FPA_FOR_FORECASTING (1).ipynb

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      "name": "FPA FOR FORECASTING.ipynb",
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        "id": "AZxaaMpLO512",
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      "source": [
        "#import librari yang diperlukan\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from levy import get_levy_flight_array\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeHe2DKyVJ2z",
        "colab_type": "code",
        "outputId": "2f8613de-f7b6-42a4-d454-e15cfa9a3dec",
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          "base_uri": "https://localhost:8080/",
          "height": 204
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      "source": [
        "#===========================initiate data ================================\n",
        "file_input = 'USDJPYterbaru'\n",
        "choose_column = 'close'\n",
        "percentage_train = 0.8\n",
        "percentage_validate = 0.2\n",
        "#=========================================================================\n",
        "\n",
        "#import data dari file csv\n",
        "data = pd.read_csv(file_input+'.csv',header=None)\n",
        "data.columns = ['date', 'time', 'open', 'high', 'low', 'close', 'volume']\n",
        "shape = data.shape\n",
        "length_data = shape[0]\n",
        "\n",
        "display(data.head())\n",
        "data = data['close']\n",
        "\n",
        "#membagi data menjadi data training dan data testing\n",
        "data_train = data[:round(length_data*percentage_train)]\n",
        "data_validate = data[round(length_data*percentage_train):]\n",
        "data_test = data_validate.reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": [
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          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>time</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2003.09.26</td>\n",
              "      <td>00:00</td>\n",
              "      <td>112.07</td>\n",
              "      <td>112.33</td>\n",
              "      <td>111.65</td>\n",
              "      <td>111.68</td>\n",
              "      <td>9297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003.09.29</td>\n",
              "      <td>00:00</td>\n",
              "      <td>111.61</td>\n",
              "      <td>111.82</td>\n",
              "      <td>110.69</td>\n",
              "      <td>110.80</td>\n",
              "      <td>10488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003.09.30</td>\n",
              "      <td>00:00</td>\n",
              "      <td>110.80</td>\n",
              "      <td>112.14</td>\n",
              "      <td>110.09</td>\n",
              "      <td>111.49</td>\n",
              "      <td>13151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2003.10.01</td>\n",
              "      <td>00:00</td>\n",
              "      <td>111.47</td>\n",
              "      <td>111.60</td>\n",
              "      <td>110.50</td>\n",
              "      <td>110.60</td>\n",
              "      <td>9832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003.10.02</td>\n",
              "      <td>00:00</td>\n",
              "      <td>110.59</td>\n",
              "      <td>111.45</td>\n",
              "      <td>110.51</td>\n",
              "      <td>110.75</td>\n",
              "      <td>9362</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "         date   time    open    high     low   close  volume\n",
              "0  2003.09.26  00:00  112.07  112.33  111.65  111.68    9297\n",
              "1  2003.09.29  00:00  111.61  111.82  110.69  110.80   10488\n",
              "2  2003.09.30  00:00  110.80  112.14  110.09  111.49   13151\n",
              "3  2003.10.01  00:00  111.47  111.60  110.50  110.60    9832\n",
              "4  2003.10.02  00:00  110.59  111.45  110.51  110.75    9362"
            ]
          },
          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "X7VbIIgq1O4Y",
        "colab": {}
      },
      "source": [
        "#======================hyper  parameter=========================\n",
        "window = 5\n",
        "\n",
        "#50, 100, 150\n",
        "popsize = 150 \n",
        "\n",
        "#:0.3 ; 0.5; 0.8\n",
        "switch_prob = 0.8\n",
        "\n",
        "max_iter = 100\n",
        "#================================================================"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "N7TJJS2q1O4J",
        "colab": {}
      },
      "source": [
        "#fungsi membuat tabel yang berisi data sliding window\n",
        "def sliding_window(data, window=5):\n",
        "    data_window = []\n",
        "    for i in range(len(data)-window):\n",
        "        sub_data = []\n",
        "        for j in range(window):\n",
        "            sub_data.append(data[i+j])\n",
        "        data_window.append(sub_data)\n",
        "    df = pd.DataFrame(data_window)\n",
        "    return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8q2nb2NU1O33",
        "colab": {}
      },
      "source": [
        "#memisahkan data target\n",
        "data_window = sliding_window(data_train,window=window+1)\n",
        "train_x = data_window.drop(window,axis=1) #window days before value\n",
        "train_y = data_window[window] #days value"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0jQo05dh1O3G",
        "colab": {}
      },
      "source": [
        "#fungsi inisialisasi populasi\n",
        "def initialize_population(popsize,window=5):\n",
        "    return np.array([[np.random.uniform(0, 1) for i in range(window+1)] for j in range(popsize)])\n",
        "\n",
        "#fungsi menghitung MSE individu\n",
        "def mse_ind(individu,x,y,window=5):\n",
        "    return np.mean((np.sum(individu[0:window]*x,axis=1)+individu[window]-y)**2)\n",
        "\n",
        "#fungsi menghitung MSE populasi (semua individu)\n",
        "def mse_all(population, window, data_x, data_y):\n",
        "    counter = 0\n",
        "    solution = []\n",
        "    for individu in population:\n",
        "        solution.append([counter,individu,mse_ind(individu,data_x,data_y, window=window)])\n",
        "        counter += 1\n",
        "    return solution\n",
        "\n",
        "#melakukan penyerbukan global\n",
        "def global_pol(gbest,flower,window=5):\n",
        "    levy_vector = get_levy_flight_array(window+1)\n",
        "    new_flower = flower + levy_vector*(gbest[1]-flower)\n",
        "    return new_flower\n",
        "\n",
        "#melakukan penyerbukan lokal\n",
        "def local_pol(flower,window=5):\n",
        "    flower1 = np.random.randint(0,len(population))\n",
        "    flower2 = np.random.randint(0,len(population))\n",
        "    new_flower = flower + np.random.uniform(0,1,window+1)*(population[flower1]-population[flower2])\n",
        "    return new_flower\n",
        "\n",
        "#fungsi penyerbukan\n",
        "def polination(gbest,population,switch_prob,window):\n",
        "    new_pop = []\n",
        "    for individu in population:\n",
        "        rand = np.random.uniform(0,1)\n",
        "        if rand < switch_prob:\n",
        "            #global polination\n",
        "            new_pop.append(global_pol(gbest,individu, window=window))\n",
        "        else:\n",
        "            #local politantion\n",
        "            new_pop.append(local_pol(individu,window=window))\n",
        "    return np.array(new_pop)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "514MW784jiMk",
        "colab_type": "text"
      },
      "source": [
        "# Flower Polination Algorithm Forecasting with Multiple Linier Regression and Evaluation with Mean Squared Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qJ-UEzjjfsF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#membuatt populasi\n",
        "population = initialize_population(popsize, window)\n",
        "\n",
        "#menghitung nilai fitness\n",
        "fitness_all = mse_all(population, window, train_x, train_y)\n",
        "\n",
        "#menyimpan individu terbaik sementara\n",
        "gbest = min(fitness_all, key = lambda x:x[2])\n",
        "\n",
        "#menjalankan algoritma penyerbukan bunga\n",
        "solution = []\n",
        "for i in range(max_iter):\n",
        "    #membuat populasi\n",
        "    new_pop = polination(gbest,population,switch_prob,window)\n",
        "    #menghitung fitness\n",
        "    fitness_all = mse_all(new_pop, window, train_x, train_y)\n",
        "    \n",
        "    #mencari individu terbaik dari populasi yang ada\n",
        "    pop_gbest = min(fitness_all, key = lambda x:x[2])\n",
        "\n",
        "    #membandingkan individu terbaik sementara dengan individu terbaik populasi\n",
        "    if pop_gbest[2] < gbest[2]:\n",
        "        gbest = pop_gbest\n",
        "    population = new_pop\n",
        "    solution.append([i+1,gbest[2]])\n",
        "\n",
        "#menyimpan data ke dalam file\n",
        "solution_df = pd.DataFrame(solution,columns=['iteration','MSE'])\n",
        "save_file = file_input+'_'+str(switch_prob)+'_'+str(popsize)\n",
        "solution_df.to_csv(save_file+'.csv')\n",
        "fo = open(save_file+'.txt','w+')\n",
        "for i in range(len(gbest[1])):\n",
        "    if i != len(gbest[1])-1:\n",
        "        fo.write('x'+str(i+1)+' '+str(gbest[1][i])+'\\n')\n",
        "    else:\n",
        "        fo.write('bias'+' '+str(gbest[1][i]))\n",
        "fo.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_plcC0ZAi9Z",
        "colab_type": "text"
      },
      "source": [
        "## Plotting MSE each Iteration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ksz0iFOxAP6R",
        "colab_type": "code",
        "outputId": "657a93cd-3146-42ea-d195-05cce9b84858",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "data_plot = pd.read_csv('USDJPYterbaru_0.3_50.csv')\n",
        "\n",
        "#membuat grafik perubahan individu terbaik\n",
        "# line 1 points \n",
        "x1 = data_plot['iteration']\n",
        "y1 = data_plot['MSE']\n",
        "# plotting the line 1 points  \n",
        "plt.plot(x1, y1, label = \"line 1\") \n",
        "  \n",
        "# naming the x axis \n",
        "plt.xlabel('x - axis') \n",
        "# naming the y axis \n",
        "plt.ylabel('y - axis') \n",
        "# giving a title to my graph \n",
        "plt.title('Two lines on same graph!') \n",
        "  \n",
        "# show a legend on the plot \n",
        "plt.legend() \n",
        "  \n",
        "# function to show the plot \n",
        "plt.show() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wJbAyIi7qYrPrgFPSaKYpwEsRsQb4NfAhSWMljQU+lNYVor7kUUxmZp0VOYppGnAy8KCk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\nKicIMzOrygnCzMyq+v9OO2WkoMb6NQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iIjk1IptEB8o",
        "colab_type": "text"
      },
      "source": [
        "## Plotting result prediction (validate)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y60xw7jtNa6P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#memisahkan data target dengan dataset data testing\n",
        "data_window = sliding_window(data_test,window=window+1)\n",
        "test_x = data_window.drop(window,axis=1) #window days before value\n",
        "test_y = data_window[window] #days value"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wRNty-fQlrt",
        "colab_type": "code",
        "outputId": "5b3db553-33a2-47cf-fa96-2e54298e08a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#membuat objek linear regresi\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(test_x, test_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nlb-Pe1LQr9y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#menggunakan nilai kromosom individu sebagai koefisien linear regeresi\n",
        "regressor.coef_ = gbest[1][:5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PUX7vmVQ_-u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#melakukan regresi pada data testing\n",
        "y_pred = regressor.predict(test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zazzYZGSFWJ",
        "colab_type": "code",
        "outputId": "56420084-cf15-43d4-8d25-155a0d20e810",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "#membuat tabel hasil regresi\n",
        "result = pd.DataFrame({'Actual': test_y, 'Predicted': y_pred+gbest[1][5:]})\n",
        "\n",
        "#mencari mean squared error dari hasil prediksi\n",
        "paramM_mse = mean_squared_error(result['Actual'], result['Predicted'])\n",
        "result"
      ],
      "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>99.558</td>\n",
              "      <td>99.375722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100.356</td>\n",
              "      <td>100.185374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>99.865</td>\n",
              "      <td>100.563050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>99.534</td>\n",
              "      <td>100.046675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>99.234</td>\n",
              "      <td>99.991603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>634</th>\n",
              "      <td>112.894</td>\n",
              "      <td>113.615369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>635</th>\n",
              "      <td>112.092</td>\n",
              "      <td>113.749135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>636</th>\n",
              "      <td>112.159</td>\n",
              "      <td>112.794479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>637</th>\n",
              "      <td>112.977</td>\n",
              "      <td>113.027126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>113.961</td>\n",
              "      <td>113.520369</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>639 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual   Predicted\n",
              "0     99.558   99.375722\n",
              "1    100.356  100.185374\n",
              "2     99.865  100.563050\n",
              "3     99.534  100.046675\n",
              "4     99.234   99.991603\n",
              "..       ...         ...\n",
              "634  112.894  113.615369\n",
              "635  112.092  113.749135\n",
              "636  112.159  112.794479\n",
              "637  112.977  113.027126\n",
              "638  113.961  113.520369\n",
              "\n",
              "[639 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHW_MwpPUhMH",
        "colab_type": "code",
        "outputId": "9e28c15c-a65d-4156-c908-0aebaff81aa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        }
      },
      "source": [
        "result_plot = result\n",
        "\n",
        "#grafik hasil sebenarnya\n",
        "# line 1 points \n",
        "x1 = result_plot.index.values\n",
        "y1 = result_plot['Actual']\n",
        "# plotting the line 1 points  \n",
        "plt.plot(x1, y1, label = \"Actual\") \n",
        "  \n",
        "#grafik hasil prediksi\n",
        "# line 2 points \n",
        "x2 = result_plot.index.values\n",
        "y2 = result_plot['Predicted']\n",
        "# plotting the line 2 points  \n",
        "plt.plot(x2, y2, label = \"Predicted\") \n",
        "  \n",
        "# naming the x axis \n",
        "plt.xlabel('x - t') \n",
        "# naming the y axis \n",
        "plt.ylabel('y - price') \n",
        "# giving a title to my graph \n",
        "plt.title('Actual vs Predicted') \n",
        "  \n",
        "# show a legend on the plot \n",
        "plt.legend() \n",
        "  \n",
        "# function to show the plot \n",
        "plt.show() \n",
        "\n",
        "print('===========================================Summary==============================================')\n",
        "print('Data \"USDJPY\"')\n",
        "print()\n",
        "print('Mean Squared Error = ',mean_squared_error(result['Actual'], result['Predicted']))\n",
        "print()\n",
        "print('sliding window = ', window,'; + bias')\n",
        "print()\n",
        "print('coefficient = ',gbest[1])\n",
        "print()\n",
        "print('column choose = ',choose_column)\n",
        "print()\n",
        "print('data split : train percentage = ', percentage_train, '; test percentage = ', percentage_validate)\n",
        "print()\n",
        "print('popsize = ', popsize, '; switch probability (p) = ', switch_prob, '; maximum iteration = ', max_iter)\n",
        "print('================================================================================================')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F8c24E/ryw9gHuT3m73hmPA3XL4aqEti+mK4bngYgXxlLeN+6aQIn9Uk+rs+Y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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "===========================================Summary==============================================\n",
            "Data \"USDJPY\"\n",
            "\n",
            "Mean Squared Error =  0.7243353920068822\n",
            "\n",
            "sliding window =  5 ; + bias\n",
            "\n",
            "coefficient =  [ 0.07727768  0.02929574  0.24208967 -0.23813027  0.90573746 -1.67319024]\n",
            "\n",
            "column choose =  close\n",
            "\n",
            "data split : train percentage =  0.8 ; test percentage =  0.2\n",
            "\n",
            "popsize =  150 ; switch probability (p) =  0.8 ; maximum iteration =  100\n",
            "================================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}