{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.3"
    },
    "colab": {
      "name": "scikit-learn-tensorflow.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/TensorFlow2/blob/master/scikit_learn_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3I_YHf1EcwI",
        "colab_type": "text"
      },
      "source": [
        "# সাইকিট লার্ন এবং টেন্সর-ফ্লো নিয়ে একসাথে কাজ\n",
        "\n",
        "সব কাজেই আমি সাইকিট লার্ন ব্যবহার করতে পারি। মেশিন লার্নিং আর ডিপ লার্নিং এর মধ্যে ফারাক নেই বললেই চলে। শুরুতেই বিভিন্ন লাইব্রেরি লোড করে নিলাম। কাজ করবো ডিপ লার্নিং নিয়ে, কিন্তু শুরুতেই প্রি-প্রসেসিং করে নিচ্ছি সাইকিট লার্ন দিয়ে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XfOEMaBuEcwJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# প্রি-প্রসেসিং করে নিচ্ছি সাইকিট লার্ন দিয়ে \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4btlLZREcwN",
        "colab_type": "text"
      },
      "source": [
        "## ১. সাইকিট লার্ন দিয়ে ট্রেইন/টেস্ট স্প্লিট \n",
        "\n",
        "মনে আছে তো শূন্য থেকে পাইথন মেশিন লার্নিং বইটার কথা?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bya8FcJgEcwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/raqueeb/TensorFlow2/master/datasets/geoloc_elev.csv')\n",
        "\n",
        "# আমরা শুধুমাত্র দুটো ফিচার ব্যবহার করছি এখানে \n",
        "X = df[['lat', 'lon']].values\n",
        "y = df['target'].values\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "    test_size = 0.3, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCGlCaeuEcwR",
        "colab_type": "text"
      },
      "source": [
        "## প্লটিং করে দেখি ডেটার ভেতরের গল্প \n",
        "\n",
        "কি মনে হচ্ছে, হবে কী বেসিক মেশিন লার্নিং দিয়ে? ঠিক ধরেছেন, এটা একটা নন-লিনিয়ার ডেটা। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6BqC6qnEcwR",
        "colab_type": "code",
        "outputId": "b7bd56ea-3b79-45e5-d159-0cff800c4210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "df.plot(kind='scatter',\n",
        "        x='lat',\n",
        "        y='lon',\n",
        "        c='target',\n",
        "        cmap='bwr');"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Og0FjLQ0zvPoq0YgRSEXy7LOYDyvn2SgCyqmKqeyN+DTDG28gdcXevdBD33MPjLTFheHD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\n6E5EPYiomhBCXUQ4nYgsR6GUrrtKMvT1gL1epTQ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+IKL1ls+Po6NhRPRKQRtakjcZb4vH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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOg_1g9sEcwV",
        "colab_type": "text"
      },
      "source": [
        "## ২. শুরুতে ১ নিউরনের (নোড) নিউরাল নেটওয়ার্ক "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqRjizlxEcwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import SGD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGyhA_teEcwZ",
        "colab_type": "code",
        "outputId": "ae33a199-443c-4536-d975-6abba96581ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "model = Sequential([\n",
        "    Dense(1, input_dim=2, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(SGD(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "h = model.fit(X_train, y_train, epochs=10, validation_split=0.1)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 945 samples, validate on 105 samples\n",
            "Epoch 1/10\n",
            "945/945 [==============================] - 0s 132us/sample - loss: 0.6640 - acc: 0.5608 - val_loss: 0.6553 - val_acc: 0.6381\n",
            "Epoch 2/10\n",
            "945/945 [==============================] - 0s 41us/sample - loss: 0.6448 - acc: 0.6582 - val_loss: 0.6549 - val_acc: 0.6381\n",
            "Epoch 3/10\n",
            "945/945 [==============================] - 0s 44us/sample - loss: 0.6443 - acc: 0.6582 - val_loss: 0.6567 - val_acc: 0.6381\n",
            "Epoch 4/10\n",
            "945/945 [==============================] - 0s 44us/sample - loss: 0.6436 - acc: 0.6582 - val_loss: 0.6539 - val_acc: 0.6381\n",
            "Epoch 5/10\n",
            "945/945 [==============================] - 0s 45us/sample - loss: 0.6435 - acc: 0.6582 - val_loss: 0.6550 - val_acc: 0.6381\n",
            "Epoch 6/10\n",
            "945/945 [==============================] - 0s 39us/sample - loss: 0.6442 - acc: 0.6582 - val_loss: 0.6544 - val_acc: 0.6381\n",
            "Epoch 7/10\n",
            "945/945 [==============================] - 0s 37us/sample - loss: 0.6442 - acc: 0.6582 - val_loss: 0.6538 - val_acc: 0.6381\n",
            "Epoch 8/10\n",
            "945/945 [==============================] - 0s 38us/sample - loss: 0.6449 - acc: 0.6582 - val_loss: 0.6539 - val_acc: 0.6381\n",
            "Epoch 9/10\n",
            "945/945 [==============================] - 0s 37us/sample - loss: 0.6438 - acc: 0.6582 - val_loss: 0.6543 - val_acc: 0.6381\n",
            "Epoch 10/10\n",
            "945/945 [==============================] - 0s 39us/sample - loss: 0.6433 - acc: 0.6582 - val_loss: 0.6577 - val_acc: 0.6381\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwytvbPSEcwc",
        "colab_type": "code",
        "outputId": "7734be24-89cd-452d-fed6-98ab79a9fdff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "pd.DataFrame(h.history).plot(ylim=(-0.05, 1.05))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f43286eeac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NS6edPZ8doGL/51R8+jkVnx5g9/7PM+MHqNhfScWnVez9++fUPbxgIB4zinpW8q3YC0z4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\nvyOhRyVIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5Au-zbOEcwf",
        "colab_type": "text"
      },
      "source": [
        "## ৩. একটু ডিপ নিউরাল নেটওয়ার্ক "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NAYjTsCEcwg",
        "colab_type": "code",
        "outputId": "03b8fe9f-35ea-4ca1-ed2e-895e02a1e14d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        }
      },
      "source": [
        "model = Sequential([\n",
        "    Dense(4, input_dim=2, activation='tanh'),\n",
        "    Dense(4, activation='tanh'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(SGD(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "h = model.fit(X_train, y_train, epochs=20, validation_split=0.1)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 945 samples, validate on 105 samples\n",
            "Epoch 1/20\n",
            "945/945 [==============================] - 0s 137us/sample - loss: 0.6381 - acc: 0.6180 - val_loss: 0.6042 - val_acc: 0.6381\n",
            "Epoch 2/20\n",
            "945/945 [==============================] - 0s 38us/sample - loss: 0.4979 - acc: 0.8159 - val_loss: 0.3797 - val_acc: 0.9524\n",
            "Epoch 3/20\n",
            "945/945 [==============================] - 0s 39us/sample - loss: 0.2458 - acc: 0.9979 - val_loss: 0.1585 - val_acc: 1.0000\n",
            "Epoch 4/20\n",
            "945/945 [==============================] - 0s 41us/sample - loss: 0.1184 - acc: 0.9989 - val_loss: 0.0942 - val_acc: 1.0000\n",
            "Epoch 5/20\n",
            "945/945 [==============================] - 0s 41us/sample - loss: 0.0731 - acc: 1.0000 - val_loss: 0.0651 - val_acc: 1.0000\n",
            "Epoch 6/20\n",
            "945/945 [==============================] - 0s 41us/sample - loss: 0.0513 - acc: 1.0000 - val_loss: 0.0454 - val_acc: 1.0000\n",
            "Epoch 7/20\n",
            "945/945 [==============================] - 0s 39us/sample - loss: 0.0379 - acc: 1.0000 - val_loss: 0.0339 - val_acc: 1.0000\n",
            "Epoch 8/20\n",
            "945/945 [==============================] - 0s 40us/sample - loss: 0.0292 - acc: 1.0000 - val_loss: 0.0273 - val_acc: 1.0000\n",
            "Epoch 9/20\n",
            "945/945 [==============================] - 0s 43us/sample - loss: 0.0226 - acc: 1.0000 - val_loss: 0.0217 - val_acc: 1.0000\n",
            "Epoch 10/20\n",
            "945/945 [==============================] - 0s 48us/sample - loss: 0.0180 - acc: 1.0000 - val_loss: 0.0181 - val_acc: 1.0000\n",
            "Epoch 11/20\n",
            "945/945 [==============================] - 0s 49us/sample - loss: 0.0148 - acc: 1.0000 - val_loss: 0.0156 - val_acc: 1.0000\n",
            "Epoch 12/20\n",
            "945/945 [==============================] - 0s 48us/sample - loss: 0.0126 - acc: 1.0000 - val_loss: 0.0131 - val_acc: 1.0000\n",
            "Epoch 13/20\n",
            "945/945 [==============================] - 0s 47us/sample - loss: 0.0108 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
            "Epoch 14/20\n",
            "945/945 [==============================] - 0s 53us/sample - loss: 0.0095 - acc: 1.0000 - val_loss: 0.0100 - val_acc: 1.0000\n",
            "Epoch 15/20\n",
            "945/945 [==============================] - 0s 46us/sample - loss: 0.0083 - acc: 1.0000 - val_loss: 0.0094 - val_acc: 1.0000\n",
            "Epoch 16/20\n",
            "945/945 [==============================] - 0s 50us/sample - loss: 0.0075 - acc: 1.0000 - val_loss: 0.0082 - val_acc: 1.0000\n",
            "Epoch 17/20\n",
            "945/945 [==============================] - 0s 50us/sample - loss: 0.0069 - acc: 1.0000 - val_loss: 0.0076 - val_acc: 1.0000\n",
            "Epoch 18/20\n",
            "945/945 [==============================] - 0s 48us/sample - loss: 0.0062 - acc: 1.0000 - val_loss: 0.0069 - val_acc: 1.0000\n",
            "Epoch 19/20\n",
            "945/945 [==============================] - 0s 49us/sample - loss: 0.0058 - acc: 1.0000 - val_loss: 0.0067 - val_acc: 1.0000\n",
            "Epoch 20/20\n",
            "945/945 [==============================] - 0s 50us/sample - loss: 0.0053 - acc: 1.0000 - val_loss: 0.0061 - val_acc: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1se5ftNEcwj",
        "colab_type": "code",
        "outputId": "9c85b576-f312-42cc-fb41-7130eba64739",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "pd.DataFrame(h.history).plot(ylim=(-0.05, 1.05))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f43286f0320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zyy238KMf/Yi1a9dy6NAhPB4Ps2bN4uGHH2bt2rXBDr9DgZy5TwS2G2OKAUTkFSAP2Nyi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\nqAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mA882gUoEcwn",
        "colab_type": "text"
      },
      "source": [
        "## ৪. বেঞ্চমার্ক, ট্রেইন এবং টেস্ট সেটের ওপর অ্যাক্যুরেসি স্কোর দেখলে কেমন হয়?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZfFd6SPEcwo",
        "colab_type": "code",
        "outputId": "e34f027b-173c-4a35-a855-e5cca92906a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "bm_score = pd.Series(y).value_counts()[0] / len(y)\n",
        "train_score = model.evaluate(X_train, y_train, verbose=0)[1]\n",
        "test_score = model.evaluate(X_test, y_test,  verbose=0)[1]\n",
        "\n",
        "print(\"\"\"Accuracy scores:\n",
        "   Benchmark:\\t{:0.3}\n",
        "   Train:\\t{:0.3}\n",
        "   Test:\\t{:0.3}\"\"\".format(bm_score, train_score, test_score))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy scores:\n",
            "   Benchmark:\t0.667\n",
            "   Train:\t1.0\n",
            "   Test:\t1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2JqfIoOEcwr",
        "colab_type": "text"
      },
      "source": [
        "## ৫. সাইকিট লার্ন দিয়ে কনফিউশন ম্যাট্রিক্স এবং ক্লাসিফিকেশন রিপোর্ট দেখি "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scrW1CWvEcws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "y_pred = model.predict_classes(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcWQpTu8Ecwv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "641384f2-10b7-4287-9b49-b11511510157"
      },
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "pd.DataFrame(cm,\n",
        "             index=[\"Miss\", \"Hit\"],\n",
        "             columns=['pred_Miss', 'pred_Hit'])"
      ],
      "execution_count": 32,
      "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>pred_Miss</th>\n",
              "      <th>pred_Hit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Miss</th>\n",
              "      <td>311</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hit</th>\n",
              "      <td>0</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      pred_Miss  pred_Hit\n",
              "Miss        311         0\n",
              "Hit           0       139"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNgvGBAiEcwx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "b103cfaf-a9d6-45b8-be69-72d826940cff"
      },
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       311\n",
            "           1       1.00      1.00      1.00       139\n",
            "\n",
            "    accuracy                           1.00       450\n",
            "   macro avg       1.00      1.00      1.00       450\n",
            "weighted avg       1.00      1.00      1.00       450\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU4Cv0dcEcw0",
        "colab_type": "text"
      },
      "source": [
        "## ৬. ডিসিশন বাউন্ডারি দেখলে কেমন হয়?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8GGBsC5Ecw0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "ed262de3-e9dc-4fef-f57d-14597b0c9b79"
      },
      "source": [
        "hticks = np.linspace(-2, 2, 101)\n",
        "vticks = np.linspace(-2, 2, 101)\n",
        "aa, bb = np.meshgrid(hticks, vticks)\n",
        "ab = np.c_[aa.ravel(), bb.ravel()]\n",
        "\n",
        "c = model.predict(ab)\n",
        "cc = c.reshape(aa.shape)\n",
        "\n",
        "ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n",
        "ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.contour.QuadContourSet at 0x7f430f504e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8DrVmmEkhEUJw6k6an2/1qdXWWi+iXe2AOv7991OY5+c7NwyU0njt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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}