predictions_Variational_Autoencoder_Medellin_100features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "278be31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e9ee6e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from skimage import io\n",
    "from skimage.transform import resize\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76091306",
   "metadata": {},
   "source": [
    "## Read dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56902f7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of images is 165\n"
     ]
    }
   ],
   "source": [
    "# Main Directory\n",
    "main_dir = 'Dataset/5001'\n",
    "print(f'The total number of images is {len(os.listdir(main_dir))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5cf6071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image list top 5 examples:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Dataset/5001/image_2015-11-01.tiff',\n",
       " 'Dataset/5001/image_2015-11-08.tiff',\n",
       " 'Dataset/5001/image_2015-11-15.tiff',\n",
       " 'Dataset/5001/image_2015-11-22.tiff',\n",
       " 'Dataset/5001/image_2015-11-29.tiff']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify the img directory path\n",
    "path = main_dir\n",
    "\n",
    "# list files in img directory\n",
    "files = os.listdir(path)\n",
    "\n",
    "image_list = []\n",
    "\n",
    "for file in files:\n",
    "    # make sure file is an image\n",
    "    if file.endswith(('.jpg', '.png', 'jpeg', 'tiff')):\n",
    "        #img_path = path + file\n",
    "        image_list.append(os.path.join(path, file))\n",
    "        \n",
    "print(f'Image list top 5 examples:')\n",
    "image_list.sort(reverse=False)\n",
    "image_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e8ea37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_size = (740, 740, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6871c2d",
   "metadata": {},
   "source": [
    "# VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c542f30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Input, Flatten, Dense, Lambda, Reshape, MaxPooling2D, UpSampling2D\n",
    "#from keras.layers import BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras import backend as K\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51294fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "\n",
    "disable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46dbfdf9",
   "metadata": {},
   "source": [
    "## Encoder Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0612c971",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = Input(shape=target_size, name='encoder_input')\n",
    "\n",
    "encoder = Conv2D(32, (5,5), activation='relu')(input_data)\n",
    "encoder = MaxPooling2D((2,2))(encoder)\n",
    "\n",
    "encoder = Conv2D(64, (3,3), activation='relu')(encoder)\n",
    "encoder = MaxPooling2D((2,2))(encoder)\n",
    "\n",
    "encoder = Conv2D(64, (3,3), activation='relu')(encoder)\n",
    "encoder = MaxPooling2D((2,2))(encoder)\n",
    "\n",
    "conv_shape = K.int_shape(encoder) #Shape of conv to be provided to decoder\n",
    "\n",
    "encoder = Flatten()(encoder)\n",
    "encoder = Dense(200, activation='relu')(encoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d7d971",
   "metadata": {},
   "source": [
    "## Latent Distribution and Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5ba6b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_latent_features(distribution):\n",
    "    distribution_mean, distribution_variance = distribution\n",
    "    batch_size = tf.shape(distribution_variance)[0]\n",
    "    random = K.random_normal(shape=(batch_size, tf.shape(distribution_variance)[1]))\n",
    "    return distribution_mean + tf.exp(0.5 * distribution_variance) * random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57e28c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 100 # Number of latent dim parameters\n",
    "\n",
    "distribution_mean = Dense(latent_dim, name='mean')(encoder)\n",
    "distribution_variance = Dense(latent_dim, name='log_variance')(encoder)\n",
    "latent_encoding = Lambda(sample_latent_features)([distribution_mean, distribution_variance])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "deb46c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " encoder_input (InputLayer)     [(None, 740, 740, 1  0           []                               \n",
      "                                2)]                                                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 736, 736, 32  9632        ['encoder_input[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 368, 368, 32  0           ['conv2d[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 366, 366, 64  18496       ['max_pooling2d[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 183, 183, 64  0          ['conv2d_1[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 181, 181, 64  36928       ['max_pooling2d_1[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 90, 90, 64)  0           ['conv2d_2[0][0]']               \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 518400)       0           ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 200)          103680200   ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " mean (Dense)                   (None, 100)          20100       ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " log_variance (Dense)           (None, 100)          20100       ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " lambda (Lambda)                (None, 100)          0           ['mean[0][0]',                   \n",
      "                                                                  'log_variance[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 103,785,456\n",
      "Trainable params: 103,785,456\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder_model = Model(input_data, latent_encoding)\n",
    "encoder_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2756726f",
   "metadata": {},
   "source": [
    "## Decoder Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3da29d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder_input = Input(shape=(latent_dim), name='decoder_input')\n",
    "decoder = Dense(conv_shape[1]*conv_shape[2]*conv_shape[3])(decoder_input)\n",
    "\n",
    "decoder = Reshape((conv_shape[1], conv_shape[2], conv_shape[3]))(decoder)\n",
    "\n",
    "\"\"\"\n",
    "encoder = Conv2D(64, (5,5), activation='relu')(input_data)\n",
    "encoder = MaxPooling2D((2,2))(encoder)\n",
    "\n",
    "encoder = Conv2D(64, (3,3), activation='relu')(encoder)\n",
    "encoder = MaxPooling2D((2,2))(encoder)\n",
    "\n",
    "encoder = Conv2D(32, (3,3), activation='relu')(encoder)\n",
    "encoder = MaxPooling2D((2,2))(encoder)\n",
    "\"\"\"\n",
    "\n",
    "decoder = UpSampling2D((2,2))(decoder)\n",
    "decoder = Conv2DTranspose(64, (3,3), activation='relu')(decoder)\n",
    "\n",
    "decoder = UpSampling2D((2,2))(decoder)\n",
    "decoder = Conv2DTranspose(64, (3,3), activation='relu')(decoder)\n",
    "\n",
    "decoder = Conv2DTranspose(32, (3,3), activation='relu')(decoder)\n",
    "decoder = UpSampling2D((2,2))(decoder)\n",
    "\n",
    "decoder_output = Conv2DTranspose(target_size[2], (5,5), activation='relu')(decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63b31f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " decoder_input (InputLayer)  [(None, 100)]             0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 518400)            52358400  \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 90, 90, 64)        0         \n",
      "                                                                 \n",
      " up_sampling2d (UpSampling2D  (None, 180, 180, 64)     0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTra  (None, 182, 182, 64)     36928     \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " up_sampling2d_1 (UpSampling  (None, 364, 364, 64)     0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2DT  (None, 366, 366, 64)     36928     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " conv2d_transpose_2 (Conv2DT  (None, 368, 368, 32)     18464     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " up_sampling2d_2 (UpSampling  (None, 736, 736, 32)     0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_transpose_3 (Conv2DT  (None, 740, 740, 12)     9612      \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 52,460,332\n",
      "Trainable params: 52,460,332\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "decoder_model = Model(decoder_input, decoder_output)\n",
    "decoder_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5551685",
   "metadata": {},
   "source": [
    "## Combining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c3ea8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded = encoder_model(input_data)\n",
    "decoded = decoder_model(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "243f0c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Model(input_data, decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891b2bc2",
   "metadata": {},
   "source": [
    "## Loss Function (Reconstruction Loss + KL-loss)\n",
    "https://towardsdatascience.com/variational-autoencoders-as-generative-models-with-keras-e0c79415a7eb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46b437f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_loss(distribution_mean, distribution_variance):\n",
    "    \n",
    "    def get_reconstruction_loss(y_true, y_pred):\n",
    "        reconstruction_loss = keras.losses.mse(y_true, y_pred)\n",
    "        reconstruction_loss_batch = tf.reduce_mean(reconstruction_loss)\n",
    "        return reconstruction_loss_batch#*target_size[0]*target_size[1]\n",
    "    \n",
    "    def get_kl_loss(distribution_mean, distribution_variance):\n",
    "        kl_loss = 1 + distribution_variance - tf.square(distribution_mean) - tf.exp(distribution_variance)\n",
    "        kl_loss_batch = tf.reduce_mean(kl_loss)\n",
    "        #return kl_loss_batch*(-5e-4)\n",
    "        return kl_loss_batch*(-.5)\n",
    "    \n",
    "    def total_loss(y_true, y_pred):\n",
    "        reconstruction_loss_batch = get_reconstruction_loss(y_true, y_pred)\n",
    "        kl_loss_batch = get_kl_loss(distribution_mean, distribution_variance)\n",
    "        #return reconstruction_loss_batch + kl_loss_batch\n",
    "        return K.mean(reconstruction_loss_batch + kl_loss_batch)\n",
    "    \n",
    "    return total_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cea4ee",
   "metadata": {},
   "source": [
    "## Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9b5e24f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " encoder_input (InputLayer)  [(None, 740, 740, 12)]    0         \n",
      "                                                                 \n",
      " model (Functional)          (None, 100)               103785456 \n",
      "                                                                 \n",
      " model_1 (Functional)        (None, 740, 740, 12)      52460332  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 156,245,788\n",
      "Trainable params: 156,245,788\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Compile VAE\n",
    "autoencoder.compile(loss=get_loss(distribution_mean, distribution_variance), optimizer='adam')\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5664da3a",
   "metadata": {},
   "source": [
    "# Model of 100 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "532b28f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-05 10:51:23.284272: 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",
      "2022-06-05 10:51:23.284581: 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",
      "2022-06-05 10:51:23.300908: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-06-05 10:51:23.302016: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-06-05 10:51:23.340814: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-06-05 10:51:23.662002: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "autoencoder.load_weights('Models/vae.h5', by_name=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27b9528",
   "metadata": {},
   "source": [
    "# Generate embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "663b9b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " model (Functional)          (None, 100)               103785456 \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 103,785,456\n",
      "Trainable params: 103,785,456\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential()\n",
    "for layer in autoencoder.layers[:-1]: # just exclude last layer from copying\n",
    "    model.add(layer)\n",
    "    \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95e581ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_image(path):\n",
    "    image_test = io.imread(path)\n",
    "    image_test = resize(image_test, (target_size[0], target_size[1]),\n",
    "                           anti_aliasing=True)\n",
    "    image_test = np.expand_dims(image_test,axis=0)\n",
    "    return image_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e980775",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_embedding(image):\n",
    "    embaedding = model.predict(image)\n",
    "    embaedding = np.squeeze(embaedding, axis=0)\n",
    "    return embaedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ec6f329",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_name(path):\n",
    "    image_name = path[path.index('/image')+7:path.index('.tiff')]\n",
    "    return image_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f7c01bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-05 10:51:24.518691: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>[-0.77316415, 0.40942943, -1.9674243, -0.19426...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
       "      <td>[-0.37579188, 1.2821914, -1.7693113, -1.768404...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
       "      <td>[0.27006036, 1.8966693, 0.16080439, 0.10046702...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>[-1.0504739, -0.4924668, -3.0571184, -2.102062...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>[0.27709633, -0.17730333, 1.5202425, 1.1475053...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>[-0.2209534, 1.9359521, -2.9964356, -0.2979858...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2018-12-02</td>\n",
       "      <td>[1.3303635, -0.22096978, -2.2959766, -5.403095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>[-0.37533164, -1.1416743, -0.72453666, -0.6877...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2018-12-16</td>\n",
       "      <td>[-0.8458848, -3.2026246, -1.2519635, -0.646646...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>[0.1472063, 0.16249888, -2.2106123, 1.9016669,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                          Embedding\n",
       "0    2015-11-01  [-0.77316415, 0.40942943, -1.9674243, -0.19426...\n",
       "1    2015-11-08  [-0.37579188, 1.2821914, -1.7693113, -1.768404...\n",
       "2    2015-11-15  [0.27006036, 1.8966693, 0.16080439, 0.10046702...\n",
       "3    2015-11-22  [-1.0504739, -0.4924668, -3.0571184, -2.102062...\n",
       "4    2015-11-29  [0.27709633, -0.17730333, 1.5202425, 1.1475053...\n",
       "..          ...                                                ...\n",
       "160  2018-11-25  [-0.2209534, 1.9359521, -2.9964356, -0.2979858...\n",
       "161  2018-12-02  [1.3303635, -0.22096978, -2.2959766, -5.403095...\n",
       "162  2018-12-09  [-0.37533164, -1.1416743, -0.72453666, -0.6877...\n",
       "163  2018-12-16  [-0.8458848, -3.2026246, -1.2519635, -0.646646...\n",
       "164  2018-12-23  [0.1472063, 0.16249888, -2.2106123, 1.9016669,...\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings = pd.DataFrame(columns=['Date', 'Embedding'])\n",
    "\n",
    "for path in image_list:\n",
    "    image = read_image(path=path)\n",
    "    embedding = generate_embedding(image)\n",
    "    date = get_image_name(path=path)\n",
    "    embeddings = embeddings.append({'Date': date, 'Embedding': embedding}, ignore_index=True )\n",
    "\n",
    "embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e923b132",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_columns(df):\n",
    "    df_aux = pd.DataFrame(df['Embedding'].tolist())\n",
    "    df_aux = pd.concat( [df['Date'], df_aux], axis=1)\n",
    "    return df_aux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c631e38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>-0.773164</td>\n",
       "      <td>0.409429</td>\n",
       "      <td>-1.967424</td>\n",
       "      <td>-0.194265</td>\n",
       "      <td>1.589418</td>\n",
       "      <td>-0.806473</td>\n",
       "      <td>3.013131</td>\n",
       "      <td>-0.131107</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.858297</td>\n",
       "      <td>-1.143137</td>\n",
       "      <td>-0.012872</td>\n",
       "      <td>2.545450</td>\n",
       "      <td>-1.408913</td>\n",
       "      <td>2.835881</td>\n",
       "      <td>-4.650385</td>\n",
       "      <td>2.829885</td>\n",
       "      <td>-3.892387</td>\n",
       "      <td>-2.027068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
       "      <td>-0.062580</td>\n",
       "      <td>3.175248</td>\n",
       "      <td>-1.147624</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433321</td>\n",
       "      <td>-1.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060055</td>\n",
       "      <td>-0.819456</td>\n",
       "      <td>-0.657092</td>\n",
       "      <td>0.556108</td>\n",
       "      <td>3.519002</td>\n",
       "      <td>-1.769988</td>\n",
       "      <td>-2.901545</td>\n",
       "      <td>0.582610</td>\n",
       "      <td>3.461969</td>\n",
       "      <td>-1.540928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>-0.220953</td>\n",
       "      <td>1.935952</td>\n",
       "      <td>-2.996436</td>\n",
       "      <td>-0.297986</td>\n",
       "      <td>0.559725</td>\n",
       "      <td>-1.316638</td>\n",
       "      <td>0.127099</td>\n",
       "      <td>0.808687</td>\n",
       "      <td>-0.865521</td>\n",
       "      <td>...</td>\n",
       "      <td>1.097104</td>\n",
       "      <td>-1.500260</td>\n",
       "      <td>0.139145</td>\n",
       "      <td>0.247580</td>\n",
       "      <td>1.519686</td>\n",
       "      <td>0.246774</td>\n",
       "      <td>-1.867187</td>\n",
       "      <td>1.393556</td>\n",
       "      <td>3.392008</td>\n",
       "      <td>-0.358894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2018-12-02</td>\n",
       "      <td>1.330364</td>\n",
       "      <td>-0.220970</td>\n",
       "      <td>-2.295977</td>\n",
       "      <td>-5.403095</td>\n",
       "      <td>-0.123737</td>\n",
       "      <td>-1.548519</td>\n",
       "      <td>1.949868</td>\n",
       "      <td>0.354802</td>\n",
       "      <td>1.288355</td>\n",
       "      <td>...</td>\n",
       "      <td>1.961233</td>\n",
       "      <td>-2.454971</td>\n",
       "      <td>-0.320453</td>\n",
       "      <td>0.415422</td>\n",
       "      <td>4.667178</td>\n",
       "      <td>1.379886</td>\n",
       "      <td>-1.097326</td>\n",
       "      <td>2.208820</td>\n",
       "      <td>1.959872</td>\n",
       "      <td>-2.071136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>-0.375332</td>\n",
       "      <td>-1.141674</td>\n",
       "      <td>-0.724537</td>\n",
       "      <td>-0.687713</td>\n",
       "      <td>-1.182274</td>\n",
       "      <td>-0.982378</td>\n",
       "      <td>1.187518</td>\n",
       "      <td>-1.785588</td>\n",
       "      <td>-0.709939</td>\n",
       "      <td>...</td>\n",
       "      <td>1.029219</td>\n",
       "      <td>-2.047943</td>\n",
       "      <td>-0.039943</td>\n",
       "      <td>1.954097</td>\n",
       "      <td>7.794002</td>\n",
       "      <td>1.010624</td>\n",
       "      <td>-1.740980</td>\n",
       "      <td>0.549338</td>\n",
       "      <td>-0.777285</td>\n",
       "      <td>-1.311492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2018-12-16</td>\n",
       "      <td>-0.845885</td>\n",
       "      <td>-3.202625</td>\n",
       "      <td>-1.251963</td>\n",
       "      <td>-0.646646</td>\n",
       "      <td>-0.755027</td>\n",
       "      <td>-0.733298</td>\n",
       "      <td>-0.165185</td>\n",
       "      <td>-0.394726</td>\n",
       "      <td>-2.383777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.604045</td>\n",
       "      <td>-3.127710</td>\n",
       "      <td>-0.387775</td>\n",
       "      <td>2.545238</td>\n",
       "      <td>-4.183140</td>\n",
       "      <td>0.834891</td>\n",
       "      <td>-2.535359</td>\n",
       "      <td>2.277708</td>\n",
       "      <td>-0.649933</td>\n",
       "      <td>-2.139267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>0.147206</td>\n",
       "      <td>0.162499</td>\n",
       "      <td>-2.210612</td>\n",
       "      <td>1.901667</td>\n",
       "      <td>2.522794</td>\n",
       "      <td>-0.552164</td>\n",
       "      <td>-0.480894</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>-0.924787</td>\n",
       "      <td>...</td>\n",
       "      <td>1.647136</td>\n",
       "      <td>-2.825460</td>\n",
       "      <td>0.598019</td>\n",
       "      <td>-0.225615</td>\n",
       "      <td>2.385600</td>\n",
       "      <td>-1.427516</td>\n",
       "      <td>-0.931740</td>\n",
       "      <td>1.786462</td>\n",
       "      <td>1.470573</td>\n",
       "      <td>-0.983926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date         0         1         2         3         4         5  \\\n",
       "0    2015-11-01 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
       "1    2015-11-08 -0.375792  1.282191 -1.769311 -1.768404  0.960811 -1.401841   \n",
       "2    2015-11-15  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3    2015-11-22 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4    2015-11-29  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "..          ...       ...       ...       ...       ...       ...       ...   \n",
       "160  2018-11-25 -0.220953  1.935952 -2.996436 -0.297986  0.559725 -1.316638   \n",
       "161  2018-12-02  1.330364 -0.220970 -2.295977 -5.403095 -0.123737 -1.548519   \n",
       "162  2018-12-09 -0.375332 -1.141674 -0.724537 -0.687713 -1.182274 -0.982378   \n",
       "163  2018-12-16 -0.845885 -3.202625 -1.251963 -0.646646 -0.755027 -0.733298   \n",
       "164  2018-12-23  0.147206  0.162499 -2.210612  1.901667  2.522794 -0.552164   \n",
       "\n",
       "            6         7         8  ...        90        91        92  \\\n",
       "0    3.013131 -0.131107 -1.274766  ...  1.858297 -1.143137 -0.012872   \n",
       "1   -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012   \n",
       "2    0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531   \n",
       "3    0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104   \n",
       "4    1.057434  1.925118  0.395960  ...  1.060055 -0.819456 -0.657092   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "160  0.127099  0.808687 -0.865521  ...  1.097104 -1.500260  0.139145   \n",
       "161  1.949868  0.354802  1.288355  ...  1.961233 -2.454971 -0.320453   \n",
       "162  1.187518 -1.785588 -0.709939  ...  1.029219 -2.047943 -0.039943   \n",
       "163 -0.165185 -0.394726 -2.383777  ...  0.604045 -3.127710 -0.387775   \n",
       "164 -0.480894  0.900387 -0.924787  ...  1.647136 -2.825460  0.598019   \n",
       "\n",
       "           93        94        95        96        97        98        99  \n",
       "0    2.545450 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1   -0.062580  3.175248 -1.147624 -2.653756  2.155063 -1.433321 -1.319588  \n",
       "2    0.623669  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3   -1.629912  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4    0.556108  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "160  0.247580  1.519686  0.246774 -1.867187  1.393556  3.392008 -0.358894  \n",
       "161  0.415422  4.667178  1.379886 -1.097326  2.208820  1.959872 -2.071136  \n",
       "162  1.954097  7.794002  1.010624 -1.740980  0.549338 -0.777285 -1.311492  \n",
       "163  2.545238 -4.183140  0.834891 -2.535359  2.277708 -0.649933 -2.139267  \n",
       "164 -0.225615  2.385600 -1.427516 -0.931740  1.786462  1.470573 -0.983926  \n",
       "\n",
       "[165 rows x 101 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new df from the column of lists\n",
    "embeddings_df = split_columns(embeddings)\n",
    "\n",
    "embeddings_df.to_csv('Embeddings/embeddings_medellin_100features.csv',index=False)\n",
    "# display the resulting df\n",
    "embeddings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ce0737c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>-0.773164</td>\n",
       "      <td>0.409429</td>\n",
       "      <td>-1.967424</td>\n",
       "      <td>-0.194265</td>\n",
       "      <td>1.589418</td>\n",
       "      <td>-0.806473</td>\n",
       "      <td>3.013131</td>\n",
       "      <td>-0.131107</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.858297</td>\n",
       "      <td>-1.143137</td>\n",
       "      <td>-0.012872</td>\n",
       "      <td>2.545450</td>\n",
       "      <td>-1.408913</td>\n",
       "      <td>2.835881</td>\n",
       "      <td>-4.650385</td>\n",
       "      <td>2.829885</td>\n",
       "      <td>-3.892387</td>\n",
       "      <td>-2.027068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
       "      <td>-0.062580</td>\n",
       "      <td>3.175248</td>\n",
       "      <td>-1.147623</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433320</td>\n",
       "      <td>-1.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060054</td>\n",
       "      <td>-0.819456</td>\n",
       "      <td>-0.657092</td>\n",
       "      <td>0.556108</td>\n",
       "      <td>3.519002</td>\n",
       "      <td>-1.769988</td>\n",
       "      <td>-2.901545</td>\n",
       "      <td>0.582610</td>\n",
       "      <td>3.461969</td>\n",
       "      <td>-1.540928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>-0.220953</td>\n",
       "      <td>1.935952</td>\n",
       "      <td>-2.996436</td>\n",
       "      <td>-0.297986</td>\n",
       "      <td>0.559725</td>\n",
       "      <td>-1.316637</td>\n",
       "      <td>0.127099</td>\n",
       "      <td>0.808687</td>\n",
       "      <td>-0.865521</td>\n",
       "      <td>...</td>\n",
       "      <td>1.097104</td>\n",
       "      <td>-1.500260</td>\n",
       "      <td>0.139145</td>\n",
       "      <td>0.247580</td>\n",
       "      <td>1.519686</td>\n",
       "      <td>0.246774</td>\n",
       "      <td>-1.867187</td>\n",
       "      <td>1.393556</td>\n",
       "      <td>3.392008</td>\n",
       "      <td>-0.358893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2018-12-02</td>\n",
       "      <td>1.330364</td>\n",
       "      <td>-0.220970</td>\n",
       "      <td>-2.295977</td>\n",
       "      <td>-5.403095</td>\n",
       "      <td>-0.123737</td>\n",
       "      <td>-1.548519</td>\n",
       "      <td>1.949868</td>\n",
       "      <td>0.354802</td>\n",
       "      <td>1.288355</td>\n",
       "      <td>...</td>\n",
       "      <td>1.961233</td>\n",
       "      <td>-2.454971</td>\n",
       "      <td>-0.320453</td>\n",
       "      <td>0.415422</td>\n",
       "      <td>4.667178</td>\n",
       "      <td>1.379886</td>\n",
       "      <td>-1.097326</td>\n",
       "      <td>2.208820</td>\n",
       "      <td>1.959872</td>\n",
       "      <td>-2.071136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>-0.375332</td>\n",
       "      <td>-1.141674</td>\n",
       "      <td>-0.724537</td>\n",
       "      <td>-0.687713</td>\n",
       "      <td>-1.182274</td>\n",
       "      <td>-0.982379</td>\n",
       "      <td>1.187518</td>\n",
       "      <td>-1.785588</td>\n",
       "      <td>-0.709939</td>\n",
       "      <td>...</td>\n",
       "      <td>1.029219</td>\n",
       "      <td>-2.047943</td>\n",
       "      <td>-0.039943</td>\n",
       "      <td>1.954097</td>\n",
       "      <td>7.794002</td>\n",
       "      <td>1.010624</td>\n",
       "      <td>-1.740980</td>\n",
       "      <td>0.549338</td>\n",
       "      <td>-0.777285</td>\n",
       "      <td>-1.311492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2018-12-16</td>\n",
       "      <td>-0.845885</td>\n",
       "      <td>-3.202625</td>\n",
       "      <td>-1.251964</td>\n",
       "      <td>-0.646647</td>\n",
       "      <td>-0.755027</td>\n",
       "      <td>-0.733298</td>\n",
       "      <td>-0.165185</td>\n",
       "      <td>-0.394726</td>\n",
       "      <td>-2.383777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.604045</td>\n",
       "      <td>-3.127710</td>\n",
       "      <td>-0.387775</td>\n",
       "      <td>2.545238</td>\n",
       "      <td>-4.183140</td>\n",
       "      <td>0.834891</td>\n",
       "      <td>-2.535359</td>\n",
       "      <td>2.277708</td>\n",
       "      <td>-0.649933</td>\n",
       "      <td>-2.139267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>0.147206</td>\n",
       "      <td>0.162499</td>\n",
       "      <td>-2.210612</td>\n",
       "      <td>1.901667</td>\n",
       "      <td>2.522794</td>\n",
       "      <td>-0.552164</td>\n",
       "      <td>-0.480894</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>-0.924787</td>\n",
       "      <td>...</td>\n",
       "      <td>1.647136</td>\n",
       "      <td>-2.825460</td>\n",
       "      <td>0.598019</td>\n",
       "      <td>-0.225615</td>\n",
       "      <td>2.385600</td>\n",
       "      <td>-1.427517</td>\n",
       "      <td>-0.931740</td>\n",
       "      <td>1.786462</td>\n",
       "      <td>1.470573</td>\n",
       "      <td>-0.983927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date         0         1         2         3         4         5  \\\n",
       "0    2015-11-01 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
       "1    2015-11-08 -0.375792  1.282191 -1.769311 -1.768404  0.960811 -1.401841   \n",
       "2    2015-11-15  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3    2015-11-22 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4    2015-11-29  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "..          ...       ...       ...       ...       ...       ...       ...   \n",
       "160  2018-11-25 -0.220953  1.935952 -2.996436 -0.297986  0.559725 -1.316637   \n",
       "161  2018-12-02  1.330364 -0.220970 -2.295977 -5.403095 -0.123737 -1.548519   \n",
       "162  2018-12-09 -0.375332 -1.141674 -0.724537 -0.687713 -1.182274 -0.982379   \n",
       "163  2018-12-16 -0.845885 -3.202625 -1.251964 -0.646647 -0.755027 -0.733298   \n",
       "164  2018-12-23  0.147206  0.162499 -2.210612  1.901667  2.522794 -0.552164   \n",
       "\n",
       "            6         7         8  ...        90        91        92  \\\n",
       "0    3.013131 -0.131107 -1.274766  ...  1.858297 -1.143137 -0.012872   \n",
       "1   -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012   \n",
       "2    0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531   \n",
       "3    0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104   \n",
       "4    1.057434  1.925118  0.395960  ...  1.060054 -0.819456 -0.657092   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "160  0.127099  0.808687 -0.865521  ...  1.097104 -1.500260  0.139145   \n",
       "161  1.949868  0.354802  1.288355  ...  1.961233 -2.454971 -0.320453   \n",
       "162  1.187518 -1.785588 -0.709939  ...  1.029219 -2.047943 -0.039943   \n",
       "163 -0.165185 -0.394726 -2.383777  ...  0.604045 -3.127710 -0.387775   \n",
       "164 -0.480894  0.900387 -0.924787  ...  1.647136 -2.825460  0.598019   \n",
       "\n",
       "           93        94        95        96        97        98        99  \n",
       "0    2.545450 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1   -0.062580  3.175248 -1.147623 -2.653756  2.155063 -1.433320 -1.319588  \n",
       "2    0.623669  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3   -1.629912  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4    0.556108  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "160  0.247580  1.519686  0.246774 -1.867187  1.393556  3.392008 -0.358893  \n",
       "161  0.415422  4.667178  1.379886 -1.097326  2.208820  1.959872 -2.071136  \n",
       "162  1.954097  7.794002  1.010624 -1.740980  0.549338 -0.777285 -1.311492  \n",
       "163  2.545238 -4.183140  0.834891 -2.535359  2.277708 -0.649933 -2.139267  \n",
       "164 -0.225615  2.385600 -1.427517 -0.931740  1.786462  1.470573 -0.983927  \n",
       "\n",
       "[165 rows x 101 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('Embeddings/embeddings_medellin_100features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb3d1f1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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