LSTM_Retail.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM_Retail.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3kH8Hd3RMqPw",
        "colab_type": "text"
      },
      "source": [
        "# Importing libraries\n",
        "\n",
        "Importing  tensorflow and basic data science libraries.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqTL5-tnJ6am",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.preprocessing  import MinMaxScaler\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9iQ4l9mM4UW",
        "colab_type": "text"
      },
      "source": [
        "# Scraping and preprocessing data\n",
        "\n",
        "Scraping data from IBGE, organizing columns and rows, converting dates into datetime (since they were originally hardcoded in portuguese).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnrG3x90tFmP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import csv from IBGE website\n",
        "df = pd.read_csv('https://sidra.ibge.gov.br/geratabela?format=us.csv&name=tabela3416.csv&terr=N&rank=-&query=t/3416/n1/all/v/564/p/all/c11046/40311/d/v564%201/l/v,c11046,t%2Bp',\n",
        "                 skiprows=3, index_col=1)\n",
        "\n",
        "# Drops unwanted column\n",
        "df = df.drop(['Unnamed: 0'], axis=1) \n",
        "\n",
        "# Drops metadata at the end of the dataframe\n",
        "df = df[:-10] \n",
        "\n",
        "# Converts dates into datetime (they were originally in hardcoded Portuguese)\n",
        "df['date'] = df.index\n",
        "new = df[\"date\"].str.split(\" \", n = 1, expand = True) \n",
        "df[\"Month\"]= new[0] \n",
        "df[\"Year\"]= new[1] \n",
        "df.drop(columns =[\"date\"], inplace = True) \n",
        "daysofweek = {\n",
        "    'janeiro':'01',\n",
        "    'fevereiro': '02',\n",
        "    'março': '03',\n",
        "    'abril': '04',\n",
        "    'maio': '05',\n",
        "    'junho': '06',\n",
        "    'julho': '07',\n",
        "    'agosto': '08',\n",
        "    'setembro': '09',\n",
        "    'outubro': '10',\n",
        "    'novembro': '11',\n",
        "    'dezembro': '12'\n",
        "}\n",
        "for key, value in daysofweek.items():\n",
        "  df = df.replace(key,value)\n",
        "df['Date'] = df['Month'] + '/'+ df['Year']\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df.index = df['Date']\n",
        "df = df.drop(['Month', 'Year', 'Date'], axis=1)\n",
        "\n",
        "# Renaming column\n",
        "df.columns = ['Retail Sales Index - Brazil - 2014=100']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkZj4BYnP42f",
        "colab_type": "code",
        "outputId": "2e3f5a85-acf2-47d0-c9b9-393ccc5b0d2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "# Plotting Time Series\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "df.plot(figsize=(10,6))"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b6d0201d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm5xB550NpEn",
        "colab_type": "text"
      },
      "source": [
        "# Splitting and creating a gridsearch pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9ZCOu0ENnvZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating a 24 months test size \n",
        "test_size = 24\n",
        "\n",
        "# Length of Batches, 12 months = 1 year to capture seasonality\n",
        "length = 12\n",
        "test_ind = len(df) - test_size\n",
        "\n",
        "# Spliting the data\n",
        "train = df.iloc[:test_ind]\n",
        "test = df.iloc[test_ind:]\n",
        "\n",
        "# Scaling the Data\n",
        "scaler = MinMaxScaler()\n",
        "scaled_train = scaler.fit_transform(train)\n",
        "scaled_test = scaler.transform(test)\n",
        "\n",
        "# Time series generator, creating batches\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train,\n",
        "                                length=length, batch_size=1)\n",
        "\n",
        "n_features = 1 # Just one feature, Sales Index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLAg0ZdGNbVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating method for model gridsearch\n",
        "\n",
        "def train_test(h_units, h_dropout, h_optimizer):\n",
        "  model = Sequential()\n",
        "  model.add(LSTM(h_units, activation='relu',input_shape=(length,n_features), return_sequences=True))\n",
        "  model.add(Dropout(h_dropout))\n",
        "  model.add(LSTM(64, activation='relu',input_shape=(length,n_features)))\n",
        "  model.add(Dropout(h_dropout))\n",
        "  model.add(Dense(1))\n",
        "  model.compile(optimizer=h_optimizer,loss='mse')\n",
        "\n",
        "  # Creates an early stop mechanism\n",
        "  early_stop = EarlyStopping(monitor='val_loss',patience=3)\n",
        "  validation_generator = TimeseriesGenerator(scaled_test, scaled_test,\n",
        "                                            length=length, batch_size=1)\n",
        "  # Fitting the model\n",
        "  model.fit(generator,epochs=200,\n",
        "            validation_data=validation_generator,\n",
        "            callbacks=[early_stop], verbose =0)\n",
        "  \n",
        "  test_predictions = []\n",
        "\n",
        "  # Storing losses\n",
        "  losses = pd.DataFrame(model.history.history)\n",
        "\n",
        "  # Updating current_batch with predictions, so the model predicts based on previous predicted values\n",
        "  first_eval_batch = scaled_train[-length:]\n",
        "  current_batch = first_eval_batch.reshape((1,length,n_features))\n",
        "\n",
        "  for i in range(len(test)):\n",
        "      current_pred = model.predict(current_batch)[0]\n",
        "      test_predictions.append(current_pred)\n",
        "      current_batch = np.append(current_batch[:,1:,:],[[current_pred]], axis=1)\n",
        "\n",
        "  # Inverse transforming predictions and adding them to a df with the test set\n",
        "  true_predictions = scaler.inverse_transform(test_predictions)\n",
        "  real_vs_pred = test\n",
        "  real_vs_pred['Predictions'] = true_predictions\n",
        "  rmse = np.sqrt(mean_squared_error(real_vs_pred.iloc[:,0], real_vs_pred.iloc[:,1]))\n",
        "  \n",
        "  # Returns prediction RMSE (Root Mean Square Error)\n",
        "  return rmse, losses, real_vs_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnA-tuj7OEYc",
        "colab_type": "code",
        "outputId": "5f289a5c-3d30-4325-bdfd-8afb8c3dd23a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# GRIDSEARCH\n",
        "# Hyperparameters List\n",
        "unitlist = [128, 172, 256]\n",
        "droplist = [0.1, 0.2, 0.3]\n",
        "optimizerlist = ['adam', 'rmsprop']\n",
        "\n",
        "# Initialising grid dictionaries\n",
        "grid_rmse = {}\n",
        "grid_losses = {}\n",
        "grid_real_pred = {}\n",
        "\n",
        "# Running the models\n",
        "for unit in unitlist:\n",
        "  for drop in droplist:\n",
        "    for optim in optimizerlist:\n",
        "      # Fetching results from the model\n",
        "      model_rmse, losses_df, real_vs_pred = train_test(unit, drop, optim)\n",
        "      \n",
        "      # Adding results to the dictionaries\n",
        "      current_model = '{} units, {} dropout, {} optimizer, {} epochs'.format(unit,drop,optim, len(losses_df))\n",
        "      grid_rmse[current_model] = model_rmse\n",
        "      grid_losses[current_model] = losses_df\n",
        "      grid_real_pred[current_model] = real_vs_pred\n",
        "\n",
        "      nr_of_models = str(len(unitlist)*len(droplist)*len(optimizerlist))\n",
        "      print(str(len(grid_rmse.items())) + \" out of \" + nr_of_models)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "4 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "5 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "6 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "7 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "8 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "9 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "10 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "11 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "12 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "13 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "14 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "15 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "16 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "17 out of 18\n",
            "18 out of 18\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dD7OfS81cITS",
        "colab_type": "code",
        "outputId": "25c0f701-53d8-4aa7-a68d-a183fba5cd98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Selecting best model based on RMSE\n",
        "grid_rmse_df = pd.DataFrame.from_dict(grid_rmse.items())\n",
        "best_model = grid_rmse_df.sort_values(by=1)[0].iloc[0]\n",
        "print(best_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "256 units, 0.3 dropout, adam optimizer, 17 epochs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtOIOvfyZ9-e",
        "colab_type": "code",
        "outputId": "65f3db01-3e2b-44f2-af98-4296b6cdbfa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "grid_losses[best_model].plot(title='Loss and validation loss throughout epochs')"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b7013f898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcAXBk3iYcfF",
        "colab_type": "code",
        "outputId": "d702574b-3351-4d33-9954-ab5cac0c1217",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "grid_real_pred[best_model].plot(figsize = (10,6), title='Real vs Predicted')\n",
        "plt.grid(None)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_KoG36GapuM",
        "colab_type": "code",
        "outputId": "1446d76d-1168-4475-e3b4-f98ed903332f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        }
      },
      "source": [
        "grid_real_pred[best_model]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Retail Sales Index - Brazil - 2014=100</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-03-01</th>\n",
              "      <td>94.5</td>\n",
              "      <td>87.918804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-01</th>\n",
              "      <td>87.9</td>\n",
              "      <td>87.669054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-05-01</th>\n",
              "      <td>91.8</td>\n",
              "      <td>88.830770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-01</th>\n",
              "      <td>89.6</td>\n",
              "      <td>87.883944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-01</th>\n",
              "      <td>89.0</td>\n",
              "      <td>88.978450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-01</th>\n",
              "      <td>93.9</td>\n",
              "      <td>89.212475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-01</th>\n",
              "      <td>89.4</td>\n",
              "      <td>88.560936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-01</th>\n",
              "      <td>92.7</td>\n",
              "      <td>89.813415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-01</th>\n",
              "      <td>101.7</td>\n",
              "      <td>95.991993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-01</th>\n",
              "      <td>120.3</td>\n",
              "      <td>114.819719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>92.6</td>\n",
              "      <td>89.814041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-02-01</th>\n",
              "      <td>85.6</td>\n",
              "      <td>84.443742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-01</th>\n",
              "      <td>90.3</td>\n",
              "      <td>88.044057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-01</th>\n",
              "      <td>89.5</td>\n",
              "      <td>87.784701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-01</th>\n",
              "      <td>92.7</td>\n",
              "      <td>88.379001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-01</th>\n",
              "      <td>89.7</td>\n",
              "      <td>87.624155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-01</th>\n",
              "      <td>92.8</td>\n",
              "      <td>88.296108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-01</th>\n",
              "      <td>95.2</td>\n",
              "      <td>88.474881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-01</th>\n",
              "      <td>91.4</td>\n",
              "      <td>88.045716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-01</th>\n",
              "      <td>96.6</td>\n",
              "      <td>88.939204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-01</th>\n",
              "      <td>104.9</td>\n",
              "      <td>94.404047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-01</th>\n",
              "      <td>123.5</td>\n",
              "      <td>109.796355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>93.9</td>\n",
              "      <td>88.947387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>89.6</td>\n",
              "      <td>85.561404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Retail Sales Index - Brazil - 2014=100  Predictions\n",
              "Date                                                           \n",
              "2018-03-01                                    94.5    87.918804\n",
              "2018-04-01                                    87.9    87.669054\n",
              "2018-05-01                                    91.8    88.830770\n",
              "2018-06-01                                    89.6    87.883944\n",
              "2018-07-01                                    89.0    88.978450\n",
              "2018-08-01                                    93.9    89.212475\n",
              "2018-09-01                                    89.4    88.560936\n",
              "2018-10-01                                    92.7    89.813415\n",
              "2018-11-01                                   101.7    95.991993\n",
              "2018-12-01                                   120.3   114.819719\n",
              "2019-01-01                                    92.6    89.814041\n",
              "2019-02-01                                    85.6    84.443742\n",
              "2019-03-01                                    90.3    88.044057\n",
              "2019-04-01                                    89.5    87.784701\n",
              "2019-05-01                                    92.7    88.379001\n",
              "2019-06-01                                    89.7    87.624155\n",
              "2019-07-01                                    92.8    88.296108\n",
              "2019-08-01                                    95.2    88.474881\n",
              "2019-09-01                                    91.4    88.045716\n",
              "2019-10-01                                    96.6    88.939204\n",
              "2019-11-01                                   104.9    94.404047\n",
              "2019-12-01                                   123.5   109.796355\n",
              "2020-01-01                                    93.9    88.947387\n",
              "2020-02-01                                    89.6    85.561404"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tw5ORq5_oEsx",
        "colab_type": "text"
      },
      "source": [
        "# FORECASTING\n",
        "\n",
        "Forecasting into the unknown future (12 months)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUSqfa4ePYAk",
        "colab_type": "code",
        "outputId": "1981474a-9b9d-4e64-8b57-677dc1b0aadb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "# Scaling the whole df\n",
        "full_scaler = MinMaxScaler()\n",
        "scaled_full_data = full_scaler.fit_transform(df)\n",
        "length = 12\n",
        "\n",
        "# Generator\n",
        "generator = TimeseriesGenerator(scaled_full_data, scaled_full_data,\n",
        "                                length=length, batch_size=1)\n",
        "# Best model:\n",
        "# 256 units, 0.3 dropout, adam optimizer, 17 epochs\n",
        "                          \n",
        "model = Sequential()\n",
        "model.add(LSTM(256, activation='relu',input_shape=(length,n_features), return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(LSTM(64, activation='relu',input_shape=(length,n_features)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam',loss='mse')\n",
        "\n",
        "model.fit(generator, epochs=17)\n",
        "\n",
        "# Forecasting the unknown future\n",
        "forecast = []\n",
        "periods = 14\n",
        "first_eval_batch = scaled_full_data[-length:]\n",
        "current_batch = first_eval_batch.reshape((1,length,n_features))\n",
        "\n",
        "# Updating batches with predicted values\n",
        "for i in range(periods):\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "    forecast.append(current_pred)\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]], axis=1)\n",
        "\n",
        "# Inverse transforming and indexing\n",
        "forecast = scaler.inverse_transform(forecast)\n",
        "forecast_index = pd.date_range(start='2020-03-01', periods=periods,\n",
        "                               freq='MS')\n",
        "\n",
        "# Merging forecast and index\n",
        "forecast_df = pd.DataFrame(data=forecast, index=forecast_index,\n",
        "                           columns=['Forecast'])"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0200\n",
            "Epoch 2/17\n",
            "230/230 [==============================] - 4s 16ms/step - loss: 0.0219\n",
            "Epoch 3/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0131\n",
            "Epoch 4/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0138\n",
            "Epoch 5/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0087\n",
            "Epoch 6/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0093\n",
            "Epoch 7/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0070\n",
            "Epoch 8/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0079\n",
            "Epoch 9/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0075\n",
            "Epoch 10/17\n",
            "230/230 [==============================] - 4s 18ms/step - loss: 0.0053\n",
            "Epoch 11/17\n",
            "230/230 [==============================] - 4s 18ms/step - loss: 0.0054\n",
            "Epoch 12/17\n",
            "230/230 [==============================] - 4s 18ms/step - loss: 0.0066\n",
            "Epoch 13/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0057\n",
            "Epoch 14/17\n",
            "230/230 [==============================] - 4s 16ms/step - loss: 0.0043\n",
            "Epoch 15/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0047\n",
            "Epoch 16/17\n",
            "230/230 [==============================] - 4s 16ms/step - loss: 0.0041\n",
            "Epoch 17/17\n",
            "230/230 [==============================] - 4s 17ms/step - loss: 0.0043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebJoXQ4QnnJe",
        "colab_type": "code",
        "outputId": "5ad86e6a-b283-405b-ccbb-e922d142f26c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "# Plotting forecast\n",
        "ax = df.plot(figsize=(10, 6))\n",
        "forecast_df.plot(ax=ax, figsize=(10, 6), title=('14 months Forecast'))\n",
        "plt.xlim('2010-01-01','2021-05-01')\n",
        "plt.tight_layout()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}