RNN Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VHjT2OpLjd7c"
   },
   "source": [
    "# Assignment 6: RNN Implementation\n",
    "-------------------\n",
    "**Dr. Dave Wanik - University of Connecticut**\n",
    "A weather-based energy forecasting homework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c9asxpfsnkNA"
   },
   "source": [
    "* **Your Name Here:** Adam Syed\n",
    "* **Your NetID Here:** Ads17007"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hcyQFoJmjylF"
   },
   "source": [
    "Energy demand forecasting as a function of weather data is an extremely interesting and relevant topic.\n",
    "\n",
    "I've given you a curated (mostly?) clean dataset that you can use to predict `df['Demand']` as a function of the weather variables.\n",
    "\n",
    "Of course, you cannot use the df[`Datetime`] column directly into the model - but you can feature engineer the column to make features temporal features for modeling like Julian day of year (0-365), week of year, and number of years since 2012.\n",
    "\n",
    "**Sorting: (5 pts)**\n",
    "Sort the data from oldest to newest.\n",
    "\n",
    "**Cleaning: (10 pts)**\n",
    "Describe and impute (or drop) the missing values using an appropriate method. At this time, you are welcome to perform feature engineering (optional).\n",
    "\n",
    "**Process as a 3D tensor: (10 pts)**\n",
    "Process the data as a 3D tensor with a lookback of your choice (use a value greater than or equal to 12).\n",
    "\n",
    "**Model Architecure: (10 pts)**\n",
    "You must use a model with:\n",
    "* at least two recurrent layers\n",
    "* at least one convolution layer\n",
    "* dropout (recurrent or between Dense layers)\n",
    "* appropriate activation functions\n",
    "\n",
    "**Experiments: (15 pts each, 30 pts total)**\n",
    "Freeze the model architecutre and perform a *walk-forward validation* where:\n",
    "* the first 50k samples predict the remaining ~46k samples\n",
    "* the first 80k samples predict the remaining ~16k samples\n",
    "\n",
    "Walk-forward validation is very common to use on the job - it shows how your model (ideally) gets better over time as it gets more data to use. Use subheads\n",
    "\n",
    "**Evaluation (15 points each, 30 pts total)**\n",
    "Evaluate your model for each of these experiments - hopefully, your model should improve as you give it more data to use! But that depends on your choice of architecture. You should have:\n",
    "* quantitative error metrics (mean absolute error and others)\n",
    "* scatterplots of actual vs. predicted\n",
    "* time series plots of actual vs. predicted\n",
    "\n",
    "You should also compare to baseline model like persistence (assume today's energy usage is the same as yesterday's - requires you to shift by 24... ask on discussion board if questions!) or ignore the sequence data and treat this as a 2D problem (a regular supervised learning problem!)\n",
    "\n",
    "**Conclusions (5 pts total)**\n",
    "* What did you learn from this assignment? Did your walk-forward validation work as you expected it to?\n",
    "* What else would you do differently next time?\n",
    "* At least five great bullets please."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qwoDp0TsjdfE",
    "outputId": "3ce0713f-ae71-4b81-abaf-712a45c20c31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1xYe3vCKTLeaJKpMDzv57ppCSWK_LCJ5V\n",
      "To: /Users/adamyeeter/OPIM 5509 Deep Learning/M6 Assignment/BDL_cleanweather_energy (1).csv\n",
      "100%|██████████████████████████████████████| 7.38M/7.38M [00:00<00:00, 20.4MB/s]\n"
     ]
    }
   ],
   "source": [
    "# https://drive.google.com/file/d/1xYe3vCKTLeaJKpMDzv57ppCSWK_LCJ5V/view?usp=sharing\n",
    "!gdown 1xYe3vCKTLeaJKpMDzv57ppCSWK_LCJ5V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "id": "iLnKeIrljmi1"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('BDL_cleanweather_energy (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pl2aA-ZtkpKR",
    "outputId": "3de457e3-b8ab-4be3-b3d9-f813ebbea690"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96427 entries, 0 to 96426\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Datetime  96427 non-null  object \n",
      " 1   Demand    96427 non-null  float64\n",
      " 2   BDL_tmpf  96160 non-null  float64\n",
      " 3   BDL_dwpf  96160 non-null  float64\n",
      " 4   BDL_relh  96160 non-null  float64\n",
      " 5   BDL_drct  96160 non-null  float64\n",
      " 6   BDL_sknt  96160 non-null  float64\n",
      " 7   BDL_p01i  96160 non-null  float64\n",
      " 8   BDL_alti  96160 non-null  float64\n",
      " 9   BDL_mslp  96160 non-null  float64\n",
      " 10  BDL_vsby  96160 non-null  float64\n",
      "dtypes: float64(10), object(1)\n",
      "memory usage: 8.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras import layers, Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D\n",
    "from tensorflow.keras.layers import Dense, Dropout, SimpleRNN, GRU, LSTM\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "id": "GQUL-bGZni7X"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Datetime  Demand  BDL_tmpf  BDL_dwpf  BDL_relh  BDL_drct  \\\n",
      "8760 2011-01-01 00:00:00  3053.0     33.98     26.96       0.0       0.0   \n",
      "8761 2011-01-01 01:00:00  2892.0     33.98     26.96       0.0       0.0   \n",
      "8762 2011-01-01 02:00:00  2774.0     30.92     26.96       0.0       0.0   \n",
      "8763 2011-01-01 03:00:00  2710.0     30.02     26.06       0.0       0.0   \n",
      "8764 2011-01-01 04:00:00  2698.0     28.94     24.98       0.0       0.0   \n",
      "\n",
      "      BDL_sknt  BDL_p01i  BDL_alti  BDL_mslp  BDL_vsby  \n",
      "8760       0.0       0.0     30.07    1018.5       6.0  \n",
      "8761       0.0       0.0     30.07    1018.5       6.0  \n",
      "8762       0.0       0.0     30.07    1018.6       6.0  \n",
      "8763       0.0       0.0     30.09    1019.2       5.0  \n",
      "8764       0.0       0.0     30.09    1019.1       5.0  \n"
     ]
    }
   ],
   "source": [
    "#convert 'Datetime' to datetime type\n",
    "df['Datetime'] = pd.to_datetime(df['Datetime'])\n",
    "\n",
    "#sort by 'Datetime'\n",
    "df = df.sort_values(by='Datetime')\n",
    "\n",
    "#display head\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Demand       0\n",
       "BDL_tmpf     0\n",
       "BDL_dwpf     0\n",
       "BDL_relh     0\n",
       "BDL_drct     0\n",
       "BDL_sknt     0\n",
       "BDL_p01i     0\n",
       "BDL_alti     0\n",
       "BDL_mslp     0\n",
       "BDL_vsby     0\n",
       "hour         0\n",
       "dayofweek    0\n",
       "dayofyear    0\n",
       "month        0\n",
       "year         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature engineering\n",
    "df['hour'] = df['Datetime'].dt.hour\n",
    "df['dayofweek'] = df['Datetime'].dt.dayofweek\n",
    "df['dayofyear'] = df['Datetime'].dt.dayofyear\n",
    "df['month'] = df['Datetime'].dt.month\n",
    "df['year'] = df['Datetime'].dt.year\n",
    "\n",
    "#drop 'Datetime' column\n",
    "df.drop(columns=['Datetime'], inplace=True)\n",
    "\n",
    "#forward fill NA's\n",
    "df.fillna(method='ffill', inplace=True)\n",
    "\n",
    "#check NA's\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96427, 15) (96427, 14) (96427, 1)\n"
     ]
    }
   ],
   "source": [
    "#define X and y variables\n",
    "Y = df[['Demand']]\n",
    "X = df.drop(axis=1, columns=['Demand'])\n",
    "print(df.shape, X.shape, Y.shape)\n",
    "\n",
    "#MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "#fit scaler\n",
    "scaler.fit(X)\n",
    "\n",
    "#transform 'X'\n",
    "X_scaled = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array\n",
    "\n",
    "#split multivariate sequence into samples\n",
    "def split_sequences(sequences, n_steps):\n",
    "    X, y = list(), list()\n",
    "    for i in np.arange(len(sequences)):\n",
    "#find the end of pattern\n",
    "        end_ix = i + n_steps\n",
    "#check if beyond dataset\n",
    "        if end_ix > len(sequences):\n",
    "            break\n",
    "#get input and output of pattern\n",
    "        seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1:]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "#n_steps\n",
    "n_steps = 12\n",
    "\n",
    "#convert df to NumPy array\n",
    "raw_seq = np.array(df)\n",
    "\n",
    "#generate dataset for RNN with specified n_steps\n",
    "X, y = split_sequences(raw_seq, n_steps=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96416, 12, 14)\n",
      "(96416, 1)\n"
     ]
    }
   ],
   "source": [
    "#print shape\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data into train and test sets\n",
    "#50% split\n",
    "train_pct_index = int(0.5 * len(X))\n",
    "X_train, X_test = X[:train_pct_index], X[train_pct_index:]\n",
    "y_train, y_test = y[:train_pct_index], y[train_pct_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96416, 12, 14) (48208, 12, 14) (48208, 12, 14)\n",
      "(96416, 1) (48208, 1) (48208, 1)\n"
     ]
    }
   ],
   "source": [
    "#print shape\n",
    "print(X.shape, X_train.shape, X_test.shape)\n",
    "print(y.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Architecure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_72\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_70 (Conv1D)          (None, 10, 128)           5504      \n",
      "                                                                 \n",
      " max_pooling1d_70 (MaxPooli  (None, 5, 128)            0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " simple_rnn_86 (SimpleRNN)   (None, 5, 64)             12352     \n",
      "                                                                 \n",
      " dropout_140 (Dropout)       (None, 5, 64)             0         \n",
      "                                                                 \n",
      " simple_rnn_87 (SimpleRNN)   (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_141 (Dropout)       (None, 64)                0         \n",
      "                                                                 \n",
      " dense_70 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 26177 (102.25 KB)\n",
      "Trainable params: 26177 (102.25 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#n_features\n",
    "n_features = X_train.shape[2]\n",
    "\n",
    "#define model\n",
    "model = Sequential()\n",
    "#convolutional layer\n",
    "model.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "#first recurrent layer\n",
    "model.add(SimpleRNN(64, activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.1))\n",
    "#second recurrent layer\n",
    "model.add(SimpleRNN(64, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "#output layer\n",
    "model.add(Dense(1, activation='linear'))\n",
    "\n",
    "#compile model\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "#model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "500/500 [==============================] - 6s 9ms/step - loss: 776463.1250 - val_loss: 277152.7812\n",
      "Epoch 2/100\n",
      "500/500 [==============================] - 4s 9ms/step - loss: 283885.4375 - val_loss: 196512.1719\n",
      "Epoch 3/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 190959.7031 - val_loss: 154615.6094\n",
      "Epoch 4/100\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 170978.8125 - val_loss: 93766.3750\n",
      "Epoch 5/100\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 157801.4531 - val_loss: 102591.6484\n",
      "Epoch 6/100\n",
      "500/500 [==============================] - 3s 7ms/step - loss: 142745.3594 - val_loss: 80186.5312\n",
      "Epoch 7/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 133873.2031 - val_loss: 106292.2500\n",
      "Epoch 8/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 125656.8125 - val_loss: 59875.4258\n",
      "Epoch 9/100\n",
      "500/500 [==============================] - 4s 9ms/step - loss: 115883.9219 - val_loss: 59652.2969\n",
      "Epoch 10/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 112204.8594 - val_loss: 55496.0430\n",
      "Epoch 11/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 106300.6953 - val_loss: 57387.6055\n",
      "Epoch 12/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 104903.9766 - val_loss: 50384.3516\n",
      "Epoch 13/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 104121.9297 - val_loss: 51290.6328\n",
      "Epoch 14/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 99516.3203 - val_loss: 63032.6328\n",
      "Epoch 15/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 98544.0547 - val_loss: 48906.2852\n",
      "Epoch 16/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 94763.4141 - val_loss: 62413.5781\n",
      "Epoch 17/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 92770.0391 - val_loss: 51326.4883\n",
      "Epoch 18/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 89454.5312 - val_loss: 60688.5078\n",
      "Epoch 19/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 85966.6562 - val_loss: 53667.2227\n",
      "Epoch 20/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 87067.5781 - val_loss: 46194.8086\n",
      "Epoch 21/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 86928.7969 - val_loss: 52043.8672\n",
      "Epoch 22/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 82040.8906 - val_loss: 48436.0469\n",
      "Epoch 23/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 83087.4531 - val_loss: 55005.1328\n",
      "Epoch 24/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 85693.5000 - val_loss: 43754.2539\n",
      "Epoch 25/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 81336.1719 - val_loss: 49875.4531\n",
      "Epoch 26/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 81156.9531 - val_loss: 67573.3906\n",
      "Epoch 27/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 80650.2266 - val_loss: 46353.8203\n",
      "Epoch 28/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 79810.0312 - val_loss: 52878.5586\n",
      "Epoch 29/100\n",
      "497/500 [============================>.] - ETA: 0s - loss: 75764.2109Restoring model weights from the end of the best epoch: 24.\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 75875.6094 - val_loss: 50788.2969\n",
      "Epoch 29: early stopping\n",
      "1451/1451 [==============================] - 3s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "model_1 = Sequential()\n",
    "model_1.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model_1.add(MaxPooling1D(pool_size=2))\n",
    "model_1.add(SimpleRNN(64, activation='relu', return_sequences=True))\n",
    "model_1.add(Dropout(0.1))\n",
    "model_1.add(SimpleRNN(64, activation='relu'))\n",
    "model_1.add(Dropout(0.1))\n",
    "model_1.add(Dense(1, activation='linear'))\n",
    "model_1.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "#split for experiment 1\n",
    "split_point_1 = 50000\n",
    "X_train_1, y_train_1 = X[:split_point_1], y[:split_point_1]\n",
    "X_test_1, y_test_1 = X[split_point_1:split_point_1+46416], y[split_point_1:split_point_1+46416]\n",
    "\n",
    "#early stopping\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', \n",
    "                   patience=5, \n",
    "                   verbose=1,\n",
    "                   restore_best_weights=True)\n",
    "\n",
    "#fit experiment 1\n",
    "model_1.fit(X_train_1, y_train_1, \n",
    "          epochs=100, \n",
    "          batch_size=100, \n",
    "          verbose=1,\n",
    "          callbacks=[es],\n",
    "          validation_data=(X_test_1, y_test_1))\n",
    "\n",
    "#store preds\n",
    "predictions_1 = model_1.predict(X_test_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 86613.8750 - val_loss: 64108.7578\n",
      "Epoch 2/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 87068.5312 - val_loss: 57878.7539\n",
      "Epoch 3/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 85584.6797 - val_loss: 61323.8984\n",
      "Epoch 4/100\n",
      "800/800 [==============================] - 5s 6ms/step - loss: 85006.5312 - val_loss: 71458.8672\n",
      "Epoch 5/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 85082.9922 - val_loss: 64342.7617\n",
      "Epoch 6/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 85676.9688 - val_loss: 43637.7617\n",
      "Epoch 7/100\n",
      "800/800 [==============================] - 5s 6ms/step - loss: 83789.4219 - val_loss: 72781.0312\n",
      "Epoch 8/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 84322.0781 - val_loss: 46465.4648\n",
      "Epoch 9/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 84223.3047 - val_loss: 73920.7031\n",
      "Epoch 10/100\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 83785.5234 - val_loss: 52632.8633\n",
      "Epoch 11/100\n",
      "798/800 [============================>.] - ETA: 0s - loss: 84307.1953Restoring model weights from the end of the best epoch: 6.\n",
      "800/800 [==============================] - 4s 5ms/step - loss: 84303.8281 - val_loss: 54208.6602\n",
      "Epoch 11: early stopping\n",
      "513/513 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "model_1 = Sequential()\n",
    "model_1.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model_1.add(MaxPooling1D(pool_size=2))\n",
    "model_1.add(SimpleRNN(64, activation='relu', return_sequences=True))\n",
    "model_1.add(Dropout(0.1))\n",
    "model_1.add(SimpleRNN(64, activation='relu'))\n",
    "model_1.add(Dropout(0.1))\n",
    "model_1.add(Dense(1, activation='linear'))\n",
    "model_1.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "#split for experiment 2\n",
    "split_point_2 = 80000\n",
    "X_train_2, y_train_2 = X[:split_point_2], y[:split_point_2]\n",
    "X_test_2, y_test_2 = X[split_point_2:split_point_2+16416], y[split_point_2:split_point_2+16416]\n",
    "\n",
    "\n",
    "#fit experiment 2\n",
    "model_2.fit(X_train_2, y_train_2, \n",
    "          epochs=100, \n",
    "          batch_size=100, \n",
    "          verbose=1,\n",
    "          callbacks=[es],\n",
    "          validation_data=(X_test_2, y_test_2))\n",
    "\n",
    "#store preds\n",
    "predictions_2 = model_2.predict(X_test_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156.24326158031107, 43754.27409938325, 209.17522343571966)"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#experiment 1 evaluation\n",
    "mae_1 = mean_absolute_error(y_test_1, predictions_1)\n",
    "mse_1 = mean_squared_error(y_test_1, predictions_1)\n",
    "rmse_1 = np.sqrt(mse_1)\n",
    "\n",
    "mae_1, mse_1, rmse_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot actual vs. pred experiment 1\n",
    "actual_1 = y_test_1\n",
    "plt.scatter(x=actual_1, y=predictions_1)\n",
    "\n",
    "#45 degree line\n",
    "x = np.linspace(0,6000)\n",
    "\n",
    "plt.plot(x, x, color='red')\n",
    "plt.suptitle('Test Results')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#time series plot experiment 1\n",
    "plt.plot(np.arange(X_test_1.shape[0]), y_test_1, color='blue')\n",
    "plt.plot(np.arange(X_test_1.shape[0]), predictions_1, color='red')\n",
    "plt.suptitle('Test Results')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Demand')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156.83019855158693, 43637.7660937023, 208.8965439965494)"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#experiment 2 evaluation\n",
    "mae_2 = mean_absolute_error(y_test_2, predictions_2)\n",
    "mse_2 = mean_squared_error(y_test_2, predictions_2)\n",
    "rmse_2 = np.sqrt(mse_2)\n",
    "\n",
    "mae_2, mse_2, rmse_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot actual vs. pred experiment 2\n",
    "actual_2 = y_test_2\n",
    "plt.scatter(x=actual_2, y=predictions_2)\n",
    "\n",
    "#45 degree line\n",
    "x = np.linspace(0,6000)\n",
    "\n",
    "plt.plot(x, x, color='red')\n",
    "plt.suptitle('Test Results')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#time series plot experiment 2\n",
    "plt.plot(np.arange(X_test_2.shape[0]), y_test_2, color='blue')\n",
    "plt.plot(np.arange(X_test_2.shape[0]), predictions_2, color='red')\n",
    "plt.suptitle('Test Results')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Demand')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. One thing I learned is how to use feature engineering to extract meaningful variables from a datetime variable. I was able to extract 'hour', 'dayofweek', 'dayofyear', 'month', and 'year'.\n",
    "\n",
    "2. Another thing I learned is how to display a time series plot. This is a good way to visualize the model's performance over time.\n",
    "\n",
    "3. Another thing I learned is how to split the data at a certain number of samples instead of doing a percentile split. This helped me with the walk-forward validation part of the assignment. \n",
    "\n",
    "4. Yes, my walk-forward validation did work as expected. The results from the walk-forward validation improved slightly from experiment 1 to experiment 2. Experiment 2 had a very slighly worse mae than experiment one but had a slightly better mse and rmse than experiment 1. \n",
    "\n",
    "5. Next time I would experiment more with different model architectures. For example, I would experiment with different reccurent units like LSTM or GRU instead of just SimpleRNN. Also, next time I could incorporate bidirectional layers to capture dependencies in both directions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}