x+bTXaF1/dLqhY+wC98cYbzJs3z3l/yJAhVKhQgerVq7N9+/aCVysi4gosFuN/zjnLZCx7wvhSclVJx2H+SPh8kBF+ylSG26cbl7xcIfyAUcfoVRA5CnDAjxONepNPml2Z6zi01piU8ttnjPBTpaXxOxs4JX/hB4w/u40HwLhNxp9hnyCjZeiTXsbyJWeOFOMbcF8FbgGqU6cOn3/+OR07dmTVqlUMGTKEefPmMX/+fGJiYli5cmVx1Vpi1AIk4sEcDmOG5A3Zy2X0exta32tuTZfKyjQ6xf7wGqQnGzMytxtrXCrxc+F/r7bNM4biZ5w3LtPcPh1qdjC7KvMk/mm02OxcZNz3Lw/dXoBWIwrfQpYcD9+/CltmAg7jkmjHh+H6x8E3qNClu7JivQTm7+/Pvn37CA8P57HHHiM1NZUPP/yQffv20a5du1IxI7QCkIiHczhyL5fRbxK0vsfUkgA4+qsx2eCJHcb96m3g5regSnNz68qv+N0wfwQk7AOLzWit6PiIZ62PmJlm/Lla96YRBi1WaD0abvxn4edl+qvY7cY8V4fXGffLhMFNz0PLu0rtZchivQRWrlw5jh49CpBrGLzD4SArK+sayhURcTEWC/T4F3R42Lj/9XhjJmWznD9tLD76cQ8j/PiVhf6T4d6V7hN+ACo1gvt/gGaDwZFlhMy5dxlz3XiCfSvg/Xbw/b8uWYNtLdz836IPP2D82Ri5FO6YDeVqQ/IJWPKwsXTJ4Z+K/vXcTIED0G233cZdd91Fjx49OHXqFH369AEgOjqa6667rsgLFBExhcUCPV+9OKT76/GweUbJ1mC3w9bP4b3W2ZczMDrHPrLZ6FfjjqN8fMvAbR8ZLVc2H9i7DD68weizUlqd+gNmDYHZQy7217rtI7hnOVRuVryvbbFAw5th3C/Gn2ffEIjbbswqPW84nD5UvK/vwgp8CSwjI4PJkydz9OhRRo0aRUREBACTJk2iTJky3HfffcVSaEnSJTARcXI4jMsIP2cvl3HLu0Y/jeJ2Yhd8EwUxG437lRrDzW9CzY7F/9ol5Xi0cUns7BEjDPWeaFwOKi2XxNJTjEtdG96FrHSwehuzjd/wlHl9cVISjNFmm6cbkyvafIyaOj/h2n3I8qnEh8GXNgpAIpKLwwHfTjBmVsaSHYKGF89rpSXDmv8z1idzZBkz/nZ9xviSutJcMO7swlljvqA9Xxv3m94O/Se5d2ddhwN2LoSVz0PSMWNf3Zugz7+NiQxdwYldRrA/+INxPyDUmBS0KDphm6jYA9Aff/zBpEmT2L17NxaLhUaNGjF+/Hjq1KlzzUW7EgUgEbmMw5G9XMYHgAUGvAcRdxft+XcvNV4j50uzYT/o84Yxw3Jp5nDAxvdh9Ytgz4QK9WDITAhrbHZlBXdilzGjeE7H47I1jPW6Gt7sei1bDgfsX2kEoVMHjH1hTaHXa1Cnq6mlXatiDUArVqzglltuoWXLlnTq1AmHw8GGDRvYtm0bS5cudS6N4c6KKwClZmSx/PdYHA64rVUp/wdNpDRyOGD508YCo1hgwPsQMazw5z19yPjS3J89jUjZGsZkhvV7Ff7c7iTmZ1hwD5w7Dl7+xhQELe80u6r8uXAWfvy/7HW7ssDLD66Pgk6Pgre/2dVdXc5EjD/+H6SeNfY16Gv0GXKVOaXyqVgDUEREBL169eL//u//cu1/5plnWLlyJVu2bCl4xS6muALQ4uhjPDY3mmpl/Vn71I3YrC72vwER+XsOhxFWfp0GWIwJ61redW3nykwz5hta+1/ITDX6iHR6zFibzCegSMt2GykJxqryf3xv3I8YDn3/47ohwm6HbbONWcRTsid4bNgPer0O5WqaWlqBnT9thKBN/zNCnNUb2j1gzN7tX9bs6vKlWAOQn58fO3bsoF693Ncx9+3bR/PmzUlNTS14xS6mOFuA2r62mqTUTGbe25Yu9SsW2blFpAQ5HLDsSdj0EUYImlrwloqDPxpLWJzab9yv3QX6vgkV6xd1te7HnmWEwh8nAg4Ia2YsBOpqrRHHNsOyp+DYb8b90PrGJUt3X/Ps5F5jksacFkn/8sY8RZH3gM3L3Nr+RrHOA1SxYkWio6Mv2x8dHU2lSpUKejqP4udt49aIagDM++2oydWIyDWzWIxWidajAQd89SBsm5u/5547AV+MhpkDjPATWAlu+5+x2rfCj8Fqg65Pw4ivILCiMffRhzfArsVmV2ZISTDmZfqomxF+fMoY80aNXe/+4QegYgMYtgDu/tJYif7CaWNpmA86GWvPlRIFjnL3338/Y8aM4eDBg3Ts2BGLxcJPP/3EG2+8wT/+8Y/iqLFUGdImnE83HmHVzhOcSUmnXKCP2SWJyLWwWIx+Ojjgt09g0VjAAi2G5n28PQs2fWxMgpeWZBzb9n648Vm3ubxQ4up0hQfWwRf3Gqudzx8B7R6EHq+Alwn/dmZlGp/1D69CaqKxr/kd0ONlCKpc8vUUt+u6Q+2uxpD5H16Hk3uMtdzq9YSer7l9YC/wJTCHw8GkSZN48803OX78OABVq1blySef5LHHHiuWIktacY8C6/fuOn4/lsQL/Rpz7/W1i/z8IlKC7HZjvp7N041lDW79EJoPyX3Msc3GEhax0cb9qhFGB9+qESVerlvKyoTvX4H1k4371VrD4BlQNrzkaji83rjsGb/TuF+5mRGAPWW19QtnjMuSv3xgjNSz2KDNfcYUDcUxi/U1KrF5gM6dOwdAUFAQKSkpbN68mS5dulzr6VxGcQegzzYe5vnFO2kQFsS34ztjcbWhkSJSMHY7fPO4MVO0xQq3ToPmg40vje/+ZbQa4DBm4e3+gtGXwo3nWjHNnmXw1Vij9cW/nDGbcr1iHnmcdNyYz+f3L4z7fmWh2/Oe+xkmHDCWMNm7zLjvV9ZYiLfNaJeYp8qUiRC3bdtGq1atSsV6YMUdgBIvZND2tdWkZdr5alwnWoaXLfLXEJESZrcby2Vs+dQIQe0fgu3zLo4Maj7UGFZcRn0lC+XMYVgw6uLSGZ3/AV3/WfSdczPTjNm/1/wHMlIAi7H8yE3PQ2CFon0td3TwR/j2nxdbxCrUM+YPqtfT1PmOirUTtBReiL83fZoa14vnbVJnaJFSwWo1Vo1vNcJYYmDje0b4Ca1vLEh52zSFn6JQrhbcuwLa3G/cX/cmfDbQ6FxeVPavhikdjKHtGSlQvS2M+dGYoVrhx1CnK4xdZ/yZDwg1OvTPHgKf3wbxu82uLl8UgEwypI1x7XrptuOcT880uRoRKRJWK/SbDG0fMIYOd3vBGBlU2/27BrgUL19jBfXbPzFGYB1eBx9cD4fWFe68pw/BnDth1iA4/YcxQm/gB0bgqtqySEovVaw2aH0PPLrFmL/K5mPM3zS1o9HnLSXB7AqvSpfA8lASS2HY7Q66/vdHYk6f57+DW3B7pGaGFilVHA7XW/qgNErYb4wOi99lXHq88VljBmZrAf5/n34efnrb6GSdlQZWL2g3Fm54ulQsEFpiTh+EVS8YS7qA0efthqeg7ZgSG7VXLH2AlixZctXHDx06RFRUlAJQAbz/wwH+s2IvbWuVZ/7YDsX2OiIipVr6eWOemuhZxv3rehiXHP9udJLDAbuXwIpnITG7O0LtG4xFSys1LN6aS7PDPxmLB8dtN+6Xr2P0f2vQt9j/U1AsAciajzRtsVgUgAogLjGVjv/3HXYHfP+PG6hTsUyxvZaISKm35TMjCGWmQnB1Y6h8eJu8j43fYyxpcmiNcT8k3OjE2+gWtdwVBXsWRM825r1Kzu6fVbuLsTBs5abF9rLF0gnabrf/7a00hJ+SVDnEj64NjE6R83/70+RqRETcXKvhcN9qKF8Xkv6E6X3g56lGS0+O1CSjxeeDTkb4sflCl6dg3K/QeIDCT1Gx2ozP45HNxkg9my8cWgsfdoYlj0JyvNkVqhO02Ya0NjpDf7H5TzKy7CZXIyLi5io3M0ZsNR4A9gz49hmjj9CFsxA9B96NNEbo2TONSzLjfoGbnvXcxWeLm2+QMRjg4U3Q5FZjhOSWT+GdVka/q6wM00pTADJZt0aVCC3jQ0JyGj/sMT8Ri4i4Pb9gGPyp0ZfH6m3083mzoTGJYkq80UI07Eu4cw6U12z8JaJcTeOS5D3fGjOgp5+D3xcaHddNogBkMm+bldtaGSPA5muBVBGRomGxQLsH4N5vjf49mRfAOxC6vwQPbYR63c2u0DPV7AD3fW9ML9D3P6bOpu3a69p7iCGtw5m29iA/7D1JfFIqlYL9zC5JRKR0qN4aHlgLe742FvcMrmp2RWK1Qss7za5CLUCu4LpKZYisWY4su4MvtqgztIhIkQoob8zQrfAjl8h3APr1119zjfL66+j5tLQ05s+fX3SVeZih2TNDL/jtz8t+tyIiIlK08h2AOnTowKlTp5z3Q0JCOHjwoPP+2bNnufNO85u03NXNzaoQ6GPjUEIKvx46bXY5IiIipVq+A9BfWyXyaqW4lpaLKVOmULt2bfz8/IiMjGTduiuv5RIbG8tdd91FgwYNsFqtjB8//rJjZsyYgcViueyWmppa4NpKUqCvF/1bGM2z89QZWkREpFgVaR8gSwEnkJo3bx7jx4/n2WefZevWrXTu3Jk+ffoQExOT5/FpaWlUrFiRZ599lhYtWlzxvMHBwcTGxua6+fm5fsfinAVSl+2IJSnVvLkRRERESjtTO0G/9dZbjB49mvvuu49GjRoxadIkwsPDmTp1ap7H16pVi8mTJzNixAhCQkKueF6LxULlypVz3dxBRHhZ6lUqQ2qGnSXRx80uR0REpNQqUADatWsX27dvZ/v27TgcDvbs2eO8v3PnzgK9cHp6Ops3b6Znz5659vfs2ZMNGzYU6Fx/lZycTM2aNalevTr9+vVj69atVz0+LS2NpKSkXDczWCwWZ2dozQkkIiJSfAo0D1C3bt1y9fPp168fYHxxOxyOAl0CS0hIICsri7CwsFz7w8LCiIuLK0hZuTRs2JAZM2bQrFkzkpKSmDx5Mp06dWLbtm3Uq1cvz+dMnDiRl19++ZpfsyjdGlGNN77dw/Y/E9kdm0SjKsW3GKuIiIinyncAOnToULEU8NfQVNAg9Vft27enffv2zvudOnWiVatWvPvuu7zzzjt5PmfChAlERUU57yclJREeHn7NNRRGhTK+dG8UxvLf45i36Sgv3dLElDpERERKs3wHoJo1a1718TNnzrB06VJGjBiRr/OFhoZis9kua+2Jj4+/rFWoMKxWK23atGH//v1XPMbX1xdfX98ie83CGtomnOW/x/FV9DEm9G2Ir5d5U4WLiIiURkXWCTomJoZ77rkn38f7+PgQGRnJqlWrcu1ftWoVHTt2LKqycDgcREdHU6VKlSI7Z3HrXK8iVUP8OHs+g5U7T5hdjoiISKlj6iiwqKgo/ve///HJJ5+we/duHn/8cWJiYhg7dixgXJr6a4tSdHQ00dHRJCcnc/LkSaKjo9m1a5fz8ZdffpkVK1Zw8OBBoqOjGT16NNHR0c5zugOb1cLtkVogVUREpLiYuhjq0KFDOXXqFK+88gqxsbE0bdqUZcuWOS+3xcbGXjYnUEREhHN78+bNzJ49m5o1a3L48GHAmJF6zJgxxMXFERISQkREBGvXrqVt27Yl9r6KwuDW4bzz/QHW7U/g6OnzhJcPMLskERGRUsPiKKKFp7Zt20arVq1yrRfmrpKSkggJCSExMZHgYPNGYQ3738+sP3CKR7vVI6pHfdPqEBERcQcF+f7OdwvQlUZQ5Th27Fh+TyX5NKR1OOsPnOKL347yWLd62KzXPjpORERELsp3AHr77bf/9pgaNWoUqhjJrVeTyoT4e3M8MZWfDiRwQ/2KZpckIiJSKpg+D5BcmZ+3jYEtq/LpxiPM33RUAUhERKSImDoKTP7e0DZGq9rKXXGcTkk3uRoREZHSId8B6JdffmH58uW59s2cOZPatWtTqVIlxowZQ1paWpEX6OkaVw2mWbUQMrIcLNqqflYiIiJFId8B6KWXXmL79u3O+zt27GD06NF0796dZ555hqVLlzJx4sRiKdLTDcleIHXephiKaNCeiIiIR8t3AIqOjqZbt27O+3PnzqVdu3Z89NFHREVF8c477zB//vxiKdLT3dKiKr5eVvadSCb66FmzyxEREXF7+Q5AZ86cybVG15o1a+jdu7fzfps2bTh6VLMWF4cQf2/6NjOW8tDM0CIiIoWX7wAUFhbmHAmWnp7Oli1b6NChg/Pxc+fO4e3tXfQVCmDMCQSwdFss59MzTa5GRETEveU7APXu3ZtnnnmGdevWMWHCBAICAujcubPz8e3bt1O3bt1iKVKgfZ3y1KwQQHJaJt9sjzW7HBEREbeW7wD06quvYrPZuOGGG/joo4/46KOP8PHxcT7+ySef0LNnz2IpUsBisThbgXQZTEREpHAKvBZYYmIiZcqUwWaz5dp/+vRpypQpkysUuStXWQvsr04kpdJh4nfYHfDdP26gbsUyZpckIiLiMgry/V3giRBDQkIuCz8A5cuXLxXhx5WFBftxY4NKgFqBRERECiPfS2Hce++9+Truk08+ueZi5O8NaRPOd3vi+XLznzzRswHeNk3mLSIiUlD5DkAzZsygZs2aREREaDI+E93UsBKhZXxJSE7j+z3x9GpS2eySRERE3E6+A9DYsWOZO3cuBw8e5N577+Xuu++mfPnyxVmb5MHbZmVQq2p8uPYg8zcdVQASERG5Bvm+fjJlyhRiY2N5+umnWbp0KeHh4QwZMoQVK1aoRaiEDc4eDfbD3nhOJKWaXI2IiIj7KVAHEl9fX+68805WrVrFrl27aNKkCQ899BA1a9YkOTm5uGqUv7iuUhla1yyH3QFfbP7T7HJERETczjX3oLVYLFgsFhwOB3a7vShrknzIWSB1wW9H1QInIiJSQAUKQGlpacyZM4cePXrQoEEDduzYwXvvvUdMTAxlymhOmpJ0c7MqlPH14vCp8/xy6LTZ5YiIiLiVfAeghx56iCpVqvDGG2/Qr18//vzzTxYsWEDfvn2xWjUUu6QF+nrRv0X2AqmbNCeQiIhIQeR7Jmir1UqNGjWIiIjAYrFc8biFCxcWWXFmcdWZoP9qa8wZbp2yAV8vK78+250Qfy1GKyIinqsg39/5HgY/YsSIqwYfKXktw8tSP6wM+04ks2TbcYa3r2l2SSIiIm6hQBMhimvJWSD11W92M3/TUQUgERGRfFLnHTd3W6vqeNss7DiWyK7jSWaXIyIi4hYUgNxc+UAfejQOA7RAqoiISH4pAJUCQ7Jnhl609RipGVkmVyMiIuL6FIBKgc71KlI1xI/ECxms3HXC7HJERERcngJQKWCzWrg9uxVIcwKJiIj8PQWgUmJwZHUsFvjpQAJHT583uxwRERGXpgBUSoSXD6BT3VDAWB9MRERErkwBqBRxLpC6+U+y7FogVURE5EoUgEqRno3DCPH3JjYxlXX7T5pdjoiIiMtSACpF/Lxt3BpRDdCcQCIiIlejAFTK5MwJtGrXCU4lp5lcjYiIiGtSACplGlcNplm1EDKyHCzaeszsckRERFySAlApNDS7M/T8347icKgztIiIyF8pAJVCt7Ssip+3lX0nktl69KzZ5YiIiLgcBaBSKNjPm75NqwCaGVpERCQvCkClVM6cQEu3HSclLdPkakRERFyLAlAp1a52eWpVCCAlPYtvdsSaXY6IiIhLUQAqpSwWC4O1QKqIiEieFIBKsdsjq2O1wG9HznAgPtnsckRERFyGAlApFhbsx40NKgFaIFVERORSCkClXM6cQF9u+ZOMLLvJ1YiIiLgGBaBS7saGlQgt40tCcjrf7Y43uxwRERGXoABUynnbrAyK1AKpIiIil1IA8gA5C6T+uDeeuMRUk6sRERExnwKQB6hbsQxtapXD7jD6AomIiHg6BSAPkdMKNP+3o9jtWiBVREQ8mwKQh7i5eRXK+Hpx5NR5fjl02uxyRERETKUA5CECfLzo3yJ7gVR1hhYREQ+nAORBhrapAcCyHbEkXsgwuRoRERHzKAB5kBbVQ2gQFkRapp0l0cfMLkdERMQ0CkAexGKxMCR7Zuh5ugwmIiIeTAHIw9waUQ1vm4XfjyWx83ii2eWIiIiYQgHIw5QP9KFn48oAzN+kViAREfFMCkAeKOcy2FfRx0nNyDK5GhERkZKnAOSBrr8ulKohfiReyGDFzjizyxERESlxCkAeyGa1cPslM0OLiIh4GgUgDzU4sjoWC6w/cIqjp8+bXY6IiEiJMj0ATZkyhdq1a+Pn50dkZCTr1q274rGxsbHcddddNGjQAKvVyvjx4/M87ssvv6Rx48b4+vrSuHFjFi1aVEzVu6/w8gFcf10ooFYgERHxPKYGoHnz5jF+/HieffZZtm7dSufOnenTpw8xMTF5Hp+WlkbFihV59tlnadGiRZ7HbNy4kaFDhzJ8+HC2bdvG8OHDGTJkCL/88ktxvhW3lLNA6heb/yRLC6SKiIgHsTgcDtO++dq1a0erVq2YOnWqc1+jRo0YOHAgEydOvOpzu3btSsuWLZk0aVKu/UOHDiUpKYnly5c79/Xu3Zty5coxZ86cfNWVlJRESEgIiYmJBAcH5/8NuZm0zCzavf4dZ89nMP2eNtzYoJLZJYmIiFyzgnx/m9YClJ6ezubNm+nZs2eu/T179mTDhg3XfN6NGzdeds5evXpd9ZxpaWkkJSXlunkCXy8bA1tWAzQnkIiIeBbTAlBCQgJZWVmEhYXl2h8WFkZc3LUPzY6LiyvwOSdOnEhISIjzFh4efs2v726GZs8JtHr3CU4lp5lcjYiISMkwvRO0xWLJdd/hcFy2r7jPOWHCBBITE523o0c9pzWkUZVgmlcPISPLwaKtWiBVREQ8g2kBKDQ0FJvNdlnLTHx8/GUtOAVRuXLlAp/T19eX4ODgXDdPktMZet6mo5jYJUxERKTEmBaAfHx8iIyMZNWqVbn2r1q1io4dO17zeTt06HDZOVeuXFmoc5Z2t7Ssip+3lf3xyWw9etbsckRERIqdl5kvHhUVxfDhw2ndujUdOnRg2rRpxMTEMHbsWMC4NHXs2DFmzpzpfE50dDQAycnJnDx5kujoaHx8fGjcuDEAjz32GF26dOGNN95gwIABLF68mNWrV/PTTz+V+PtzF8F+3vRtVoWFW44x79ejtKpRzuySREREipWpAWjo0KGcOnWKV155hdjYWJo2bcqyZcuoWbMmYEx8+Nc5gSIiIpzbmzdvZvbs2dSsWZPDhw8D0LFjR+bOnctzzz3H888/T926dZk3bx7t2rUrsffljoa2DmfhlmN8vf04L/RvTKCvqX80REREipWp8wC5Kk+ZB+hSDoeDm95cw6GEFP49qLlzxXgRERF34RbzAIlrsVgsDG5dHYB5WhpDRERKOQUgcbq9VXVsVgubj5zhQPw5s8sREREpNgpA4lQp2I8bG1QEYP5vf5pcjYiISPFRAJJccuYEWrjlTzKy7CZXIyIiUjwUgCSXGxtWomKQLwnJ6Xy3O97sckRERIqFApDk4m2zMqhVdmfoTTF/c7SIiIh7UgCSywzJHg22Zt9J4hJTTa5GRESk6CkAyWXqVCxD21rlsTvgi80aEi8iIqWPApDkKWcixPm//YndrrkyRUSkdFEAkjz1bVaZMr5exJw+z8+HTpldjoiISJFSAJI8Bfh40b9FVQDmb9JlMBERKV0UgOSKhmZfBlv+exyJFzJMrkZERKToKADJFbWoHkLDykGkZdpZEn3M7HJERESKjAKQXJHFYnHODD1Xl8FERKQUUQCSq7o1oho+Nis7jyfx+7FEs8sREREpEgpAclXlAn3o0SQMgPm/qRVIRERKBwUg+VtDsy+DfbX1GKkZWSZXIyIiUngKQPK3rr8ulGpl/UlKzWTFzjizyxERESk0BSD5W1arhdsjcxZI1WUwERFxfwpAki+DW1fHYoENf5wi5tR5s8sREREpFAUgyZfq5QK4/rpQABZogVQREXFzCkCSbzkzQy/47U+ytECqiIi4MQUgybcejcMoF+BNXFIqa/edNLscERGRa6YAJPnm62VjYEQ1QJ2hRUTEvSkASYHkXAZbvfsECclpJlcjIiJybRSApEAaVg6mRfUQMu0OFm3RAqkiIuKeFICkwIZktwJ9sv4QW2POmFyNiIhIwSkASYHd0qIq1cr6E5uYym1TN/DSkp0kp2WaXZaIiEi+KQBJgQX5ebP0keu5rVU1HA6YseEwPd9aw3e7T5hdmoiISL4oAMk1KR/ow1tDWvLZ6LbUKB/A8cRURn/6G+NmbSH+XKrZ5YmIiFyVApAUSud6FVkxvgsP3FAHm9XCNzti6f7mGub8GoNdkyWKiIiLUgCSQvP3sTGhTyMWj+tEs2ohJKVmMmHhDu746Gf+OJlsdnkiIiKXUQCSItO0WgiLHurIczc3wt/bxq+HTtNn0jre+W4/6Zl2s8sTERFxUgCSIuVls3Jf5zqsfLwLN9SvSHqWnbdW7aPfu+vYfERD5kVExDUoAEmxCC8fwIx72jD5jpZUCPRh34lkbv9gA89/9TvnUjPMLk9ERDycApAUG4vFwoCW1VgddQO3R1bH4YDPfj5Cj7fWsmJnnNnliYiIB1MAkmJXLtCH/w5uwez72lGzQgBxSak88Nlmxn62mRNJGjIvIiIlTwFISkzH60JZMb4LD3Wti5fVwrc74+j+5ho+//mIhsyLiEiJUgCSEuXnbeOp3g1Z+sj1tKgewrm0TJ776neGTtvIgfhzZpcnIiIeQgFITNGoSjALH+rEi/0bE+BjY9PhM/SZvI63V+0jLTPL7PJERKSUUwAS09isFu7pVJtVUTdwU8NKZGQ5mPzdfvpOXsemw6fNLk9EREoxBSAxXbWy/nw8sjXv3RVBaBlf/jiZwuAPNvLPRTtIvKAh8yIiUvQUgMQlWCwW+jWvyndRN3BHm3AAZv8SQ4+31rB8RywOhzpJi4hI0VEAEpcSEuDN/w1qzpz721MnNJD4c2k8OGsLYz7bTGziBbPLExGRUkIBSFxSh7oVWPZYZx656Tq8rBZW7TpBj7fWMnPjYQ2ZFxGRQlMAEpfl523jHz0b8M2jnYmoUZbktExeWLyT2z/YwN44DZkXEZFrpwAkLq9B5SC+GNuRVwY0oYyvF1tiztLv3XW8uXIvqRkaMi8iIgWnACRuwWa1MKJDLVZFdaF7ozAyshy8+/0B+k5ex88HT5ldnoiIuBkFIHErVUL8+WhEJFOHtaJikC8HE1K4Y9rPPPPldhLPa8i8iIjkjwKQuB2LxUKfZlVYHXUDd7WrAcDcTUfp9tYavtmuIfMiIvL3FIDEbYX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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MSLE Loss')\n",
    "plt.legend(['loss', 'val_loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-20 20:48:07.427229: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:48:07.475611: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-10-20 20:48:07.543915: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "X_train_pred = model.predict(X_train)\n",
    "train_mae_loss = np.mean(np.abs(X_train_pred - X_train), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_mae_loss, bins=50)\n",
    "plt.xlabel(\"Train MAE loss\")\n",
    "plt.ylabel(\"No of samples\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We’ll pick a threshold of 0.65, as not much of the loss is larger than that. When the error is larger than that, we’ll declare that example an anomaly:\n",
    "THRESHOLD = 0.65\n",
    "# calc MAE on test data\n",
    "X_test_pred = model.predict(X_test)\n",
    "test_mae_loss = np.mean(np.abs(X_test_pred - X_test), axis=1)\n",
    "test_mae_loss = test_mae_loss.reshape((-1))\n",
    "\n",
    "plt.hist(test_mae_loss, bins=50)\n",
    "plt.xlabel(\"test MAE loss\")\n",
    "plt.ylabel(\"No of samples\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.781395</td>\n",
       "      <td>0.65</td>\n",
       "      <td>True</td>\n",
       "      <td>3.193456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.720457</td>\n",
       "      <td>0.65</td>\n",
       "      <td>True</td>\n",
       "      <td>3.234646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.737082</td>\n",
       "      <td>0.65</td>\n",
       "      <td>True</td>\n",
       "      <td>3.245897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.744951</td>\n",
       "      <td>0.65</td>\n",
       "      <td>True</td>\n",
       "      <td>3.201889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0.758562</td>\n",
       "      <td>0.65</td>\n",
       "      <td>True</td>\n",
       "      <td>3.233219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>0.759748</td>\n",
       "      <td>0.65</td>\n",
       "      <td>True</td>\n",
       "      <td>3.237088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss  threshold  anomaly     close\n",
       "305  0.656027       0.65     True  3.435135\n",
       "306  0.672940       0.65     True  3.431698\n",
       "307  0.686633       0.65     True  3.319282\n",
       "308  0.718965       0.65     True  3.106679\n",
       "309  0.781395       0.65     True  3.193456\n",
       "..        ...        ...      ...       ...\n",
       "405  0.720457       0.65     True  3.234646\n",
       "406  0.737082       0.65     True  3.245897\n",
       "407  0.744951       0.65     True  3.201889\n",
       "408  0.758562       0.65     True  3.233219\n",
       "409  0.759748       0.65     True  3.237088\n",
       "\n",
       "[76 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We’ll build a DataFrame containing the loss and the anomalies (values above the threshold):\n",
    "test_score_df = pd.DataFrame(index=test[TIME_STEPS:].index)\n",
    "test_score_df['loss'] = test_mae_loss\n",
    "test_score_df['threshold'] = THRESHOLD\n",
    "test_score_df['anomaly'] = test_score_df.loss > test_score_df.threshold\n",
    "test_score_df['close'] = test[TIME_STEPS:].close\n",
    "anomalies = test_score_df[test_score_df.anomaly == True]\n",
    "anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of anomaly samples:  76\n",
      "Indices of anomaly samples:  [305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322\n",
      " 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340\n",
      " 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358\n",
      " 359 361 362 363 364 365 387 388 389 390 398 399 400 401 402 403 404 405\n",
      " 406 407 408 409]\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of anomaly samples: \", len(anomalies))\n",
    "print(\"Indices of anomaly samples: \", np.array(anomalies.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have a thorough look at the chart. The red dots (anomalies) are covering most of the points with abrupt changes to the closing price. You can play around with the threshold and try to get even better results. You just combined two powerful concepts in Deep Learning - LSTMs and Autoencoders. The result is a model that can find anomalies in S&P 500 closing price data. You can try to tune the model and/or the threshold to get even better results."
   ]
  }
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