SimpleMLP.ipynb

{
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   "source": [
    "# Artificial Neural NetworksAssignment 2\n",
    "### Comparison between MLP Network with PCA and MLP Network without PCA\n",
    "<p>This Notebook contains a comparison between MLP network and MLP network after appling PCA on Jm1 Dataset</p>\n",
    "<br>\n",
    "<strong>Run the Following Commands before Starting the notebook</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "LiAHQsEOnku2",
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   },
   "outputs": [],
   "source": [
    "#install dataset that works with imbalanced Data by running the following command\n",
    "#!pip install -U imbalanced-learn\n",
    "#!pip install datapackage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cptE0XAbnku3"
   },
   "source": [
    "**First We Import the Necessary Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h7Y61j_9nku4",
    "outputId": "134fe2c5-117b-470a-a2ec-225e117f881d"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from imblearn.under_sampling import NearMiss\n",
    "\n",
    "from sklearn import model_selection,preprocessing\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,learning_curve,validation_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,f1_score,plot_confusion_matrix,precision_recall_fscore_support\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e8HeKViwnku5"
   },
   "source": [
    "## Dataset \n",
    "**Here the dataset in retrieved from the original website**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "oOJuhWu_nku6"
   },
   "outputs": [],
   "source": [
    "# TO load dataset from the orginal website\n",
    "import datapackage\n",
    "data_url = 'https://datahub.io/machine-learning/jm1/datapackage.json'\n",
    "# to load Data Package into storage\n",
    "package = datapackage.Package(data_url)\n",
    "# to load only tabular data\n",
    "resources = package.resources\n",
    "for resource in resources:\n",
    "    if resource.tabular:\n",
    "        Dataset = pd.read_csv(resource.descriptor['path'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "YbV8DkN4nku6",
    "outputId": "e2669b42-f53f-43da-c768-b9d57a2a1700"
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   "outputs": [
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       "      <th></th>\n",
       "      <th>loc</th>\n",
       "      <th>v(g)</th>\n",
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       "      <th>uniq_Opnd</th>\n",
       "      <th>total_Op</th>\n",
       "      <th>total_Opnd</th>\n",
       "      <th>branchCount</th>\n",
       "      <th>defects</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>1134.13</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20.31</td>\n",
       "      <td>55.85</td>\n",
       "      <td>23029.10</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>4348.76</td>\n",
       "      <td>0.06</td>\n",
       "      <td>17.06</td>\n",
       "      <td>254.87</td>\n",
       "      <td>74202.67</td>\n",
       "      <td>...</td>\n",
       "      <td>129</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>599.12</td>\n",
       "      <td>0.06</td>\n",
       "      <td>17.19</td>\n",
       "      <td>34.86</td>\n",
       "      <td>10297.30</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>50.0</td>\n",
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       "      <td>True</td>\n",
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       "<p>5 rows × 22 columns</p>\n",
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      "text/plain": [
       "     loc  v(g)  ev(g)  iv(g)      n        v     l      d       i         e  \\\n",
       "0    1.1   1.4    1.4    1.4    1.3     1.30  1.30   1.30    1.30      1.30   \n",
       "1    1.0   1.0    1.0    1.0    1.0     1.00  1.00   1.00    1.00      1.00   \n",
       "2   72.0   7.0    1.0    6.0  198.0  1134.13  0.05  20.31   55.85  23029.10   \n",
       "3  190.0   3.0    1.0    3.0  600.0  4348.76  0.06  17.06  254.87  74202.67   \n",
       "4   37.0   4.0    1.0    4.0  126.0   599.12  0.06  17.19   34.86  10297.30   \n",
       "\n",
       "   ...  lOCode  lOComment  lOBlank  locCodeAndComment  uniq_Op  uniq_Opnd  \\\n",
       "0  ...       2          2        2                  2      1.2        1.2   \n",
       "1  ...       1          1        1                  1      1.0        1.0   \n",
       "2  ...      51         10        8                  1     17.0       36.0   \n",
       "3  ...     129         29       28                  2     17.0      135.0   \n",
       "4  ...      28          1        6                  0     11.0       16.0   \n",
       "\n",
       "   total_Op  total_Opnd  branchCount  defects  \n",
       "0       1.2         1.2          1.4    False  \n",
       "1       1.0         1.0          1.0     True  \n",
       "2     112.0        86.0         13.0     True  \n",
       "3     329.0       271.0          5.0     True  \n",
       "4      76.0        50.0          7.0     True  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset file\n",
    "Dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bUS3f-PPnku8",
    "outputId": "97a90929-6ab5-457c-82ac-b75beabc36cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loc                  0\n",
       "v(g)                 0\n",
       "ev(g)                0\n",
       "iv(g)                0\n",
       "n                    0\n",
       "v                    0\n",
       "l                    0\n",
       "d                    0\n",
       "i                    0\n",
       "e                    0\n",
       "b                    0\n",
       "t                    0\n",
       "lOCode               0\n",
       "lOComment            0\n",
       "lOBlank              0\n",
       "locCodeAndComment    0\n",
       "uniq_Op              5\n",
       "uniq_Opnd            5\n",
       "total_Op             5\n",
       "total_Opnd           5\n",
       "branchCount          5\n",
       "defects              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the  of missing values \n",
    "Dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qGwwKC4nku8",
    "outputId": "0d7eaa34-f2f8-4f16-a547-071bfb63e498"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loc                  float64\n",
       "v(g)                 float64\n",
       "ev(g)                float64\n",
       "iv(g)                float64\n",
       "n                    float64\n",
       "v                    float64\n",
       "l                    float64\n",
       "d                    float64\n",
       "i                    float64\n",
       "e                    float64\n",
       "b                    float64\n",
       "t                    float64\n",
       "lOCode                 int64\n",
       "lOComment              int64\n",
       "lOBlank                int64\n",
       "locCodeAndComment      int64\n",
       "uniq_Op              float64\n",
       "uniq_Opnd            float64\n",
       "total_Op             float64\n",
       "total_Opnd           float64\n",
       "branchCount          float64\n",
       "defects                 bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since defects are boolean we need to make it Integer\n",
    "Dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ainWCNMtnku9"
   },
   "outputs": [],
   "source": [
    "# Converting defect to 1 and 0\n",
    "Dataset['defects']=Dataset['defects'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "ea-WKdgEnku9"
   },
   "outputs": [],
   "source": [
    "# Dealing with missing data by dropping rows with missing values\n",
    "Dataset.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RZxfLws5nku9"
   },
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "ijcumGNOnku-",
    "outputId": "e5ec6f82-847d-4aeb-f55e-472617999fb5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".output_png {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a code to center the plots this works on jupyter notebook only\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "}\n",
    "</style>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 879
    },
    "id": "YfVjtFF3nku-",
    "outputId": "42cd1d69-4e05-4958-de7f-51f267705335",
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting the data distrubtion of each feature and target\n",
    "Dataset.iloc[:,:-1].hist(figsize=(20, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "jB9CUOSxnku-",
    "outputId": "d27272be-586a-485e-c152-ae914ecbb603"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First step is checking the frequency of each class \n",
    "ax=Dataset['defects'].value_counts().plot(kind='bar',color = ['#BB0000', '#0000BB'],\n",
    "                                          title='Number of defects and no defects',figsize=(8, 6))\n",
    "ax.set_xlabel(\"Defects\",fontsize=14)\n",
    "ax.set_ylabel(\"Number of instaces\",fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Pgbdiwknku_"
   },
   "source": [
    "<h4>As we can see from the figure above that the data is heavily biased with the ratio 80% 20% which is making this dataset un-balanced.</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ckEpv9PXnku_"
   },
   "source": [
    "## Creating the Necessary Functions\n",
    "\n",
    "**Creating the Model build and Training Function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "ArIcSB7rnku_"
   },
   "outputs": [],
   "source": [
    "#defining the set_seed to 42 to make the results stable with each run\n",
    "tf.random.set_seed(42)\n",
    "# Defining Function that builds the model not to repeat it , it returns ths trained model and history of losses and accuaries\n",
    "def build_Model(act,opti,los,x,y,n_feature=21,ver=0):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(1000, input_shape=(n_feature,), activation=act))\n",
    "    model.add(Dense(500, activation=act))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(optimizer=opti,loss=los,metrics=['accuracy'])\n",
    "    history =model.fit(x, y, epochs=10, batch_size=250, verbose=ver, validation_split=0.2)\n",
    "    return [model,history]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0HTExCgznku_"
   },
   "source": [
    "**Createing the Model Evaluation Function**\n",
    "<br>\n",
    "here we use 2 evaluation metrics to evaluate the model: Accuarcy Score, and Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "e-Ogbxe2nku_"
   },
   "outputs": [],
   "source": [
    "# Defining Function that shows the accuracies of models\n",
    "# It calculates the accuracy, loss and confusion matrix of the model \n",
    "def showAcc(model,x_train,y_train,x_test,y_test):\n",
    "    evaluation=model.evaluate(x_test, y_test,verbose=0)\n",
    "    print('Neural Network Accuracy is {:.4f}\\nNeural Network Loss is {:.4f}'.format(evaluation[1],evaluation[0]))\n",
    "    y_pred=model.predict(x_test)\n",
    "    y_pred=(y_pred>0.5)\n",
    "    cm=confusion_matrix(y_test, y_pred)\n",
    "    print('True Postive  : '+str(cm[0][0])+'\\tFalse Postive  : '+str(cm[0][1]))\n",
    "    print('False Negative  : '+str(cm[1][0])+'\\tTrue Negative  : '+str(cm[1][1]))\n",
    "    plt.imshow(cm, cmap=plt.cm.Purples,)\n",
    "    plt.xlabel(\"Predicted labels\")\n",
    "    plt.ylabel(\"True labels\")\n",
    "    plt.xticks([], [])\n",
    "    plt.yticks([], [])\n",
    "    plt.title('Confusion matrix ')\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    return evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KplfHyzknkvA"
   },
   "source": [
    "## MLP Before Under-sampling\n",
    "\n",
    "**Here we try the model on the befor taking any action regarding the un-balanced data we have**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "yRlyothtnkvA"
   },
   "outputs": [],
   "source": [
    "# Dividing the data to training features and target \n",
    "x_Dataset=Dataset.iloc[:,:-1]\n",
    "y_Dataset=Dataset['defects']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z3Xl5vdonkvA",
    "outputId": "d6dd6492-efc8-4d75-ad60-e5c1d62487a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8704, 21) (2176, 21)\n"
     ]
    }
   ],
   "source": [
    "# Dividing the data by 0.8 train and 0.2 test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_Dataset, y_Dataset, test_size=0.2,random_state=42)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "ZyvpHIxGnkvA"
   },
   "outputs": [],
   "source": [
    "# Standardizing the data\n",
    "sc= StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "id": "cZ6ok15BnkvB",
    "outputId": "e0c39810-9d98-42bf-bd05-7dfb881290c1",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Accuracy is 0.8111\n",
      "Neural Network Loss is 0.4438\n",
      "True Postive  : 1717\tFalse Postive  : 40\n",
      "False Negative  : 371\tTrue Negative  : 48\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building a model to see the first impression \n",
    "UnSample=build_Model('sigmoid','adam','binary_crossentropy',x_train,y_train)\n",
    "UnSample_res=showAcc(UnSample[0],x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ytw1nYYMnkvB"
   },
   "source": [
    "**We can see that the accuracy on the validation dataste is 81.1%.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p5aeu9PXnkvB"
   },
   "source": [
    "## Under-sampling the dataset\n",
    "\n",
    "**Here we try to solve the problem on the un-balanced dataset by performing an unber-sampling technique called NearMiss.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tr4HAQusnkvB",
    "outputId": "0630ff97-41d1-4634-f4c1-0ab9fd6a8904"
   },
   "outputs": [],
   "source": [
    "# Using NearMiss method to undersample the data\n",
    "sampler = NearMiss(version=1,sampling_strategy={0: 3000,1: 2103})\n",
    "x_DS, y_DS = sampler.fit_sample(x_Dataset, y_Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "5-xlDjOBnkvB",
    "outputId": "b5b9dc3c-fe4d-40d2-a252-ca1bff6034b4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#y_DS = pd.DataFrame(y_DS,columns=['Defects'])\n",
    "ax=y_DS.value_counts().plot(kind='bar',color = ['#BB0000', '#0000BB'],title='Number of defects and no defects After under sampling',figsize=(8, 6))\n",
    "ax.set_xlabel(\"Defects\",fontsize=14)\n",
    "ax.set_ylabel(\"Number of instaces\",fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TOsj9q_ZnkvC"
   },
   "source": [
    "**Now After the Under-Sampling we can notice the difference in the biasing of the dataset, and it is much better now as the data is more balanced.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3k_8Vg3xnkvC"
   },
   "source": [
    "## Data preparation\n",
    "\n",
    "**Now we operate on the new dataset produced from the under-sampling technique.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vG9NIvW0nkvD",
    "outputId": "2ae05c7e-a574-4dd6-b08c-8d16ccb7dcd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4082, 21) (1021, 21)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the Data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_DS, y_DS, test_size=0.2,random_state=42)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Z_jzvKT4nkvD"
   },
   "outputs": [],
   "source": [
    "# Standardization of the data\n",
    "sc= StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_TpHitkLnkvD"
   },
   "source": [
    "**Building the same model built before up-sampling to compare the differences.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "id": "URieZwuxnkvD",
    "outputId": "e7d7a120-4607-411c-99d9-ad6036be70f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Accuracy is 0.8962\n",
      "Neural Network Loss is 0.2904\n",
      "True Postive  : 594\tFalse Postive  : 12\n",
      "False Negative  : 94\tTrue Negative  : 321\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_MLP=build_Model('sigmoid','adam','binary_crossentropy',x_train,y_train)\n",
    "res=showAcc(init_MLP[0],x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bRQgCOm4nkvD"
   },
   "source": [
    "**As we can see that there is imrovement in the performance of the model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vHkVM-XgnkvE"
   },
   "source": [
    "## Creating Variations of the Model \n",
    "\n",
    "**In this section we try different hyperparameters to create more variations of the MLP model to see the best variation of the network**\n",
    "\n",
    "### 1) Activation & Optimizer Variations\n",
    "**In This section , The MLP will be trained by changing the hyperparameters of both activation functions and optimizers to find the best model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "boHj8RFHnkvE",
    "outputId": "38bf5f4a-23a9-4df7-dfd0-43729a5b5a02",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             \u001b[1mMLP With relu Activation Function And Adamax Optimizer\u001b[0m                             \n",
      "Neural Network Accuracy is 0.8874\n",
      "Neural Network Loss is 0.2761\n",
      "True Postive  : 582\tFalse Postive  : 24\n",
      "False Negative  : 91\tTrue Negative  : 324\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            \u001b[1mMLP With relu Activation Function And RMSprop Optimizer\u001b[0m                             \n",
      "Neural Network Accuracy is 0.8864\n",
      "Neural Network Loss is 0.2898\n",
      "True Postive  : 578\tFalse Postive  : 28\n",
      "False Negative  : 88\tTrue Negative  : 327\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              \u001b[1mMLP With relu Activation Function And Adam Optimizer\u001b[0m                              \n",
      "Neural Network Accuracy is 0.8942\n",
      "Neural Network Loss is 0.2706\n",
      "True Postive  : 590\tFalse Postive  : 16\n",
      "False Negative  : 92\tTrue Negative  : 323\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           \u001b[1mMLP With sigmoid Activation Function And Adamax Optimizer\u001b[0m                            \n",
      "Neural Network Accuracy is 0.8874\n",
      "Neural Network Loss is 0.3233\n",
      "True Postive  : 591\tFalse Postive  : 15\n",
      "False Negative  : 100\tTrue Negative  : 315\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           \u001b[1mMLP With sigmoid Activation Function And RMSprop Optimizer\u001b[0m                           \n",
      "Neural Network Accuracy is 0.8492\n",
      "Neural Network Loss is 0.3377\n",
      "True Postive  : 522\tFalse Postive  : 84\n",
      "False Negative  : 70\tTrue Negative  : 345\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            \u001b[1mMLP With sigmoid Activation Function And Adam Optimizer\u001b[0m                             \n",
      "Neural Network Accuracy is 0.8932\n",
      "Neural Network Loss is 0.2998\n",
      "True Postive  : 602\tFalse Postive  : 4\n",
      "False Negative  : 105\tTrue Negative  : 310\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             \u001b[1mMLP With tanh Activation Function And Adamax Optimizer\u001b[0m                             \n",
      "Neural Network Accuracy is 0.8883\n",
      "Neural Network Loss is 0.2879\n",
      "True Postive  : 575\tFalse Postive  : 31\n",
      "False Negative  : 83\tTrue Negative  : 332\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            \u001b[1mMLP With tanh Activation Function And RMSprop Optimizer\u001b[0m                             \n",
      "Neural Network Accuracy is 0.8707\n",
      "Neural Network Loss is 0.3181\n",
      "True Postive  : 556\tFalse Postive  : 50\n",
      "False Negative  : 82\tTrue Negative  : 333\n"
     ]
    },
    {
     "data": {
      "image/png": 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1nzcAPky6W9rMMpbZc43LHAZeWbsu6VLgxrb1yMz6zYA6DOzFdsA7Wt0RM+t/GWVVqTmr53hjzqqL4qbDk9rZKTNrP0l0DconreqGVXr2+m7An1LRa40e42Bm+cjpMLDu7TYpmK6OiDVpcVCZDSAD7d7AOyWNb3tPzKzf5fTewHrPYO9+FOnfAMdKehR4ieKEZ0SEA8wsc50yaiqj3pzVncB4YFI/9cXM+pFa+Cqu/lAvrATFW5j7qS9m1q80YO4N3ErSiX1tjIjvtqE/ZtaPBsrIahDFm5jz+TVm1pSMsqpuWC2NiNP7rSdm1q+kpt7IvM41nLMys4FroIys9u+3XpjZOjEg5qwiou6rnM0sfwMirMxsgOugq9PLcFiZVZTonPv+ynBYmVVYRlnlsDKrMo+szKzzaYC9isvMBqbM3hfhsDKrMoeVmWVBGd2o4rAyqzCPrMys4ymzCfYyz2A3swGp3MsiylzeIGm0pN9KelDSAkmfT+VbSLpR0sL0d/NULknnSlok6b4y73lwWJlVWAtfGLEa+GJEvAuYAJwgaReKd4zOioixwCzeeOfoQcDYtEwDzm+0A4eVWYW1amQVEUsj4q70+QXgQWAkMBGYnqpN5413OkwELonCHGCopBH19uGwMqsylVxgmKR5Ncu0PpuUxgC7A3OB4RGxFIpAA7ZO1UYCT9R8bXEq65Mn2M2qqrm32yyPiD0bNiltAlwFfCEi/lyn/d421H2JssPKrKJEa88GSlqPIqj+MyJ+mYqfkjQiIpamw7xlqXwxMLrm66OAJfXa92GgWYWVPwps0E4xhLoIeLDHm69mAlPS5ynANTXlR6ezghOAFd2Hi33xyMqswlr41IV9gKOA+yXdk8pOBs4EZkiaCvwRmJy2XQ8cDCwCVgLHNNqBw8qswlqVVRFxO30Pwv7qfQ4REcAJzezDYWVWUWUvS+gUDiuzCssoqxxWZlWW072BDiuzCvPIysw6npq7KHSd83VWZpYFj6zMKiynkZXDyqzCMsoqh5VZlTmszCwLfmGEmeUhn6xyWJlVVROPLO4IDiuzypIPA80sDx5ZmVkWfJ2VmeUhn6xyWJlVWUZZ5bAyqyqR12Ggb2Q2syx4ZGVWVcrr4XseWZlZFjyyMquwjKasHFZm1eUr2M0sF/lklcPKrKoEZDS/7rAyq6ziQqt13YvSHFZmFZZPVDmszCoto4GVw8qs0jJKK4eVWYXlE1UOK7PKyu1GZoeVWYVllFW+N9DM8uCRlVlVZfZ2G4+szCpNJZcGrUj/IWmZpAdqyraQdKOkhenv5qlcks6VtEjSfZLGl+mpw8qswrrfHdhoKeFi4MAeZScBsyJiLDArrQMcBIxNyzTg/DI7cFiZVVlrBlZExK3Asz2KJwLT0+fpwKSa8kuiMAcYKmlEo304rMwqSk38DxgmaV7NMq3ELoZHxFKA9HfrVD4SeKKm3uJUVpcn2M2sjOURsWeL2uptrBaNvuSRlVmFtXDOqjdPdR/epb/LUvliYHRNvVHAkkaNOazMrF1mAlPS5ynANTXlR6ezghOAFd2Hi/X4MNCsqtS6220kXQ7sSzG3tRj4OnAmMEPSVOCPwORU/XrgYGARsBI4psw+HFZmVdaii0Ij4hN9bNq/l7oBnNDsPhxWZhWW0QXsDiuzSsvofhtPsJtZFjyyMquwfMZVDiuzyvLD98wsH/lklcPKrMoyyiqHlVlllXyiQqdwWJlVWj5p5bAyq7B8osphZVZpGZ0MdFiZVVdeb4zwFexmlgWPrMwqLKOBlUdWZpYHj6zMKiq3221UPAerxY1KTwOPt7xhM6v1jojYam2/LOlXwLCS1ZdHRM/3AvartoSVmVmrec7KzLLgsDKzLDiszCwLDqt+JGmNpHskPSDp55I2egtt7Svp2vT5Q5JOqlN3qKTPrsU+TpP0pbLlPepcLOmjTexrjKQHmu2jVYfDqn+9HBHjImJX4C/AcbUb00sfm/5nEhEzI+LMOlWGAk2HlVkncVitO7cBO6YRxYOSfgjcBYyWdICk2ZLuSiOwTQAkHSjpIUm3Ax/pbkjSpySdlz4Pl3S1pHvTsjfFyyZ3SKO676R6X5b0O0n3SfpGTVunSHpY0m+AnRv9CEnHpnbulXRVj9HiByXdJukRSYem+oMkfadm33/fS5vvlnRn6u99ksY2/3+vDTQOq3VA0mDgIOD+VLQzcElE7A68BJwKfDAixgPzgBMlbQBcCBwGvA94ex/NnwvcEhG7AeOBBcBJwKNpVPdlSQcAY4G9gHHAHpLeL2kP4HBgd4owfG+Jn/PLiHhv2t+DwNSabWOADwCHAD9Kv2EqxevC35vaP1bSdj3aPA74XkSMA/YEFpfohw1wvoK9f20o6Z70+TbgImAb4PGImJPKJwC7AHekq4vfBswG3gn8PiIWAkj6GTCtl33sBxwNEBFrgBWSNu9R54C03J3WN6EIryHA1RGxMu1jZonftKukMygONTcBbqjZNiMiXgMWSnos/YYDgPfUzGdtlvb9SM33ZgOnSBpFEYYLS/TDBjiHVf96OY0WXpcC6aXaIuDGnq/jljQOaNUVvAK+HRE/7rGPL6zFPi4GJkXEvZI+Bexbs61nW5H2/bmIqA01JI15vVLEZZLmUozIbpD0mYi4qcl+2QDjw8DOMwfYR9KOAJI2krQT8BCwnaQdUr1P9PH9WcDx6buDJG0KvEAxaup2A/DpmrmwkZK2Bm4FPixpQ0lDKA45GxkCLJW0HnBkj22TJXWlPm8PPJz2fXyqj6SdJG1c+yVJ2wOPRcS5wEzgPSX6YQOcR1YdJiKeTiOUyyWtn4pPjYhHJE0DrpO0HLgd2LWXJj4PXCBpKrAGOD4iZku6I10a8D9p3updwOw0snsR+GRE3CXpSuAeins7byvR5X8G5qb69/PmUHwYuAUYDhwXEa9I+gnFXNZdKnb+NDCpR5sfBz4paRXwJHB6iX7YAOd7A80sCz4MNLMsOKzMLAsOKzPLgsPKzLLgsDKzLDiszCwLDiszy8L/AxTKsw2NAc1oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              \u001b[1mMLP With tanh Activation Function And Adam Optimizer\u001b[0m                              \n",
      "Neural Network Accuracy is 0.8932\n",
      "Neural Network Loss is 0.2771\n",
      "True Postive  : 584\tFalse Postive  : 22\n",
      "False Negative  : 87\tTrue Negative  : 328\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Results=[]\n",
    "act=['relu','sigmoid','tanh']\n",
    "opts=['Adamax','RMSprop','Adam']\n",
    "for  activation in act:\n",
    "    for optimizer in opts:\n",
    "        string='\\033[1m'+'MLP With '+activation+' Activation Function And '+optimizer+' Optimizer'+\"\\033[0m\"\n",
    "        print(string.center(120) )\n",
    "        MLP=build_Model(activation,optimizer,'binary_crossentropy',x_train,y_train)\n",
    "        res=showAcc(MLP[0],x_train,y_train,x_test,y_test)\n",
    "        Results.append(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vP00vcxankvE"
   },
   "source": [
    "**Based On the outputs and confusion matrix we can see that the \"ReLU\" activation function with adam optimizer produces the best performance model among the variations.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "kbrSRcPTnkvF",
    "outputId": "89078696-63bf-48d0-8908-f2654d0f66e8"
   },
   "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>MLP</th>\n",
       "      <th>Adamax</th>\n",
       "      <th>RMSprop</th>\n",
       "      <th>Adam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Relu</td>\n",
       "      <td>0.887365</td>\n",
       "      <td>0.886386</td>\n",
       "      <td>0.894221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>0.887365</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.893242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Tanh</td>\n",
       "      <td>0.888345</td>\n",
       "      <td>0.870715</td>\n",
       "      <td>0.893242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MLP    Adamax   RMSprop      Adam\n",
       "0     Relu  0.887365  0.886386  0.894221\n",
       "1  Sigmoid  0.887365  0.849167  0.893242\n",
       "2     Tanh  0.888345  0.870715  0.893242"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_accuracy = pd.DataFrame({\n",
    "    'MLP': ['Relu', 'Sigmoid', 'Tanh'],\n",
    "    'Adamax': [Results[x][1] for x in range (0,len(Results),3)],\n",
    "    'RMSprop': [Results[x][1] for x in range (1,len(Results),3)],\n",
    "    'Adam' : [Results[x][1] for x in range (2,len(Results),3)],\n",
    "})\n",
    "model_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "oTwecOEenkvF",
    "outputId": "a7fad3de-c0c7-4435-f0a4-4cbd21cac039"
   },
   "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>MLP</th>\n",
       "      <th>Adamax</th>\n",
       "      <th>RMSprop</th>\n",
       "      <th>Adam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Relu</td>\n",
       "      <td>0.276057</td>\n",
       "      <td>0.289766</td>\n",
       "      <td>0.270605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>0.323323</td>\n",
       "      <td>0.337685</td>\n",
       "      <td>0.299762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Tanh</td>\n",
       "      <td>0.287871</td>\n",
       "      <td>0.318093</td>\n",
       "      <td>0.277094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MLP    Adamax   RMSprop      Adam\n",
       "0     Relu  0.276057  0.289766  0.270605\n",
       "1  Sigmoid  0.323323  0.337685  0.299762\n",
       "2     Tanh  0.287871  0.318093  0.277094"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_loss = pd.DataFrame({\n",
    "    'MLP': ['Relu', 'Sigmoid', 'Tanh'],\n",
    "    'Adamax': [Results[x][0] for x in range (0,len(Results),3)],\n",
    "    'RMSprop': [Results[x][0] for x in range (1,len(Results),3)],\n",
    "    'Adam' : [Results[x][0] for x in range (2,len(Results),3)],\n",
    "})\n",
    "model_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fBDIpNisnkvG"
   },
   "source": [
    "### Building the Best Variation of MLP\n",
    "**After testing all the variations above, now we build the best MLP variation based on the results we got.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "id": "rNKV0iWfnkvG",
    "outputId": "b158dc4a-ea73-42b8-c34f-6ac542960b44",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Accuracy is 0.8942\n",
      "Neural Network Loss is 0.2710\n",
      "True Postive  : 590\tFalse Postive  : 16\n",
      "False Negative  : 92\tTrue Negative  : 323\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Best_MLP=build_Model('relu','adam','binary_crossentropy',x_train,y_train)\n",
    "res=showAcc(Best_MLP[0],x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EJ2ozfO_nkvG"
   },
   "source": [
    "##  Learning Curves\n",
    "**Now we plot the learning curve for the best MLP variation created above.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "E-cn4jX7nkvG",
    "outputId": "9d1eb044-f0fe-457e-c65c-7e942523564b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(15,5))\n",
    "fig.suptitle('Learning Curve', fontsize=16)\n",
    "ax1.plot(Best_MLP[1].history['accuracy'])\n",
    "ax1.plot(Best_MLP[1].history['val_accuracy'])\n",
    "ax1.set_title('model accuracy')\n",
    "ax1.set(xlabel='epoch', ylabel='accuracy')\n",
    "ax1.legend(['train', 'Valid'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "ax2.plot(Best_MLP[1].history['loss'])\n",
    "ax2.plot(Best_MLP[1].history['val_loss'])\n",
    "ax2.set_title('model loss')\n",
    "ax2.set(xlabel='epoch', ylabel='loss')\n",
    "ax2.legend(['train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gQYMucx3nkvH"
   },
   "source": [
    "## Applying PCA to MLP\n",
    "**Here we apply PCA method to the MLP network to compare between the enhanced MLP and the basic MLP to see which has the better performance.**\n",
    "<br>First, we check all the \"n_components\" values possible to test every variation of PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "zV5PZto8nkvH",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# We set the parameters of the model to the best variation obtained from the basic MLP variation testing.\n",
    "Result=[]\n",
    "for x in range(1,22):\n",
    "    pca_data = PCA(n_components=x)\n",
    "    x_trainNew=pca_data.fit_transform(x_train)\n",
    "    x_testNew=pca_data.transform(x_test)\n",
    "    MLP=build_Model('relu','adam','binary_crossentropy',x_trainNew,y_train,x,0)\n",
    "    Result.append(MLP[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T5Ort4c2nkvH"
   },
   "source": [
    "**Plotting the accuracies in each \"n_components\" value to see the impact of the PCA.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "-7YzIFttnkvH",
    "outputId": "53a2f4bc-48ca-4e0a-c77e-fb775fe0f8fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                            The best result with 16 Features                                            \n"
     ]
    }
   ],
   "source": [
    "yTab=[]\n",
    "xTab=[i for i in range(1,22)]\n",
    "maxCom=-1\n",
    "numOfComp=None\n",
    "for i in range(len(Result)):\n",
    "    if Result[i].history['accuracy'][-1]>maxCom:\n",
    "        maxCom=Result[i].history['accuracy'][-1]\n",
    "        numOfComp=i\n",
    "    yTab.append(Result[i].history['accuracy'][-1])\n",
    "plt.plot(xTab,yTab)\n",
    "plt.show()\n",
    "print(('The best result with '+str(numOfComp)+' Features').center(120))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UTZLfVY5nkvH"
   },
   "source": [
    "**Based on the plot, we can see that we can get the same accuracy of the basic MLP with less features.**\n",
    "<br> So we create a PCA and setting the \"n_components\" to the value of numOfComp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "Urs9fWTYnkvH"
   },
   "outputs": [],
   "source": [
    "pca_data = PCA(n_components=numOfComp)\n",
    "x_trainNew2=pca_data.fit_transform(x_train)\n",
    "x_testNew2=pca_data.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6QUlCZZ1nkvI"
   },
   "source": [
    "## Creating Variations of the Model \n",
    "\n",
    "**In this section we try different hyperparameters to create more variations of the PCA-MLP model to see the best variation of the network**\n",
    "\n",
    "### 1) Activation Functions And Optimizer Variation\n",
    "**In This section , The PCA-MLP will be trained by changing the hyperparameters of both activation functions and optimizers to find the best model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "oMCfh4M4nkvI",
    "outputId": "be30b585-4a45-4516-9cf7-00af8b80e28d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           \u001b[1mPCA-MLP With relu Activation Function And Adamax Optimizer\u001b[0m                           \n",
      "Neural Network Accuracy is 0.8893\n",
      "Neural Network Loss is 0.2739\n",
      "True Postive  : 583\tFalse Postive  : 23\n",
      "False Negative  : 90\tTrue Negative  : 325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          \u001b[1mPCA-MLP With relu Activation Function And RMSprop Optimizer\u001b[0m                           \n",
      "Neural Network Accuracy is 0.8864\n",
      "Neural Network Loss is 0.2843\n",
      "True Postive  : 577\tFalse Postive  : 29\n",
      "False Negative  : 87\tTrue Negative  : 328\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            \u001b[1mPCA-MLP With relu Activation Function And Adam Optimizer\u001b[0m                            \n",
      "Neural Network Accuracy is 0.8942\n",
      "Neural Network Loss is 0.2698\n",
      "True Postive  : 589\tFalse Postive  : 17\n",
      "False Negative  : 91\tTrue Negative  : 324\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         \u001b[1mPCA-MLP With sigmoid Activation Function And Adamax Optimizer\u001b[0m                          \n",
      "Neural Network Accuracy is 0.8972\n",
      "Neural Network Loss is 0.3399\n",
      "True Postive  : 600\tFalse Postive  : 6\n",
      "False Negative  : 99\tTrue Negative  : 316\n"
     ]
    },
    {
     "data": {
      "image/png": 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YnoJqbYrn330D+AXwYYrpTlOBq9NXZqX1O9L+mxuNV0H551mZ2RDT5kmhmwEz0rhVDzAzIq6R9FvgJ5K+CtwD1O6MuRC4RNICih7Voc0O4LAyq7A2Xg28H9iln+2PA7v3s/1VYEorxygdVpLWjIjXWqnczLpYevheLso8KXR3SQ8A89P6zpK+0/GWmVnHtWsG+2AoM4P9bOAg4DmAiLgP325jlr3aPKsySzcocxrYExFP9EnXFR1qj5kNoi7pNJVSJqyekrQ7EGmk/wTg0c42y8w6rotO8cooE1bHUZwKbgE8A/w8bTOzzOU0wF7m3sDFlJgDYWZ5GTIvjKiRdAH93LMTEdM70iIzGzQZZVWp08Cf131eC/gg6W5pM8tYZs81LnMaeHn9uqRLgJs61iIzGzRD6jSwH1sCb293Q8xs8GWUVaXGrF7gjTGrHoqbDk/uZKPMrPMk0TMsn7RqGFbp2es7A39Im/7S7DEOZpaPnE4DG95uk4LpqohYkRYHldkQMtTuDfy1pIkdb4mZDbqc3hvY6BnstUeR/i1wjKTHgFcoLnhGRDjAzDLXLb2mMhqNWf0amAhMHqS2mNkgUhtfxTUYGoWVoHgL8yC1xcwGlYbMvYGbSDppoJ0R8a0OtMfMBtFQ6VkNo3gTcz6/xsxaklFWNQyrRRFx+qC1xMwGldTSG5lXu6ZjVmY2dA2VntU+g9YKM1sthsSYVUQ0fJWzmeVvSISVmQ1xXTQ7vQyHlVlFie65768Mh5VZhWWUVQ4rsyrLqWdV5qkLZjYUqXgVV5mlaVXSWEm/kPSQpHmSTkzbN5J0k6T56e+GabsknS1pgaT7yzzZxWFlVlG190W06RExrwOfiYh3AJOA4yXtQPFU4dkRMR6YzRtPGd4fGJ+W6cC5zQ7gsDKrsHaFVUQsioi70+eXgIeA0cAhwIxUbAZvPMXlEODiKNwJjJC0WaNjOKzMKkwl/wFGSrqrbhnwvaGSxgG7AHOAURGxCIpAAzZNxUbz5lf69aZtA/IAu1mFtTC+viQidmten9YFrgQ+HRF/bDCA39+Oho9Nd1iZVZTSAHv76tMaFEH144j4Wdr8jKTNImJROs1bnLb3AmPrvj4GWNiofp8GmlVWuZdFlJnekN6EdSHwUJ9n3c0CpqbPU4Gr67Yfla4KTgKW1k4XB+KelVmFtXGa1Z7AkcADku5N204BzgBmSpoGPAlMSfuuAw4AFgDLgKObHcBhZVZh7ZoUGhG3M/Bjpf7fE1zSa/2Ob+UYDiuzKstnArvDyqyyhtDbbcxsCBPtvRrYaQ4rswrLJ6ocVmaV5tNAM8tCRlnlsDKrqrITPruFw8qswjLKKoeVWZX5aqCZZcE9KzPrespsUqifumBmWXDPyqzCcupZOazMKiyjrHJYmVWZw8rMsqCM7g50WJlVWT5Z5bAyq6oWXmDaFRxWZpUlnwaaWR7cszKzLHielZnlIZ+scliZVVlGWeWwMqsqkddpoG9kNrMsuGdlVlXK6+F77lmZWRYcVmYVVpvF3mxpXo/+U9JiSQ/WbdtI0k2S5qe/G6btknS2pAWS7pc0sUxbHVZmlaXS/5RwEbBfn20nA7MjYjwwO60D7A+MT8t04NwyB3BYmVWZSi5NRMQvgef7bD4EmJE+zwAm122/OAp3AiMkbdbsGB5gN6soAS2Mr4+UdFfd+vkRcX6T74yKiEUAEbFI0qZp+2jgqbpyvWnbokaVOazMqqqYaFW29JKI2K2NR+4rmn3Jp4FmFdams8CBPFM7vUt/F6ftvcDYunJjgIXNKnNYmVVYu64GDmAWMDV9ngpcXbf9qHRVcBKwtHa62IhPA82qrE2320i6DNiLYmyrF/hX4AxgpqRpwJPAlFT8OuAAYAGwDDi6zDEcVmYV1q756xFx2AC79umnbADHt3oMh5VZReV2I7PDyqzCMsoqD7CbWR7cszKrKr/dxszykU9aOazMKsw9KzPLg8PKzLpdC49/6Qq+GmhmWXDPyqzCchqzcs/KzLLgnpVZVcm325hZLvLJKoeVWZVllFUOK7NKy+g00APsZpYF96zMKiyffpXDyqyy/PA9M8tHPlnlsDKrsoyyymFlVlmr+FLAweawMqu0fNLKYWVWYflElcPKrNIyuhjosDKrrrzeGOEZ7GaWBfeszCoso46Ve1Zmlgf3rMwqKrfbbRQR7a9UehZ4ou0Vm1m9t0fEJiv7ZUnXAyNLFl8SEfut7LHaoSNhZWbWbh6zMrMsOKzMLAsOKzPLgsNqEElaIeleSQ9K+qmkdVahrr0kXZM+f0DSyQ3KjpD0qZU4xmmSPlt2e58yF0n6cAvHGifpwVbbaNXhsBpcf4qICRGxI/Bn4Nj6nSq0/P9JRMyKiDMaFBkBtBxWZt3EYbX63AZsk3oUD0n6HnA3MFbSvpLukHR36oGtCyBpP0kPS7od+FCtIkkfl3RO+jxK0lWS7kvLHsAZwNapV/fNVO5zkn4j6X5JX66r60uSHpH0c2C7Zj9C0jGpnvskXdmnt/h+SbdJelTSQan8MEnfrDv23/dT5zsl/Tq1935J41v/n9eGGofVaiBpOLA/8EDatB1wcUTsArwCnAq8PyImAncBJ0laC7gAOBh4N/C2Aao/G7g1InYGJgLzgJOBx1Kv7nOS9gXGA7sDE4BdJb1H0q7AocAuFGH4rhI/52cR8a50vIeAaXX7xgHvBQ4Evp9+wzRgaUS8K9V/jKQt+9R5LHBWREwAdgN6S7TDhjjPYB9ca0u6N32+DbgQ2Bx4IiLuTNsnATsAv0qzi98C3AFsD/wuIuYDSPoRML2fY+wNHAUQESuApZI27FNm37Tck9bXpQiv9YCrImJZOsasEr9pR0lfpTjVXBe4oW7fzIj4CzBf0uPpN+wL7FQ3nrVBOvajdd+7A/iSpDEUYTi/RDtsiHNYDa4/pd7CX6VAeqV+E3BTRBzWp9wEoF0zeAV8PSLO63OMT6/EMS4CJkfEfZI+DuxVt69vXZGOfUJE1Icaksb9tVDEpZLmUPTIbpD0yYi4ucV22RDj08Ducyewp6RtACStI2lb4GFgS0lbp3KHDfD92cBx6bvDJK0PvETRa6q5AfhE3VjYaEmbAr8EPihpbUnrUZxyNrMesEjSGsARffZNkdST2rwV8Eg69nGpPJK2lfTW+i9J2gp4PCLOBmYBO5Vohw1x7ll1mYh4NvVQLpO0Ztp8akQ8Kmk6cK2kJcDtwI79VHEicL6kacAK4LiIuEPSr9LUgP9J41bvAO5IPbuXgY9FxN2SLgfupbi387YSTf5nYE4q/wBvDsVHgFuBUcCxEfGqpB9QjGXdreLgzwKT+9T5UeBjkpYDTwOnl2iHDXG+N9DMsuDTQDPLgsPKzLLgsDKzLDiszCwLDiszy4LDysyy4LAysyz8H0lcIGv+j3wuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         \u001b[1mPCA-MLP With sigmoid Activation Function And RMSprop Optimizer\u001b[0m                         \n",
      "Neural Network Accuracy is 0.8364\n",
      "Neural Network Loss is 0.3603\n",
      "True Postive  : 502\tFalse Postive  : 104\n",
      "False Negative  : 63\tTrue Negative  : 352\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          \u001b[1mPCA-MLP With sigmoid Activation Function And Adam Optimizer\u001b[0m                           \n",
      "Neural Network Accuracy is 0.8903\n",
      "Neural Network Loss is 0.3015\n",
      "True Postive  : 597\tFalse Postive  : 9\n",
      "False Negative  : 103\tTrue Negative  : 312\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           \u001b[1mPCA-MLP With tanh Activation Function And Adamax Optimizer\u001b[0m                           \n",
      "Neural Network Accuracy is 0.8903\n",
      "Neural Network Loss is 0.2848\n",
      "True Postive  : 578\tFalse Postive  : 28\n",
      "False Negative  : 84\tTrue Negative  : 331\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          \u001b[1mPCA-MLP With tanh Activation Function And RMSprop Optimizer\u001b[0m                           \n",
      "Neural Network Accuracy is 0.8707\n",
      "Neural Network Loss is 0.3175\n",
      "True Postive  : 556\tFalse Postive  : 50\n",
      "False Negative  : 82\tTrue Negative  : 333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            \u001b[1mPCA-MLP With tanh Activation Function And Adam Optimizer\u001b[0m                            \n",
      "Neural Network Accuracy is 0.8972\n",
      "Neural Network Loss is 0.2730\n",
      "True Postive  : 586\tFalse Postive  : 20\n",
      "False Negative  : 85\tTrue Negative  : 330\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ResultsPCA = []\n",
    "for  activation in act:\n",
    "    for optimizer in opts:\n",
    "        string='\\033[1m'+'PCA-MLP With '+activation+' Activation Function And '+optimizer+' Optimizer'+\"\\033[0m\"\n",
    "        print(string.center(120) )\n",
    "        MLP_PCA=build_Model(activation,optimizer,'binary_crossentropy',x_trainNew2,y_train,numOfComp,0)\n",
    "        resPCA=showAcc(MLP_PCA[0],x_trainNew2,y_train,x_testNew2,y_test)\n",
    "        ResultsPCA.append(resPCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5P-wsF6tnkvI"
   },
   "source": [
    "**Based On the outputs and confusion matrix we can see that the \"Tanh\" activation with adam optimizer function produces the best performance among the activation functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>PCA-MLP</th>\n",
       "      <th>Adamax</th>\n",
       "      <th>RMSprop</th>\n",
       "      <th>Adam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Relu</td>\n",
       "      <td>0.889324</td>\n",
       "      <td>0.886386</td>\n",
       "      <td>0.894221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>0.897160</td>\n",
       "      <td>0.836435</td>\n",
       "      <td>0.890304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Tanh</td>\n",
       "      <td>0.890304</td>\n",
       "      <td>0.870715</td>\n",
       "      <td>0.897160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PCA-MLP    Adamax   RMSprop      Adam\n",
       "0     Relu  0.889324  0.886386  0.894221\n",
       "1  Sigmoid  0.897160  0.836435  0.890304\n",
       "2     Tanh  0.890304  0.870715  0.897160"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_accuracyPCA = pd.DataFrame({\n",
    "    'PCA-MLP': ['Relu', 'Sigmoid', 'Tanh'],\n",
    "    'Adamax': [ResultsPCA[x][1] for x in range (0,len(Results),3)],\n",
    "    'RMSprop': [ResultsPCA[x][1] for x in range (1,len(Results),3)],\n",
    "    'Adam' : [ResultsPCA[x][1] for x in range (2,len(Results),3)],\n",
    "})\n",
    "model_accuracyPCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "n4kimUdOnkvJ",
    "outputId": "7caee32b-167b-4cb1-c2bb-864f6329b146"
   },
   "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>PCA-MLP</th>\n",
       "      <th>Adamax</th>\n",
       "      <th>RMSprop</th>\n",
       "      <th>Adam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Relu</td>\n",
       "      <td>0.273903</td>\n",
       "      <td>0.284260</td>\n",
       "      <td>0.269752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sigmoid</td>\n",
       "      <td>0.339911</td>\n",
       "      <td>0.360345</td>\n",
       "      <td>0.301549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Tanh</td>\n",
       "      <td>0.284850</td>\n",
       "      <td>0.317464</td>\n",
       "      <td>0.273000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PCA-MLP    Adamax   RMSprop      Adam\n",
       "0     Relu  0.273903  0.284260  0.269752\n",
       "1  Sigmoid  0.339911  0.360345  0.301549\n",
       "2     Tanh  0.284850  0.317464  0.273000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lossPCA = pd.DataFrame({\n",
    "    'PCA-MLP': ['Relu', 'Sigmoid', 'Tanh'],\n",
    "    'Adamax': [ResultsPCA[x][0] for x in range (0,len(Results),3)],\n",
    "    'RMSprop': [ResultsPCA[x][0] for x in range (1,len(Results),3)],\n",
    "    'Adam' : [ResultsPCA[x][0] for x in range (2,len(Results),3)],\n",
    "})\n",
    "model_lossPCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Niz2mV7NnkvJ"
   },
   "source": [
    "### Building the Best Variation of PCA-MLP\n",
    "**After testing all the variations above, now we build the best PCA-MLP variation based on the results we got.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "id": "xCvw1zFmnkvJ",
    "outputId": "8e74f637-85ab-454d-d11a-47025c4f3207"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Accuracy is 0.8962\n",
      "Neural Network Loss is 0.2729\n",
      "True Postive  : 585\tFalse Postive  : 21\n",
      "False Negative  : 85\tTrue Negative  : 330\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MLP_PCA=build_Model('tanh','adam','binary_crossentropy',x_trainNew2,y_train,numOfComp,0)\n",
    "PCA_Acc= showAcc(MLP_PCA[0],x_trainNew2,y_train,x_testNew2,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qlU2OoZ9nkvJ"
   },
   "source": [
    "##  Learning Curves\n",
    "**Now we plot the learning curve for the best PCA-MLP variation created above.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "tmxfjSe5nkvK",
    "outputId": "22a8bcf2-dedc-4516-f4db-40a980163ff0",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(15,5))\n",
    "fig.suptitle('Learning Curve', fontsize=16)\n",
    "ax1.plot(MLP_PCA[1].history['accuracy'])\n",
    "ax1.plot(MLP_PCA[1].history['val_accuracy'])\n",
    "ax1.set_title('model accuracy')\n",
    "ax1.set(xlabel='epoch', ylabel='accuracy')\n",
    "ax1.legend(['train', 'Valid'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "ax2.plot(MLP_PCA[1].history['loss'])\n",
    "ax2.plot(MLP_PCA[1].history['val_loss'])\n",
    "ax2.set_title('model loss')\n",
    "ax2.set(xlabel='epoch', ylabel='loss')\n",
    "ax2.legend(['train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L36dEfgHnkvK"
   },
   "source": [
    "### After Applying PCA to the MLP we obtained slightly better results but with significantly smaller number of features than the basic MLP. So, the \"PCA-MLP\" considered much better model than the basic MLP."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Artificial_Neural_Networks_Assignment_2_CODE_DONE!!.ipynb",
   "provenance": []
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}