Combined Input Source v1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d4218c1",
   "metadata": {},
   "source": [
    "# Training NN-model for tabular and image data\n",
    "In this notebook we test whether considering not only tabular but also image satellite data can improve the accuracy of predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7013a2ab",
   "metadata": {},
   "source": [
    "This notebook is in partially very similar to \"As in Paper v1.1.ipynb\". Therefore, it includes less comments (only differences between notebooks are commented). Please get first familiar with \"As in Paper v1.1.ipynb\" before starting with this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f304721e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras as K\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877fc4ce",
   "metadata": {},
   "source": [
    "Make shure, you use GPU for training. CPU is extremely slow (but also a good GPU might need many hours for some code in this nodebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b5591c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 3117593634670920738\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 14285799424\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 4111643706473280735\n",
      "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 3080 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d665d3ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sprin\\anaconda3\\envs\\ML\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: DtypeWarning: Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(69068, 106)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_and_input_OpenLandMap = pd.read_csv(\"Target_and_input.csv\")\n",
    "target_and_input_OpenLandMap = target_and_input_OpenLandMap[target_and_input_OpenLandMap.olc_id != \"5G55HPG4+MM7\"]\n",
    "target_and_input_OpenLandMap.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fd34bad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15478, 14)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_Sentinel_2 = pd.read_csv(\"pixels_df.csv\")\n",
    "input_Sentinel_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "71f689a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69068, 119)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = target_and_input_OpenLandMap.merge(input_Sentinel_2, on='olc_id', how='left')\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7ee65ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.dropna(subset=['oc'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4a37376f",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.drop(['uuid','confidence_degree','site_obsdate','source_db','layer_sequence.f','hzn_top', 'hzn_bot', 'n_tot', 'ph_h2o'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "520fff2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(all_data['oc'], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d63091f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data[all_data['oc'] < 120]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3149f9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(all_data['oc'], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ca69c3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, validate, test = \\\n",
    "              np.split(all_data.sample(frac=1, random_state=42), \n",
    "                       [int(.6*len(all_data)), int(.8*len(all_data))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fbc26804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55885, 110), (33531, 110), (11177, 110), (11177, 110))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape, train.shape, validate.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d813c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_targets = train[\"oc\"]\n",
    "validate_targets = validate[\"oc\"]\n",
    "test_targets = test[\"oc\"]\n",
    "\n",
    "train_targets = np.log(train_targets+1)\n",
    "validate_targets = np.log(validate_targets+1)\n",
    "test_targets = np.log(test_targets+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5f2035",
   "metadata": {},
   "source": [
    "Tabular Input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f94128ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs_1 = train.drop(['oc', 'olc_id'], axis=1)\n",
    "validate_inputs_1 = validate.drop(['oc', 'olc_id'], axis=1)\n",
    "test_inputs_1 = test.drop(['oc', 'olc_id'], axis=1)\n",
    "\n",
    "train_inputs_1.fillna(0, inplace = True)\n",
    "validate_inputs_1.fillna(0, inplace = True)\n",
    "test_inputs_1.fillna(0, inplace = True)\n",
    "\n",
    "min_values = train_inputs_1.min(axis=0)\n",
    "max_values = train_inputs_1.max(axis=0)\n",
    "\n",
    "train_inputs_1=(train_inputs_1-min_values)/(max_values-min_values)\n",
    "validate_inputs_1=(validate_inputs_1-min_values)/(max_values-min_values)\n",
    "test_inputs_1=(test_inputs_1-min_values)/(max_values-min_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b737cac0",
   "metadata": {},
   "source": [
    "For image input, we need olc_id as reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a8888619",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs_2 = train[\"olc_id\"]\n",
    "validate_inputs_2 = validate[\"olc_id\"]\n",
    "test_inputs_2 = test[\"olc_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8308601f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train_targets, train_inputs_2, train_inputs_1], axis=1).reset_index(drop=True)\n",
    "validate = pd.concat([validate_targets, validate_inputs_2, validate_inputs_1], axis=1).reset_index(drop=True)\n",
    "test = pd.concat([test_targets, test_inputs_2, test_inputs_1], axis=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a18572",
   "metadata": {},
   "source": [
    "I have 32GB RAM but it is not sufficient to store all images. So, I defined an custom data generation function that loads only one batch of images (batch size is 32) into the RAM during the training process. I.e., images are loaded iteratively during the training. In the end of an epoche, the data points are shuffeled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "051c5d0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomDataGen(tf.keras.utils.Sequence):\n",
    "    \n",
    "    def __init__(self, df,\n",
    "                 batch_size,\n",
    "                 shuffle = True):\n",
    "        \n",
    "        self.df = df.copy()\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        \n",
    "        self.n = len(self.df)\n",
    "        self.n_target = df['oc'].nunique()\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        if self.shuffle:\n",
    "            self.df = self.df.sample(frac=1).reset_index(drop=True)\n",
    "            \n",
    "    def __get_image_input(self, image):\n",
    "        \n",
    "        loaded_image = np.load(\"images/\" + image + \".npz\")[\"arr_0\"]\n",
    "        \n",
    "        return K.applications.resnet50.preprocess_input(loaded_image)\n",
    "    \n",
    "    def __get_data(self, batches):\n",
    "        # Generates data containing batch_size samples\n",
    "        \n",
    "        tabular_batch = batches.drop(['oc', 'olc_id'], axis=1)\n",
    "        \n",
    "        image_batch = batches[\"olc_id\"]\n",
    "        \n",
    "        target_batch = batches[\"oc\"]\n",
    "        \n",
    "        X1_batch = np.asarray(tabular_batch)\n",
    "        \n",
    "        X2_batch = np.asarray([self.__get_image_input(x) for x in image_batch])\n",
    "        \n",
    "        y_batch = np.asarray(target_batch)\n",
    "        \n",
    "        return [X1_batch, X2_batch], y_batch\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        \n",
    "        batches = self.df[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "        X, y = self.__get_data(batches)        \n",
    "        \n",
    "        return X, y\n",
    "    \n",
    "    def __len__(self):\n",
    "        \n",
    "        return self.n // self.batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c689bf00",
   "metadata": {},
   "source": [
    "Define the actual model. It consists of two branches, one for tabular and one for image data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e86b96",
   "metadata": {},
   "source": [
    "The first branch operates on the first (tabular) input and is basically the pretrained model from the previous experiment (\"As in Paper v1.1.ipynb\"). But the last batch normalization layer and the layer for final prediction (that with linear activation function) are removed. So, the output of this branch is a 64-dimensional vector. All the coefficients of this branch are not trainable. We call this branch x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8f90ff0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_1 False\n",
      "dense False\n",
      "batch_normalization False\n",
      "dense_1 False\n",
      "batch_normalization_1 False\n",
      "dense_2 False\n",
      "batch_normalization_2 False\n",
      "dense_3 False\n"
     ]
    }
   ],
   "source": [
    "x = K.models.load_model('model_as_in_paper_with_sentinel_regression.h5')\n",
    "\n",
    "x = K.Model(x.inputs, x.layers[-3].output)\n",
    "\n",
    "for layer in x.layers:\n",
    "    layer.trainable = False\n",
    "    print(layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3311bbd6",
   "metadata": {},
   "source": [
    "The second branch is the famous ResNet50 model pretrained on ImageNet data set. Only the last classification layer is removed. We do not re-train ResNet50 and keep all coefficients not trainable. After reduction of dimensionality (a trainable dense layer), the output of this branch is connected to the output of the first branch and is fed into the final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "10bee927",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_model = K.applications.ResNet50(include_top = False,\n",
    "                                  weights = \"imagenet\",\n",
    "                                  input_tensor = K.Input(shape=(224,224,3,)))\n",
    "\n",
    "for layer in res_model.layers[:175]:\n",
    "    layer.trainable = False \n",
    "    \n",
    "    \n",
    "input_2 = K.Input(shape=(224,224,3,))\n",
    "y = K.layers.RandomFlip(mode=\"horizontal_and_vertical\", seed=123)(input_2)\n",
    "y = res_model(y)\n",
    "y = K.layers.Flatten()(y)\n",
    "y = K.layers.Dense(32, activation=\"relu\")(y)\n",
    "y = K.layers.BatchNormalization()(y)\n",
    "y = K.Model(inputs=input_2, outputs=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "17051397",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine the output of the two branches\n",
    "combined = K.layers.concatenate([x.output, y.output])\n",
    "\n",
    "\n",
    "# apply a fully connected Dense layers and then a regression prediction on the\n",
    "# combined outputs\n",
    "z = K.layers.Dense(128, activation=\"relu\")(combined)\n",
    "z = K.layers.BatchNormalization()(z)\n",
    "z = K.layers.Dense(64, activation=\"relu\")(z)\n",
    "z = K.layers.BatchNormalization()(z)\n",
    "z = K.layers.Dense(32, activation=\"relu\")(z)\n",
    "z = K.layers.BatchNormalization()(z)\n",
    "z = K.layers.Dense(1, activation=\"linear\")(z)\n",
    "\n",
    "\n",
    "# the model accepts the inputs of the two branches and\n",
    "# then output a single value\n",
    "model = K.Model(inputs=[x.input, y.input], outputs=z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "74734378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_38\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 108)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 512)          55808       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 512)          2048        dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "input_26 (InputLayer)           [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          131328      batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "random_flip_12 (RandomFlip)     (None, 224, 224, 3)  0           input_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 256)          1024        dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "resnet50 (Functional)           (None, 7, 7, 2048)   23587712    random_flip_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          32896       batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_12 (Flatten)            (None, 100352)       0           resnet50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 128)          512         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_60 (Dense)                (None, 32)           3211296     flatten_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 64)           8256        batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 32)           128         dense_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_12 (Concatenate)    (None, 96)           0           dense_3[0][0]                    \n",
      "                                                                 batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_61 (Dense)                (None, 128)          12416       concatenate_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 128)          512         dense_61[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_62 (Dense)                (None, 64)           8256        batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 64)           256         dense_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_63 (Dense)                (None, 32)           2080        batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 32)           128         dense_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_64 (Dense)                (None, 1)            33          batch_normalization_51[0][0]     \n",
      "==================================================================================================\n",
      "Total params: 27,054,689\n",
      "Trainable params: 3,234,593\n",
      "Non-trainable params: 23,820,096\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770ca2bb",
   "metadata": {},
   "source": [
    "We again use early stopping, but now patience is 10 (instead of 30). Otherwise calcualtion time is too big."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "53c0d809",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_siz = 32\n",
    "num_epochs = 300\n",
    "callback = K.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d1b221fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mse',\n",
    "              optimizer=K.optimizers.Adam(learning_rate=0.0001, #decay=0.000001\n",
    "                                         )\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abfc396",
   "metadata": {},
   "source": [
    "Use custom data generation function from above to generate batches of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0e96e266",
   "metadata": {},
   "outputs": [],
   "source": [
    "traingen = CustomDataGen(train,\n",
    "                         batch_size=batch_siz)\n",
    "\n",
    "valgen = CustomDataGen(validate,\n",
    "                       batch_size=batch_siz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "10205d1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "1047/1047 [==============================] - 159s 149ms/step - loss: 2.7384 - val_loss: 2.1226\n",
      "Epoch 2/300\n",
      "1047/1047 [==============================] - 155s 148ms/step - loss: 0.7140 - val_loss: 0.9743\n",
      "Epoch 3/300\n",
      "1047/1047 [==============================] - 146s 140ms/step - loss: 0.2242 - val_loss: 0.3273\n",
      "Epoch 4/300\n",
      "1047/1047 [==============================] - 153s 146ms/step - loss: 0.1743 - val_loss: 0.2874\n",
      "Epoch 5/300\n",
      "1047/1047 [==============================] - 151s 144ms/step - loss: 0.1526 - val_loss: 0.3171\n",
      "Epoch 6/300\n",
      "1047/1047 [==============================] - 150s 143ms/step - loss: 0.1427 - val_loss: 0.2153\n",
      "Epoch 7/300\n",
      "1047/1047 [==============================] - 155s 148ms/step - loss: 0.1339 - val_loss: 0.2159\n",
      "Epoch 8/300\n",
      "1047/1047 [==============================] - 154s 147ms/step - loss: 0.1274 - val_loss: 0.2063\n",
      "Epoch 9/300\n",
      "1047/1047 [==============================] - 152s 145ms/step - loss: 0.1222 - val_loss: 0.2350\n",
      "Epoch 10/300\n",
      "1047/1047 [==============================] - 153s 146ms/step - loss: 0.1177 - val_loss: 0.2155\n",
      "Epoch 11/300\n",
      "1047/1047 [==============================] - 152s 145ms/step - loss: 0.1194 - val_loss: 0.2364\n",
      "Epoch 12/300\n",
      "1047/1047 [==============================] - 151s 144ms/step - loss: 0.1145 - val_loss: 0.2255\n",
      "Epoch 13/300\n",
      "1047/1047 [==============================] - 155s 148ms/step - loss: 0.1128 - val_loss: 0.2244\n",
      "Epoch 14/300\n",
      "1047/1047 [==============================] - 151s 144ms/step - loss: 0.1125 - val_loss: 0.2890\n",
      "Epoch 15/300\n",
      "1047/1047 [==============================] - 155s 148ms/step - loss: 0.1119 - val_loss: 0.2303\n",
      "Epoch 16/300\n",
      "1047/1047 [==============================] - 154s 147ms/step - loss: 0.1095 - val_loss: 0.2155\n",
      "Epoch 17/300\n",
      "1047/1047 [==============================] - 150s 144ms/step - loss: 0.1090 - val_loss: 0.2008\n",
      "Epoch 18/300\n",
      "1047/1047 [==============================] - 156s 149ms/step - loss: 0.1068 - val_loss: 0.2087\n",
      "Epoch 19/300\n",
      "1047/1047 [==============================] - 154s 147ms/step - loss: 0.1082 - val_loss: 0.2053\n",
      "Epoch 20/300\n",
      "1047/1047 [==============================] - 155s 148ms/step - loss: 0.1072 - val_loss: 0.2041\n",
      "Epoch 21/300\n",
      "1047/1047 [==============================] - 152s 145ms/step - loss: 0.1067 - val_loss: 0.2033\n",
      "Epoch 22/300\n",
      "1047/1047 [==============================] - 151s 144ms/step - loss: 0.1054 - val_loss: 0.2150\n",
      "Epoch 23/300\n",
      "1047/1047 [==============================] - 152s 145ms/step - loss: 0.1060 - val_loss: 0.2118\n",
      "Epoch 24/300\n",
      "1047/1047 [==============================] - 151s 144ms/step - loss: 0.1059 - val_loss: 0.1932\n",
      "Epoch 25/300\n",
      "1047/1047 [==============================] - 148s 141ms/step - loss: 0.1047 - val_loss: 0.2021\n",
      "Epoch 26/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1053 - val_loss: 0.2082\n",
      "Epoch 27/300\n",
      "1047/1047 [==============================] - 151s 144ms/step - loss: 0.1031 - val_loss: 0.2004\n",
      "Epoch 28/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1041 - val_loss: 0.2023\n",
      "Epoch 29/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1040 - val_loss: 0.2070\n",
      "Epoch 30/300\n",
      "1047/1047 [==============================] - 150s 143ms/step - loss: 0.1026 - val_loss: 0.2174\n",
      "Epoch 31/300\n",
      "1047/1047 [==============================] - 150s 143ms/step - loss: 0.1017 - val_loss: 0.2049\n",
      "Epoch 32/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1038 - val_loss: 0.1944\n",
      "Epoch 33/300\n",
      "1047/1047 [==============================] - 149s 143ms/step - loss: 0.1047 - val_loss: 0.1949\n",
      "Epoch 34/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1025 - val_loss: 0.1946\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(traingen,\n",
    "                    validation_data=valgen,\n",
    "                    epochs=num_epochs,\n",
    "                    callbacks=[callback]\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "469da62d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sprin\\anaconda3\\envs\\ML\\lib\\site-packages\\keras\\utils\\generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n"
     ]
    }
   ],
   "source": [
    "#model.save('model_with_images_regression.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f46c51c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o5syZnHHGGXTp0oVu3bpx6qmnVi1btGgRRx99NMOGDePhhx9m8eLFdcbz7rvvMmDAAA4++GAALrroIl5//fWq5RMmTADg8MMPr+rQqzazZs3iggsuAGruZvf2229n48aNtG/fnpEjR3L//fczdepUFi5cSH5+fp3bbogG3SlqZrmEZP6wu/85cXl8gnf3Z8zsTjPr7e4t8yRnldBFkuPVV2tf1qVL3ct79657eS1OO+00rrzySubOncuWLVs4/PDD+eijj7j11lt566236NGjB5MmTaKiia3YJk2axIwZMzjssMN44IEHeLUJMcaLdcHbnO53W6ub3Ya0cjHgPmCJu/+ylnX6ROthZkdE2y1tVmR1UQldJG3l5eUxfvx4Jk+eXFU637RpE127dqWgoIC1a9dWVcnU5phjjmHGjBls3bqV8vJynnrqqapl5eXl7LPPPlRWVlZ1eQuQn59PeXn5HtsaNGgQy5cvZ9myZQD88Y9/5Itf/GKT/rZUd7PbkBL6WOACYKGZzYvmXQfsD+DudwNnAVPMbAewFTjPW7Ibxy5doH17ldBF0tTEiRM544wzqqpeYt3NDh48mP3224+xY8fW+f4RI0Zw7rnncthhh7HXXnsxcuTIqmU/+clPGDVqFIWFhYwaNaoqiZ933nl885vf5Pbbb6+6GArQqVMn7r//fs4++2x27NjByJEjufTSS5v0d8WedTp8+HC6dOmyWze7r7zyCu3atWPo0KGccMIJTJs2jVtuuYXc3Fzy8vJ46KGHmvSZ8dKz+1wIp3vnngt33JG8oEQynLrPTS/Z0X0uhGoXldBFRKqkb0LXU4tERHaTvgldPS6KNEmqqlmlcZryPaVvQlef6CKN1qlTJ0pLS5XU2zh3p7S0lE6dOjXqfen5xCJQCV2kCfr168fKlStp1p3a0io6depEv379GvWe9E3oKqGLNFpubi4DBgxIdRjSQtK3yqV7dygvh507Ux2JiEibkN4JHWBTYrcyIiLZKX0TuvpEFxHZTfomdPXnIiKym/RN6OpxUURkN+mb0FVCFxHZTfomdJXQRUR2k74JXSV0EZHdpG9C79YtjFVCFxEB0jmht28PeXkqoYuIRNI3oYP6RBcRiZPeCV19oouIVEnvhK4eF0VEqqR3QlePiyIiVdI7oauELiJSJb0TukroIiJV0i+hL1kCF18MH35YXULX47RERNIwoW/cCPfeC0uXhhL6jh2wdWuqoxIRSbn0S+ixZ+ytWqXb/0VE4qRfQu/TB8x2T+iqRxcRScOEnpsbkvrKlXpqkYhInPRL6AADBsD27apyERGJU29CN7P9zOwVM3vHzBab2RU1rGNmdruZLTOzBWY2omXCjcyaBQ89pD7RRUTitG/AOjuA77n7XDPLB+aY2Yvu/k7cOicAB0XDKOCuaNwyzMJYJXQRkSr1ltDdfbW7z42my4ElQN+E1U4DHvLgH0B3M9sn6dHGPPssnHhiqE8HldBFRGhkHbqZ9QeKgX8mLOoLfBz3eiV7Jv3kWbcuJPUNG0JSVwldRKThCd3M8oAngO+6+6amfJiZXWxms81s9rp165qyiSCxLbpK6CIiDUvoZpZLSOYPu/ufa1hlFbBf3Ot+0bzduPs97l7i7iWFhYVNiTfoGxX+V61Sn+giIpGGtHIx4D5gibv/spbVngQujFq7jAbK3H11EuPcXXxCV4+LIiJAw1q5jAUuABaa2bxo3nXA/gDufjfwDHAisAzYAnw96ZHGy8uDYcOgQwf1uCgiEqk3obv7LMDqWceBy5IVVIMsWBDGb7wBq1vuZEBEJF2k552i8VRCFxEB0jmh/+pX8KUvqQ5dRCSSvgn9s8/gtddCffrnn4d+0UVEslj6JvS+fWHXLsjJCa9V7SIiWS59E3rs5qJYyVwJXUSyXPom9Fhb9G3bwlj16CKS5dI3oe+3Hxx1FPTqFV4roYtIlkvfhN6rF8ycCV/9anitKhcRyXLpm9Bj1Ce6iAjQsFv/265Jk2Dt2jCtErqIZLn0LqFv3w7vvx+mVUIXkSyX3gm9b9/Q42JenkroIpL10j+hV1RAt24qoYtI1kvvhB67uahLF5XQRSTrpXdCHzwYzjtPJXQREdI9oR96KDz6KOy7rxK6iGS99E7oMfn5qnIRkayX3u3QIVS7tGunErqIZL30L6Hn5YUOusrKwD3V0YiIpEz6J/R+/WDLFti5MzzoQkQkS2VGQt+0KUyrHl1Eslj6J/S+fUMJHVSPLiJZLf0T+tFHw9lnh2mV0EUki6V/K5ejjoLcXPjTn1RCF5Gslv4ldID20XFJCV1Eslj6J/TPP4eSkjCtKhcRyWLpn9C7doWCgjCtErqIZLH0T+gQmi6aqYQuIlktMxJ6376Qk6MSuohktXoTupn9wcw+NbNFtSwfZ2ZlZjYvGq5Pfpj16Ncv3PavErqIZLGGlNAfAI6vZ52Z7l4UDTc2P6xGOvfcUEpXCV1Esli9Cd3dXwc2tEIsTffVr8KgQUroIpLVklWHfqSZzTezZ81saG0rmdnFZjbbzGavW7cuSR8NbN8eLopuaNvHHRGRlpSMhD4XOMDdDwN+A8yobUV3v8fdS9y9pLCwMAkfHXnnHXjhBfj00+RtU0QkzTQ7obv7JnffHE0/A+SaWe9mR9YYffuGsbrPFZEs1uyEbmZ9zMyi6SOibZY2d7uN0rt3aLZYWRkGEZEsVG/nXGb2KDAO6G1mK4EbgFwAd78bOAuYYmY7gK3Aee6t/OggM+jeHUpLQ9PF3q17giAi0hbUm9DdfWI9y38L/DZpETVV794hoW/cqIQuIlkpM+4UBZgYHXd0c5GIZKnMSejjx4ex2qKLSJbKnIQeq7Zfuza1cYiIpEjmJPR33gnj999PbRwiIimSOQn9wAPDeOXK1MYhIpIimZPQDzoojNesSW0cIiIpkjkJPXa3qG7/F5EslTkJPTcX2rWDzz5LdSQiIimROQkdoH9/2H//VEchIpISmZXQ+/WDnTtTHYWISEpkVkJv1w4+/DDVUYiIpERmJfSNG+Hf/4YtW1IdiYhIq8ushB7rlGvVqtTGISKSApmV0PfeO4x1c5GIZKHMSuj77hvGy5alNg4RkRTIrIQea7KoC6MikoUyK6HHqlxOOSW1cYiIpEBmJfTu3cN4166UhiEikgqZmdCnT09pGCIiqZBZCb2gIIwffji1cYiIpEBmJfRYCX3jRtixI5WRiIi0usxK6LESurv6RReRrJNZCb1jx9CNLujmIhHJOpmV0AHy88NYCV1Eskz7VAeQdL17w1FHwYQJqY5ERKRVZV4JvUcP2LYtdKUrIpJFMi/rFRTA0qVw772pjkREpFVlXkLv3j08KFpt0UUky9Sb0M3sD2b2qZktqmW5mdntZrbMzBaY2Yjkh9kIBQWh2aIuiopIlmlICf0B4Pg6lp8AHBQNFwN3NT+sZujeHSorw0Mu3FMaiohIa6o3obv768CGOlY5DXjIg38A3c1sn2QF2GgFBeFB0RUVsKGusEVEMksy6tD7Ah/HvV4ZzUuN2O3/HTqEunQRkSzRqhdFzexiM5ttZrPXrVvXMh8Su/1/4UIYMqRlPkNEpA1KRkJfBewX97pfNG8P7n6Pu5e4e0lhYWESProGsRJ6WVnLbF9EpI1KRkJ/Ergwau0yGihz99VJ2G7TxBL6L34Bt9ySsjBERFpbvbf+m9mjwDigt5mtBG4AcgHc/W7gGeBEYBmwBfh6SwXbILEql0WL4KOP4Ac/SGk4IiKtpd6E7u4T61nuwGVJi6i5YiX0ffaBWbNCv+jtM6/LGhGRRJl3p2ishF5YGJouvvdeauMREWklmZfQ8/JCx1yxbnTffju18YiItJLMS+jt2kG3buFBF4MHh7tGRUSyQGZWLnfvDps2wZIlqY5ERKTVZF4JHUI9utqhi0iWycyE3r07bNwIzz4LBxwA//53qiMSEWlxmZvQy8rC+N//1oVREckKmZnQCwpCCX34cDCDefNSHZGISIvLzIQeq3Lp2hUOPlgldBHJCpmZ0AsKQiuXXbuguFgldBHJCpnbbNEdysvhlFOgZ8/w0IucnFRHJiLSYjIzocdu/y8rg699LQwiIhkuM6tcYh10bdwYxjt2VE+LiGSozEzo8SV0CE8u+s53UhePiEgryMyEnlhCHzxYLV1EJONldkL/7LMwLiqCpUth69ZURSQi0uIyM6H37Rt6W1y8OLwuLg6tXBYtSm1cIiItKDMTeufOMGoUvPJKeF1UFMaqdhGRDJaZCR1g/HiYMyfcYDRgANx8M4weneqoRERaTOYm9HHjwp2is2aF/lyuvTb07SIikqEyN6GPHg0dOlRXu5SVwUsvhbp0EZEMlLkJvUuXUI/+6qvh9YwZ8JWv6KHRIpKxMjehQ6hHnzs3lM6Li8M8ddQlIhkqsxN6fD36kCGhCkYtXUQkQ2V2Qo+vR8/NhUMPVUIXkYyV2Qm9c2c48sjqevTi4pDQ3VMalohIS8jshA6h2uXtt0O/LlddBS+8kOqIRERaRHYk9F27YOZMOOQQGDEitEsXEckwmZ/QR4+Gjh2rq10efhhefDGlIYmItIQGJXQzO97M3jWzZWZ2TQ3LJ5nZOjObFw3fSH6oTdSp0+716P/1X3DXXSkNSUSkJdSb0M0sB7gDOAE4BJhoZofUsOp0dy+Kht8nOc7midWjf/ZZ9YVREZEM05AS+hHAMnf/0N23A9OA01o2rCQbNy60bJk5MyT05cur+0oXEckQDUnofYGP416vjOYlOtPMFpjZ42a2X00bMrOLzWy2mc1et25dE8JtolGjQtXLq69Wd6U7f37rfb6ISCtI1kXRp4D+7j4ceBF4sKaV3P0edy9x95LCwsIkfXQDxNejx7oAiD38QkQkQzQkoa8C4kvc/aJ5Vdy91N23RS9/DxyenPCSaNy40I9Lbi588gl861upjkhEJKkaktDfAg4yswFm1gE4D3gyfgUz2yfu5anAkuSFmCTjx1fXo++zj9qii0jGqTehu/sO4NvA84RE/Zi7LzazG83s1Gi1y81ssZnNBy4HJrVUwE12xBHV9egzZ8L550NFRaqjEhFJGvMU9WtSUlLis2fPbt0P/fKXobQU/vM/4eyzYfZsOLzt1Q6JiNTGzOa4e0lNyzL/TtF448fDggXhGaOg9ugiklGyK6HH2qOvWAH5+UroIpJRsiuhjxwZutR97bXQHl1PLxKRDJJdCb1jRxgzJlwYHTUqtHRR3+gikiHapzqAVjd+fLgo+vLL0KtXqqMREUma7CqhQ6hHh1DtIiKSQbIvoY8cCV26hOeMHndc6BLgb3+DHTtSHZmISLNkX0Lv0AHGjg0l9NGjQzPGY4+FPn3gG98IbdMb4tNP4Ykn4IorwlOQDjxQpX4RSansS+gQql0WLoTvfAfWrQuJ+bjj4LHHqjvtWrMGZsyArVvD6xUr4Mm4Hg8uvBDOOgvuvRd69IB27eDRR1v7LxERqZJdd4rGvPlmKKU//jiceWb1/G3bQquXTp3gt78NCb9rV+jZEz7+OLSKKS0NCfzvfw+vR4wIpf6ystAkskMHWLkylPjbZ98152ZZvx7+53/Cvr344nDQFJHd6E7RRLF69Nhj6WI6dgzJHOCSS+CFF+CCC0JTx9/8JtyIVFAQlh95ZKiy6dAhvC4oCNNbtsAXvwgnnqiHaDRUeXl4NODAgfDLX4bqrH/+s3p57CxJROqUnUXI3Fw46qg9E3riOl/5Shgao0sX+NGP4NJLQ8J/6ik4+OBmhZvxfv1rmDoVJkyAn/wEhgyp7jht5kw44wz47nfh29+G7t1TGKhI25adJXQI9eiLFoU69GSbPDm0c9+wIdzA9OKLyf+MdFZZCb//fTgDglC19a9/hWsZhxwSqrI6dw7LCgrCgfHHP4b994drr4W1a1MXu0gblp116BDqaceMgT/9KVzcbAnLl8Opp4Y6+Fdeafk+2Ldvh7lzYdasMKxZA1OmwEUXwc6d8N57oTVObm7t2ygtDReGFy8OB7wbbww3YD3ySLiusPfesNdeYbz33qGeOz8fNm6EzZvDxeH4oVev8HdXVIQYnnoKrr8e3n8fJk2C++9v2N82bx7893+H76tfP/joI8jJCa2SevcO83TNQrJAXXXo2fsfUFICeXlwzTWhxPcf/1FdP54s/fvDG2+Ei61msHp1KLm7w65dYdyhA0ycGNZ/6aVwEOjWLSTC3r2hsBD23bfm7W/cGC4kHnhg+IzCwlAfDWHefnEPmvrww1D6bd8+LBs8OFRtTJwIw4bBc8/B178eDgIx+fnhbKNXr3B9oUsXWLYs/E3r14f4Y7H//Och4Sbavj0cQL73PbjzzjBv2LDQYujkkxu+L4uKYPr0UCXz3nshmUNoclpWFg4effvCAQfAueeG6hkIZ0ex+V27Nvzz2qJdu8J+LywMv6elS+GDD8JvqGPHMHToAMOHh/2zaVP4jpL9u26L3MNvPycnfM+lpfCXv4TrMR07hv+hffeFoUPD/1WGyt4SOsDTT4fS4ty5IVl97Wuh7rsl+kjftQu++tVwE1O8Hj1C1QyEFjd//vPuy/ffPzSZhJBcFy4MP8hPPgnT48aFgwTAL34RugYeMya0son32Wfw17+GJLBkSRiWLQtJcsKEUCK/5RY49NDwoz/00FDqre2sYufOkFz22ius849/hG3s3Bn+1tjwrW+FZPvSS+Gi8sCBoU68XRJq+9zDdlesCMO//x3GJ54IV18d/sG7datev2fPkNgvvzycHVRUhH3SsWM4OG7cGPbTySeH1kuLFoUDw2efhXVjifNnPwsHkvnzQ91/LJl27BgSyne+Ew6eCxbAgw+GebEzlpwc+OY3w/c6f344sLVrF/ZhbDx5ckjaTz0Fd98dDrJr1oSCx86dIUkVFoZrNTffvOd+2bIlVFldcQXcfnv4vQwaFK7lHHxw2Dft2oX9V9dZ48cfh8/asCEMZWVhWxMmhOXXXRf2d3l5ODsrLw+/vV//Oiy/9trwG+jRI1z76N49fP6IEWH5iy+GG/rihy98ISyvrIR77gl/y5Yt4cL4li3hmtYpp4R4zjsvJO5160Kc27bBr34Vrre8+24otCS6//7w3c+ZEx5yE0v0ffqEM84zzwy/0c2bw2fstVd1Q4k2QiX02px0Uhhmzw7/OI88Eup2S0pCYj/vvOSV6tq1C//cn39e/Y9rVl3SBPjd7+C220LJav36MMTbf//wj71uXfgBnnVWdVcGEErBtenRI7TYiVdZWd052dCh8MADDf97cnLCP0DM6NFhqM2xx4YhmczqvmjdqVOoeool+tg49g/64YfhQSeJ9tknJJXc3LB/+vcPCXL79pA0Yu/fvDmUkLdtqx527oRzzgkJ/aOPwne6a1f1gW7nTjjhhPBdvv12KFAkOvHE6rOtNWvCd11cHMZ9+oQDB8Bll8Hpp+/++du3Vy8/++xwUH7//XBW8+yzoRBzzTVh+cSJ8NZboRBQURGS4/77w/PPh+Wnnx4KO/GOOqo6oc+aFQoW+fnhbLewcPeCxBNPhP29fXv1vPhqtpNOCr/BeN/+dtj37tVnWRAOpl26hN/cKadUn4H06RPOSPbaK3z+MceE9QcODN93YWHYL598EoYhQ8Ly3NxwpvjJJ+GMc82asA+Ki8N7n3uu+reRnx+237Nn+B8eMiQUvG66afezbfdwgO7fHx56KBSwYgfM2P/7c8+FbbWQ7C6hJyorg//9X7jrrlDa7NYt1BFPnBh+NHl5qY5QkmnbtnDGsn17OOD16BGqJ1qrLj52FhOfFHbtCgeMZJzB1GTr1uoLznffHVp6LV9efb/FoEHw05+G5c8/H/ZRz55hKCgI/xP5+Y37zIqK6jOgzp3DWRKE+0FycsL+jg29eoUk7R4KLl26hPfEF3xagnsobHXoEIaPPgpn059+Goa1a8OZ2u23h7OM554L15TiC2dmYV7fvvB//1d94IolewjzmtkpYF0ldCX0mriHH9vdd4eLcNu2hfkDB4ajevxw0EG6GCcirUYJvTlKS8Mp2YIFoc564cJw+rpzZ1jesWM4BRsyJJwOxi5kxsax6Z49W67UJSJZQ3XozdGrV2h6eOqp1fMqKsJFxViCX7gwXBRcty7Uq9akXbuQ1Hv12n1c03RBQTitjZ3e5uXpYCAi9VJCb4pOncLFk+LiPZdVVFRf0Fy3Lgyx6fXrw5Xz0lJYtSocCEpLaz8IxMvP3zPJd+2659Cly+6vO3cOQ6wuMnG6U6dwtrFzZ2hlkDjesSPU68aaLca22dJ1miLSaEroydapU2hZ0K9fw9+zfXu44FJaGhL+pk1hKC/fczo23rw5HBQ+/3z3IbHVQEuJT/BduoShQ4fqi1uxi13xF71izffir/zHj+On4y82JV54ijUBbN8+tFaoa0h8b+IQa0oYv62aptu1qz7wxYZYq5X417HY4uNMHBI/v6aY4ps5xvZb/DgnJ6wb38Iifjr2OlFN+xr2bFqZOJ2Ts/u2a/o8992/l/hxQ88w47fbkLhj+6y1JF7AThzcq3/vsd9NK1JCbws6dKi+87K5Kiurk3t8+92tW6uH+NcVFXsm35qS8LZt1W2Ct2yp3n5sOnYwiZXuKyvDtmsq7UP1P2z8P278vMSEkTi9a1fYXmVl9aCHlLRd8Yk+J2f3ppyJCbEp244d5BIPgPGv6zrYQvVvqLZxU35fZnsWDtq3D/cqXHdd47dXDyX0TJObW30TR7Zx3zPJ11SKTCxl7tq1+0Ghpuldu/ZMGomJI3azTmJzxPhh5876S7qx14lnAvFnBLHtJd6UVFPJP37/1DYdH19iso19Zn1nFrGHrteVGCsrw7YSzzpqGhoad0POnGo6cCS+rinxxo/jz4zqOvOKFWhq+i3FxoMGJf/3jxK6ZJL40pBIFlLTCRGRDKGELiKSIRqU0M3seDN718yWmdk1NSzvaGbTo+X/NLP+SY9URETqVG9CN7Mc4A7gBOAQYKKZHZKw2v8DPnP3A4FfAf+T7EBFRKRuDSmhHwEsc/cP3X07MA04LWGd04AHo+nHgS+btWbjUBERaUhC7wt8HPd6ZTSvxnXcfQdQBjSvSzEREWmUVr0oamYXm9lsM5u9riWe5SkiksUaktBXAXHPMqNfNK/GdcysPVAAlCZuyN3vcfcSdy8pLCxsWsQiIlKjhtxY9BZwkJkNICTu84CvJazzJHAR8HfgLOBlr6df3jlz5qw3sxWNDxmA3sD6etdqm9I1dsXduhR360qnuA+obUG9Cd3dd5jZt4HngRzgD+6+2MxuBGa7+5PAfcAfzWwZsIGQ9OvbbpOL6GY2u7b+gNu6dI1dcbcuxd260jXuRA269d/dnwGeSZh3fdx0BVDDwxlFRKS16E5REZEMka4J/Z5UB9AM6Rq74m5dirt1pWvcu0nZM0VFRCS50rWELiIiCZTQRUQyRNol9Pp6fmyrzGy5mS00s3lmNjvV8dTGzP5gZp+a2aK4eT3N7EUzez8a90hljLWpJfapZrYq2u/zzOzEVMaYyMz2M7NXzOwdM1tsZldE89v0Pq8j7ja9vwHMrJOZ/cvM5kex/1c0f0DUW+yyqPfYDqmOtbHSqg496vnxPeArhD5l3gImuvs7KQ2sAcxsOVDi7m365gUzOwbYDDzk7odG834ObHD3n0UH0R7u/sNUxlmTWmKfCmx291tTGVttzGwfYB93n2tm+cAc4HRgEm14n9cR9zm04f0NEHUc2NXdN5tZLjALuAK4Cvizu08zs7uB+e5+Vypjbax0K6E3pOdHaQZ3f51wc1i8+N40HyT847Y5tcTeprn7anefG02XA0sInd216X1eR9xtngebo5e50eDAlwi9xUIb3OcNkW4JvSE9P7ZVDrxgZnPM7OJUB9NIe7v76mh6DbB3KoNpgm+b2YKoSqZNVV3Eix4MUwz8kzTa5wlxQxrsbzPLMbN5wKfAi8AHwMaot1hIr9xSJd0Sejo7yt1HEB4UcllUPZB2oj560qeeDu4CvgAUAauBX6Q0mlqYWR7wBPBdd98Uv6wt7/Ma4k6L/e3uO929iNDZ4BHA4NRGlBzpltAb0vNjm+Tuq6Lxp8BfCD+idLE2qjON1Z1+muJ4Gszd10b/vLuAe2mD+z2qx30CeNjd/xzNbvP7vKa402F/x3P3jcArwJFA96i3WEij3BIv3RJ6Vc+P0RXo8wg9PbZpZtY1unCEmXUFvgosqvtdbUqsN02i8f+lMJZGiSXFyBm0sf0eXaC7D1ji7r+MW9Sm93ltcbf1/Q1gZoVm1j2a7kxoZLGEkNjPilZrc/u8IdKqlQtA1AzqNqp7frwptRHVz8wGEkrlEDpEe6Stxm1mjwLjCN2JrgVuAGYAjwH7AyuAc9y9zV18rCX2cYTTfweWA5fE1U2nnJkdBcwEFgK7otnXEeqj2+w+ryPuibTh/Q1gZsMJFz1zCIXax9z9xuj/dBrQE3gb+A9335a6SBsv7RK6iIjULN2qXEREpBZK6CIiGUIJXUQkQyihi4hkCCV0EZEMoYQuIpIhlNBFRDLE/we3dwm+3HlJ4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#acc = history.history['accuracy']\n",
    "#val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(loss))\n",
    "\n",
    "#plt.plot(epochs, acc, 'b', label='Training acc')\n",
    "#plt.plot(epochs, val_acc, 'b--', label='Validation acc')\n",
    "#plt.title('Training and validation accuracy')\n",
    "#plt.legend()\n",
    "\n",
    "#plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r--', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b386379",
   "metadata": {},
   "source": [
    "Create a custom data generation function also for predictions. It is the same as function for training but does not shuffle data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a35bbc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomPredGen(tf.keras.utils.Sequence):\n",
    "    \n",
    "    def __init__(self, df,\n",
    "                 batch_size,\n",
    "                 shuffle = False):\n",
    "        \n",
    "        self.df = df.copy()\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        \n",
    "        self.n = len(self.df)\n",
    "        self.n_target = df['oc'].nunique()\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        if self.shuffle:\n",
    "            self.df = self.df.sample(frac=1).reset_index(drop=True)\n",
    "            \n",
    "    def __get_image_input(self, image):\n",
    "        \n",
    "        loaded_image = np.load(\"images/\" + image + \".npz\")[\"arr_0\"]\n",
    "        \n",
    "        return K.applications.resnet50.preprocess_input(loaded_image)\n",
    "    \n",
    "    def __get_data(self, batches):\n",
    "        # Generates data containing batch_size samples\n",
    "        \n",
    "        tabular_batch = batches.drop(['oc', 'olc_id'], axis=1)\n",
    "        \n",
    "        image_batch = batches[\"olc_id\"]\n",
    "        \n",
    "        target_batch = batches[\"oc\"]\n",
    "        \n",
    "        X1_batch = np.asarray(tabular_batch)\n",
    "        \n",
    "        X2_batch = np.asarray([self.__get_image_input(x) for x in image_batch])\n",
    "        \n",
    "        return [X1_batch, X2_batch]\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        \n",
    "        batches = self.df[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "        X = self.__get_data(batches)        \n",
    "        \n",
    "        return X\n",
    "    \n",
    "    def __len__(self):\n",
    "        \n",
    "        return self.n // self.batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fedee3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Required if model is loaded\n",
    "\n",
    "#batch_siz = 32\n",
    "#model = K.models.load_model('model_with_images_regression.h5')\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "22585b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions = pd.DataFrame(model.predict(CustomPredGen(train,batch_size=batch_siz)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "51acf5c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.DataFrame(train[\"oc\"][0:33504]) # since we lose a couple of points due to batches, modify also the original df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cf699ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions.clip(lower=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d104c0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28378329592571"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.square(train_predictions[0] - train_df[\"oc\"]).sum(axis=0)/train_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "696865fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.794114135625597"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.round(train_predictions[0]) == np.round(train_df[\"oc\"])).sum(axis=0)/train_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8268f6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(np.round(train_df), bins='auto', alpha=0.5)  # arguments are passed to np.histogram\n",
    "_ = plt.hist(np.round(train_predictions), bins='auto', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "81653252",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_predictions = pd.DataFrame(model.predict(CustomPredGen(validate,batch_size=batch_siz)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "23b6f5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_df = pd.DataFrame(validate[\"oc\"][0:11168])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f1946d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_predictions.clip(lower=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "be163b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4394987090645643"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.square(validate_predictions[0] - validate_df[\"oc\"]).sum(axis=0)/validate_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9c44cda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6943946991404012"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.round(validate_predictions[0]) == np.round(validate_df[\"oc\"])).sum(axis=0)/validate_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a430895b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(np.round(validate_df), bins='auto', alpha=0.5)  # arguments are passed to np.histogram\n",
    "_ = plt.hist(np.round(validate_predictions), bins='auto', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b25a8f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = pd.DataFrame(model.predict(CustomPredGen(test,batch_size=batch_siz)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8b81c5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.DataFrame(test[\"oc\"][0:11168])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9ef1c523",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions.clip(lower=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "758683e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43568125618742437"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.square(test_predictions[0] - test_df[\"oc\"]).sum(axis=0)/test_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ffe235d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7003939828080229"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.round(test_predictions[0]) == np.round(test_df[\"oc\"])).sum(axis=0)/test_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6639dafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qWulj/izwYeDPE+7HuBXw3SR726NpRmYlhr46kuTlwNeBD1bV7ybdn+VWVU9X1enM3c1+ZpIVO52X5O3AgaraO+m+TMBbq+oM4CLgsjaFOxIrMfR91EMn2rz214EvV9U3Jt2fcaqqx4HdwKYJd2U5nQO8o81vXw+cl+S/Jtul8aiq/e39APBN5qatR2Ilhr6PeuhA+1HzWuC+qvrMpPszDkmmkpzYll/G3MUKP5top5ZRVV1ZVWuraj1z/49vqap/nHC3ll2SE9rFCSQ5AdgIjOzKvBUX+lX1FHD4UQ/3ATcs86MeJi7JV4AfAm9Isi/J1kn3aQzOAd7L3NnfXe118aQ7tcxOAXYnuZu5k5uZqurmMsaOrAZ+kOTHwO3A/1TVd0Z18BV3yaYkaWEr7kxfkrQwQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR15P8BJXId4I4MmooAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(np.round(test_df), bins='auto', alpha=0.5)  # arguments are passed to np.histogram\n",
    "_ = plt.hist(np.round(test_predictions), bins='auto', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ca8b6a",
   "metadata": {},
   "source": [
    "## Summary for one model\n",
    " - RMSE\n",
    "   - Train: 0.28378329592571\n",
    "   - Valid: 0.4394987090645643\n",
    "   - Test: 0.43568125618742437\n",
    "\n",
    " - Accuracy (rounded in classes)\n",
    "   - Train: 0.794114135625597\n",
    "   - Valid: 0.6943946991404012\n",
    "   - Test: 0.7003939828080229"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ccc1b7",
   "metadata": {},
   "source": [
    "## Error Estimation\n",
    "Now, as already mentioned above we repeat the same process 10 times to estimate the error of our metrices. Thereby, different seeds are used to split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2debb288",
   "metadata": {},
   "outputs": [],
   "source": [
    "seeds = [24,3,123,7,666,5,10,1993,13,12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8adf9730",
   "metadata": {},
   "source": [
    "Before running the code below, make shure that classes CustomDataGen and CustomPredGen are loaded.\n",
    "ATTENTION: on my GPU, NVIDIA GeForce RTX 3080 with 16GB, this code needs ca 18-19 hours. On CPU, it might need factor 2-10 longer (I did not test it)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a9401dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "1047/1047 [==============================] - 240s 221ms/step - loss: 2.7055 - val_loss: 214.1637\n",
      "Epoch 2/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.7196 - val_loss: 0.6307\n",
      "Epoch 3/300\n",
      "1047/1047 [==============================] - 184s 176ms/step - loss: 0.2492 - val_loss: 0.3760\n",
      "Epoch 4/300\n",
      "1047/1047 [==============================] - 162s 155ms/step - loss: 0.1973 - val_loss: 0.3266\n",
      "Epoch 5/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1796 - val_loss: 0.2754\n",
      "Epoch 6/300\n",
      "1047/1047 [==============================] - 138s 131ms/step - loss: 0.1682 - val_loss: 0.7761\n",
      "Epoch 7/300\n",
      "1047/1047 [==============================] - 139s 132ms/step - loss: 0.1608 - val_loss: 0.3243\n",
      "Epoch 8/300\n",
      "1047/1047 [==============================] - 143s 136ms/step - loss: 0.1534 - val_loss: 0.5796\n",
      "Epoch 9/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1497 - val_loss: 0.2340\n",
      "Epoch 10/300\n",
      "1047/1047 [==============================] - 141s 135ms/step - loss: 0.1464 - val_loss: 0.2113\n",
      "Epoch 11/300\n",
      "1047/1047 [==============================] - 140s 133ms/step - loss: 0.1438 - val_loss: 0.2443\n",
      "Epoch 12/300\n",
      "1047/1047 [==============================] - 147s 141ms/step - loss: 0.1413 - val_loss: 0.2378\n",
      "Epoch 13/300\n",
      "1047/1047 [==============================] - 141s 135ms/step - loss: 0.1395 - val_loss: 0.2196\n",
      "Epoch 14/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.1381 - val_loss: 0.2863\n",
      "Epoch 15/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1369 - val_loss: 0.2099\n",
      "Epoch 16/300\n",
      "1047/1047 [==============================] - 141s 134ms/step - loss: 0.1350 - val_loss: 0.2030\n",
      "Epoch 17/300\n",
      "1047/1047 [==============================] - 138s 131ms/step - loss: 0.1357 - val_loss: 0.2066\n",
      "Epoch 18/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.1350 - val_loss: 0.1994\n",
      "Epoch 19/300\n",
      "1047/1047 [==============================] - 141s 135ms/step - loss: 0.1319 - val_loss: 0.2123\n",
      "Epoch 20/300\n",
      "1047/1047 [==============================] - 143s 136ms/step - loss: 0.1323 - val_loss: 0.2185\n",
      "Epoch 21/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.1337 - val_loss: 0.2068\n",
      "Epoch 22/300\n",
      "1047/1047 [==============================] - 139s 132ms/step - loss: 0.1325 - val_loss: 0.2034\n",
      "Epoch 23/300\n",
      "1047/1047 [==============================] - 139s 132ms/step - loss: 0.1313 - val_loss: 0.2092\n",
      "Epoch 24/300\n",
      "1047/1047 [==============================] - 143s 136ms/step - loss: 0.1294 - val_loss: 0.2329\n",
      "Epoch 25/300\n",
      "1047/1047 [==============================] - 140s 133ms/step - loss: 0.1300 - val_loss: 0.2027\n",
      "Epoch 26/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.1288 - val_loss: 0.2021\n",
      "Epoch 27/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1290 - val_loss: 0.2002\n",
      "Epoch 28/300\n",
      "1047/1047 [==============================] - 143s 136ms/step - loss: 0.1283 - val_loss: 0.2198\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sprin\\anaconda3\\envs\\ML\\lib\\site-packages\\keras\\utils\\generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "Epoch 1/300\n",
      "1047/1047 [==============================] - 142s 132ms/step - loss: 2.7397 - val_loss: 1.8735\n",
      "Epoch 2/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.7163 - val_loss: 0.4817\n",
      "Epoch 3/300\n",
      "1047/1047 [==============================] - 140s 134ms/step - loss: 0.2230 - val_loss: 0.5973\n",
      "Epoch 4/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1733 - val_loss: 0.2978\n",
      "Epoch 5/300\n",
      "1047/1047 [==============================] - 140s 133ms/step - loss: 0.1572 - val_loss: 0.3681\n",
      "Epoch 6/300\n",
      "1047/1047 [==============================] - 142s 136ms/step - loss: 0.1457 - val_loss: 0.2224\n",
      "Epoch 7/300\n",
      "1047/1047 [==============================] - 204s 195ms/step - loss: 0.1398 - val_loss: 0.4505\n",
      "Epoch 8/300\n",
      "1047/1047 [==============================] - 142s 135ms/step - loss: 0.1349 - val_loss: 0.4773\n",
      "Epoch 9/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1295 - val_loss: 0.2334\n",
      "Epoch 10/300\n",
      "1047/1047 [==============================] - 141s 134ms/step - loss: 0.1264 - val_loss: 0.2288\n",
      "Epoch 11/300\n",
      "1047/1047 [==============================] - 138s 131ms/step - loss: 0.1240 - val_loss: 0.2265\n",
      "Epoch 12/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1214 - val_loss: 0.2079\n",
      "Epoch 13/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1191 - val_loss: 0.2058\n",
      "Epoch 14/300\n",
      "1047/1047 [==============================] - 138s 131ms/step - loss: 0.1193 - val_loss: 1.8402\n",
      "Epoch 15/300\n",
      "1047/1047 [==============================] - 140s 134ms/step - loss: 0.1175 - val_loss: 0.2145\n",
      "Epoch 16/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1177 - val_loss: 0.1972\n",
      "Epoch 17/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1151 - val_loss: 0.3360\n",
      "Epoch 18/300\n",
      "1047/1047 [==============================] - 141s 135ms/step - loss: 0.1158 - val_loss: 0.2032\n",
      "Epoch 19/300\n",
      "1047/1047 [==============================] - 137s 131ms/step - loss: 0.1123 - val_loss: 0.2103\n",
      "Epoch 20/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1132 - val_loss: 0.2086\n",
      "Epoch 21/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1116 - val_loss: 0.1965\n",
      "Epoch 22/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.1123 - val_loss: 0.2043\n",
      "Epoch 23/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1110 - val_loss: 0.2081\n",
      "Epoch 24/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1102 - val_loss: 0.1981\n",
      "Epoch 25/300\n",
      "1047/1047 [==============================] - 140s 133ms/step - loss: 0.1095 - val_loss: 0.1983\n",
      "Epoch 26/300\n",
      "1047/1047 [==============================] - 142s 135ms/step - loss: 0.1093 - val_loss: 0.6362\n",
      "Epoch 27/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1094 - val_loss: 0.1970\n",
      "Epoch 28/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.1096 - val_loss: 0.1971\n",
      "Epoch 29/300\n",
      "1047/1047 [==============================] - 138s 132ms/step - loss: 0.1083 - val_loss: 0.1966\n",
      "Epoch 30/300\n",
      "1047/1047 [==============================] - 141s 135ms/step - loss: 0.1087 - val_loss: 0.1990\n",
      "Epoch 31/300\n",
      "1047/1047 [==============================] - 139s 132ms/step - loss: 0.1094 - val_loss: 0.2022\n",
      "3\n",
      "Epoch 1/300\n",
      "1047/1047 [==============================] - 153s 143ms/step - loss: 2.7215 - val_loss: 4.2578\n",
      "Epoch 2/300\n",
      "1047/1047 [==============================] - 146s 140ms/step - loss: 0.7033 - val_loss: 0.9768\n",
      "Epoch 3/300\n",
      "1047/1047 [==============================] - 139s 133ms/step - loss: 0.2346 - val_loss: 0.6202\n",
      "Epoch 4/300\n",
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      "123\n",
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      "Epoch 13/300\n",
      "1047/1047 [==============================] - 148s 141ms/step - loss: 0.1221 - val_loss: 0.2083\n",
      "Epoch 14/300\n",
      "1047/1047 [==============================] - 141s 135ms/step - loss: 0.1224 - val_loss: 0.2007\n",
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      "Epoch 21/300\n",
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      "Epoch 25/300\n",
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      "7\n",
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      "Epoch 26/300\n",
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      "Epoch 27/300\n",
      "1047/1047 [==============================] - 159s 152ms/step - loss: 0.1109 - val_loss: 0.2035\n",
      "Epoch 28/300\n",
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      "Epoch 31/300\n",
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      "Epoch 40/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1047/1047 [==============================] - 143s 137ms/step - loss: 0.1083 - val_loss: 0.2197\n",
      "Epoch 41/300\n",
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      "Epoch 44/300\n",
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      "666\n",
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      "Epoch 10/300\n",
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      "Epoch 13/300\n",
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      "Epoch 14/300\n",
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      "Epoch 21/300\n",
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      "5\n",
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      "Epoch 17/300\n",
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      "Epoch 18/300\n",
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      "Epoch 19/300\n",
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      "Epoch 20/300\n",
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      "Epoch 21/300\n",
      "1047/1047 [==============================] - 147s 140ms/step - loss: 0.1065 - val_loss: 0.2062\n",
      "Epoch 22/300\n",
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      "Epoch 23/300\n",
      "1047/1047 [==============================] - 150s 144ms/step - loss: 0.1054 - val_loss: 0.2064\n",
      "Epoch 24/300\n",
      "1047/1047 [==============================] - 147s 141ms/step - loss: 0.1045 - val_loss: 0.2045\n",
      "Epoch 25/300\n",
      "1047/1047 [==============================] - 147s 141ms/step - loss: 0.1038 - val_loss: 0.2127\n",
      "Epoch 26/300\n",
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      "Epoch 27/300\n",
      "1047/1047 [==============================] - 148s 141ms/step - loss: 0.1066 - val_loss: 0.2231\n",
      "Epoch 28/300\n",
      "1047/1047 [==============================] - 148s 141ms/step - loss: 0.1067 - val_loss: 0.2228\n",
      "Epoch 29/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1042 - val_loss: 0.1986\n",
      "Epoch 30/300\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1047/1047 [==============================] - 143s 137ms/step - loss: 0.1071 - val_loss: 0.1985\n",
      "Epoch 31/300\n",
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      "Epoch 33/300\n",
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      "Epoch 34/300\n",
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      "Epoch 35/300\n",
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      "Epoch 37/300\n",
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      "Epoch 39/300\n",
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      "Epoch 40/300\n",
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      "Epoch 41/300\n",
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      "Epoch 42/300\n",
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      "10\n",
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      "Epoch 9/300\n",
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      "Epoch 10/300\n",
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      "Epoch 11/300\n",
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      "Epoch 12/300\n",
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      "Epoch 13/300\n",
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      "Epoch 14/300\n",
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      "Epoch 17/300\n",
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      "1993\n",
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      "Epoch 20/300\n",
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      "Epoch 21/300\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1047/1047 [==============================] - 146s 139ms/step - loss: 0.1080 - val_loss: 0.1948\n",
      "Epoch 22/300\n",
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      "Epoch 29/300\n",
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      "Epoch 30/300\n",
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      "Epoch 31/300\n",
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      "Epoch 32/300\n",
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      "Epoch 33/300\n",
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      "Epoch 34/300\n",
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      "Epoch 36/300\n",
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      "Epoch 37/300\n",
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      "Epoch 38/300\n",
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      "Epoch 39/300\n",
      "1047/1047 [==============================] - 143s 137ms/step - loss: 0.1030 - val_loss: 0.1996\n",
      "Epoch 40/300\n",
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      "Epoch 41/300\n",
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      "Epoch 42/300\n",
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      "Epoch 43/300\n",
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      "Epoch 44/300\n",
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      "Epoch 45/300\n",
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      "Epoch 46/300\n",
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      "Epoch 47/300\n",
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      "Epoch 48/300\n",
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      "Epoch 50/300\n",
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      "Epoch 51/300\n",
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      "Epoch 52/300\n",
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      "Epoch 53/300\n",
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      "13\n",
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      "Epoch 3/300\n",
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      "Epoch 7/300\n",
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      "Epoch 17/300\n",
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      "Epoch 18/300\n",
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      "Epoch 19/300\n",
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      "Epoch 20/300\n",
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      "Epoch 21/300\n",
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      "Epoch 22/300\n",
      "1047/1047 [==============================] - 144s 138ms/step - loss: 0.1159 - val_loss: 0.1987\n",
      "Epoch 23/300\n",
      "1047/1047 [==============================] - 148s 142ms/step - loss: 0.1146 - val_loss: 0.2006\n",
      "Epoch 24/300\n",
      "1047/1047 [==============================] - 148s 142ms/step - loss: 0.1136 - val_loss: 0.1924\n",
      "Epoch 25/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1143 - val_loss: 0.2114\n",
      "Epoch 26/300\n",
      "1047/1047 [==============================] - 145s 138ms/step - loss: 0.1130 - val_loss: 0.2000\n",
      "Epoch 27/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1131 - val_loss: 0.2024\n",
      "Epoch 28/300\n",
      "1047/1047 [==============================] - 148s 142ms/step - loss: 0.1124 - val_loss: 0.2085\n",
      "Epoch 29/300\n",
      "1047/1047 [==============================] - 148s 142ms/step - loss: 0.1147 - val_loss: 0.1930\n",
      "Epoch 30/300\n",
      "1047/1047 [==============================] - 145s 138ms/step - loss: 0.1125 - val_loss: 0.1949\n",
      "Epoch 31/300\n",
      "1047/1047 [==============================] - 148s 141ms/step - loss: 0.1112 - val_loss: 0.2055\n",
      "Epoch 32/300\n",
      "1047/1047 [==============================] - 149s 142ms/step - loss: 0.1123 - val_loss: 0.2142\n",
      "Epoch 33/300\n",
      "1047/1047 [==============================] - 148s 141ms/step - loss: 0.1105 - val_loss: 0.2017\n",
      "Epoch 34/300\n",
      "1047/1047 [==============================] - 145s 138ms/step - loss: 0.1116 - val_loss: 0.2011\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "rmse_train = []\n",
    "rmse_valid = []\n",
    "rmse_test = []\n",
    "\n",
    "acc_train = []\n",
    "acc_valid = []\n",
    "acc_test = []\n",
    "\n",
    "for i in seeds:\n",
    "    target_and_input_OpenLandMap = pd.read_csv(\"Target_and_input.csv\")\n",
    "    target_and_input_OpenLandMap = target_and_input_OpenLandMap[target_and_input_OpenLandMap.olc_id != \"5G55HPG4+MM7\"]\n",
    "    input_Sentinel_2 = pd.read_csv(\"pixels_df.csv\")\n",
    "    all_data = target_and_input_OpenLandMap.merge(input_Sentinel_2, on='olc_id', how='left')\n",
    "    all_data.dropna(subset=['oc'], inplace=True)\n",
    "    all_data.drop(['uuid','confidence_degree','site_obsdate','source_db','layer_sequence.f','hzn_top', 'hzn_bot', 'n_tot', 'ph_h2o'],\n",
    "                  axis=1, inplace=True)\n",
    "    all_data = all_data[all_data['oc'] < 120] \n",
    "       \n",
    "    train, validate, test = \\\n",
    "              np.split(all_data.sample(frac=1, random_state=i), \n",
    "                       [int(.6*len(all_data)), int(.8*len(all_data))])\n",
    "    \n",
    "    train_targets = train[\"oc\"]\n",
    "    validate_targets = validate[\"oc\"]\n",
    "    test_targets = test[\"oc\"]\n",
    "    \n",
    "    train_targets = np.log(train_targets+1)\n",
    "    validate_targets = np.log(validate_targets+1)\n",
    "    test_targets = np.log(test_targets+1)\n",
    "    \n",
    "    train_inputs_1 = train.drop(['oc', 'olc_id'], axis=1)\n",
    "    validate_inputs_1 = validate.drop(['oc', 'olc_id'], axis=1)\n",
    "    test_inputs_1 = test.drop(['oc', 'olc_id'], axis=1)\n",
    "    \n",
    "    train_inputs_1.fillna(0, inplace = True)\n",
    "    validate_inputs_1.fillna(0, inplace = True)\n",
    "    test_inputs_1.fillna(0, inplace = True)\n",
    "    \n",
    "    min_values = train_inputs_1.min(axis=0)\n",
    "    max_values = train_inputs_1.max(axis=0)\n",
    "    \n",
    "    train_inputs_1=(train_inputs_1-min_values)/(max_values-min_values)\n",
    "    validate_inputs_1=(validate_inputs_1-min_values)/(max_values-min_values)\n",
    "    test_inputs_1=(test_inputs_1-min_values)/(max_values-min_values)\n",
    "    \n",
    "    train_inputs_2 = train[\"olc_id\"]\n",
    "    validate_inputs_2 = validate[\"olc_id\"]\n",
    "    test_inputs_2 = test[\"olc_id\"]\n",
    "    \n",
    "    train = pd.concat([train_targets, train_inputs_2, train_inputs_1], axis=1).reset_index(drop=True)\n",
    "    validate = pd.concat([validate_targets, validate_inputs_2, validate_inputs_1], axis=1).reset_index(drop=True)\n",
    "    test = pd.concat([test_targets, test_inputs_2, test_inputs_1], axis=1).reset_index(drop=True)\n",
    "    \n",
    "    #Model\n",
    "    x = K.models.load_model('Models_eval_as_in_paper/model_without_images_regression_seed_'+str(i)+'.h5')\n",
    "    x = K.Model(x.inputs, x.layers[-3].output)\n",
    "    \n",
    "    for layer in x.layers:\n",
    "        layer.trainable = False\n",
    "        \n",
    "    res_model = K.applications.ResNet50(include_top = False,\n",
    "                                  weights = \"imagenet\",\n",
    "                                  input_tensor = K.Input(shape=(224,224,3,)))\n",
    "    \n",
    "    for layer in res_model.layers[:175]:\n",
    "        layer.trainable = False \n",
    "        \n",
    "    input_2 = K.Input(shape=(224,224,3,))\n",
    "    y = K.layers.RandomFlip(mode=\"horizontal_and_vertical\", seed=123)(input_2)\n",
    "    y = res_model(y)\n",
    "    y = K.layers.Flatten()(y)\n",
    "    y = K.layers.Dense(32, activation=\"relu\")(y)\n",
    "    y = K.layers.BatchNormalization()(y)\n",
    "    y = K.Model(inputs=input_2, outputs=y)\n",
    "    \n",
    "    combined = K.layers.concatenate([x.output, y.output])\n",
    "    \n",
    "    z = K.layers.Dense(128, activation=\"relu\")(combined)\n",
    "    z = K.layers.BatchNormalization()(z)\n",
    "    z = K.layers.Dense(64, activation=\"relu\")(z)\n",
    "    z = K.layers.BatchNormalization()(z)\n",
    "    z = K.layers.Dense(32, activation=\"relu\")(z)\n",
    "    z = K.layers.BatchNormalization()(z)\n",
    "    z = K.layers.Dense(1, activation=\"linear\")(z)\n",
    "    \n",
    "    model = K.Model(inputs=[x.input, y.input], outputs=z)\n",
    "\n",
    "    \n",
    "    batch_siz = 32\n",
    "    num_epochs = 300\n",
    "    callback = K.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights = True)\n",
    "    \n",
    "    model.compile(loss='mse',\n",
    "              optimizer=K.optimizers.Adam(learning_rate=0.0001, #decay=0.000001\n",
    "                                         ),\n",
    "              #metrics=['accuracy']\n",
    "             )\n",
    "    \n",
    "    traingen = CustomDataGen(train,\n",
    "                             batch_size=batch_siz)\n",
    "    valgen = CustomDataGen(validate,\n",
    "                           batch_size=batch_siz)\n",
    "    \n",
    "    history = model.fit(traingen,\n",
    "                        validation_data=valgen,\n",
    "                        epochs=num_epochs,\n",
    "                        callbacks=[callback]\n",
    "                       )\n",
    "    \n",
    "    \n",
    "    train_predictions = pd.DataFrame(model.predict(CustomPredGen(train,batch_size=batch_siz)))\n",
    "    train_df = pd.DataFrame(train[\"oc\"][0:33504])\n",
    "    train_predictions.clip(lower=0, inplace=True)\n",
    "    \n",
    "    rmse_train.append(np.sqrt(np.square(train_predictions[0] - train_df[\"oc\"]).sum(axis=0)/train_df.shape[0]))\n",
    "    acc_train.append((np.round(train_predictions[0]) == np.round(train_df[\"oc\"])).sum(axis=0)/train_df.shape[0])\n",
    "    \n",
    "    validate_predictions = pd.DataFrame(model.predict(CustomPredGen(validate,batch_size=batch_siz)))\n",
    "    validate_df = pd.DataFrame(validate[\"oc\"][0:11168])\n",
    "    validate_predictions.clip(lower=0, inplace=True)\n",
    "    \n",
    "    rmse_valid.append(np.sqrt(np.square(validate_predictions[0] - validate_df[\"oc\"]).sum(axis=0)/validate_df.shape[0]))\n",
    "    acc_valid.append((np.round(validate_predictions[0]) == np.round(validate_df[\"oc\"])).sum(axis=0)/validate_df.shape[0])\n",
    "    \n",
    "    test_predictions = pd.DataFrame(model.predict(CustomPredGen(test,batch_size=batch_siz)))\n",
    "    test_df = pd.DataFrame(test[\"oc\"][0:11168])\n",
    "    test_predictions.clip(lower=0, inplace=True)\n",
    "    \n",
    "    rmse_test.append(np.sqrt(np.square(test_predictions[0] - test_df[\"oc\"]).sum(axis=0)/test_df.shape[0]))\n",
    "    acc_test.append((np.round(test_predictions[0]) == np.round(test_df[\"oc\"])).sum(axis=0)/test_df.shape[0])\n",
    "    \n",
    "    model.save('Models_eval_Combined_Input_Source/model_with_images_regression_seed_'+str(i)+'.h5')\n",
    "\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1bc4bcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.32131005005679175,\n",
       " 0.2914595003961931,\n",
       " 0.269508979274405,\n",
       " 0.29939520928942764,\n",
       " 0.2898124773527674,\n",
       " 0.28179312077333685,\n",
       " 0.28336765802273534,\n",
       " 0.28394492886447426,\n",
       " 0.27770242645861803,\n",
       " 0.29017155854041615]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90f286c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7673710601719198,\n",
       " 0.7895176695319962,\n",
       " 0.8054560649474689,\n",
       " 0.7822349570200573,\n",
       " 0.7913383476599809,\n",
       " 0.7970391595033429,\n",
       " 0.791248806112703,\n",
       " 0.7933978032473734,\n",
       " 0.7990687679083095,\n",
       " 0.7893982808022922]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "170ef627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4463641571968481,\n",
       " 0.443278121833637,\n",
       " 0.44356066824095064,\n",
       " 0.4399865104751898,\n",
       " 0.4411733241575992,\n",
       " 0.44070664333071485,\n",
       " 0.4446573458287871,\n",
       " 0.43619222681561026,\n",
       " 0.4365567746830732,\n",
       " 0.4386040789545298]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7686d745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6856196275071633,\n",
       " 0.6946633237822349,\n",
       " 0.6958273638968482,\n",
       " 0.6880372492836676,\n",
       " 0.6943051575931232,\n",
       " 0.6923352435530086,\n",
       " 0.6940365329512894,\n",
       " 0.6997671919770774,\n",
       " 0.6953796561604585,\n",
       " 0.6977077363896849]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55089a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.45767762502076276,\n",
       " 0.4392500846005559,\n",
       " 0.4349267904246865,\n",
       " 0.44188495324433874,\n",
       " 0.4371186210710677,\n",
       " 0.43234531889925565,\n",
       " 0.435803444488765,\n",
       " 0.4361156136290402,\n",
       " 0.4363038842526551,\n",
       " 0.448349641822445]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53ddea05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6823961318051576,\n",
       " 0.6883954154727794,\n",
       " 0.6993194842406877,\n",
       " 0.6852614613180515,\n",
       " 0.698871776504298,\n",
       " 0.6995881088825215,\n",
       " 0.6959169054441261,\n",
       " 0.6942156160458453,\n",
       " 0.6934992836676218,\n",
       " 0.6890222063037249]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0bd4d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_errors = pd.DataFrame({'seeds':seeds,\n",
    "                               'rmse_train': rmse_train, 'acc_train': acc_train,\n",
    "                              'rmse_valid': rmse_valid, 'acc_valid': acc_valid,\n",
    "                              'rmse_test': rmse_test, 'acc_test': acc_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7c1a2d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seeds</th>\n",
       "      <th>rmse_train</th>\n",
       "      <th>acc_train</th>\n",
       "      <th>rmse_valid</th>\n",
       "      <th>acc_valid</th>\n",
       "      <th>rmse_test</th>\n",
       "      <th>acc_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>0.321310</td>\n",
       "      <td>0.767371</td>\n",
       "      <td>0.446364</td>\n",
       "      <td>0.685620</td>\n",
       "      <td>0.457678</td>\n",
       "      <td>0.682396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.291460</td>\n",
       "      <td>0.789518</td>\n",
       "      <td>0.443278</td>\n",
       "      <td>0.694663</td>\n",
       "      <td>0.439250</td>\n",
       "      <td>0.688395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123</td>\n",
       "      <td>0.269509</td>\n",
       "      <td>0.805456</td>\n",
       "      <td>0.443561</td>\n",
       "      <td>0.695827</td>\n",
       "      <td>0.434927</td>\n",
       "      <td>0.699319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.299395</td>\n",
       "      <td>0.782235</td>\n",
       "      <td>0.439987</td>\n",
       "      <td>0.688037</td>\n",
       "      <td>0.441885</td>\n",
       "      <td>0.685261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>0.289812</td>\n",
       "      <td>0.791338</td>\n",
       "      <td>0.441173</td>\n",
       "      <td>0.694305</td>\n",
       "      <td>0.437119</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.281793</td>\n",
       "      <td>0.797039</td>\n",
       "      <td>0.440707</td>\n",
       "      <td>0.692335</td>\n",
       "      <td>0.432345</td>\n",
       "      <td>0.699588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>0.283368</td>\n",
       "      <td>0.791249</td>\n",
       "      <td>0.444657</td>\n",
       "      <td>0.694037</td>\n",
       "      <td>0.435803</td>\n",
       "      <td>0.695917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1993</td>\n",
       "      <td>0.283945</td>\n",
       "      <td>0.793398</td>\n",
       "      <td>0.436192</td>\n",
       "      <td>0.699767</td>\n",
       "      <td>0.436116</td>\n",
       "      <td>0.694216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "      <td>0.277702</td>\n",
       "      <td>0.799069</td>\n",
       "      <td>0.436557</td>\n",
       "      <td>0.695380</td>\n",
       "      <td>0.436304</td>\n",
       "      <td>0.693499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>0.290172</td>\n",
       "      <td>0.789398</td>\n",
       "      <td>0.438604</td>\n",
       "      <td>0.697708</td>\n",
       "      <td>0.448350</td>\n",
       "      <td>0.689022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seeds  rmse_train  acc_train  rmse_valid  acc_valid  rmse_test  acc_test\n",
       "0     24    0.321310   0.767371    0.446364   0.685620   0.457678  0.682396\n",
       "1      3    0.291460   0.789518    0.443278   0.694663   0.439250  0.688395\n",
       "2    123    0.269509   0.805456    0.443561   0.695827   0.434927  0.699319\n",
       "3      7    0.299395   0.782235    0.439987   0.688037   0.441885  0.685261\n",
       "4    666    0.289812   0.791338    0.441173   0.694305   0.437119  0.698872\n",
       "5      5    0.281793   0.797039    0.440707   0.692335   0.432345  0.699588\n",
       "6     10    0.283368   0.791249    0.444657   0.694037   0.435803  0.695917\n",
       "7   1993    0.283945   0.793398    0.436192   0.699767   0.436116  0.694216\n",
       "8     13    0.277702   0.799069    0.436557   0.695380   0.436304  0.693499\n",
       "9     12    0.290172   0.789398    0.438604   0.697708   0.448350  0.689022"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55c4b2e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seeds         3.000000\n",
       "rmse_train    0.269509\n",
       "acc_train     0.767371\n",
       "rmse_valid    0.436192\n",
       "acc_valid     0.685620\n",
       "rmse_test     0.432345\n",
       "acc_test      0.682396\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_errors.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21fc589c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seeds         1993.000000\n",
       "rmse_train       0.321310\n",
       "acc_train        0.805456\n",
       "rmse_valid       0.446364\n",
       "acc_valid        0.699767\n",
       "rmse_test        0.457678\n",
       "acc_test         0.699588\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_errors.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "934ed435",
   "metadata": {},
   "outputs": [],
   "source": [
    "#results_errors.to_csv('Combined_Input_Source_results_errors_reg.csv', index=False)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361665f7",
   "metadata": {},
   "source": [
    "Now, also add results from the oiginal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "83eaa164",
   "metadata": {},
   "outputs": [],
   "source": [
    "Combined_Input_Source_results_errors_reg = pd.read_csv(\"Combined_Input_Source_results_errors_reg.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "156d0087",
   "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>seeds</th>\n",
       "      <th>rmse_train</th>\n",
       "      <th>acc_train</th>\n",
       "      <th>rmse_valid</th>\n",
       "      <th>acc_valid</th>\n",
       "      <th>rmse_test</th>\n",
       "      <th>acc_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>0.321310</td>\n",
       "      <td>0.767371</td>\n",
       "      <td>0.446364</td>\n",
       "      <td>0.685620</td>\n",
       "      <td>0.457678</td>\n",
       "      <td>0.682396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.291460</td>\n",
       "      <td>0.789518</td>\n",
       "      <td>0.443278</td>\n",
       "      <td>0.694663</td>\n",
       "      <td>0.439250</td>\n",
       "      <td>0.688395</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123</td>\n",
       "      <td>0.269509</td>\n",
       "      <td>0.805456</td>\n",
       "      <td>0.443561</td>\n",
       "      <td>0.695827</td>\n",
       "      <td>0.434927</td>\n",
       "      <td>0.699319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.299395</td>\n",
       "      <td>0.782235</td>\n",
       "      <td>0.439987</td>\n",
       "      <td>0.688037</td>\n",
       "      <td>0.441885</td>\n",
       "      <td>0.685261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>0.289812</td>\n",
       "      <td>0.791338</td>\n",
       "      <td>0.441173</td>\n",
       "      <td>0.694305</td>\n",
       "      <td>0.437119</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.281793</td>\n",
       "      <td>0.797039</td>\n",
       "      <td>0.440707</td>\n",
       "      <td>0.692335</td>\n",
       "      <td>0.432345</td>\n",
       "      <td>0.699588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>0.283368</td>\n",
       "      <td>0.791249</td>\n",
       "      <td>0.444657</td>\n",
       "      <td>0.694037</td>\n",
       "      <td>0.435803</td>\n",
       "      <td>0.695917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1993</td>\n",
       "      <td>0.283945</td>\n",
       "      <td>0.793398</td>\n",
       "      <td>0.436192</td>\n",
       "      <td>0.699767</td>\n",
       "      <td>0.436116</td>\n",
       "      <td>0.694216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "      <td>0.277702</td>\n",
       "      <td>0.799069</td>\n",
       "      <td>0.436557</td>\n",
       "      <td>0.695380</td>\n",
       "      <td>0.436304</td>\n",
       "      <td>0.693499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>0.290172</td>\n",
       "      <td>0.789398</td>\n",
       "      <td>0.438604</td>\n",
       "      <td>0.697708</td>\n",
       "      <td>0.448350</td>\n",
       "      <td>0.689022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seeds  rmse_train  acc_train  rmse_valid  acc_valid  rmse_test  acc_test\n",
       "0     24    0.321310   0.767371    0.446364   0.685620   0.457678  0.682396\n",
       "1      3    0.291460   0.789518    0.443278   0.694663   0.439250  0.688395\n",
       "2    123    0.269509   0.805456    0.443561   0.695827   0.434927  0.699319\n",
       "3      7    0.299395   0.782235    0.439987   0.688037   0.441885  0.685261\n",
       "4    666    0.289812   0.791338    0.441173   0.694305   0.437119  0.698872\n",
       "5      5    0.281793   0.797039    0.440707   0.692335   0.432345  0.699588\n",
       "6     10    0.283368   0.791249    0.444657   0.694037   0.435803  0.695917\n",
       "7   1993    0.283945   0.793398    0.436192   0.699767   0.436116  0.694216\n",
       "8     13    0.277702   0.799069    0.436557   0.695380   0.436304  0.693499\n",
       "9     12    0.290172   0.789398    0.438604   0.697708   0.448350  0.689022"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combined_Input_Source_results_errors_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b8ba7074",
   "metadata": {},
   "outputs": [],
   "source": [
    "Combined_Input_Source_results_errors_reg = Combined_Input_Source_results_errors_reg.append({'seeds': 42, 'rmse_train': 0.28378329592571, 'acc_train': 0.794114135625597,\n",
    "                                   'rmse_valid': 0.4394987090645643, 'acc_valid': 0.6943946991404012,\n",
    "                                   'rmse_test': 0.43568125618742437, 'acc_test': 0.7003939828080229}, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3aa5b9d0",
   "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>seeds</th>\n",
       "      <th>rmse_train</th>\n",
       "      <th>acc_train</th>\n",
       "      <th>rmse_valid</th>\n",
       "      <th>acc_valid</th>\n",
       "      <th>rmse_test</th>\n",
       "      <th>acc_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.321310</td>\n",
       "      <td>0.767371</td>\n",
       "      <td>0.446364</td>\n",
       "      <td>0.685620</td>\n",
       "      <td>0.457678</td>\n",
       "      <td>0.682396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.291460</td>\n",
       "      <td>0.789518</td>\n",
       "      <td>0.443278</td>\n",
       "      <td>0.694663</td>\n",
       "      <td>0.439250</td>\n",
       "      <td>0.688395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123.0</td>\n",
       "      <td>0.269509</td>\n",
       "      <td>0.805456</td>\n",
       "      <td>0.443561</td>\n",
       "      <td>0.695827</td>\n",
       "      <td>0.434927</td>\n",
       "      <td>0.699319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.299395</td>\n",
       "      <td>0.782235</td>\n",
       "      <td>0.439987</td>\n",
       "      <td>0.688037</td>\n",
       "      <td>0.441885</td>\n",
       "      <td>0.685261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666.0</td>\n",
       "      <td>0.289812</td>\n",
       "      <td>0.791338</td>\n",
       "      <td>0.441173</td>\n",
       "      <td>0.694305</td>\n",
       "      <td>0.437119</td>\n",
       "      <td>0.698872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.281793</td>\n",
       "      <td>0.797039</td>\n",
       "      <td>0.440707</td>\n",
       "      <td>0.692335</td>\n",
       "      <td>0.432345</td>\n",
       "      <td>0.699588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.283368</td>\n",
       "      <td>0.791249</td>\n",
       "      <td>0.444657</td>\n",
       "      <td>0.694037</td>\n",
       "      <td>0.435803</td>\n",
       "      <td>0.695917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1993.0</td>\n",
       "      <td>0.283945</td>\n",
       "      <td>0.793398</td>\n",
       "      <td>0.436192</td>\n",
       "      <td>0.699767</td>\n",
       "      <td>0.436116</td>\n",
       "      <td>0.694216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.277702</td>\n",
       "      <td>0.799069</td>\n",
       "      <td>0.436557</td>\n",
       "      <td>0.695380</td>\n",
       "      <td>0.436304</td>\n",
       "      <td>0.693499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.290172</td>\n",
       "      <td>0.789398</td>\n",
       "      <td>0.438604</td>\n",
       "      <td>0.697708</td>\n",
       "      <td>0.448350</td>\n",
       "      <td>0.689022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.283783</td>\n",
       "      <td>0.794114</td>\n",
       "      <td>0.439499</td>\n",
       "      <td>0.694395</td>\n",
       "      <td>0.435681</td>\n",
       "      <td>0.700394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     seeds  rmse_train  acc_train  rmse_valid  acc_valid  rmse_test  acc_test\n",
       "0     24.0    0.321310   0.767371    0.446364   0.685620   0.457678  0.682396\n",
       "1      3.0    0.291460   0.789518    0.443278   0.694663   0.439250  0.688395\n",
       "2    123.0    0.269509   0.805456    0.443561   0.695827   0.434927  0.699319\n",
       "3      7.0    0.299395   0.782235    0.439987   0.688037   0.441885  0.685261\n",
       "4    666.0    0.289812   0.791338    0.441173   0.694305   0.437119  0.698872\n",
       "5      5.0    0.281793   0.797039    0.440707   0.692335   0.432345  0.699588\n",
       "6     10.0    0.283368   0.791249    0.444657   0.694037   0.435803  0.695917\n",
       "7   1993.0    0.283945   0.793398    0.436192   0.699767   0.436116  0.694216\n",
       "8     13.0    0.277702   0.799069    0.436557   0.695380   0.436304  0.693499\n",
       "9     12.0    0.290172   0.789398    0.438604   0.697708   0.448350  0.689022\n",
       "10    42.0    0.283783   0.794114    0.439499   0.694395   0.435681  0.700394"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combined_Input_Source_results_errors_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2b916411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot = Combined_Input_Source_results_errors_reg.boxplot(column=[\"rmse_test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7a2ac328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot = Combined_Input_Source_results_errors_reg.boxplot(column=[\"acc_test\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b555aba",
   "metadata": {},
   "source": [
    "Plot together with results from \"As in Paper v1.1.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7c4a7a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "as_paper_results_errors_reg = pd.read_csv(\"as_paper_results_errors_reg.csv\")\n",
    "as_paper_results_errors_reg = as_paper_results_errors_reg.append({'seeds': 42, 'rmse_train': 0.290209, 'acc_train': 0.789836,\n",
    "                                   'rmse_valid': 0.444209, 'acc_valid': 0.692494,\n",
    "                                   'rmse_test': 0.439565, 'acc_test': 0.699472}, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "65c5042b",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_compare = pd.DataFrame({\"rmse_only_tabular\":as_paper_results_errors_reg['rmse_test'],\n",
    "                          \"acc_only_tabular\":as_paper_results_errors_reg['acc_test'],\n",
    "                          \"rmse_with_images\":Combined_Input_Source_results_errors_reg['rmse_test'],\n",
    "                          \"acc_with_Images\":Combined_Input_Source_results_errors_reg['acc_test']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c2f76cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lAbAfWFYyf3oqG7UEaAYK6U3+FKBP0mqKR/VXSfoscBJwRNJBYNsk2zQzs6OskgDYCqyQtJzim/TVwEdHF0bEAWDp6LykAnBj+hTQxSXltwLDEXGXpPqJtmkzZ6IzL20Yfz3fd91s/pt0EDgiRoDrgC3AEPC1iNgpaX06yq/aeNucyrZsYuP9qEZ/f/+4y/zmb5aHisYAImIzsHlM2S3j1G0bp/zWybZpZmazxzeDMzPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDoDM9Pb20tzczKpVq2hubqa3t7fWTTKzGpn2bwLbsaO3t5euri56eno4fPgwdXV1dHZ2AtDR0VHj1pnZbPMZQEa6u7vp6emhvb2d+vp62tvb6enpobu7u9ZNM7MacABkZGhoiNbW1reVtba2MjQ0VKMWmVktOQAy0tTUxMDAwNvKBgYGaGpqqlGLzKyWHAAZ6erqorOzk/7+fkZGRujv76ezs5Ourq5aN83MasCDwBkZHehdu3YtQ0NDNDU10d3d7QFgO6qWNK3jnHvXVb/ivdU8B8CHq3+OzDkAMtPR0UFHRweFQoG2trZaN8cy8NrQ7ey9vbo352r7Z+O6TVW2ysCXgMzMsuUAMDPLlAPAzCxTHgMws6NuStfoH618nRNPWFj99s0BYGZHV7UDwFAMjKmsZ9Wp6BKQpMskvShpt6RxP88l6UpJIaklza+UtD09npP0kZK6eyXtSMsGp78rZmZWjUnPACTVAXcDlwD7gK2S+iJi15h6S4AbgKdLil8AWiJiRNKpwHOS/iEiRtLy9oh4dSZ2xMzMqlPJGcBKYHdE7ImIN4H7gcvL1LsN2AAcHC2IiJ+VvNkfD8Q022tmZjOkkjGA04CXS+b3AReUVpB0HrAsIjZJ+uSYZRcA9wBnAB8vCYQAHpMUwN9GxMZyTy5pDbAGoKGhgUKhUEGTbTLDw8N+LW1Oc/88+qY9CCxpAXAHcE255RHxNHC2pCbgXkmPRMRBoDUi9kt6D/C4pO9HxLfLrL8R2AjQ0tIS/vbqzPA3gW1Oe3ST++csqOQS0H5gWcn86als1BKgGShI2gtcCPSNDgSPioghYDjVJSL2p3//BXiQ4qUmMzObJZUEwFZghaTlko4Drgb6RhdGxIGIWBoRjRHRCDwFrI6IwbROPYCkM4Azgb2SFqVBYyQtAi6lOGBsZmazZNJLQOkTPNcBW4A64J6I2ClpPTAYEX0TrN4KrJN0CDgCXBsRr0p6H/CgpNE2fDUiHp3uzpiZWeUqGgOIiM3A5jFlt4xTt61k+svAl8vU2QOcW01DzcxsZvleQGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmZr2L4KZmU1VuiV8+WUbypdH+KfFZ4rPAMysZiKi7KO/v3/cZTZzHABmZplyAJiZZcoBYGaWKQeAmVmmHABmZplyAJiZZcoBYGaWKQeAmVmmdCx9sULS/wV+WOt2zBNLgVdr3Qizcbh/zqwzIuLksYXHVADYzJE0GBEttW6HWTnun7PDl4DMzDLlADAzy5QDIF8ba90Aswm4f84CjwGYmWXKZwBmZplyAJiZZcoBYGaWKQdARiR9UdJVU1jv5grrDVe53TZJD1fbHjv2Sdos6aT0uLakvKo+IekLks46Oq2c/xwAM0xF8+11rSgAjjZJ/g3raZhLfTMifi8i/h9wEnDtxLUn3M5/iIhdM9Wu3MyJznCsk9Qo6UVJXwKGgZfS0fb/kvQVSb8j6TuS/reklWmd35K0PT2elbQklX9S0lZJz0v69CTP+58lvZAef1rSliFJn5e0U9Jjkk4Ys95vS3qoZP4SSQ+O8xy3Ayekdn4llT0kaVva/pox9e9M5d+UdHIqK0hqSdNLJe0t8zwrJT2ZXovvSvq1VH6NpD5J/xP45kSvh71TLfpmqnd9mr4z/d+N9rvRPrRX0lLgduBX0nP9RdrEYknfkPT91MZxfzl+TN8alvQXqf/9Y+pTBUl7JK0ueT2ekPRMevxGKl8g6a/Scz6ezlCuSsvOl/St1Oe3SDo1lV8vaVd6Pe6f8n9SLY33w8t+VP4AGoEjwIVpegQ4h2LAbgPuAQRcDjyU1vkH4DfT9GKgHriU4uefldZ9GPjgOM95PrADWJTW3wn8m5Ln/0Cq9zXgY2n6i8BVafvfB05O5V8Ffn+C/RseM/+v0r8nAC8Av5zmA/iDNH0LcFeaLgAtaXopsDdNtwEPp+l3A/Vp+neAB9L0NcC+0ef045jomxcCX0/TTwDfAxYCnwL+JJXvTX2hEXihZN024ABwenqeJ4HWCfavtG8F8KE0/SDwWHrec4HtqfxdwPFpegUwmKavAjan5zwF+EkqWwh8t+Rv5d8B96TpfwJ+KU2fVOv/66k8fAYwc34YEU+l6R9ExI6IOELxjfmbUewlOyh2eIDvAHekI6WTImKE4h/ZpcCzwDPAmRQ7aTmtwIMR8XpEDAP/A7i45Pm3p+ltJc8JQGrLl4GPSToJuAh4pIp9vV7Sc8BTwLKSNh4B/nuavi+1sVInAl+X9AJwJ3B2ybLHI+LHVWzL3m62++Y24HxJ7wZ+TvFNvIVi/3yigvZ+LyL2pTZuZ0z/ncCbwKNpegfwrYg4NGbfFgKfl7QD+DowOn7QSjG0jkTE/wH6U/mvAc3A45K2A39GMZwAnge+IuljFIP1mONrqjPn9ZLpn5dMHymZP0J6zSPidkmbgN8DviPpdykeXX0mIv52mm0pff7DFI/Ux/o7ikd6Byl2/Io6sKQ2ikfoF0XEzyQVgOPHqT76LcMRfnG5cby6twH9EfERSY0Uj+xGvV52DavUrPbNiDgk6QcUz96+S/GNsh34VWCogvaO7b+Vvk8dSmEGJfsWEUf0i/Gj/wT8M8WzggUU+/9EBOyMiIvKLPsw8EHg94EuSedU+nc0V/gMoEYk/Uo6EtsAbKV4RLUF+PeSFqc6p0l6zzibeAK4QtK7JC0CPkJlR1cARMQ/UTyF/TOKYTCRQ5IWpukTgZ+kN/8zKZ7uj1pA8bQZ4KPAQJreS/GSFSXLxzoR2J+mr6lgF+womYG+CcW+eCPw7TT9CeDZkjfoUa8BS2Z6HyZwIvBKOrv4OFCXyr8DXJnGAhooXooCeBE4WdJFAJIWSjpbxcH0ZRHRD9yUtrt4FvdjRjgAaudPVRy8fR44BDwSEY9RvB7/ZDpF/Qbj/HFExDMUr+l/D3ga+EJEPFtlG74CvBwRkx2VbQSeTwN4jwL1koYoDuA9VVLvdWBluozz28D6VP6XwH+U9CzF677lfBb4TKrjM9PamlbfTJ4ATgWejIh/pnik/Y4DlIj4EcWzjBdKBoGPpr8C/ihdwjyTX5wdPUBxrGkXxcuXzwAHIuJNigctG9I624HfoBgc96XX4lngc1H8VNMxxfcCypikuygelfXUui1mtSZpcUQMS/pligdWv5nGA+YtH2llStI2ikc//6XWbTGbIx5OH4o4Drhtvr/5g88A5rx0NFLu8++r0unzTD7X08AvjSn+eETsmMnnsflhlvvmg8DyMcU3RcSWmXye3DgAzMwy5UFgM7NMOQDMzDLlADAzy5QDwMwsU/8fbjS0MnfnAjAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot = to_compare.boxplot(column=[\"rmse_only_tabular\",\"rmse_with_images\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3800f4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot = to_compare.boxplot(column=[\"acc_only_tabular\",\"acc_with_Images\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aff5c52",
   "metadata": {},
   "source": [
    "The improvement is present but is marginal.\n",
    "\n",
    "The reason for that seems to be bad quality of the images in the first place: there are images with a lot of clouds and images created by night. Sometime both.\n",
    "\n",
    "The fact that there are clouds on images is very strange, because while doing API calls to Sentinel Hub we say: “take an image from the last two month with lowest cloud coverage”. It looks like some areas are very cloudy. Maybe we should consider more than 2 months. I have no idea how to control whether day- or nighttime images are downloaded. I have to read documentation more carefully. In any case, the problem seems to be quite big: from 10 images I randomly looked at, 4 were trash. \n",
    "\n",
    "Of course, the quality of images has a large impact on the results. We should pay more attention on data preparation. But it will be probably not an easy task since we will have to look at thousands of images manually and try to identify all reasons for bad quality of images and try to find corresponding solution. Here we might have to acquire some better understanding of satellite imagery in general.\n",
    "\n",
    "In any case, increasing quality of images is the next step in the project.\n",
    "\n",
    "Aelow is some good and some bad examples of images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0008cb",
   "metadata": {},
   "source": [
    "Examples for good quality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7dbffcef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2028fcd0550>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = np.load(\"images/4FRWMG86+VQP.npz\")[\"arr_0\"]\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "12a93f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16dd6208520>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = np.load(\"images/4FRX78HM+VMM.npz\")[\"arr_0\"]\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6873eb",
   "metadata": {},
   "source": [
    "Rather bad quality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c0cb946d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16a208a6af0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = np.load(\"images/6G6V7F52+4GG.npz\")[\"arr_0\"]\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b275b62a",
   "metadata": {},
   "source": [
    "Definitely bad quality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f33f973b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16dcbf06f70>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = np.load(\"images/6FJGRMMF+686.npz\")[\"arr_0\"]\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "cdd2218a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16a20bf16d0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = np.load(\"images/6FJJC7M8+8MM.npz\")[\"arr_0\"]\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3477b4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16a298c8550>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = np.load(\"images/6FJJ93MX+8XQ.npz\")[\"arr_0\"]\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf37b9ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}