Deep Learning/12. Sequencial Processing with Tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing Sequence using RNN and CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version:  2.6.0\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"Tensorflow version: \", tf.__version__)\n",
    "\n",
    "def cls():\n",
    "    tf.keras.backend.clear_session()\n",
    "    tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets download Ridership data\n",
    "tf.keras.utils.get_file(\n",
    "    \"ridership.tgz\",\n",
    "    \"https://github.com/ageron/data/raw/main/ridership.tgz\",\n",
    "    cache_dir=\".\",\n",
    "    extract=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>day_type</th>\n",
       "      <th>bus</th>\n",
       "      <th>rail</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>U</td>\n",
       "      <td>297192</td>\n",
       "      <td>126455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-02</th>\n",
       "      <td>W</td>\n",
       "      <td>780827</td>\n",
       "      <td>501952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-03</th>\n",
       "      <td>W</td>\n",
       "      <td>824923</td>\n",
       "      <td>536432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-04</th>\n",
       "      <td>W</td>\n",
       "      <td>870021</td>\n",
       "      <td>550011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-05</th>\n",
       "      <td>W</td>\n",
       "      <td>890426</td>\n",
       "      <td>557917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           day_type     bus    rail\n",
       "date                               \n",
       "2001-01-01        U  297192  126455\n",
       "2001-01-02        W  780827  501952\n",
       "2001-01-03        W  824923  536432\n",
       "2001-01-04        W  870021  550011\n",
       "2001-01-05        W  890426  557917"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do some preprocessing\n",
    "path = Path(\"datasets/ridership/CTA_-_Ridership_-_Daily_Boarding_Totals.csv\")\n",
    "df = pd.read_csv(path, parse_dates=[\"service_date\"])\n",
    "df.columns = [\"date\", \"day_type\", \"bus\", \"rail\", \"total\"]  # shorter names\n",
    "df = df.sort_values(\"date\").set_index(\"date\")\n",
    "df = df.drop(\"total\", axis=1)  # no need for total, it's just bus + rail\n",
    "df = df.drop_duplicates()  # remove duplicated months (2011-10 and 2014-07)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    5336\n",
       "U    1216\n",
       "A    1087\n",
       "Name: day_type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.day_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot some month of 2019\n",
    "df[\"2019-03\":\"2019-04\"].plot(grid=True, marker=\".\", figsize=(8, 3.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To remove weekly trend\n",
    "diff_7 = df[[\"bus\", \"rail\"]].diff(7)[\"2019-03\":\"2019-05\"] # Difference of current value and its next 7th value\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, sharex=True, figsize=(16, 6))\n",
    "df.plot(ax=axs[0], legend=False, marker=\".\")  # original time series\n",
    "df.shift(7).plot(ax=axs[0], grid=True, legend=False, linestyle=\":\")  # 7 day lagged value\n",
    "\n",
    "diff_7.plot(ax=axs[1], grid=True, marker=\".\")  # 7-day difference time series\n",
    "axs[0].set_ylim([170_000, 900_000])  # extra code – beautifies the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'U', 'U']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.loc[\"2019-05-25\":\"2019-05-27\"][\"day_type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus     0.082938\n",
       "rail    0.089948\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAPE of two series\n",
    "targets = df[[\"bus\", \"rail\"]][\"2019-03\":\"2019-05\"]\n",
    "(diff_7 / targets).abs().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets look at the yearly seasonality and the long term trends.\n",
    "\n",
    "period = slice(\"2001\", \"2019\")\n",
    "df_monthly = df.resample('M').mean()  # compute the mean for each month\n",
    "rolling_average_12_months = df_monthly[period].rolling(window=12).mean()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "df_monthly[period].plot(ax=ax, marker=\".\")\n",
    "rolling_average_12_months.plot(ax=ax, grid=True, legend=False)\n",
    "plt.show()\n",
    "# Green and Red is normalized or 12 month moving average value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_monthly.diff(12)[period].plot(grid=True, marker=\".\", figsize=(8, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-06-01    427758.626282\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "origin, today = \"2019-01-01\", \"2019-05-31\"\n",
    "rail_series = df.loc[origin:today][\"rail\"].asfreq(\"D\")\n",
    "model = ARIMA(rail_series,\n",
    "              order=(1, 0, 0),\n",
    "              seasonal_order=(0, 1, 1, 7))\n",
    "model = model.fit()\n",
    "y_pred = model.forecast()\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "379044"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rail\"].loc[\"2019-06-01\"]  # target value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "426932"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rail\"].loc[\"2019-05-25\"]  # naive forecast (value from one week earlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32040.720093289623"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin, start_date, end_date = \"2019-01-01\", \"2019-03-01\", \"2019-05-31\"\n",
    "time_period = pd.date_range(start_date, end_date)\n",
    "rail_series = df.loc[origin:end_date][\"rail\"].asfreq(\"D\")\n",
    "y_preds = []\n",
    "for today in time_period.shift(-1):\n",
    "    model = ARIMA(rail_series[origin:today],  # train on data up to \"today\"\n",
    "                  order=(1, 0, 0),\n",
    "                  seasonal_order=(0, 1, 1, 7))\n",
    "    model = model.fit()  # note that we retrain the model every day!\n",
    "    y_pred = model.forecast()[0]\n",
    "    y_preds.append(y_pred)\n",
    "\n",
    "y_preds = pd.Series(y_preds, index=time_period)\n",
    "mae = (y_preds - rail_series[time_period]).abs().mean()\n",
    "mae  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the SARIMA forecast\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "rail_series.loc[time_period].plot(label=\"True\", ax=ax, marker=\".\", grid=True)\n",
    "ax.plot(y_preds, color=\"r\", marker=\".\", label=\"SARIMA Forecasts\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Autocorrelation Function (ACF) and the\n",
    "# Partial Autocorrelation Function (PACF)\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
    "plot_acf(df[period][\"rail\"], ax=axs[0], lags=35)\n",
    "axs[0].grid()\n",
    "plot_pacf(df[period][\"rail\"], ax=axs[1], lags=35, method=\"ywm\")\n",
    "axs[1].grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B. Sequence-to-Vector Modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "  array([[0, 1, 2],\n",
       "         [1, 2, 3]])>,\n",
       "  <tf.Tensor: shape=(2,), dtype=int32, numpy=array([3, 4])>),\n",
       " (<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "  array([[2, 3, 4],\n",
       "         [3, 4, 5]])>,\n",
       "  <tf.Tensor: shape=(2,), dtype=int32, numpy=array([5, 6])>)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################################\n",
    "# Create empty series using tensorflow #\n",
    "########################################\n",
    "\n",
    "# Here we first store the series value in array or list\n",
    "my_series = [0, 1, 2, 3, 4, 5, 6]\n",
    "my_dataset = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    my_series,\n",
    "    targets=my_series[3:],  # the targets are 3 steps into the future\n",
    "    sequence_length=3,\n",
    "    batch_size=2,\n",
    ")\n",
    "list(my_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 \n",
      "1 2 3 4 \n",
      "2 3 4 5 \n",
      "3 4 5 6 \n",
      "4 5 6 \n",
      "5 6 \n",
      "6 \n"
     ]
    }
   ],
   "source": [
    "# Same series using tf.data module\n",
    "for window_dataset in tf.data.Dataset.range(7).window(4, shift=1):\n",
    "    for element in window_dataset:\n",
    "        print(f\"{element}\", end=\" \")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[1 2 3 4]\n",
      "[2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(6).window(4, shift=1, drop_remainder=True)\n",
    "dataset = dataset.flat_map(lambda window_dataset: window_dataset.batch(4))\n",
    "for window_tensor in dataset:\n",
    "    print(f\"{window_tensor}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<tf.Tensor: shape=(2, 3), dtype=int64, numpy=\n",
       "  array([[0, 1, 2],\n",
       "         [1, 2, 3]], dtype=int64)>,\n",
       "  <tf.Tensor: shape=(2,), dtype=int64, numpy=array([3, 4], dtype=int64)>),\n",
       " (<tf.Tensor: shape=(2, 3), dtype=int64, numpy=\n",
       "  array([[2, 3, 4],\n",
       "         [3, 4, 5]], dtype=int64)>,\n",
       "  <tf.Tensor: shape=(2,), dtype=int64, numpy=array([5, 6], dtype=int64)>)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_windows(dataset, length):\n",
    "    dataset = dataset.window(length, shift=1, drop_remainder=True) # This window function convert data into window chunk in Dataset format not Tensor\n",
    "    return dataset.flat_map(lambda window_ds: window_ds.batch(length)) # This function will convert tf.Dataset into tf.Tensor that our model need.\n",
    "dataset = to_windows(tf.data.Dataset.range(7), 4)\n",
    "dataset = dataset.map(lambda window: (window[:-1], window[-1])) # This function will convert tf.Tensor into window chunks that we did earlier\n",
    "list(dataset.batch(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<tf.Tensor: shape=(2, 3), dtype=int64, numpy=\n",
       "  array([[0, 1, 2],\n",
       "         [1, 2, 3]], dtype=int64)>,\n",
       "  <tf.Tensor: shape=(2,), dtype=int64, numpy=array([3, 4], dtype=int64)>),\n",
       " (<tf.Tensor: shape=(2, 3), dtype=int64, numpy=\n",
       "  array([[2, 3, 4],\n",
       "         [3, 4, 5]], dtype=int64)>,\n",
       "  <tf.Tensor: shape=(2,), dtype=int64, numpy=array([5, 6], dtype=int64)>)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(7)\n",
    "dataset = dataset.window(4, shift=1, drop_remainder=True)\n",
    "dataset = dataset.flat_map(lambda window: window.batch(4))\n",
    "#dataset = dataset.shuffle(20)\n",
    "dataset = dataset.map(lambda window: (window[:-1], window[-1]))\n",
    "dataset = dataset.batch(2).prefetch(1)\n",
    "list(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can combine above operation into below funtion as well\n",
    "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
    "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
    "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
    "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
    "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
    "  dataset = dataset.batch(batch_size).prefetch(1)\n",
    "  return dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "# Data splitting #\n",
    "##################\n",
    "\n",
    "# data normalizing\n",
    "rail_train = df[\"rail\"][\"2016-01\":\"2018-12\"] / 1e6\n",
    "rail_valid = df[\"rail\"][\"2019-01\":\"2019-05\"] / 1e6\n",
    "rail_test = df[\"rail\"][\"2019-06\":] / 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2016-01-01    0.319835\n",
       "2016-01-02    0.365509\n",
       "2016-01-03    0.287661\n",
       "2016-01-04    0.703185\n",
       "2016-01-05    0.727716\n",
       "Name: rail, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rail_train.head() # univarient series,  3 years of data single value for 1 day ==> 365*3 +1 = 1096 total values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_length = 5\n",
    "batch_size = 12\n",
    "cls()\n",
    "# Sequence to vector kind of data formatting\n",
    "# 56 sequence of values give 57th prediction.\n",
    "# It drop the last sequence /drop_remainder=True\n",
    "train_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    rail_train.to_numpy(),\n",
    "    targets=rail_train[seq_length:],\n",
    "    sequence_length=seq_length,\n",
    "    shuffle=True,\n",
    "    batch_size=batch_size,\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "valid_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    rail_valid.to_numpy(),\n",
    "    targets=rail_valid[seq_length:],\n",
    "    sequence_length=seq_length,\n",
    "    batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(12, 5), dtype=float64, numpy=\n",
       " array([[0.430711, 0.704247, 0.735234, 0.732813, 0.74543 ],\n",
       "        [0.737535, 0.726872, 0.720483, 0.405211, 0.296805],\n",
       "        [0.721661, 0.729861, 0.75728 , 0.861954, 0.831628],\n",
       "        [0.514174, 0.58774 , 0.591081, 0.572525, 0.292077],\n",
       "        [0.766072, 0.750353, 0.758014, 0.767974, 0.406616],\n",
       "        [0.748703, 0.75806 , 0.732007, 0.404779, 0.304418],\n",
       "        [0.477767, 0.351048, 0.716772, 0.700422, 0.734845],\n",
       "        [0.344784, 0.336441, 0.722529, 0.745189, 0.771134],\n",
       "        [0.426851, 0.327855, 0.74655 , 0.77778 , 0.752143],\n",
       "        [0.336109, 0.740387, 0.763274, 0.779477, 0.785637],\n",
       "        [0.891357, 1.146516, 0.531687, 0.380648, 0.809878],\n",
       "        [0.323263, 0.245376, 0.419604, 0.710906, 0.71368 ]])>,\n",
       " <tf.Tensor: shape=(12,), dtype=float64, numpy=\n",
       " array([0.756699, 0.702129, 0.521622, 0.226604, 0.345432, 0.690624,\n",
       "        0.751673, 0.796256, 0.744199, 0.760661, 0.79187 , 0.720369])>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(train_ds)[0] # 12*5 => 12 batch 5 cotinuous sequence number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2016-01-01    0.319835\n",
       "2016-01-02    0.365509\n",
       "2016-01-03    0.287661\n",
       "2016-01-04    0.703185\n",
       "2016-01-05    0.727716\n",
       "2016-01-06    0.733167\n",
       "2016-01-07    0.736809\n",
       "2016-01-08    0.732495\n",
       "2016-01-09    0.391597\n",
       "2016-01-10    0.263073\n",
       "2016-01-11    0.694734\n",
       "2016-01-12    0.705694\n",
       "Name: rail, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rail_train.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Simple Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 3ms/step - loss: 0.6543 - mae: 1.1400 - val_loss: 0.4228 - val_mae: 0.8729\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.3549 - mae: 0.7873 - val_loss: 0.2119 - val_mae: 0.5699\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 1ms/step - loss: 0.1718 - mae: 0.5131 - val_loss: 0.1088 - val_mae: 0.4039\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 1ms/step - loss: 0.0906 - mae: 0.3667 - val_loss: 0.0702 - val_mae: 0.3240\n",
      "13/13 [==============================] - 0s 767us/step - loss: 0.0702 - mae: 0.3240\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "323983.8182926178"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###########################################\n",
    "# Lets first build a simple Linear model. #\n",
    "###########################################\n",
    "# Suppose these 5 are not sequence they are features: we try to fit a linear combination of these 5 features\n",
    "cls()\n",
    "model = tf.keras.Sequential([\n",
    "                tf.keras.layers.Dense(1, input_shape=[seq_length])]) # seq_length = 5 \n",
    "\n",
    "model.compile(loss=tf.keras.losses.Huber(), optimizer=\"adam\", metrics=[\"mae\"])\n",
    "history = model.fit(train_ds, validation_data=valid_ds, epochs=4) # (12,5) (12,)\n",
    "valid_loss, valid_mae = model.evaluate(valid_ds)\n",
    "valid_mae * 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights, bias = model.get_weights()\n",
    "len(weights), len(bias) # Total 5 parameters: 4 of feature neuron + 1 bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Simple RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda (Lambda)              (None, None, 1)           0         \n",
      "_________________________________________________________________\n",
      "simple_rnn (SimpleRNN)       (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 3\n",
      "Trainable params: 3\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "###########################################\n",
    "# Building the same model using RNN layer #\n",
    "###########################################\n",
    "\n",
    "#defines a utility function we'll reuse several time\n",
    "def fit_and_evaluate(model, train_set, valid_set, learning_rate, epochs=4, metrics=[\"mae\"]):\n",
    "    opt = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=0.9)\n",
    "    model.compile(loss=tf.keras.losses.Huber(), optimizer=opt, metrics=metrics)\n",
    "    history = model.fit(train_set, validation_data=valid_set, epochs=epochs)\n",
    "    valid_loss, valid_mae = model.evaluate(valid_set)\n",
    "    return valid_mae\n",
    "\n",
    "cls()\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1), input_shape=[None]), # Axis -1 insert 1 at last position [12,5]--> [12,5,1]\n",
    "    tf.keras.layers.SimpleRNN(1) # Default activation function is tanh so output would be in range -1 to 1\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.5700599]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([0.], dtype=float32)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights() # Wxx, Wxh, Wb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 3ms/step - loss: 0.0192 - mae: 0.1597 - val_loss: 0.0210 - val_mae: 0.1547\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.0166 - mae: 0.1573 - val_loss: 0.0207 - val_mae: 0.1483\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.0161 - mae: 0.1551 - val_loss: 0.0189 - val_mae: 0.1524\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.0157 - mae: 0.1547 - val_loss: 0.0187 - val_mae: 0.1503\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.0187 - mae: 0.1503\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.15025538206100464"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_and_evaluate(model, train_ds, valid_ds, learning_rate=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.4148249]], dtype=float32),\n",
       " array([[0.5648627]], dtype=float32),\n",
       " array([0.15581825], dtype=float32)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights() # Wxx, Wxh, Wb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Deep RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([12, 5, 1])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds2 = train_ds.map(lambda x,y: (tf.expand_dims(x, axis=-1), tf.expand_dims(y, axis=-1))) # [12,5,1]\n",
    "valid_ds2 = valid_ds.map(lambda x,y: (tf.expand_dims(x, axis=-1), tf.expand_dims(y, axis=-1))) # [12,5,1]\n",
    "x_test, y_test = list(valid_ds2.take(1))[0][0], list(valid_ds2.take(1))[0][1]\n",
    "list(train_ds2.take(1))[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn (SimpleRNN)       (None, None, 10)          120       \n",
      "_________________________________________________________________\n",
      "simple_rnn_1 (SimpleRNN)     (None, None, 5)           80        \n",
      "_________________________________________________________________\n",
      "simple_rnn_2 (SimpleRNN)     (None, 1)                 7         \n",
      "=================================================================\n",
      "Total params: 207\n",
      "Trainable params: 207\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "###################################################################################\n",
    "# input_feature*neuron + neuron*neuron(self weight and output weight) + bias term #\n",
    "###################################################################################\n",
    "cls()\n",
    "# We don't need to pass the sequence length\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 1]),\n",
    "    tf.keras.layers.SimpleRNN(5, return_sequences=True),\n",
    "    tf.keras.layers.SimpleRNN(1) # RNN expect input in 3D so this layer expect input [None, None, 50]\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 2s 6ms/step - loss: 0.0140 - mae: 0.1396 - val_loss: 0.0145 - val_mae: 0.1256\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.1258 - val_loss: 0.0109 - val_mae: 0.1151\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0130 - mae: 0.1290 - val_loss: 0.0120 - val_mae: 0.1229\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0114 - mae: 0.1215 - val_loss: 0.0168 - val_mae: 0.1398\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.0168 - mae: 0.1398\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.13982592523097992"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_and_evaluate(model, train_ds2, valid_ds2, learning_rate=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.6590329 ],\n",
       "        [0.6375895 ],\n",
       "        [0.8503542 ],\n",
       "        [0.7543295 ],\n",
       "        [0.75746006]], dtype=float32),\n",
       " <tf.Tensor: shape=(5, 1), dtype=float64, numpy=\n",
       " array([[0.260556],\n",
       "        [0.66247 ],\n",
       "        [0.701693],\n",
       "        [0.685444],\n",
       "        [0.690162]])>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction of first 5 instances\n",
    "model.predict(x_test[:5]), y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn (SimpleRNN)       (None, None, 10)          120       \n",
      "_________________________________________________________________\n",
      "simple_rnn_1 (SimpleRNN)     (None, 5)                 80        \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 206\n",
      "Trainable params: 206\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cls()\n",
    "####################################################################################################\n",
    "# It is preferable to replace the last layer with Dense bcz\n",
    "# 1. RNN learn the sequence pattern most with the previous hidden layers. So we can ignore RNN layer at last\n",
    "# 2. RNN use tanh default means output would be -1 to 1. so if we need to use another activation function then dense will help.\n",
    "###################################################################################################\n",
    "\n",
    "# if you use return_sequence True in 2nd last layer then you get \n",
    "# output of all time step\n",
    "\n",
    "univar_model = tf.keras.models.Sequential([\n",
    "        tf.keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 1]),\n",
    "        tf.keras.layers.SimpleRNN(5),\n",
    "        tf.keras.layers.Dense(1),\n",
    "])\n",
    "univar_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 5ms/step - loss: 0.0186 - mae: 0.1572 - val_loss: 0.0142 - val_mae: 0.1245\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0107 - mae: 0.1212 - val_loss: 0.0137 - val_mae: 0.1264\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0101 - mae: 0.1160 - val_loss: 0.0109 - val_mae: 0.1120\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0100 - mae: 0.1153 - val_loss: 0.0161 - val_mae: 0.1468\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.0161 - mae: 0.1468\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.14680089056491852"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_and_evaluate(univar_model, train_ds2, valid_ds2, learning_rate=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.6590329 ],\n",
       "        [0.6375895 ],\n",
       "        [0.8503542 ],\n",
       "        [0.7543295 ],\n",
       "        [0.75746006]], dtype=float32),\n",
       " <tf.Tensor: shape=(5, 1), dtype=float64, numpy=\n",
       " array([[0.260556],\n",
       "        [0.66247 ],\n",
       "        [0.701693],\n",
       "        [0.685444],\n",
       "        [0.690162]])>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction of first 5 instances\n",
    "model.predict(x_test[:5]), y_test[:5]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 6ms/step - loss: 0.0244 - mae: 0.1789 - val_loss: 0.0200 - val_mae: 0.1464\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0163 - mae: 0.1540 - val_loss: 0.0202 - val_mae: 0.1417\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0160 - mae: 0.1518 - val_loss: 0.0189 - val_mae: 0.1387\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0155 - mae: 0.1487 - val_loss: 0.0194 - val_mae: 0.1384\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.0194 - mae: 0.1384\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.13843156397342682"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When you use return_sequence in last layer\n",
    "univar_model = tf.keras.models.Sequential([\n",
    "        tf.keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 1]),\n",
    "        tf.keras.layers.SimpleRNN(5, return_sequences=True), # It will give 3D matrix to next layer\n",
    "        tf.keras.layers.Dense(1),\n",
    "])\n",
    "fit_and_evaluate(univar_model, train_ds2, valid_ds2, learning_rate=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0.6392189 ],\n",
       "         [0.68810093],\n",
       "         [0.74831986],\n",
       "         [0.6426396 ],\n",
       "         [0.7086762 ]],\n",
       " \n",
       "        [[0.65313405],\n",
       "         [0.66444   ],\n",
       "         [0.6644804 ],\n",
       "         [0.6567253 ],\n",
       "         [0.7032294 ]],\n",
       " \n",
       "        [[0.6539015 ],\n",
       "         [0.662523  ],\n",
       "         [0.6778616 ],\n",
       "         [0.747851  ],\n",
       "         [0.77306837]],\n",
       " \n",
       "        [[0.6539093 ],\n",
       "         [0.6682202 ],\n",
       "         [0.7714013 ],\n",
       "         [0.7250874 ],\n",
       "         [0.7818424 ]],\n",
       " \n",
       "        [[0.64542115],\n",
       "         [0.68744004],\n",
       "         [0.81177104],\n",
       "         [0.65422666],\n",
       "         [0.7147461 ]]], dtype=float32),\n",
       " <tf.Tensor: shape=(5, 1), dtype=float64, numpy=\n",
       " array([[0.260556],\n",
       "        [0.66247 ],\n",
       "        [0.701693],\n",
       "        [0.685444],\n",
       "        [0.690162]])>)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction of first 5 instances\n",
    "univar_model.predict(x_test[:5]), y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As you can see model returns the output of each timestep as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. MultiVarient Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>bus</th>\n",
       "      <th>rail</th>\n",
       "      <th>next_day_type_A</th>\n",
       "      <th>next_day_type_U</th>\n",
       "      <th>next_day_type_W</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.303321</td>\n",
       "      <td>0.319835</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.448859</td>\n",
       "      <td>0.365509</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>0.340540</td>\n",
       "      <td>0.287661</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.829429</td>\n",
       "      <td>0.703185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.846789</td>\n",
       "      <td>0.727716</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.853904</td>\n",
       "      <td>0.733167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>0.846274</td>\n",
       "      <td>0.736809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 bus      rail  next_day_type_A  next_day_type_U  \\\n",
       "date                                                               \n",
       "2016-01-01  0.303321  0.319835                1                0   \n",
       "2016-01-02  0.448859  0.365509                0                1   \n",
       "2016-01-03  0.340540  0.287661                0                0   \n",
       "2016-01-04  0.829429  0.703185                0                0   \n",
       "2016-01-05  0.846789  0.727716                0                0   \n",
       "2016-01-06  0.853904  0.733167                0                0   \n",
       "2016-01-07  0.846274  0.736809                0                0   \n",
       "\n",
       "            next_day_type_W  \n",
       "date                         \n",
       "2016-01-01                0  \n",
       "2016-01-02                0  \n",
       "2016-01-03                1  \n",
       "2016-01-04                1  \n",
       "2016-01-05                1  \n",
       "2016-01-06                1  \n",
       "2016-01-07                1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mulvar = df[[\"bus\", \"rail\"]] / 1e6  # use both bus & rail series as input\n",
    "df_mulvar[\"next_day_type\"] = df[\"day_type\"].shift(-1)  # we know tomorrow's type\n",
    "df_mulvar = pd.get_dummies(df_mulvar)  # one-hot encode the day type\n",
    "\n",
    "mulvar_train = df_mulvar[\"2016-01\":\"2018-12\"]\n",
    "mulvar_valid = df_mulvar[\"2019-01\":\"2019-05\"]\n",
    "mulvar_test  = df_mulvar[\"2019-06\":]\n",
    "mulvar_train.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls()\n",
    "train_mulvar_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    mulvar_train.to_numpy(),  # use all 5 columns as input\n",
    "    targets=mulvar_train[\"rail\"][seq_length:],  # forecast only the rail series\n",
    "    sequence_length=seq_length,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    seed=42\n",
    ")\n",
    "valid_mulvar_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    mulvar_valid.to_numpy(),\n",
    "    targets=mulvar_valid[\"rail\"][seq_length:],\n",
    "    sequence_length=seq_length,\n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<tf.Tensor: shape=(12, 5, 5), dtype=float64, numpy=\n",
       "  array([[[0.303321, 0.319835, 1.      , 0.      , 0.      ],\n",
       "          [0.448859, 0.365509, 0.      , 1.      , 0.      ],\n",
       "          [0.34054 , 0.287661, 0.      , 0.      , 1.      ],\n",
       "          [0.829429, 0.703185, 0.      , 0.      , 1.      ],\n",
       "          [0.846789, 0.727716, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.448859, 0.365509, 0.      , 1.      , 0.      ],\n",
       "          [0.34054 , 0.287661, 0.      , 0.      , 1.      ],\n",
       "          [0.829429, 0.703185, 0.      , 0.      , 1.      ],\n",
       "          [0.846789, 0.727716, 0.      , 0.      , 1.      ],\n",
       "          [0.853904, 0.733167, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.34054 , 0.287661, 0.      , 0.      , 1.      ],\n",
       "          [0.829429, 0.703185, 0.      , 0.      , 1.      ],\n",
       "          [0.846789, 0.727716, 0.      , 0.      , 1.      ],\n",
       "          [0.853904, 0.733167, 0.      , 0.      , 1.      ],\n",
       "          [0.846274, 0.736809, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.829429, 0.703185, 0.      , 0.      , 1.      ],\n",
       "          [0.846789, 0.727716, 0.      , 0.      , 1.      ],\n",
       "          [0.853904, 0.733167, 0.      , 0.      , 1.      ],\n",
       "          [0.846274, 0.736809, 0.      , 0.      , 1.      ],\n",
       "          [0.841494, 0.732495, 1.      , 0.      , 0.      ]],\n",
       "  \n",
       "         [[0.846789, 0.727716, 0.      , 0.      , 1.      ],\n",
       "          [0.853904, 0.733167, 0.      , 0.      , 1.      ],\n",
       "          [0.846274, 0.736809, 0.      , 0.      , 1.      ],\n",
       "          [0.841494, 0.732495, 1.      , 0.      , 0.      ],\n",
       "          [0.469539, 0.391597, 0.      , 1.      , 0.      ]],\n",
       "  \n",
       "         [[0.853904, 0.733167, 0.      , 0.      , 1.      ],\n",
       "          [0.846274, 0.736809, 0.      , 0.      , 1.      ],\n",
       "          [0.841494, 0.732495, 1.      , 0.      , 0.      ],\n",
       "          [0.469539, 0.391597, 0.      , 1.      , 0.      ],\n",
       "          [0.306896, 0.263073, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.846274, 0.736809, 0.      , 0.      , 1.      ],\n",
       "          [0.841494, 0.732495, 1.      , 0.      , 0.      ],\n",
       "          [0.469539, 0.391597, 0.      , 1.      , 0.      ],\n",
       "          [0.306896, 0.263073, 0.      , 0.      , 1.      ],\n",
       "          [0.753006, 0.694734, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.841494, 0.732495, 1.      , 0.      , 0.      ],\n",
       "          [0.469539, 0.391597, 0.      , 1.      , 0.      ],\n",
       "          [0.306896, 0.263073, 0.      , 0.      , 1.      ],\n",
       "          [0.753006, 0.694734, 0.      , 0.      , 1.      ],\n",
       "          [0.760149, 0.705694, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.469539, 0.391597, 0.      , 1.      , 0.      ],\n",
       "          [0.306896, 0.263073, 0.      , 0.      , 1.      ],\n",
       "          [0.753006, 0.694734, 0.      , 0.      , 1.      ],\n",
       "          [0.760149, 0.705694, 0.      , 0.      , 1.      ],\n",
       "          [0.78722 , 0.716663, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.306896, 0.263073, 0.      , 0.      , 1.      ],\n",
       "          [0.753006, 0.694734, 0.      , 0.      , 1.      ],\n",
       "          [0.760149, 0.705694, 0.      , 0.      , 1.      ],\n",
       "          [0.78722 , 0.716663, 0.      , 0.      , 1.      ],\n",
       "          [0.859099, 0.758116, 0.      , 0.      , 1.      ]],\n",
       "  \n",
       "         [[0.753006, 0.694734, 0.      , 0.      , 1.      ],\n",
       "          [0.760149, 0.705694, 0.      , 0.      , 1.      ],\n",
       "          [0.78722 , 0.716663, 0.      , 0.      , 1.      ],\n",
       "          [0.859099, 0.758116, 0.      , 0.      , 1.      ],\n",
       "          [0.858032, 0.742622, 1.      , 0.      , 0.      ]],\n",
       "  \n",
       "         [[0.760149, 0.705694, 0.      , 0.      , 1.      ],\n",
       "          [0.78722 , 0.716663, 0.      , 0.      , 1.      ],\n",
       "          [0.859099, 0.758116, 0.      , 0.      , 1.      ],\n",
       "          [0.858032, 0.742622, 1.      , 0.      , 0.      ],\n",
       "          [0.486797, 0.404212, 0.      , 1.      , 0.      ]]])>,\n",
       "  <tf.Tensor: shape=(12,), dtype=float64, numpy=\n",
       "  array([0.733167, 0.736809, 0.732495, 0.391597, 0.263073, 0.694734,\n",
       "         0.705694, 0.716663, 0.758116, 0.742622, 0.404212, 0.24116 ])>)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(train_mulvar_ds.take(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset shapes: ((None, None, 5), (None,)), types: (tf.float64, tf.float64)>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mulvar_ds # this dataset has 5 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 6ms/step - loss: 0.0152 - mae: 0.1199 - val_loss: 0.0091 - val_mae: 0.1221\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0051 - mae: 0.0784 - val_loss: 0.0092 - val_mae: 0.1240\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0050 - mae: 0.0780 - val_loss: 0.0086 - val_mae: 0.1188\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0050 - mae: 0.0790 - val_loss: 0.0077 - val_mae: 0.1101\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.0077 - mae: 0.1101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.11010503023862839"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls()\n",
    "mulvar_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.SimpleRNN(5),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "#mulvar_model.summary()\n",
    "fit_and_evaluate(mulvar_model, train_mulvar_ds, valid_mulvar_ds, learning_rate=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(1, 5, 5), dtype=float64, numpy=\n",
       " array([[[0.248879, 0.245852, 0.      , 0.      , 1.      ],\n",
       "         [0.591006, 0.573542, 0.      , 0.      , 1.      ],\n",
       "         [0.664442, 0.627781, 0.      , 0.      , 1.      ],\n",
       "         [0.668812, 0.628514, 1.      , 0.      , 0.      ],\n",
       "         [0.444434, 0.348257, 0.      , 1.      , 0.      ]]])>,\n",
       " <tf.Tensor: shape=(1,), dtype=float64, numpy=array([0.260556])>)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_mul, y_test_mul = list(valid_mulvar_ds.take(1))[0][0][:1], list(valid_mulvar_ds.take(1))[0][1][:1]\n",
    "x_test_mul, y_test_mul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.21070781]], dtype=float32),\n",
       " <tf.Tensor: shape=(1,), dtype=float64, numpy=array([0.260556])>)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mulvar_model.predict(x_test_mul), y_test_mul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252991.39726027398"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluates the naive forecasts for bus\n",
    "bus_naive = mulvar_valid[\"bus\"].shift(5)[seq_length:]\n",
    "bus_target = mulvar_valid[\"bus\"][seq_length:]\n",
    "(bus_target - bus_naive).abs().mean() * 1e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Multiple Series Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 5ms/step - loss: 0.0210 - mae: 0.1158 - val_loss: 0.0063 - val_mae: 0.0719\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0024 - mae: 0.0488 - val_loss: 0.0040 - val_mae: 0.0519\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.0022 - mae: 0.0471 - val_loss: 0.0037 - val_mae: 0.0482\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.0020 - mae: 0.0452 - val_loss: 0.0045 - val_mae: 0.0588\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.0045 - mae: 0.0588\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.058799803256988525"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#####################################################################\n",
    "# build and train a multitask RNN that forecasts both bus and rail  #\n",
    "# Just change the target variable                                   #\n",
    "#####################################################################\n",
    "cls()\n",
    "train_multask_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    mulvar_train.to_numpy(),\n",
    "    targets=mulvar_train[[\"bus\", \"rail\"]][seq_length:],  # 2 targets per day\n",
    "    sequence_length=seq_length,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    seed=42\n",
    ")\n",
    "valid_multask_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    mulvar_valid.to_numpy(),\n",
    "    targets=mulvar_valid[[\"bus\", \"rail\"]][seq_length:],\n",
    "    sequence_length=seq_length,\n",
    "    batch_size=batch_size\n",
    ")\n",
    "\n",
    "multask_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.SimpleRNN(32, input_shape=[None, 5]),\n",
    "    tf.keras.layers.Dense(2) # Dense Layer always expect input in 2D\n",
    "])\n",
    "fit_and_evaluate(multask_model, train_multask_ds, valid_multask_ds, learning_rate=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(1, 5, 5), dtype=float64, numpy=\n",
       " array([[[0.248879, 0.245852, 0.      , 0.      , 1.      ],\n",
       "         [0.591006, 0.573542, 0.      , 0.      , 1.      ],\n",
       "         [0.664442, 0.627781, 0.      , 0.      , 1.      ],\n",
       "         [0.668812, 0.628514, 1.      , 0.      , 0.      ],\n",
       "         [0.444434, 0.348257, 0.      , 1.      , 0.      ]]])>,\n",
       " <tf.Tensor: shape=(1, 2), dtype=float64, numpy=array([[0.318623, 0.260556]])>)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X and Y\n",
    "list(valid_multask_ds.take(1))[0][0][:1], list(valid_multask_ds.take(1))[0][1][:1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.24051149, 0.26643598]], dtype=float32),\n",
       " <tf.Tensor: shape=(1, 2), dtype=float64, numpy=array([[0.318623, 0.260556]])>)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the predicted and actual value\n",
    "multask_model.predict(list(valid_multask_ds.take(1))[0][0][:1]), list(valid_multask_ds.take(1))[0][1][:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bus 59543\n",
      "rail 58055\n"
     ]
    }
   ],
   "source": [
    "#evaluates the multitask RNN's forecasts both bus and rail\n",
    "Y_preds_valid = multask_model.predict(valid_multask_ds)\n",
    "for idx, name in enumerate([\"bus\", \"rail\"]):\n",
    "    mae = 1e6 * tf.keras.metrics.mean_absolute_error(mulvar_valid[name][seq_length:], Y_preds_valid[:, idx])\n",
    "    print(name, int(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Forecasting Several Step Aheads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 5, 1)\n"
     ]
    }
   ],
   "source": [
    "##############################################\n",
    "# Method-1: Get forecast in loop with same   #\n",
    "# trained model                              #\n",
    "##############################################\n",
    "#import numpy as np\n",
    "\n",
    "# Let predict the next 5 values after  \n",
    "X = rail_valid.to_numpy()[np.newaxis, :seq_length, np.newaxis]\n",
    "print(X.shape)\n",
    "for step_ahead in range(5):\n",
    "    y_pred_one = univar_model.predict(X)# since univar_model return 5 time step forecast so we will take only last one\n",
    "    y_pred_one = y_pred_one[0][-1].reshape(1,1,1)\n",
    "    X = np.concatenate([X, y_pred_one.reshape(1, 1, 1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Method 1: Looping\n"
     ]
    },
    {
     "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",
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       "    }\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>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predicted  Actual  Difference\n",
       "0       0.25    0.25        0.00\n",
       "1       0.57    0.57        0.00\n",
       "2       0.63    0.63        0.00\n",
       "3       0.63    0.63        0.00\n",
       "4       0.35    0.35        0.00\n",
       "5       0.71    0.26        0.45\n",
       "6       0.83    0.66        0.17\n",
       "7       0.92    0.70        0.22\n",
       "8       0.85    0.69        0.17\n",
       "9       0.75    0.69        0.06"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Using Method 1: Looping\")\n",
    "pd.DataFrame({\"Predicted\":X.flatten(), \"Actual\":rail_valid.to_numpy()[:10], \"Difference\":np.abs((rail_valid.to_numpy()[:10]-X.flatten()))}).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################\n",
    "# Method-2: Train RNN to predict all values  #\n",
    "##############################################\n",
    "cls()\n",
    "\n",
    "def split_inputs_and_targets(mulvar_series, ahead=5, target_col=1):\n",
    "    return mulvar_series[:, :-ahead], mulvar_series[:, -ahead:, target_col] # splitting first 5 instance into X and next 5 into Y\n",
    "\n",
    "ahead_train_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    mulvar_train.to_numpy(),\n",
    "    targets=None,\n",
    "    sequence_length=seq_length + seq_length, # We are making sequence of 10 instance\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    seed=42\n",
    ").map(split_inputs_and_targets)\n",
    "ahead_valid_ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "    mulvar_valid.to_numpy(),\n",
    "    targets=None,\n",
    "    sequence_length=seq_length + seq_length,\n",
    "    batch_size=batch_size\n",
    ").map(split_inputs_and_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>bus</th>\n",
       "      <th>rail</th>\n",
       "      <th>next_day_type_A</th>\n",
       "      <th>next_day_type_U</th>\n",
       "      <th>next_day_type_W</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.303321</td>\n",
       "      <td>0.319835</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.448859</td>\n",
       "      <td>0.365509</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>0.340540</td>\n",
       "      <td>0.287661</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.829429</td>\n",
       "      <td>0.703185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.846789</td>\n",
       "      <td>0.727716</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.853904</td>\n",
       "      <td>0.733167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>0.846274</td>\n",
       "      <td>0.736809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.841494</td>\n",
       "      <td>0.732495</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>0.469539</td>\n",
       "      <td>0.391597</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>0.306896</td>\n",
       "      <td>0.263073</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 bus      rail  next_day_type_A  next_day_type_U  \\\n",
       "date                                                               \n",
       "2016-01-01  0.303321  0.319835                1                0   \n",
       "2016-01-02  0.448859  0.365509                0                1   \n",
       "2016-01-03  0.340540  0.287661                0                0   \n",
       "2016-01-04  0.829429  0.703185                0                0   \n",
       "2016-01-05  0.846789  0.727716                0                0   \n",
       "2016-01-06  0.853904  0.733167                0                0   \n",
       "2016-01-07  0.846274  0.736809                0                0   \n",
       "2016-01-08  0.841494  0.732495                1                0   \n",
       "2016-01-09  0.469539  0.391597                0                1   \n",
       "2016-01-10  0.306896  0.263073                0                0   \n",
       "\n",
       "            next_day_type_W  \n",
       "date                         \n",
       "2016-01-01                0  \n",
       "2016-01-02                0  \n",
       "2016-01-03                1  \n",
       "2016-01-04                1  \n",
       "2016-01-05                1  \n",
       "2016-01-06                1  \n",
       "2016-01-07                1  \n",
       "2016-01-08                0  \n",
       "2016-01-09                0  \n",
       "2016-01-10                1  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mulvar_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(1, 5, 5), dtype=float64, numpy=\n",
       " array([[[0.303321, 0.319835, 1.      , 0.      , 0.      ],\n",
       "         [0.448859, 0.365509, 0.      , 1.      , 0.      ],\n",
       "         [0.34054 , 0.287661, 0.      , 0.      , 1.      ],\n",
       "         [0.829429, 0.703185, 0.      , 0.      , 1.      ],\n",
       "         [0.846789, 0.727716, 0.      , 0.      , 1.      ]]])>,\n",
       " <tf.Tensor: shape=(1, 5), dtype=float64, numpy=array([[0.733167, 0.736809, 0.732495, 0.391597, 0.263073]])>)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ahead_train_ds.take(1))[0][0][:1], list(ahead_train_ds.take(1))[0][1][:1] # Showing first batch's first instance X and Y\n",
    "# You can see 5 rows feed into the model will predict 5 next values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 2s 5ms/step - loss: 0.0454 - mae: 0.2251 - val_loss: 0.0205 - val_mae: 0.1470\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0165 - mae: 0.1552 - val_loss: 0.0187 - val_mae: 0.1387\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0151 - mae: 0.1472 - val_loss: 0.0173 - val_mae: 0.1325\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 2ms/step - loss: 0.0139 - mae: 0.1396 - val_loss: 0.0161 - val_mae: 0.1285\n",
      "12/12 [==============================] - 0s 1ms/step - loss: 0.0161 - mae: 0.1285\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.12851522862911224"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls()\n",
    "ahead_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.SimpleRNN(10, return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.SimpleRNN(5),\n",
    "    tf.keras.layers.Dense(5) # As we want 5 next values\n",
    "])\n",
    "fit_and_evaluate(ahead_model, ahead_train_ds, ahead_valid_ds,learning_rate=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.48013747, 0.550444  , 0.65839565, 0.68533003, 0.73855877]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = ahead_model.predict(list(ahead_valid_ds.take(1))[0][0][:1])  # shape [1, 5]\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Method 2: Without Looping\n"
     ]
    },
    {
     "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>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predicted  Actual  Difference\n",
       "0       0.48    0.26        0.22\n",
       "1       0.55    0.66        0.11\n",
       "2       0.66    0.70        0.04\n",
       "3       0.69    0.69        0.00\n",
       "4       0.74    0.69        0.05"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Using Method 2: Without Looping\")\n",
    "pd.DataFrame({\"Predicted\":Y_pred.flatten(), \"Actual\":rail_valid.iloc[5:10].values, \"Difference\":np.abs((rail_valid.iloc[5:10].values-Y_pred.flatten()))}).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can see result are better then Method:1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C. Sequence-to-Sequence Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "Sequence to sequence kind of architecture\n",
    "\n",
    "Now let's create an RNN that predicts the next 5 steps at each time step. That is, instead of just forecasting time steps 5 to 10 based on time steps 0 to 5, it will forecast time steps 1 to 6 at time step 0, then time steps 2 to 7 at time step 1, and so on, and finally it will forecast time steps 6 to 11 at the last time step. \n",
    "`Notice that the model is causal: when it makes predictions at any time step, it can only see past time steps.`\n",
    "\n",
    "To prepare the datasets, we can use to_windows() twice, to get sequences of consecutive windows, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(4, 3), dtype=int64, numpy=\n",
       " array([[0, 1, 2],\n",
       "        [1, 2, 3],\n",
       "        [2, 3, 4],\n",
       "        [3, 4, 5]], dtype=int64)>,\n",
       " <tf.Tensor: shape=(4, 3), dtype=int64, numpy=\n",
       " array([[1, 2, 3],\n",
       "        [2, 3, 4],\n",
       "        [3, 4, 5],\n",
       "        [4, 5, 6]], dtype=int64)>]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series = tf.data.Dataset.range(7)\n",
    "dataset = to_windows(to_windows(my_series, 3), 4)\n",
    "list(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 1, 2, 3], dtype=int64)>,\n",
       "  <tf.Tensor: shape=(4, 2), dtype=int64, numpy=\n",
       "  array([[1, 2],\n",
       "         [2, 3],\n",
       "         [3, 4],\n",
       "         [4, 5]], dtype=int64)>),\n",
       " (<tf.Tensor: shape=(4,), dtype=int64, numpy=array([1, 2, 3, 4], dtype=int64)>,\n",
       "  <tf.Tensor: shape=(4, 2), dtype=int64, numpy=\n",
       "  array([[2, 3],\n",
       "         [3, 4],\n",
       "         [4, 5],\n",
       "         [5, 6]], dtype=int64)>)]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.map(lambda S: (S[:, 0], S[:, 1:]))\n",
    "list(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_seq2seq_dataset(series, seq_length=5, ahead=6, target_col=1, batch_size=12, shuffle=False, seed=None):\n",
    "    ds = to_windows(tf.data.Dataset.from_tensor_slices(series), ahead + 1)\n",
    "    ds = to_windows(ds, seq_length).map(lambda S: (S[:, 0], S[:, 1:, 1]))\n",
    "    if shuffle:\n",
    "        ds = ds.shuffle(8 * batch_size, seed=seed)\n",
    "    return ds.batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq2seq_train = to_seq2seq_dataset(mulvar_train, shuffle=False, seed=42)\n",
    "seq2seq_valid = to_seq2seq_dataset(mulvar_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "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>bus</th>\n",
       "      <th>rail</th>\n",
       "      <th>next_day_type_A</th>\n",
       "      <th>next_day_type_U</th>\n",
       "      <th>next_day_type_W</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.303321</td>\n",
       "      <td>0.319835</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.448859</td>\n",
       "      <td>0.365509</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>0.340540</td>\n",
       "      <td>0.287661</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.829429</td>\n",
       "      <td>0.703185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.846789</td>\n",
       "      <td>0.727716</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.853904</td>\n",
       "      <td>0.733167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>0.846274</td>\n",
       "      <td>0.736809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.841494</td>\n",
       "      <td>0.732495</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>0.469539</td>\n",
       "      <td>0.391597</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>0.306896</td>\n",
       "      <td>0.263073</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 bus      rail  next_day_type_A  next_day_type_U  \\\n",
       "date                                                               \n",
       "2016-01-01  0.303321  0.319835                1                0   \n",
       "2016-01-02  0.448859  0.365509                0                1   \n",
       "2016-01-03  0.340540  0.287661                0                0   \n",
       "2016-01-04  0.829429  0.703185                0                0   \n",
       "2016-01-05  0.846789  0.727716                0                0   \n",
       "2016-01-06  0.853904  0.733167                0                0   \n",
       "2016-01-07  0.846274  0.736809                0                0   \n",
       "2016-01-08  0.841494  0.732495                1                0   \n",
       "2016-01-09  0.469539  0.391597                0                1   \n",
       "2016-01-10  0.306896  0.263073                0                0   \n",
       "\n",
       "            next_day_type_W  \n",
       "date                         \n",
       "2016-01-01                0  \n",
       "2016-01-02                0  \n",
       "2016-01-03                1  \n",
       "2016-01-04                1  \n",
       "2016-01-05                1  \n",
       "2016-01-06                1  \n",
       "2016-01-07                1  \n",
       "2016-01-08                0  \n",
       "2016-01-09                0  \n",
       "2016-01-10                1  "
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By passing first 5 sequence we try to predict rail's next 5 sequence\n",
    "mulvar_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(1, 5, 5), dtype=float64, numpy=\n",
       " array([[[0.303321, 0.319835, 1.      , 0.      , 0.      ],\n",
       "         [0.448859, 0.365509, 0.      , 1.      , 0.      ],\n",
       "         [0.34054 , 0.287661, 0.      , 0.      , 1.      ],\n",
       "         [0.829429, 0.703185, 0.      , 0.      , 1.      ],\n",
       "         [0.846789, 0.727716, 0.      , 0.      , 1.      ]]])>,\n",
       " <tf.Tensor: shape=(1, 5, 6), dtype=float64, numpy=\n",
       " array([[[0.365509, 0.287661, 0.703185, 0.727716, 0.733167, 0.736809],\n",
       "         [0.287661, 0.703185, 0.727716, 0.733167, 0.736809, 0.732495],\n",
       "         [0.703185, 0.727716, 0.733167, 0.736809, 0.732495, 0.391597],\n",
       "         [0.727716, 0.733167, 0.736809, 0.732495, 0.391597, 0.263073],\n",
       "         [0.733167, 0.736809, 0.732495, 0.391597, 0.263073, 0.694734]]])>)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This would be our X and Y\n",
    "list(seq2seq_train.take(1))[0][0][:1], list(seq2seq_train.take(1))[0][1][:1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 1s 5ms/step - loss: 0.0291 - mae: 0.1632 - val_loss: 0.0148 - val_mae: 0.1472\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0115 - mae: 0.1168 - val_loss: 0.0086 - val_mae: 0.0913\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0095 - mae: 0.1041 - val_loss: 0.0085 - val_mae: 0.0902\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0086 - mae: 0.0977 - val_loss: 0.0089 - val_mae: 0.0933\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.0089 - mae: 0.0933\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.09327250719070435"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls() \n",
    "seq2seq_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.SimpleRNN(32, return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.SimpleRNN(32, return_sequences=True),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])\n",
    "fit_and_evaluate(seq2seq_model, seq2seq_train, seq2seq_valid, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predicted  Actual  Difference\n",
       "0       0.16    0.66        0.51\n",
       "1       0.55    0.70        0.15\n",
       "2       0.59    0.69        0.09\n",
       "3       0.53    0.69        0.16\n",
       "4       0.80    0.68        0.12\n",
       "5       0.80    0.33        0.47"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = mulvar_valid.to_numpy()[np.newaxis, :seq_length]\n",
    "y_pred_6 = seq2seq_model.predict(X)[0][-1] # Take last row. This would be the [5,6] dimension 5 time step 6 neuron output\n",
    "\n",
    "pd.DataFrame({\"Predicted\":y_pred_6.flatten(), \"Actual\":mulvar_valid.iloc[6:12,1].values, \"Difference\":np.abs((mulvar_valid.iloc[6:12,1].values-y_pred_6.flatten()))}).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "91/91 [==============================] - 2s 6ms/step - loss: 0.0291 - mae: 0.1632 - val_loss: 0.0148 - val_mae: 0.1472\n",
      "Epoch 2/4\n",
      "91/91 [==============================] - 0s 3ms/step - loss: 0.0115 - mae: 0.1168 - val_loss: 0.0086 - val_mae: 0.0913\n",
      "Epoch 3/4\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0095 - mae: 0.1041 - val_loss: 0.0085 - val_mae: 0.0902\n",
      "Epoch 4/4\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0086 - mae: 0.0977 - val_loss: 0.0089 - val_mae: 0.0933\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0089 - mae: 0.0933\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.09327250719070435"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TimeDistributed wrap any layer and applies it at every time step of its input sequence.\n",
    "# For sake of clarity we use TimeDistributed(Dense(.)) bcz it makes clear that the dense layer is applied\n",
    "# independently at each time step and that the model will output a sequence not just a single vector.\n",
    "# This equivalent to: Dense(6) when previous layer return_sequence=True\n",
    "# also equivalent: tf.keras.layers.Conv1D(6, kernel_size=1)\n",
    "cls() \n",
    "seq2seq_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.SimpleRNN(32, return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.SimpleRNN(32, return_sequences=True),\n",
    "    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(6))\n",
    "])\n",
    "fit_and_evaluate(seq2seq_model, seq2seq_train, seq2seq_valid, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When we do this the loss will calculated using all time step output. We can change that with last time step\n",
    "\n",
    "# def last_time_step_mse(Y_true, Y_pred):\n",
    "#     return tf.keras.metrics.MeanSquaredError(Y_true, Y_pred)\n",
    "#fit_and_evaluate(seq2seq_model, seq2seq_train, seq2seq_valid, learning_rate=0.1, metrics=[last_time_step_mse])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "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>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predicted  Actual  Difference\n",
       "0       0.23    0.66        0.43\n",
       "1       0.51    0.70        0.19\n",
       "2       0.68    0.69        0.00\n",
       "3       0.73    0.69        0.04\n",
       "4       0.79    0.68        0.11\n",
       "5       0.75    0.33        0.43"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = mulvar_valid.to_numpy()[np.newaxis, :seq_length]\n",
    "y_pred_6 = seq2seq_model.predict(X)[0][-1] # Take last row. This would be the [5,6] dimension 5 time step 6 neuron output\n",
    "\n",
    "pd.DataFrame({\"Predicted\":y_pred_6.flatten(), \"Actual\":mulvar_valid.iloc[6:12,1].values, \"Difference\":np.abs((mulvar_valid.iloc[6:12,1].values-y_pred_6.flatten()))}).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# D. Deal with Gradient Explode/Vanishing\n",
    "### 1. Layer Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LNSimpleRNNCell(tf.keras.layers.Layer):\n",
    "    def __init__(self, units, activation=\"tanh\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.state_size = units\n",
    "        self.output_size = units\n",
    "        self.simple_rnn_cell = tf.keras.layers.SimpleRNNCell(units,activation=None)\n",
    "        self.layer_norm = tf.keras.layers.LayerNormalization()\n",
    "        self.activation = tf.keras.activations.get(activation)\n",
    "\n",
    "    def call(self, inputs, states):\n",
    "        outputs, new_states = self.simple_rnn_cell(inputs, states)\n",
    "        norm_outputs = self.activation(self.layer_norm(outputs))\n",
    "        return norm_outputs, [norm_outputs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "91/91 [==============================] - 2s 6ms/step - loss: 0.0338 - mae: 0.1758 - val_loss: 0.0173 - val_mae: 0.1617\n",
      "Epoch 2/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0116 - mae: 0.1172 - val_loss: 0.0166 - val_mae: 0.1580\n",
      "Epoch 3/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0113 - mae: 0.1155 - val_loss: 0.0158 - val_mae: 0.1534\n",
      "Epoch 4/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.1196 - val_loss: 0.0130 - val_mae: 0.1346\n",
      "Epoch 5/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0128 - mae: 0.1247 - val_loss: 0.0099 - val_mae: 0.1122\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0099 - mae: 0.1122\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.11215344071388245"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is simple RNN but data will not directly feed to \n",
    "# activation layer it will first normalize\n",
    "\n",
    "custom_ln_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.RNN(LNSimpleRNNCell(32), return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])\n",
    "fit_and_evaluate(custom_ln_model, seq2seq_train, seq2seq_valid, learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Custom RNN Cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyRNN(tf.keras.layers.Layer):\n",
    "    def __init__(self, cell, return_sequences=False, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.cell = cell\n",
    "        self.return_sequences = return_sequences\n",
    "\n",
    "    def get_initial_state(self, inputs):\n",
    "        try:\n",
    "            return self.cell.get_initial_state(inputs)\n",
    "        except AttributeError:\n",
    "            # fallback to zeros if self.cell has no get_initial_state() method\n",
    "            batch_size = tf.shape(inputs)[0]\n",
    "            return [tf.zeros([batch_size, self.cell.state_size],\n",
    "                             dtype=inputs.dtype)]\n",
    "\n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        states = self.get_initial_state(inputs)\n",
    "        shape = tf.shape(inputs)\n",
    "        batch_size = shape[0]\n",
    "        n_steps = shape[1]\n",
    "        sequences = tf.TensorArray(\n",
    "            inputs.dtype, size=(n_steps if self.return_sequences else 0))\n",
    "        outputs = tf.zeros(shape=[batch_size, self.cell.output_size],\n",
    "                           dtype=inputs.dtype)\n",
    "        for step in tf.range(n_steps):\n",
    "            outputs, states = self.cell(inputs[:, step], states)\n",
    "            if self.return_sequences:\n",
    "                sequences = sequences.write(step, outputs)\n",
    "\n",
    "        if self.return_sequences:\n",
    "            # stack the outputs into an array of shape\n",
    "            # [time steps, batch size, dims], then transpose it to shape\n",
    "            # [batch size, time steps, dims]\n",
    "            return tf.transpose(sequences.stack(), [1, 0, 2])\n",
    "        else:\n",
    "            return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `@tf.function` requires the `outputs` variable to be created before the `for` loop, which is why we initialize its value to a zero tensor, even though we don't use that value at all. Once the function is converted to a graph, this unused value will be pruned from the graph, so it doesn't impact performance. Similarly, `@tf.function` requires the `sequences` variable to be created before the `if` statement where it is used, even if `self.return_sequences` is `False`, so we create a `TensorArray` of size 0 in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "91/91 [==============================] - 1s 6ms/step - loss: 0.0264 - mae: 0.1652 - val_loss: 0.0211 - val_mae: 0.1784\n",
      "Epoch 2/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.1201 - val_loss: 0.0152 - val_mae: 0.1483\n",
      "Epoch 3/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0109 - mae: 0.1117 - val_loss: 0.0140 - val_mae: 0.1418\n",
      "Epoch 4/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.1165 - val_loss: 0.0104 - val_mae: 0.1139\n",
      "Epoch 5/5\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0120 - mae: 0.1171 - val_loss: 0.0089 - val_mae: 0.0945\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0089 - mae: 0.0945\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.09446323662996292"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls()\n",
    "\n",
    "custom_model = tf.keras.Sequential([\n",
    "    MyRNN(LNSimpleRNNCell(32), return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])\n",
    "fit_and_evaluate(custom_model, seq2seq_train, seq2seq_valid, learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. LSTM Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "91/91 [==============================] - 2s 9ms/step - loss: 0.0288 - mae: 0.1919 - val_loss: 0.0189 - val_mae: 0.1404\n",
      "Epoch 2/5\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0141 - mae: 0.1405 - val_loss: 0.0162 - val_mae: 0.1306\n",
      "Epoch 3/5\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.1285 - val_loss: 0.0153 - val_mae: 0.1294\n",
      "Epoch 4/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.1226 - val_loss: 0.0147 - val_mae: 0.1286\n",
      "Epoch 5/5\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0113 - mae: 0.1193 - val_loss: 0.0144 - val_mae: 0.1275\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0144 - mae: 0.1275\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.1274643838405609"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls()\n",
    "lstm_model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])\n",
    "\n",
    "fit_and_evaluate(lstm_model, seq2seq_train, seq2seq_valid, learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. GRU Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "91/91 [==============================] - 2s 7ms/step - loss: 0.0284 - mae: 0.1784 - val_loss: 0.0138 - val_mae: 0.1287\n",
      "Epoch 2/5\n",
      "91/91 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.1215 - val_loss: 0.0134 - val_mae: 0.1238\n",
      "Epoch 3/5\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0112 - mae: 0.1169 - val_loss: 0.0133 - val_mae: 0.1215\n",
      "Epoch 4/5\n",
      "91/91 [==============================] - 0s 5ms/step - loss: 0.0109 - mae: 0.1146 - val_loss: 0.0133 - val_mae: 0.1195\n",
      "Epoch 5/5\n",
      "91/91 [==============================] - 1s 6ms/step - loss: 0.0106 - mae: 0.1127 - val_loss: 0.0131 - val_mae: 0.1178\n",
      "12/12 [==============================] - 0s 6ms/step - loss: 0.0131 - mae: 0.1178\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.11778771877288818"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls()\n",
    "lstm_model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.GRU(32, return_sequences=True, input_shape=[None, 5]),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])\n",
    "\n",
    "fit_and_evaluate(lstm_model, seq2seq_train, seq2seq_valid,learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Conv1D Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#   |-----0-----|      |-----3----|      |--... |-------52------|\n",
    "#           |-----1----|      |-----4----|   ... |       |-------53------|\n",
    "#                 |-----2----|     |------5--...-51------|       |-------54------|\n",
    "# X:  0  1  2  3  4  5  6  7  8  9 10 11 12 ...  104 105 106 107 108 109 110 111\n",
    "# Y:      from 4     6     8    10    12    ...      106     108     110     112\n",
    "#          to  9    11    13    15    17    ...      111     112     115     117"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, None, 32)          672       \n",
      "_________________________________________________________________\n",
      "gru (GRU)                    (None, None, 32)          6336      \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, None, 6)           198       \n",
      "=================================================================\n",
      "Total params: 7,206\n",
      "Trainable params: 7,206\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cls()\n",
    "conv_rnn_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv1D(filters=32, kernel_size=4, strides=2, activation=\"relu\", input_shape=[None, 5]),\n",
    "    tf.keras.layers.GRU(32, return_sequences=True),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])\n",
    "conv_rnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "# Data preparing #\n",
    "##################\n",
    "longer_train = to_seq2seq_dataset(mulvar_train, seq_length=112, shuffle=True, seed=42)\n",
    "longer_valid = to_seq2seq_dataset(mulvar_valid, seq_length=112)\n",
    "# cropping the first 3 value of target bcz kernel is 4 \n",
    "# it will use first 4 values in calculation part\n",
    "# also downsampling by 2 bcz stride is 2 which half the input\n",
    "# sequence so we have to downsample the target as well\n",
    "downsampled_train = longer_train.map(lambda X, Y: (X, Y[:, 3::2]))\n",
    "downsampled_valid = longer_valid.map(lambda X, Y: (X, Y[:, 3::2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([1, 112, 5]), TensorShape([1, 55, 6]))"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our input is a sequence of 112 numbers or 55 time step. and 5 features\n",
    "# our target is sequence of 55 numbers or 55 time step but alternate as we use stride 2.\n",
    "list(downsampled_train.take(1))[0][0][:1].shape, list(downsampled_train.take(1))[0][1][:1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "82/82 [==============================] - 4s 26ms/step - loss: 0.0290 - mae: 0.1827 - val_loss: 0.0144 - val_mae: 0.1232\n",
      "Epoch 2/5\n",
      "82/82 [==============================] - 2s 22ms/step - loss: 0.0091 - mae: 0.1096 - val_loss: 0.0090 - val_mae: 0.0957\n",
      "Epoch 3/5\n",
      "82/82 [==============================] - 2s 22ms/step - loss: 0.0062 - mae: 0.0831 - val_loss: 0.0070 - val_mae: 0.0823\n",
      "Epoch 4/5\n",
      "82/82 [==============================] - 2s 23ms/step - loss: 0.0053 - mae: 0.0732 - val_loss: 0.0061 - val_mae: 0.0737\n",
      "Epoch 5/5\n",
      "82/82 [==============================] - 2s 30ms/step - loss: 0.0049 - mae: 0.0684 - val_loss: 0.0060 - val_mae: 0.0724\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 0.0060 - mae: 0.0724\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.07243182510137558"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_and_evaluate(conv_rnn_model, downsampled_train, downsampled_valid, learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 55, 6)"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = conv_rnn_model.predict(list(downsampled_valid.take(1))[0][0][:1])\n",
    "y_pred.shape # One sample, 55 time step, 6 predicted values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. WaveNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  ⋮\n",
    "# C2  /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\...\n",
    "#    \\  /  \\  /  \\  /  \\  /  \\  /  \\  /  \\     \n",
    "#      /    \\      /    \\      /    \\          \n",
    "# C1  /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\  /\\ /...\\\n",
    "# X: 0  1  2  3  4  5  6  7  8  9  10 11 12 ... 111\n",
    "# Y: 1  2  3  4  5  6  7  8  9  10 11 12 13 ... 112\n",
    "#  /14 15 16 17 18 19 20 21 22  23 24 25 26 ... 125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, None, 32)          352       \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_8 (Conv1D)            (None, None, 6)           198       \n",
      "=================================================================\n",
      "Total params: 15,110\n",
      "Trainable params: 15,110\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cls()\n",
    "wavenet_model = tf.keras.Sequential()\n",
    "wavenet_model.add(tf.keras.layers.InputLayer(input_shape=[None, 5]))\n",
    "for rate in (1, 2, 4, 8) * 2:\n",
    "    wavenet_model.add(tf.keras.layers.Conv1D(filters=32, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=rate))\n",
    "wavenet_model.add(tf.keras.layers.Conv1D(filters=6, kernel_size=1))\n",
    "wavenet_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "82/82 [==============================] - 3s 21ms/step - loss: 0.0312 - mae: 0.1988 - val_loss: 0.0203 - val_mae: 0.1417\n",
      "Epoch 2/5\n",
      "82/82 [==============================] - 1s 12ms/step - loss: 0.0152 - mae: 0.1527 - val_loss: 0.0183 - val_mae: 0.1286\n",
      "Epoch 3/5\n",
      "82/82 [==============================] - 1s 12ms/step - loss: 0.0097 - mae: 0.1125 - val_loss: 0.0096 - val_mae: 0.1029\n",
      "Epoch 4/5\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 0.0058 - mae: 0.0786 - val_loss: 0.0075 - val_mae: 0.0861\n",
      "Epoch 5/5\n",
      "82/82 [==============================] - 1s 12ms/step - loss: 0.0050 - mae: 0.0685 - val_loss: 0.0073 - val_mae: 0.0840\n",
      "3/3 [==============================] - 0s 17ms/step - loss: 0.0073 - mae: 0.0840\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.08399646729230881"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_and_evaluate(wavenet_model, longer_train, longer_valid, learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original WaveNet defined in the paper: it uses Gated Activation Units instead of ReLU and parametrized skip connections, plus it pads with zeros on the left to avoid getting shorter and shorter sequences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GatedActivationUnit(tf.keras.layers.Layer):\n",
    "    def __init__(self, activation=\"tanh\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.activation = tf.keras.activations.get(activation)\n",
    "\n",
    "    def call(self, inputs):\n",
    "        n_filters = inputs.shape[-1] // 2\n",
    "        linear_output = self.activation(inputs[..., :n_filters])\n",
    "        gate = tf.keras.activations.sigmoid(inputs[..., n_filters:])\n",
    "        return self.activation(linear_output) * gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wavenet_residual_block(inputs, n_filters, dilation_rate):\n",
    "    z = tf.keras.layers.Conv1D(2 * n_filters, kernel_size=2, padding=\"causal\", dilation_rate=dilation_rate)(inputs)\n",
    "    z = GatedActivationUnit()(z)\n",
    "    z = tf.keras.layers.Conv1D(n_filters, kernel_size=1)(z)\n",
    "    return tf.keras.layers.Add()([z, inputs]), z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, None, 5)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, None, 32)     352         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, None, 64)     4160        conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gated_activation_unit (GatedAct (None, None, 32)     0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, None, 32)     1056        gated_activation_unit[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, None, 32)     0           conv1d_2[0][0]                   \n",
      "                                                                 conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, None, 64)     4160        add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "gated_activation_unit_1 (GatedA (None, None, 32)     0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, None, 32)     1056        gated_activation_unit_1[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, None, 32)     0           conv1d_4[0][0]                   \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, None, 64)     4160        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "gated_activation_unit_2 (GatedA (None, None, 32)     0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, None, 32)     1056        gated_activation_unit_2[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, None, 32)     0           conv1d_2[0][0]                   \n",
      "                                                                 conv1d_4[0][0]                   \n",
      "                                                                 conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf.nn.relu (TFOpLambda)         (None, None, 32)     0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, None, 32)     1056        tf.nn.relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, None, 6)      198         conv1d_7[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 17,254\n",
      "Trainable params: 17,254\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cls()\n",
    "\n",
    "n_layers_per_block = 3  # 10 in the paper\n",
    "n_blocks = 1  # 3 in the paper\n",
    "n_filters = 32  # 128 in the paper\n",
    "n_outputs = 6  # 256 in the paper\n",
    "\n",
    "inputs = tf.keras.layers.Input(shape=[None, 5])\n",
    "z = tf.keras.layers.Conv1D(n_filters, kernel_size=2, padding=\"causal\")(inputs)\n",
    "skip_to_last = []\n",
    "for dilation_rate in [2**i for i in range(n_layers_per_block)] * n_blocks:\n",
    "    z, skip = wavenet_residual_block(z, n_filters, dilation_rate)\n",
    "    skip_to_last.append(skip)\n",
    "\n",
    "z = tf.keras.activations.relu(tf.keras.layers.Add()(skip_to_last))\n",
    "z = tf.keras.layers.Conv1D(n_filters, kernel_size=1, activation=\"relu\")(z)\n",
    "Y_preds = tf.keras.layers.Conv1D(n_outputs, kernel_size=1)(z)\n",
    "\n",
    "full_wavenet_model = tf.keras.Model(inputs=[inputs], outputs=[Y_preds])\n",
    "full_wavenet_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "82/82 [==============================] - 2s 18ms/step - loss: 0.0212 - mae: 0.1505 - val_loss: 0.0081 - val_mae: 0.0888\n",
      "Epoch 2/5\n",
      "82/82 [==============================] - 1s 17ms/step - loss: 0.0047 - mae: 0.0668 - val_loss: 0.0052 - val_mae: 0.0655\n",
      "Epoch 3/5\n",
      "82/82 [==============================] - 1s 14ms/step - loss: 0.0039 - mae: 0.0577 - val_loss: 0.0047 - val_mae: 0.0608\n",
      "Epoch 4/5\n",
      "82/82 [==============================] - 1s 17ms/step - loss: 0.0036 - mae: 0.0543 - val_loss: 0.0044 - val_mae: 0.0568\n",
      "Epoch 5/5\n",
      "82/82 [==============================] - 2s 18ms/step - loss: 0.0034 - mae: 0.0520 - val_loss: 0.0044 - val_mae: 0.0571\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 0.0044 - mae: 0.0571\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0570661723613739"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_and_evaluate(full_wavenet_model, longer_train, longer_valid, learning_rate=0.1, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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