notebook_4_multivariate_lstm.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "%%capture\n",
    "!pip install numpy pandas matplotlib timeseriesfcst tensorflow"
   ],
   "id": "cdafa71ba3ae42ad",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Setup\n",
   "id": "f19845a328dd0c00"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T14:59:35.715054Z",
     "start_time": "2024-07-29T14:59:35.705599Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%capture\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import timeseriesfcst.preprocessing as tsprep\n",
    "import timeseriesfcst.lstm as tslstm\n",
    "import timeseriesfcst.evaluation as eval\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam"
   ],
   "id": "96a874728f6d5ff",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Define Constants\n",
   "id": "883e7d55c7812661"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T14:59:39.140921Z",
     "start_time": "2024-07-29T14:59:39.127940Z"
    }
   },
   "cell_type": "code",
   "source": [
    "REPO_PATH = \"hf://datasets/Creatorin/solarpower/\"\n",
    "REPO_PATH_SELECTED = \"hf://datasets/Creatorin/solar_selected/\"\n",
    "REPO_PATH_PCA = \"hf://datasets/Creatorin/solar_pca/\"\n",
    "\n",
    "SEQUENCE_LENGTH = 24\n",
    "BATCH_SIZE = 8\n",
    "TARGET_COLUMN = 'Leistung'\n",
    "NUM_EPOCHS = 100\n",
    "OPTIMIZER = Adam(learning_rate=0.0001)\n",
    "\n",
    "# Set random seed\n",
    "np.random.seed(42)"
   ],
   "id": "4c375a7f369f6730",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Load Data from Huggingface\n",
   "id": "699057030834951a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T15:00:38.680403Z",
     "start_time": "2024-07-29T14:59:41.719470Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Original Dataset (No Feature Engineering)\n",
    "splits = {'train': 'train_ts.csv', 'validation': 'val_ts.csv', 'test': 'test_ts.csv'}\n",
    "\n",
    "train_ts = pd.read_csv(REPO_PATH + splits[\"train\"], index_col=0, date_format=\"%Y-%m-%d %H:%M:%S\")\n",
    "val_ts = pd.read_csv(REPO_PATH + splits[\"validation\"], index_col=0, date_format=\"%Y-%m-%d %H:%M:%S\")\n",
    "test_ts = pd.read_csv(REPO_PATH + splits[\"test\"], index_col=0, date_format=\"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "\n",
    "# Dataset with Selected Features\n",
    "splits = {'train': 'data/train-00000-of-00001.parquet', 'validation': 'data/validation-00000-of-00001.parquet', 'test': 'data/test-00000-of-00001.parquet'}\n",
    "\n",
    "train_selected = pd.read_parquet(REPO_PATH_SELECTED + splits[\"train\"])\n",
    "val_selected = pd.read_parquet(REPO_PATH_SELECTED + splits[\"validation\"])\n",
    "test_selected = pd.read_parquet(REPO_PATH_SELECTED + splits[\"test\"])\n",
    "\n",
    "# set index\n",
    "train_selected.set_index(\"__index_level_0__\", inplace=True)\n",
    "val_selected.set_index(\"__index_level_0__\", inplace=True)\n",
    "test_selected.set_index(\"__index_level_0__\", inplace=True)\n",
    "\n",
    "# Datetime\n",
    "train_selected.index = pd.to_datetime(train_selected.index)\n",
    "val_selected.index = pd.to_datetime(val_selected.index)\n",
    "test_selected.index = pd.to_datetime(test_selected.index)\n",
    "\n",
    "# PCA Dataset\n",
    "splits = {'train': 'data/train-00000-of-00001.parquet', 'validation': 'data/validation-00000-of-00001.parquet', 'test': 'data/test-00000-of-00001.parquet'}\n",
    "\n",
    "train_pca = pd.read_parquet(\"hf://datasets/Creatorin/solar_pca/\" + splits[\"train\"])\n",
    "val_pca = pd.read_parquet(\"hf://datasets/Creatorin/solar_pca/\" + splits[\"validation\"])\n",
    "test_pca = pd.read_parquet(\"hf://datasets/Creatorin/solar_pca/\" + splits[\"test\"])\n",
    "\n",
    "# set index\n",
    "train_pca.set_index(\"__index_level_0__\", inplace=True)\n",
    "val_pca.set_index(\"__index_level_0__\", inplace=True)\n",
    "test_pca.set_index(\"__index_level_0__\", inplace=True)\n",
    "\n",
    "# Datetime\n",
    "train_pca.index = pd.to_datetime(train_pca.index)\n",
    "val_pca.index = pd.to_datetime(val_pca.index)\n",
    "test_pca.index = pd.to_datetime(test_pca.index)\n",
    "\n",
    "# Confirm Shapes\n",
    "print(f\"Train Shape Original: {train_ts.shape}, Validation Shape: {val_ts.shape}, Test Shape: {test_ts.shape}\")\n",
    "print(f\"Train Shape Selected: {train_selected.shape}, Validation Shape: {val_selected.shape}, Test Shape: {test_selected.shape}\")\n",
    "print(f\"Train Shape PCA: {train_pca.shape}, Validation Shape: {val_pca.shape}, Test Shape: {test_pca.shape}\")"
   ],
   "id": "f56b704c89214c66",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape Original: (70129, 31), Validation Shape: (8760, 31), Test Shape: (2926, 31)\n",
      "Train Shape Selected: (61368, 50), Validation Shape: (8759, 50), Test Shape: (2925, 50)\n",
      "Train Shape PCA: (61368, 617), Validation Shape: (8759, 617), Test Shape: (2925, 617)\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Verify Dates\n",
    "print(train_ts.index.min())\n",
    "print(train_ts.index.max())\n",
    "\n",
    "print(train_selected.index.min())\n",
    "print(train_selected.index.max())\n",
    "\n",
    "print(train_pca.index.min())\n",
    "print(train_pca.index.max())"
   ],
   "id": "aaba451ebe4ef002",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Prepare Data for LSTM\n",
   "id": "ad403d343aadb770"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T15:00:38.755500Z",
     "start_time": "2024-07-29T15:00:38.687650Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Normalise original dataset (the others are already normalised)\n",
    "train_ts, val_ts, test_ts = tsprep.normalise_ts(train_ts, val_ts, test_ts)"
   ],
   "id": "ba756f06c4f52815",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T15:00:46.044291Z",
     "start_time": "2024-07-29T15:00:38.760824Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create Sequences\n",
    "X_train, y_train = tslstm.create_sequences(train_ts, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "X_val, y_val = tslstm.create_sequences(val_ts, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "X_test, y_test = tslstm.create_sequences(test_ts, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "\n",
    "# Create Sequences for selected features\n",
    "X_train_selected, y_train_selected = tslstm.create_sequences(train_selected, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "X_val_selected, y_val_selected = tslstm.create_sequences(val_selected, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "X_test_selected, y_test_selected = tslstm.create_sequences(test_selected, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "\n",
    "# Create Sequences for PCA features\n",
    "X_train_pca, y_train_pca = tslstm.create_sequences(train_pca, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "X_val_pca, y_val_pca = tslstm.create_sequences(val_pca, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "X_test_pca, y_test_pca = tslstm.create_sequences(test_pca, TARGET_COLUMN, SEQUENCE_LENGTH, BATCH_SIZE)\n",
    "\n",
    "# Print the shapes\n",
    "print(\"Training set - X shape:\", X_train.shape, \"y shape:\", y_train.shape)\n",
    "print(\"Validation set - X shape:\", X_val.shape, \"y shape:\", y_val.shape)\n",
    "print(\"Test set - X shape:\", X_test.shape, \"y shape:\", y_test.shape)\n",
    "\n",
    "print(\"\")\n",
    "print(\"Training set selected - X shape:\", X_train_selected.shape, \"y shape:\", y_train_selected.shape)\n",
    "print(\"Validation set selected - X shape:\", X_val_selected.shape, \"y shape:\", y_val_selected.shape)\n",
    "print(\"Test set selected - X shape:\", X_test_selected.shape, \"y shape:\", y_test_selected.shape)\n",
    "\n",
    "print(\"\")\n",
    "print(\"Training set pca - X shape:\", X_train_pca.shape, \"y shape:\", train_pca.shape)\n",
    "print(\"Validation set pca - X shape:\", X_val_pca.shape, \"y shape:\", y_val_pca.shape)\n",
    "print(\"Test set pca - X shape:\", X_test_pca.shape, \"y shape:\", y_test_pca.shape)"
   ],
   "id": "94819c085e81021c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set - X shape: (70105, 3, 31) y shape: (70105,)\n",
      "Validation set - X shape: (8736, 3, 31) y shape: (8736,)\n",
      "Test set - X shape: (2902, 3, 31) y shape: (2902,)\n",
      "\n",
      "Training set selected - X shape: (61344, 3, 50) y shape: (61344,)\n",
      "Validation set selected - X shape: (8735, 3, 50) y shape: (8735,)\n",
      "Test set selected - X shape: (2901, 3, 50) y shape: (2901,)\n",
      "\n",
      "Training set pca - X shape: (61344, 3, 617) y shape: (61368, 617)\n",
      "Validation set pca - X shape: (8735, 3, 617) y shape: (8735,)\n",
      "Test set pca - X shape: (2901, 3, 617) y shape: (2901,)\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# LSTM with Original Dataset\n",
    "## Train"
   ],
   "id": "71545b1061a3d591"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T15:14:36.706732Z",
     "start_time": "2024-07-29T15:00:46.048511Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create the model\n",
    "input_shape = (X_train.shape[1], X_train.shape[2])\n",
    "model = tslstm.create_lstm_model(input_shape, optimizer=OPTIMIZER)\n",
    "\n",
    "# Train the model\n",
    "history = tslstm.train_lstm_model(\n",
    "    model, X_train, y_train, X_val, y_val, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"Test Loss: {test_loss}\")\n",
    "\n",
    "# Plot train val losses\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss Original Dataset')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ],
   "id": "1ea31e61951040af",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/moonchild/PycharmProjects/solar-prediction/solarvenv/lib/python3.12/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m74s\u001B[0m 8ms/step - loss: 0.2321 - val_loss: 0.1247\n",
      "Epoch 2/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m50s\u001B[0m 6ms/step - loss: 0.0660 - val_loss: 0.1104\n",
      "Epoch 3/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m48s\u001B[0m 5ms/step - loss: 0.0598 - val_loss: 0.1047\n",
      "Epoch 4/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m47s\u001B[0m 5ms/step - loss: 0.0563 - val_loss: 0.1030\n",
      "Epoch 5/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m85s\u001B[0m 6ms/step - loss: 0.0544 - val_loss: 0.1042\n",
      "Epoch 6/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m51s\u001B[0m 6ms/step - loss: 0.0533 - val_loss: 0.0948\n",
      "Epoch 7/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m68s\u001B[0m 8ms/step - loss: 0.0521 - val_loss: 0.0990\n",
      "Epoch 8/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m41s\u001B[0m 5ms/step - loss: 0.0515 - val_loss: 0.1007\n",
      "Epoch 9/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m43s\u001B[0m 5ms/step - loss: 0.0513 - val_loss: 0.1060\n",
      "Epoch 10/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m39s\u001B[0m 4ms/step - loss: 0.0503 - val_loss: 0.1133\n",
      "Epoch 11/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m47s\u001B[0m 5ms/step - loss: 0.0497 - val_loss: 0.1052\n",
      "Epoch 12/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m51s\u001B[0m 6ms/step - loss: 0.0480 - val_loss: 0.1153\n",
      "Epoch 13/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m48s\u001B[0m 5ms/step - loss: 0.0487 - val_loss: 0.1048\n",
      "Epoch 14/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m57s\u001B[0m 6ms/step - loss: 0.0479 - val_loss: 0.0958\n",
      "Epoch 15/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m41s\u001B[0m 5ms/step - loss: 0.0476 - val_loss: 0.0970\n",
      "Epoch 16/100\n",
      "\u001B[1m8764/8764\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m39s\u001B[0m 4ms/step - loss: 0.0467 - val_loss: 0.1055\n",
      "Test Loss: 0.16491760313510895\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Predict",
   "id": "f13daa2c3e3f4ca8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T15:14:37.854625Z",
     "start_time": "2024-07-29T15:14:36.708601Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Make predictions\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Plot predictions \n",
    "plt.plot(figsize=(10, 6))\n",
    "plt.plot(y_test, label='True')\n",
    "plt.plot(predictions, label='Predicted')\n",
    "plt.title('Predictions Original Dataset')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Get performance metrics\n",
    "metrics = eval.evaluate_model(y_test, predictions)\n",
    "print(metrics)"
   ],
   "id": "474a948773a27455",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mae': 0.2110655594508874, 'mape': 0.7926247305349324, 'mse': 0.16491760087719506, 'r2': 0.888623510108859, 'rmse': 0.4061004812570345}\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "\n",
   "id": "7e11462c853b21e6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Feature Importance\n",
   "id": "f05c33a70397b865"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T15:16:54.075910Z",
     "start_time": "2024-07-29T15:14:37.856430Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Calculate permutation importance\n",
    "importances = eval.permutation_importance(model, X_test, y_test)\n",
    "\n",
    "# get feature names (column names)\n",
    "feature_names = train_ts.columns\n",
    "\n",
    "# Create a DataFrame for easier plotting\n",
    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
    "importance_df = importance_df.sort_values('Importance', ascending=False)\n",
    "\n",
    "# Plot feature importance\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(importance_df['Feature'], importance_df['Importance'])\n",
    "plt.title('Feature Importance')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance (Increase in MSE)')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print feature importance\n",
    "print(importance_df)"
   ],
   "id": "8a99d3e203dc59cd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 6ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 6ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 6ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 5ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n",
      "\u001B[1m91/91\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                Feature  Importance\n",
      "0                              Leistung    1.348709\n",
      "3           shortwave_radiation_templin    0.032672\n",
      "7                cloud_cover_kastellaun    0.028070\n",
      "18       shortwave_radiation_ingolstadt    0.027907\n",
      "28       shortwave_radiation_neumunster    0.021319\n",
      "17               cloud_cover_ingolstadt    0.019930\n",
      "20  direct_normal_irradiance_ingolstadt    0.018336\n",
      "1                temperature_2m_templin    0.014917\n",
      "13       shortwave_radiation_guetersloh    0.013132\n",
      "4             diffuse_radiation_templin    0.011195\n",
      "12               cloud_cover_guetersloh    0.011018\n",
      "8        shortwave_radiation_kastellaun    0.009488\n",
      "23           shortwave_radiation_erfurt    0.008098\n",
      "27               cloud_cover_neumunster    0.007781\n",
      "25      direct_normal_irradiance_erfurt    0.007621\n",
      "22                   cloud_cover_erfurt    0.007518\n",
      "19         diffuse_radiation_ingolstadt    0.007250\n",
      "9          diffuse_radiation_kastellaun    0.006559\n",
      "24             diffuse_radiation_erfurt    0.005876\n",
      "15  direct_normal_irradiance_guetersloh    0.005714\n",
      "29         diffuse_radiation_neumunster    0.004895\n",
      "5      direct_normal_irradiance_templin    0.004546\n",
      "11            temperature_2m_guetersloh    0.004034\n",
      "6             temperature_2m_kastellaun    0.003197\n",
      "26            temperature_2m_neumunster    0.002508\n",
      "16            temperature_2m_ingolstadt    0.002237\n",
      "2                   cloud_cover_templin    0.002059\n",
      "10  direct_normal_irradiance_kastellaun    0.001678\n",
      "14         diffuse_radiation_guetersloh    0.001112\n",
      "30  direct_normal_irradiance_neumunster    0.000958\n",
      "21                temperature_2m_erfurt   -0.000361\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# LSTM with Selected Features\n",
    "## Train"
   ],
   "id": "c4f7dacccf2e057b"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Create the model\n",
    "input_shape = (X_train_selected.shape[1], X_train_selected.shape[2])\n",
    "model = tslstm.create_lstm_model(input_shape)\n",
    "\n",
    "# Train the model\n",
    "history = tslstm.train_lstm_model(\n",
    "    model, X_train_selected, y_train_selected, X_val_selected, y_val_selected, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss = model.evaluate(X_test_selected, y_test_selected, verbose=0)\n",
    "print(f\"Test Loss: {test_loss}\")\n",
    "\n",
    "# Plot train val losses\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss Selected Features')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ],
   "id": "ccb6cb29337a5e20",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Predict\n",
   "id": "f044b18cce026a03"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Make predictions\n",
    "predictions = model.predict(X_test_selected)\n",
    "\n",
    "# Plot predictions\n",
    "plt.plot(figsize=(10, 6))\n",
    "plt.plot(y_test_selected, label='True')\n",
    "plt.plot(predictions, label='Predicted')\n",
    "plt.title('Predictions Selected Features')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Get performance metrics\n",
    "metrics = eval.evaluate_model(y_test_selected, predictions)\n",
    "print(metrics)"
   ],
   "id": "ab2c087f4496f629",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# LSTM with PCA Features\n",
    "## Train"
   ],
   "id": "198981c5f359be7b"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "c1ec232608a7fad3"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Create the model\n",
    "input_shape = (X_train_pca.shape[1], X_train_pca.shape[2])\n",
    "model = tslstm.create_lstm_model(input_shape)\n",
    "\n",
    "# Train the model\n",
    "history = tslstm.train_lstm_model(\n",
    "    model, X_train_pca, y_train_pca, X_val_pca, y_val_pca, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss = model.evaluate(X_test_pca, y_test_pca, verbose=0)\n",
    "print(f\"Test Loss: {test_loss}\")\n",
    "\n",
    "# Plot train val losses\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss PCA Features')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ],
   "id": "714762352e00104d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Predict",
   "id": "b55d38223e6e4219"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Make predictions\n",
    "predictions = model.predict(X_test_pca)\n",
    "\n",
    "# Get performance metrics\n",
    "metrics = eval.evaluate_model(y_test_pca, predictions)\n",
    "print(metrics)\n",
    "\n",
    "# Plot predictions\n",
    "plt.plot(figsize=(10, 6))\n",
    "plt.plot(y_test_pca, label='True')\n",
    "plt.plot(predictions, label='Predicted')\n",
    "plt.title('Predictions PCA Features')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "c50d484eaca73581",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "a1e233739a5e46a6"
  }
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