From f0dabd7f1f2c2e72ba07a79c590b9ff510217144 Mon Sep 17 00:00:00 2001
From: Mohamed Hisham Abdelzaher <mohamed.hisham.abdelzaher@gmail.com>
Date: Thu, 28 Dec 2023 15:18:55 +0200
Subject: [PATCH] a

---
 .idea/.gitignore                             |    3 -
 .idea/codestream.xml                         |    3 +-
 .idea/facial-emotions-detection.iml          |    8 +
 .idea/inspectionProfiles/Project_Default.xml |   27 +-
 .idea/misc.xml                               |    9 +-
 .idea/modules.xml                            |    2 +-
 .idea/vcs.xml                                |    2 +-
 "Facial Emotion Detection\357\273\277.docx"  |  Bin 27565 -> 0 bytes
 facial_emotions_detection.ipynb              | 3098 +++++++++++++++++-
 9 files changed, 3124 insertions(+), 28 deletions(-)
 create mode 100644 .idea/facial-emotions-detection.iml
 delete mode 100644 "Facial Emotion Detection\357\273\277.docx"

diff --git a/.idea/.gitignore b/.idea/.gitignore
index 1c52318c..13566b81 100644
--- a/.idea/.gitignore
+++ b/.idea/.gitignore
@@ -6,6 +6,3 @@
 # Datasource local storage ignored files
 /dataSources/
 /dataSources.local.xml
-# Zeppelin ignored files
-/ZeppelinRemoteNotebooks/
-/sonarlint/
\ No newline at end of file
diff --git a/.idea/codestream.xml b/.idea/codestream.xml
index ec44c448..1c8f7bc6 100644
--- a/.idea/codestream.xml
+++ b/.idea/codestream.xml
@@ -1,7 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="CodeStream">
-    <option name="teamId" value="658829d2eed21b8c8fef599e" />
-    <option name="webViewContext" value="{&quot;chatProviderAccess&quot;:&quot;strict&quot;,&quot;currentTeamId&quot;:&quot;658829d2eed21b8c8fef599e&quot;,&quot;currentStreamId&quot;:&quot;&quot;,&quot;pullRequestCheckoutBranch&quot;:false,&quot;isRepositioning&quot;:false,&quot;issueProvider&quot;:&quot;github*com&quot;,&quot;onboardStep&quot;:0,&quot;panelStack&quot;:[&quot;landing-redirect&quot;],&quot;hasFocus&quot;:true,&quot;channelFilter&quot;:&quot;all&quot;,&quot;channelsMuteAll&quot;:false,&quot;codemarkFileFilter&quot;:&quot;all&quot;,&quot;codemarkTypeFilter&quot;:&quot;all&quot;,&quot;codemarkTagFilter&quot;:&quot;all&quot;,&quot;codemarkBranchFilter&quot;:&quot;all&quot;,&quot;codemarkAuthorFilter&quot;:&quot;all&quot;,&quot;codemarksFileViewStyle&quot;:&quot;inline&quot;,&quot;codemarksShowArchived&quot;:false,&quot;codemarksShowResolved&quot;:false,&quot;codemarksWrapComments&quot;:false,&quot;showFeedbackSmiley&quot;:true,&quot;route&quot;:{&quot;name&quot;:&quot;providerAuth&quot;,&quot;params&quot;:{&quot;type&quot;:&quot;createTeam&quot;,&quot;fromSignup&quot;:false,&quot;mode&quot;:&quot;strict&quot;,&quot;provider&quot;:&quot;newrelicidp&quot;}},&quot;spatialViewShowPRComments&quot;:false,&quot;currentPullRequestNeedsRefresh&quot;:{&quot;needsRefresh&quot;:false,&quot;providerId&quot;:&quot;&quot;,&quot;pullRequestId&quot;:&quot;&quot;},&quot;__teamless__&quot;:{&quot;selectedRegion&quot;:&quot;us&quot;},&quot;sessionStart&quot;:1703422433857,&quot;isFirstPageview&quot;:false}" />
+    <option name="webViewContext" value="{&quot;chatProviderAccess&quot;:&quot;strict&quot;,&quot;currentTeamId&quot;:&quot;&quot;,&quot;currentStreamId&quot;:&quot;&quot;,&quot;pullRequestCheckoutBranch&quot;:false,&quot;isRepositioning&quot;:false,&quot;onboardStep&quot;:0,&quot;panelStack&quot;:[&quot;landing-redirect&quot;],&quot;hasFocus&quot;:false,&quot;channelFilter&quot;:&quot;all&quot;,&quot;channelsMuteAll&quot;:false,&quot;codemarkFileFilter&quot;:&quot;all&quot;,&quot;codemarkTypeFilter&quot;:&quot;all&quot;,&quot;codemarkTagFilter&quot;:&quot;all&quot;,&quot;codemarkBranchFilter&quot;:&quot;all&quot;,&quot;codemarkAuthorFilter&quot;:&quot;all&quot;,&quot;codemarksFileViewStyle&quot;:&quot;inline&quot;,&quot;codemarksShowArchived&quot;:false,&quot;codemarksShowResolved&quot;:false,&quot;codemarksWrapComments&quot;:false,&quot;showFeedbackSmiley&quot;:true,&quot;route&quot;:{&quot;name&quot;:&quot;newUserEntry&quot;,&quot;params&quot;:{}},&quot;spatialViewShowPRComments&quot;:false,&quot;currentPullRequestNeedsRefresh&quot;:{&quot;needsRefresh&quot;:false,&quot;providerId&quot;:&quot;&quot;,&quot;pullRequestId&quot;:&quot;&quot;},&quot;__teamless__&quot;:{&quot;selectedRegion&quot;:&quot;us&quot;},&quot;sessionStart&quot;:1703519260592}" />
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/facial-emotions-detection.iml b/.idea/facial-emotions-detection.iml
new file mode 100644
index 00000000..b3a11046
--- /dev/null
+++ b/.idea/facial-emotions-detection.iml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Anaconda (WSL)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
index 6e8d7336..f50bb87f 100644
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -1,25 +1,22 @@
 <component name="InspectionProjectProfileManager">
   <profile version="1.0">
     <option name="myName" value="Project Default" />
-    <inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
-      <Languages>
-        <language minSize="151" name="Python" />
-      </Languages>
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="4">
+            <item index="0" class="java.lang.String" itemvalue="fastapi" />
+            <item index="1" class="java.lang.String" itemvalue="pathlib" />
+            <item index="2" class="java.lang.String" itemvalue="uvicorn" />
+            <item index="3" class="java.lang.String" itemvalue="gunicorn" />
+          </list>
+        </value>
+      </option>
     </inspection_tool>
     <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
       <option name="ignoredErrors">
         <list>
-          <option value="N802" />
-          <option value="N803" />
-          <option value="N806" />
-        </list>
-      </option>
-    </inspection_tool>
-    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
-      <option name="ignoredIdentifiers">
-        <list>
-          <option value="flet.page.clean" />
-          <option value="flet.page.close" />
+          <option value="N801" />
         </list>
       </option>
     </inspection_tool>
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 235f8032..e11826b1 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,11 +1,10 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="Black">
-    <option name="sdkName" value="Anaconda" />
-  </component>
-  <component name="DiscordProjectSettings">
-    <option name="show" value="ASK" />
-    <option name="description" value="" />
+    <option name="sdkName" value="Anaconda (WSL)" />
   </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Anaconda (WSL)" project-jdk-type="Python SDK" />
+  <component name="PyCharmDSProjectLayout">
+    <option name="id" value="JupyterRightHiddenStructureLayout" />
+  </component>
 </project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
index 1dc1233f..1c35f394 100644
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@@ -2,7 +2,7 @@
 <project version="4">
   <component name="ProjectModuleManager">
     <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/Facial Emotions Detection.iml" filepath="$PROJECT_DIR$/.idea/Facial Emotions Detection.iml" />
+      <module fileurl="file://$PROJECT_DIR$/.idea/facial-emotions-detection.iml" filepath="$PROJECT_DIR$/.idea/facial-emotions-detection.iml" />
     </modules>
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index 94a25f7f..35eb1ddf 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+    <mapping directory="" vcs="Git" />
   </component>
 </project>
\ No newline at end of file
diff --git "a/Facial Emotion Detection\357\273\277.docx" "b/Facial Emotion Detection\357\273\277.docx"
deleted file mode 100644
index f88a8beec8949a34a59a36e319dbefe7862d4765..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/facial_emotions_detection.ipynb b/facial_emotions_detection.ipynb
index b87048ce..28b1fc68 100644
--- a/facial_emotions_detection.ipynb
+++ b/facial_emotions_detection.ipynb
@@ -1 +1,3097 @@
-{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":1351797,"sourceType":"datasetVersion","datasetId":786787}],"dockerImageVersionId":30529,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/mh0386/facial-emotions-detection?scriptVersionId=156333364\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"#----------------------------------------#\n!pip install xgboost                     #\n!pip install --upgrade                   #\n#----------------------------------------#","metadata":{"execution":{"iopub.status.busy":"2023-12-24T15:22:02.344592Z","iopub.execute_input":"2023-12-24T15:22:02.344918Z","iopub.status.idle":"2023-12-24T15:22:11.086951Z","shell.execute_reply.started":"2023-12-24T15:22:02.344887Z","shell.execute_reply":"2023-12-24T15:22:11.085892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport tensorflow as tf\nprint(\"Num GPUs Available:\", (tf.config.experimental.list_physical_devices('TPU')))\nfrom keras.applications.xception import Xception\nimport bz2\nimport os\nimport xgboost as xgb\nimport requests\nimport matplotlib.pyplot as plt\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.utils import img_to_array , load_img\nfrom keras.models import load_model\nimport scipy\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg16 import preprocess_input as p_input\nfrom keras.utils import to_categorical\nimport numpy as np\nimport cv2\nimport requests\nfrom keras.utils import plot_model\nimport numpy as np\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\nfrom sklearn.datasets import make_classification\nfrom deap import base, creator, tools, algorithms","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:31:21.501207100Z","start_time":"2023-08-26T15:31:21.440809700Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:35:19.44509Z","iopub.execute_input":"2023-12-24T15:35:19.445415Z","iopub.status.idle":"2023-12-24T15:35:19.451425Z","shell.execute_reply.started":"2023-12-24T15:35:19.445388Z","shell.execute_reply":"2023-12-24T15:35:19.450692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# #!pip install requests\n# # import requests\n# # import bz2\n# import dlib\n\n# # # Download the shape predictor file compressed in bz2 format\n# # url = \"http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2\"\n# # response = requests.get(url)\n# # with open(\"shape_predictor_68_face_landmarks.dat.bz2\", \"wb\") as f:\n# #     f.write(response.content)\n\n# # # Decompress the downloaded bz2 file\n# # with open(\"shape_predictor_68_face_landmarks.dat.bz2\", \"rb\") as f:\n# #     compressed_data = f.read()\n# #\n# # decompressed_data = bz2.decompress(compressed_data)\n# #\n# # # Save the decompressed data to a file\n# # with open(\"shape_predictor_68_face_landmarks.dat\", \"wb\") as f:\n# #     f.write(decompressed_data)\n# #\n# # print(\"Shape predictor file downloaded and decompressed.\")\n\n# # Now you can use the dlib detector and predictor\n# datFile = \"shape_predictor_68_face_landmarks.dat\"\n# detector = dlib.get_frontal_face_detector()\n# predictor = dlib.shape_predictor(datFile)\n","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:24:10.378549700Z","start_time":"2023-08-26T15:24:08.556866Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T14:06:26.345243Z","iopub.status.idle":"2023-12-24T14:06:26.345735Z","shell.execute_reply.started":"2023-12-24T14:06:26.345485Z","shell.execute_reply":"2023-12-24T14:06:26.345508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#reading and Augmenting data and preprocess it for VGG16\n# def custom_preprocess_input(img):\n#     processed_img = p_input(img)\n#     return processed_img","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:11.732268300Z","start_time":"2023-08-26T15:22:11.714657300Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T14:06:26.782726Z","iopub.execute_input":"2023-12-24T14:06:26.783478Z","iopub.status.idle":"2023-12-24T14:06:26.787244Z","shell.execute_reply.started":"2023-12-24T14:06:26.783445Z","shell.execute_reply":"2023-12-24T14:06:26.786331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load images and labels\ngeneral_path = \"/kaggle/input/fer2013/\"\npath_train_dataset = general_path + \"train/\"\npath_validation_dataset = general_path + \"test/\"\npicture_size=224 #input of VGG16 should be 224 AND input for mibile network should be 227\nbatch_size=256\nnumber_of_classes=7\n# Data Augmentation using ImageDataGenerator\ndatagen = ImageDataGenerator(\n    preprocessing_function=p_input,\n    rotation_range=20,\n    width_shift_range=0.2,\n    height_shift_range=0.2,\n    zoom_range=0.2,\n    horizontal_flip=True\n)\n\n# Load train and test datasets\ntrain_data_set = datagen.flow_from_directory(\n    directory=path_train_dataset,\n    target_size=(picture_size, picture_size),\n    batch_size=batch_size,\n    class_mode='categorical',\n    shuffle=True\n)\n\ntest_data_set = datagen.flow_from_directory(\n    directory=path_validation_dataset,\n    target_size=(picture_size, picture_size),\n    batch_size=batch_size,\n    class_mode='categorical',\n    shuffle=True\n)","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:16.657435900Z","start_time":"2023-08-26T15:22:13.928278500Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:35:28.213029Z","iopub.execute_input":"2023-12-24T15:35:28.213383Z","iopub.status.idle":"2023-12-24T15:35:35.490857Z","shell.execute_reply.started":"2023-12-24T15:35:28.213352Z","shell.execute_reply":"2023-12-24T15:35:35.489968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(train_data_set.filenames)","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:17.782173300Z","start_time":"2023-08-26T15:22:17.751621300Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:35:53.455133Z","iopub.execute_input":"2023-12-24T15:35:53.455886Z","iopub.status.idle":"2023-12-24T15:35:53.460722Z","shell.execute_reply.started":"2023-12-24T15:35:53.455852Z","shell.execute_reply":"2023-12-24T15:35:53.46002Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"expression = \"happy\"\nplt.figure(figsize=(12, 12))\n\nfor i in range(1, 13):\n    plt.subplot(4, 4, i)\n    img_path = os.path.join(path_train_dataset, expression,\n                            os.listdir(os.path.join(path_train_dataset, expression))[i])\n    img = load_img(img_path, target_size=(48, 48))\n    plt.imshow(img)\n    plt.title(f\"Image {i}\")\n    plt.axis(\"off\")\n    plt.title(expression)\nplt.show()\n\nlabels = []\nimages=[]\n\n\n","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:21.298005700Z","start_time":"2023-08-26T15:22:19.893201700Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:35:56.80947Z","iopub.execute_input":"2023-12-24T15:35:56.809784Z","iopub.status.idle":"2023-12-24T15:35:57.355141Z","shell.execute_reply.started":"2023-12-24T15:35:56.809755Z","shell.execute_reply":"2023-12-24T15:35:57.354323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"expression = \"angry\"\nplt.figure(figsize=(10, 10))\n\nfor i in range(1, 10):\n    plt.subplot(3, 3, i)\n    img_path = os.path.join(path_train_dataset, expression,\n                            os.listdir(os.path.join(path_train_dataset, expression))[i])\n    img = load_img(img_path, target_size=(48, 48))\n    plt.imshow(img)\n    plt.title(f\"Image {i}\")\n    plt.axis(\"off\")\n    plt.title(expression)\nplt.show()\n","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:23.504962600Z","start_time":"2023-08-26T15:22:22.099909100Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:36:01.674545Z","iopub.execute_input":"2023-12-24T15:36:01.674874Z","iopub.status.idle":"2023-12-24T15:36:02.344062Z","shell.execute_reply.started":"2023-12-24T15:36:01.674846Z","shell.execute_reply":"2023-12-24T15:36:02.343151Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data_set.next()[0].shape #get shape of each patch ","metadata":{"ExecuteTime":{"end_time":"2023-08-26T16:20:53.037363300Z","start_time":"2023-08-26T16:20:50.714488500Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:36:19.474994Z","iopub.execute_input":"2023-12-24T15:36:19.475365Z","iopub.status.idle":"2023-12-24T15:36:23.273874Z","shell.execute_reply.started":"2023-12-24T15:36:19.475332Z","shell.execute_reply":"2023-12-24T15:36:23.273154Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Now we will try to use exception Feature extraction architicture ","metadata":{}},{"cell_type":"code","source":"from keras.utils import plot_model\nnon_trainable_model = Xception(weights='imagenet', include_top=False, input_shape=(picture_size, picture_size, 3))\nfor layer in non_trainable_model.layers:\n    layer.trainable = False\n# output_features_of_freezed_model = non_trainable_model.predict(train_data_set)\nnon_trainable_model.summary()\nplot_model(non_trainable_model, to_file='Xception_base_model.png', show_shapes=True)","metadata":{"execution":{"iopub.status.busy":"2023-12-24T15:36:28.183945Z","iopub.execute_input":"2023-12-24T15:36:28.184264Z","iopub.status.idle":"2023-12-24T15:36:29.628055Z","shell.execute_reply.started":"2023-12-24T15:36:28.184236Z","shell.execute_reply":"2023-12-24T15:36:29.618048Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Know we want to access extracted features of data to feed into XG_boost with genatic algorithm optimizer**","metadata":{}},{"cell_type":"code","source":"train_data=non_trainable_model.predict(train_data_set)\ntest_data = train_data=non_trainable_model.predict(test_data_set)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-24T16:02:26.747554Z","iopub.execute_input":"2023-12-24T16:02:26.747931Z","iopub.status.idle":"2023-12-24T16:14:35.279061Z","shell.execute_reply.started":"2023-12-24T16:02:26.747898Z","shell.execute_reply":"2023-12-24T16:14:35.278023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data.shape ","metadata":{"execution":{"iopub.status.busy":"2023-12-24T16:33:03.561551Z","iopub.execute_input":"2023-12-24T16:33:03.561917Z","iopub.status.idle":"2023-12-24T16:33:03.567126Z","shell.execute_reply.started":"2023-12-24T16:33:03.561886Z","shell.execute_reply":"2023-12-24T16:33:03.56645Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\n# Assuming you have a dataset X and corresponding labels y\n# For illustration purposes, I'm using a synthetic dataset\n\n# X, y = make_classification(n_samples=1000, n_features=20, n_classes=3, random_state=42)\n\n\n# Define the XGBoost fitness function for multi-class classification\n\n# we use compination of f1_scores_ and recall to give high penality into false negative \ndef xgboost_fitness(hyperparameters):\n    params = {\n        'objective': 'multi:softmax',\n        'num_class': len(np.unique(y)),  # Number of classes\n        'eval_metric': 'mlogloss',\n        'eta': hyperparameters[0],  # Learning rate\n        'max_depth': int(hyperparameters[1]),  # Maximum depth of a tree\n        'subsample': hyperparameters[2],  # Subsample ratio of the training instances\n        'colsample_bytree': hyperparameters[3],  # Subsample ratio of columns when constructing each tree\n        'min_child_weight': int(hyperparameters[4]),  # Minimum sum of instance weight (hessian) needed in a child\n    }\n\n    # Train XGBoost model\n    model = xgb.XGBClassifier(**params)\n    model.fit(X_train, y_train)\n\n    # Make predictions on the test set\n    y_pred = model.predict(X_test)\n\n    # Evaluate metrics\n    precision = precision_score(y_test, y_pred, average='weighted')\n    recall = recall_score(y_test, y_pred, average='weighted')\n    f1 = f1_score(y_test, y_pred, average='weighted')\n    accuracy = accuracy_score(y_test, y_pred)\n\n    # Combine metrics into a single fitness score\n    fitness = f1 * (1 + 0.1 * (1 - recall))\n\n    return fitness,\n\n# Define genetic algorithm parameters\nNUM_GENERATIONS = 10\nPOPULATION_SIZE = 10\nCXPB, MUTPB = 0.7, 0.2\n\n# Define the hyperparameter space\nHYPERPARAMETER_SPACE = [\n    ('eta', 0.01, 1.0),  # Learning rate\n    ('max_depth', 1, 10),  # Maximum depth of a tree\n    ('subsample', 0.1, 1.0),  # Subsample ratio of the training instances\n    ('colsample_bytree', 0.1, 1.0),  # Subsample ratio of columns when constructing each tree\n    ('min_child_weight', 1, 10),  # Minimum sum of instance weight (hessian) needed in a child\n]\n\n# Create a fitness function for the genetic algorithm\ncreator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\ncreator.create(\"Individual\", list, fitness=creator.FitnessMax)\n\n\n\n\n#--------------------------------------------------------------------------------------------------------------------\n#this is the steps of genatic algorithm \n#first initialize individual at the start of first cycle of generation \n#second initialize the population of last initialized  individuales\n#check the fitness value\n#check make crossover and mutation  using gaussian distibutionwith CXPB, MUTPB probabilities of crossover and mutation \n# select the high fitness value and make a new generation cycle and so on until you reach the optimal hyperparams \n#---------------------------------------------------------------------------------------------------------------------\n#toolbox is a container for using genatic algorithm \n#---------------------------------------------------------------------------------------------------------------------\n\n\n\ntoolbox = base.Toolbox()\ntoolbox.register(\"attr_float\", np.random.uniform, low=0, high=1)\ntoolbox.register(\"individual\", tools.initCycle, creator.Individual, (toolbox.attr_float, ), n=len(HYPERPARAMETER_SPACE))\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\ntoolbox.register(\"evaluate\", xgboost_fitness)\ntoolbox.register(\"mate\", tools.cxBlend, alpha=0.5)\ntoolbox.register(\"mutate\", tools.mutGaussian, mu=0, sigma=0.2, indpb=0.2)\ntoolbox.register(\"select\", tools.selTournament, tournsize=3)\n\n# Main genetic algorithm loop\npopulation = toolbox.population(n=POPULATION_SIZE)\n\nfor gen in range(NUM_GENERATIONS):\n    offspring = algorithms.varAnd(population, toolbox, cxpb=CXPB, mutpb=MUTPB)\n    fits = toolbox.map(toolbox.evaluate, offspring)\n    for ind, fit in zip(offspring, fits):\n        ind.fitness.values = fit\n    population = toolbox.select(offspring, k=len(population))\n\n# Get the best individual (best set of hyperparameters)\nbest_individual = tools.selBest(population, k=1)[0]\nbest_hyperparameters = [param[1].low + value * (param[1].high - param[1].low) for param, value in zip(HYPERPARAMETER_SPACE, best_individual)]\n\nprint(\"Best Hyperparameters:\", best_hyperparameters)\n\n","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:33.310134400Z","start_time":"2023-08-26T15:22:33.298764Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T15:50:04.97891Z","iopub.execute_input":"2023-12-24T15:50:04.97917Z","iopub.status.idle":"2023-12-24T15:50:05.128675Z","shell.execute_reply.started":"2023-12-24T15:50:04.979145Z","shell.execute_reply":"2023-12-24T15:50:05.127632Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"VGG16 Model","metadata":{"jupyter":{"outputs_hidden":false}}},{"cell_type":"code","source":"","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:22:38.060888600Z","start_time":"2023-08-26T15:22:37.040967600Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:49:13.311554Z","iopub.status.idle":"2023-12-24T13:49:13.312031Z","shell.execute_reply.started":"2023-12-24T13:49:13.311762Z","shell.execute_reply":"2023-12-24T13:49:13.311782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Model=tf.keras.Sequential(\n# [\n#         non_trainable_model, #the extracted features from VGG16 will be used as input of dense layers which is new model (\"we applied transfer learning \")\n#         tf.keras.layers.Flatten(),\n#         tf.keras.layers.Dense(512, activation=tf.nn.relu),\n#         tf.keras.layers.Dense(256, activation=tf.nn.relu),\n#         tf.keras.layers.Dense(128, activation=tf.nn.relu),\n#         tf.keras.layers.Dense(number_of_classes, activation=tf.nn.softmax)\n#     ]\n# )\n# Model.summary()\n# from keras.utils import plot_model\n# Model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n#               loss='categorical_crossentropy',\n#               metrics=['accuracy'])\n\n","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:23:23.345690100Z","start_time":"2023-08-26T15:23:23.005880800Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:49:13.312941Z","iopub.status.idle":"2023-12-24T13:49:13.313305Z","shell.execute_reply.started":"2023-12-24T13:49:13.313142Z","shell.execute_reply":"2023-12-24T13:49:13.313158Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Model.fit_generator(\n#     train_data_set,\n#     steps_per_epoch=train_data_set.n // train_data_set.batch_size,\n#     epochs=10,\n#     validation_data=test_data_set,\n#     validation_steps=test_data_set.n // test_data_set.batch_size\n    \n# )","metadata":{"ExecuteTime":{"end_time":"2023-08-26T15:23:26.999432100Z","start_time":"2023-08-26T15:23:25.701049500Z"},"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:49:13.314436Z","iopub.status.idle":"2023-12-24T13:49:13.314755Z","shell.execute_reply.started":"2023-12-24T13:49:13.314594Z","shell.execute_reply":"2023-12-24T13:49:13.31461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # plot_model(Model, to_file='model_architecture.png', show_shapes=True)\n# # Save the model\n# Model.save('face_emotion_model_VGG16.h5')\n\n","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:49:13.315958Z","iopub.status.idle":"2023-12-24T13:49:13.316294Z","shell.execute_reply.started":"2023-12-24T13:49:13.31614Z","shell.execute_reply":"2023-12-24T13:49:13.316155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Plot the accuracy and loss curves\n# fig, axes = plt.subplots(1, 2, figsize=(20, 5))\n# axes[0].plot(history.history['accuracy'], label='Train accuracy')\n# axes[0].plot(history.history['val_accuracy'], label='Validation accuracy')\n# axes[0].set_xlabel('Epochs')\n# axes[0].legend()\n# axes[1].plot(history.history['loss'], label='Train loss')\n# axes[1].plot(history.history['val_loss'], label='Validation loss')\n# axes[1].set_xlabel('Epochs')\n# axes[1].legend()\n# plt.show()","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:46:16.392551Z","iopub.status.idle":"2023-12-24T13:46:16.392854Z","shell.execute_reply.started":"2023-12-24T13:46:16.392705Z","shell.execute_reply":"2023-12-24T13:46:16.392719Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import cv2\n# import dlib\n# import numpy as np\n# from keras.models import load_model\n# from keras.preprocessing.image import img_to_array\n\n\n# model=load_model(\".h5\")\n# # Load face detector from dlib\n# detector = dlib.get_frontal_face_detector()\n\n# # Define the emotion labels\n# emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n\n# # Initialize the webcam\n# cap = cv2.VideoCapture(0)\n\n# while True:\n#     # Read a frame from the webcam\n#     ret, frame = cap.read()\n\n#     if not ret:\n#         break\n\n#     # Convert the frame to grayscale\n#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n#     # Detect faces in the frame\n#     faces = detector(gray)\n\n#     for face in faces:\n#         x, y, w, h = face.left(), face.top(), face.width(), face.height()\n\n#         # Extract the face region\n#         face_roi = gray[y:y+h, x:x+w]\n#         face_roi = cv2.resize(face_roi, (224, 224))\n#         face_roi = p_input(face_roi)\n#         face_roi = img_to_array(face_roi)\n#         face_roi = np.expand_dims(face_roi, axis=0)\n\n#         # Predict the emotion using the model\n#         emotion_probabilities = model.predict(face_roi)[0]\n#         predicted_emotion = emotion_labels[np.argmax(emotion_probabilities)]\n\n#         # Draw a rectangle around the detected face and label the emotion\n#         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n#         # lanmarks=predictor(face)\n#         # for point in range(68):\n#         #     x=lanmarks.part(point).x\n#         #     y=lanmarks.part(point).y\n#         #     cv2.circle(frame, (x, y), 5, (0, 255, 0), -1)\n#         cv2.putText(frame, predicted_emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)\n\n#     # Display the frame\n#     cv2.imshow(\"Emotion Detection\", frame)\n\n#     # Exit when the 'q' key is pressed\n#     if cv2.waitKey(1) & 0xFF == ord('q'):\n#         break\n\n# # Release the webcam and close all windows\n# cap.release()\n# cv2.destroyAllWindows()\n","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:49:17.578725Z","iopub.execute_input":"2023-12-24T13:49:17.579374Z","iopub.status.idle":"2023-12-24T13:49:17.633677Z","shell.execute_reply.started":"2023-12-24T13:49:17.57934Z","shell.execute_reply":"2023-12-24T13:49:17.63246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"/","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:46:16.396013Z","iopub.status.idle":"2023-12-24T13:46:16.396321Z","shell.execute_reply.started":"2023-12-24T13:46:16.396169Z","shell.execute_reply":"2023-12-24T13:46:16.396184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"/","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-24T13:46:16.397532Z","iopub.status.idle":"2023-12-24T13:46:16.397872Z","shell.execute_reply.started":"2023-12-24T13:46:16.397692Z","shell.execute_reply":"2023-12-24T13:46:16.397713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
+{
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.12",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "sourceId": 1351797,
+     "sourceType": "datasetVersion",
+     "datasetId": 786787
+    },
+    {
+     "sourceId": 2887117,
+     "sourceType": "datasetVersion",
+     "datasetId": 1768625
+    }
+   ],
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook",
+   "isGpuEnabled": true
+  }
+ },
+ "nbformat_minor": 4,
+ "nbformat": 4,
+ "cells": [
+  {
+   "cell_type": "code",
+   "source": [
+    "! pip install sklearn-genetic-opt xgboost"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:24.318917Z",
+     "iopub.execute_input": "2023-12-26T12:21:24.319727Z",
+     "iopub.status.idle": "2023-12-26T12:21:37.459501Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:24.319691Z",
+     "shell.execute_reply": "2023-12-26T12:21:37.458415Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:47.853132Z",
+     "start_time": "2023-12-26T21:31:44.801430100Z"
+    }
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\r\n",
+      "Requirement already satisfied: sklearn-genetic-opt in /home/mohamed/anaconda3/lib/python3.11/site-packages (0.10.1)\r\n",
+      "Requirement already satisfied: xgboost in /home/mohamed/anaconda3/lib/python3.11/site-packages (2.0.3)\r\n",
+      "Requirement already satisfied: scikit-learn>=1.1.0 in /home/mohamed/anaconda3/lib/python3.11/site-packages (from sklearn-genetic-opt) (1.2.2)\r\n",
+      "Requirement already satisfied: numpy>=1.19.0 in /home/mohamed/anaconda3/lib/python3.11/site-packages (from sklearn-genetic-opt) (1.26.2)\r\n",
+      "Requirement already satisfied: deap>=1.3.3 in /home/mohamed/anaconda3/lib/python3.11/site-packages (from sklearn-genetic-opt) (1.4.1)\r\n",
+      "Requirement already satisfied: tqdm>=4.61.1 in /home/mohamed/anaconda3/lib/python3.11/site-packages (from sklearn-genetic-opt) (4.65.0)\r\n",
+      "Requirement already satisfied: scipy in /home/mohamed/anaconda3/lib/python3.11/site-packages (from xgboost) (1.11.4)\r\n",
+      "Requirement already satisfied: joblib>=1.1.1 in /home/mohamed/anaconda3/lib/python3.11/site-packages (from scikit-learn>=1.1.0->sklearn-genetic-opt) (1.2.0)\r\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/mohamed/anaconda3/lib/python3.11/site-packages (from scikit-learn>=1.1.0->sklearn-genetic-opt) (2.2.0)\r\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import tensorflow as tf\n",
+    "from keras.applications.xception import Xception\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, \\\n",
+    "    confusion_matrix\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn_genetic import GASearchCV\n",
+    "from sklearn_genetic.plots import plot_search_space\n",
+    "from sklearn_genetic.space import Continuous, Categorical, Integer\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:37.461447Z",
+     "iopub.execute_input": "2023-12-26T12:21:37.461752Z",
+     "iopub.status.idle": "2023-12-26T12:21:49.801040Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:37.461722Z",
+     "shell.execute_reply": "2023-12-26T12:21:49.800047Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:52.067364300Z",
+     "start_time": "2023-12-26T21:31:47.855132100Z"
+    }
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-26 23:31:48.644863: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+      "2023-12-26 23:31:49.081277: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2023-12-26 23:31:49.081349: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2023-12-26 23:31:49.113550: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2023-12-26 23:31:49.240747: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-12-26 23:31:50.798214: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Load images and labels\n",
+    "path_local = \"../facial_emotion_dataset/\"\n",
+    "path_kaggle = \"/kaggle/input/facial-emotion/\"\n",
+    "\n",
+    "general_path = path_local if os.path.exists(path_local) else path_kaggle\n",
+    "path_train_images = os.path.join(general_path, \"train\")  # Path to training images\n",
+    "path_test_images = os.path.join(general_path, \"test\")  # Path to test images"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:49.802016Z",
+     "iopub.execute_input": "2023-12-26T12:21:49.802474Z",
+     "iopub.status.idle": "2023-12-26T12:21:49.810457Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:49.802448Z",
+     "shell.execute_reply": "2023-12-26T12:21:49.809281Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:52.082608300Z",
+     "start_time": "2023-12-26T21:31:52.067364300Z"
+    }
+   },
+   "execution_count": 4,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def limit_data(data_dir):\n",
+    "    a = []\n",
+    "    for i in os.listdir(data_dir):\n",
+    "        for k, j in enumerate(os.listdir(data_dir + '/' + i)):\n",
+    "            a.append((f'{data_dir}/{i}/{j}', i))\n",
+    "    return pd.DataFrame(a, columns=['filename', 'class'])"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:49.814899Z",
+     "iopub.execute_input": "2023-12-26T12:21:49.815214Z",
+     "iopub.status.idle": "2023-12-26T12:21:49.828382Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:49.815188Z",
+     "shell.execute_reply": "2023-12-26T12:21:49.827591Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:52.084117400Z",
+     "start_time": "2023-12-26T21:31:52.072877600Z"
+    }
+   },
+   "execution_count": 5,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "# Use the function to limit the data\n",
+    "limited_train_data = limit_data(path_train_images)\n",
+    "limited_test_data = limit_data(path_test_images)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Data augmentation\n",
+    "datagen = ImageDataGenerator(rescale=1. / 255)\n",
+    "train_dataset = datagen.flow_from_dataframe(dataframe=limited_train_data)\n",
+    "test_dataset = datagen.flow_from_dataframe(dataframe=limited_test_data)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:49.829389Z",
+     "iopub.execute_input": "2023-12-26T12:21:49.829668Z",
+     "iopub.status.idle": "2023-12-26T12:21:54.503920Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:49.829644Z",
+     "shell.execute_reply": "2023-12-26T12:21:54.503040Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:52.471251900Z",
+     "start_time": "2023-12-26T21:31:52.076879400Z"
+    }
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 112 validated image filenames belonging to 8 classes.\n",
+      "Found 40 validated image filenames belonging to 8 classes.\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "train_dataset.image_shape"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:54.505125Z",
+     "iopub.execute_input": "2023-12-26T12:21:54.505441Z",
+     "iopub.status.idle": "2023-12-26T12:21:54.512390Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:54.505415Z",
+     "shell.execute_reply": "2023-12-26T12:21:54.511349Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:52.472756700Z",
+     "start_time": "2023-12-26T21:31:52.462251500Z"
+    }
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(256, 256, 3)"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "plt.imshow(train_dataset.next()[0][0])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:54.513860Z",
+     "iopub.execute_input": "2023-12-26T12:21:54.514204Z",
+     "iopub.status.idle": "2023-12-26T12:21:55.279745Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:54.514171Z",
+     "shell.execute_reply": "2023-12-26T12:21:55.278812Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:31:55.824417300Z",
+     "start_time": "2023-12-26T21:31:52.467254400Z"
+    }
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<matplotlib.image.AxesImage at 0x7ff40254e610>"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Now we will try to use exception Feature extraction architecture "
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "non_trainable_model = Xception(\n",
+    "    weights='imagenet',\n",
+    "    include_top=False,\n",
+    "    input_shape=(train_dataset.image_shape[0], train_dataset.image_shape[1], train_dataset.image_shape[2])\n",
+    ")"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:21:55.280814Z",
+     "iopub.execute_input": "2023-12-26T12:21:55.281090Z",
+     "iopub.status.idle": "2023-12-26T12:22:03.532846Z",
+     "shell.execute_reply.started": "2023-12-26T12:21:55.281066Z",
+     "shell.execute_reply": "2023-12-26T12:22:03.531537Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:36:28.708671800Z",
+     "start_time": "2023-12-26T21:36:27.592388300Z"
+    }
+   },
+   "execution_count": 16,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "for layer in non_trainable_model.layers:\n",
+    "    layer.trainable = False"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:03.534235Z",
+     "iopub.execute_input": "2023-12-26T12:22:03.535204Z",
+     "iopub.status.idle": "2023-12-26T12:22:03.545579Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:03.535165Z",
+     "shell.execute_reply": "2023-12-26T12:22:03.544551Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:36:28.710676400Z",
+     "start_time": "2023-12-26T21:36:28.692676900Z"
+    }
+   },
+   "execution_count": 17,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = non_trainable_model.output\n",
+    "x = tf.keras.layers.Conv2D(filters=100, kernel_size=(3, 3), activation='relu')(x)\n",
+    "x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
+    "flatten = tf.keras.layers.Flatten()(x)\n",
+    "predictions = tf.keras.layers.Dense(8, activation='softmax')(flatten)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:03.551587Z",
+     "iopub.execute_input": "2023-12-26T12:22:03.551863Z",
+     "iopub.status.idle": "2023-12-26T12:22:03.603576Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:03.551840Z",
+     "shell.execute_reply": "2023-12-26T12:22:03.602330Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:36:28.750843700Z",
+     "start_time": "2023-12-26T21:36:28.694684400Z"
+    }
+   },
+   "execution_count": 18,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "#x = tf.keras.layers.GlobalAveragePooling2D()(x)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:36:28.752348600Z",
+     "start_time": "2023-12-26T21:36:28.723693700Z"
+    }
+   },
+   "execution_count": 19
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "model = tf.keras.Model(inputs=non_trainable_model.input, outputs=predictions)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:03.605745Z",
+     "iopub.execute_input": "2023-12-26T12:22:03.606156Z",
+     "iopub.status.idle": "2023-12-26T12:22:03.627429Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:03.606119Z",
+     "shell.execute_reply": "2023-12-26T12:22:03.626456Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:36:28.753353200Z",
+     "start_time": "2023-12-26T21:36:28.727695100Z"
+    }
+   },
+   "execution_count": 20,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:03.628588Z",
+     "iopub.execute_input": "2023-12-26T12:22:03.634931Z",
+     "iopub.status.idle": "2023-12-26T12:22:03.666049Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:03.634895Z",
+     "shell.execute_reply": "2023-12-26T12:22:03.664953Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:36:28.754353Z",
+     "start_time": "2023-12-26T21:36:28.743827800Z"
+    }
+   },
+   "execution_count": 21,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "model.fit(train_dataset, epochs=30)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:03.670770Z",
+     "iopub.execute_input": "2023-12-26T12:22:03.671403Z",
+     "iopub.status.idle": "2023-12-26T12:22:52.727077Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:03.671375Z",
+     "shell.execute_reply": "2023-12-26T12:22:52.726088Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:23.795884200Z",
+     "start_time": "2023-12-26T21:36:28.745826900Z"
+    }
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/30\n",
+      "4/4 [==============================] - 12s 3s/step - loss: 2.8978 - accuracy: 0.1429\n",
+      "Epoch 2/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 2.0169 - accuracy: 0.2054\n",
+      "Epoch 3/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.9960 - accuracy: 0.2143\n",
+      "Epoch 4/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.9504 - accuracy: 0.1964\n",
+      "Epoch 5/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.8508 - accuracy: 0.2946\n",
+      "Epoch 6/30\n",
+      "4/4 [==============================] - 9s 2s/step - loss: 1.7631 - accuracy: 0.3214\n",
+      "Epoch 7/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.6397 - accuracy: 0.4196\n",
+      "Epoch 8/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.4746 - accuracy: 0.4643\n",
+      "Epoch 9/30\n",
+      "4/4 [==============================] - 9s 2s/step - loss: 1.3895 - accuracy: 0.5000\n",
+      "Epoch 10/30\n",
+      "4/4 [==============================] - 9s 2s/step - loss: 1.2800 - accuracy: 0.6071\n",
+      "Epoch 11/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.1499 - accuracy: 0.6696\n",
+      "Epoch 12/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 1.0320 - accuracy: 0.6786\n",
+      "Epoch 13/30\n",
+      "4/4 [==============================] - 10s 3s/step - loss: 0.8999 - accuracy: 0.7411\n",
+      "Epoch 14/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.7756 - accuracy: 0.7857\n",
+      "Epoch 15/30\n",
+      "4/4 [==============================] - 9s 2s/step - loss: 0.6765 - accuracy: 0.8304\n",
+      "Epoch 16/30\n",
+      "4/4 [==============================] - 10s 3s/step - loss: 0.5638 - accuracy: 0.9018\n",
+      "Epoch 17/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.4931 - accuracy: 0.9107\n",
+      "Epoch 18/30\n",
+      "4/4 [==============================] - 10s 3s/step - loss: 0.4140 - accuracy: 0.9196\n",
+      "Epoch 19/30\n",
+      "4/4 [==============================] - 10s 3s/step - loss: 0.3501 - accuracy: 0.9375\n",
+      "Epoch 20/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.2969 - accuracy: 0.9554\n",
+      "Epoch 21/30\n",
+      "4/4 [==============================] - 10s 3s/step - loss: 0.2497 - accuracy: 0.9643\n",
+      "Epoch 22/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.2142 - accuracy: 0.9732\n",
+      "Epoch 23/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.1825 - accuracy: 0.9732\n",
+      "Epoch 24/30\n",
+      "4/4 [==============================] - 9s 2s/step - loss: 0.1599 - accuracy: 0.9643\n",
+      "Epoch 25/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.1416 - accuracy: 0.9732\n",
+      "Epoch 26/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.1268 - accuracy: 0.9732\n",
+      "Epoch 27/30\n",
+      "4/4 [==============================] - 10s 3s/step - loss: 0.1254 - accuracy: 0.9732\n",
+      "Epoch 28/30\n",
+      "4/4 [==============================] - 9s 2s/step - loss: 0.1105 - accuracy: 0.9821\n",
+      "Epoch 29/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.0926 - accuracy: 0.9732\n",
+      "Epoch 30/30\n",
+      "4/4 [==============================] - 10s 2s/step - loss: 0.0805 - accuracy: 0.9911\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<keras.src.callbacks.History at 0x7ff20f5f0510>"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "model2 = tf.keras.Model(inputs=non_trainable_model.input, outputs=flatten)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:52.728524Z",
+     "iopub.execute_input": "2023-12-26T12:22:52.728863Z",
+     "iopub.status.idle": "2023-12-26T12:22:52.745822Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:52.728835Z",
+     "shell.execute_reply": "2023-12-26T12:22:52.745113Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:23.796937600Z",
+     "start_time": "2023-12-26T21:41:23.739705700Z"
+    }
+   },
+   "execution_count": 23,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "#plot_model(non_trainable_model, to_file='Xception_base_model.png', show_shapes=True)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:52.747156Z",
+     "iopub.execute_input": "2023-12-26T12:22:52.747774Z",
+     "iopub.status.idle": "2023-12-26T12:22:52.751771Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:52.747740Z",
+     "shell.execute_reply": "2023-12-26T12:22:52.750906Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:23.798518600Z",
+     "start_time": "2023-12-26T21:41:23.747220800Z"
+    }
+   },
+   "execution_count": 24,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "train_data_flatten = model2.predict(train_dataset)\n",
+    "test_data_flatten = model2.predict(test_dataset)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:22:52.752912Z",
+     "iopub.execute_input": "2023-12-26T12:22:52.753210Z",
+     "iopub.status.idle": "2023-12-26T12:23:05.079241Z",
+     "shell.execute_reply.started": "2023-12-26T12:22:52.753185Z",
+     "shell.execute_reply": "2023-12-26T12:23:05.078327Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:45.206335Z",
+     "start_time": "2023-12-26T21:41:23.754226800Z"
+    }
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4/4 [==============================] - 11s 2s/step\n",
+      "2/2 [==============================] - 5s 3s/step\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y_train = np.array(train_dataset.classes)\n",
+    "y_test = np.array(test_dataset.classes)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:23:05.080657Z",
+     "iopub.execute_input": "2023-12-26T12:23:05.081447Z",
+     "iopub.status.idle": "2023-12-26T12:23:05.086496Z",
+     "shell.execute_reply.started": "2023-12-26T12:23:05.081407Z",
+     "shell.execute_reply": "2023-12-26T12:23:05.085520Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:45.208340600Z",
+     "start_time": "2023-12-26T21:41:45.194801700Z"
+    }
+   },
+   "execution_count": 26,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "train_dataset.next()[0].shape, y_train.shape, train_data_flatten.shape"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:23:05.087886Z",
+     "iopub.execute_input": "2023-12-26T12:23:05.088158Z",
+     "iopub.status.idle": "2023-12-26T12:23:05.168558Z",
+     "shell.execute_reply.started": "2023-12-26T12:23:05.088134Z",
+     "shell.execute_reply": "2023-12-26T12:23:05.167672Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.812203500Z",
+     "start_time": "2023-12-26T21:41:45.249516600Z"
+    }
+   },
+   "execution_count": 27,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "((32, 256, 256, 3), (112,), (112, 180))"
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n       4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6,\n       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n       7, 7])"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.815259700Z",
+     "start_time": "2023-12-26T21:41:47.793003700Z"
+    }
+   },
+   "execution_count": 28
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 1, 2, 3, 4, 5, 6, 7])"
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.unique(y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.817259800Z",
+     "start_time": "2023-12-26T21:41:47.797056Z"
+    }
+   },
+   "execution_count": 29
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# SVM"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "svm_model = SVC(kernel='poly', C=1, gamma='auto')"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:23:05.169766Z",
+     "iopub.execute_input": "2023-12-26T12:23:05.170069Z",
+     "iopub.status.idle": "2023-12-26T12:23:05.174418Z",
+     "shell.execute_reply.started": "2023-12-26T12:23:05.170042Z",
+     "shell.execute_reply": "2023-12-26T12:23:05.173526Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.820259900Z",
+     "start_time": "2023-12-26T21:41:47.804057100Z"
+    }
+   },
+   "execution_count": 30,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "svm_model.fit(train_data_flatten, y_train)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:23:05.176097Z",
+     "iopub.execute_input": "2023-12-26T12:23:05.176460Z",
+     "iopub.status.idle": "2023-12-26T12:23:08.745479Z",
+     "shell.execute_reply.started": "2023-12-26T12:23:05.176425Z",
+     "shell.execute_reply": "2023-12-26T12:23:08.744624Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.848818600Z",
+     "start_time": "2023-12-26T21:41:47.808057300Z"
+    }
+   },
+   "execution_count": 31,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SVC(C=1, gamma='auto', kernel='poly')",
+      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=&#x27;auto&#x27;, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=&#x27;auto&#x27;, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_svm = svm_model.score(test_data_flatten, y_test)\n",
+    "print(\"Accuracy:\", accuracy_svm)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.908214800Z",
+     "start_time": "2023-12-26T21:41:47.817259800Z"
+    }
+   },
+   "execution_count": 32
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Define the search space for hyperparameters\n",
+    "search_space_svm = {\n",
+    "    'C': Continuous(0.1, 10),\n",
+    "    'gamma': Categorical(['scale', 'auto']),\n",
+    "    'kernel': Categorical(['linear', 'poly', 'rbf', 'sigmoid']),\n",
+    "    'degree': Integer(1, 6),\n",
+    "    'coef0': Continuous(0, 1),\n",
+    "    'shrinking': Categorical([True, False]),\n",
+    "    'probability': Categorical([True, False]),\n",
+    "    'tol': Continuous(1e-5, 1e-2),\n",
+    "}"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:26:38.478726Z",
+     "iopub.execute_input": "2023-12-26T12:26:38.479116Z",
+     "iopub.status.idle": "2023-12-26T12:26:38.484391Z",
+     "shell.execute_reply.started": "2023-12-26T12:26:38.479076Z",
+     "shell.execute_reply": "2023-12-26T12:26:38.483365Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.947748400Z",
+     "start_time": "2023-12-26T21:41:47.825278200Z"
+    }
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Use the genetic algorithm to search for the best hyperparameters\n",
+    "genetic_algo_svm = GASearchCV(\n",
+    "    estimator=svm_model,\n",
+    "    param_grid=search_space_svm,\n",
+    "    scoring='accuracy',\n",
+    "    cv=2,\n",
+    "    verbose=True,\n",
+    "    generations=40,\n",
+    "    n_jobs=-1\n",
+    ")"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:26:39.289131Z",
+     "iopub.execute_input": "2023-12-26T12:26:39.289462Z",
+     "iopub.status.idle": "2023-12-26T12:26:39.294621Z",
+     "shell.execute_reply.started": "2023-12-26T12:26:39.289437Z",
+     "shell.execute_reply": "2023-12-26T12:26:39.293435Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.949262500Z",
+     "start_time": "2023-12-26T21:41:47.834305600Z"
+    }
+   },
+   "execution_count": 34,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "train_data_flatten.shape, y_train.shape"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:26:39.771077Z",
+     "iopub.execute_input": "2023-12-26T12:26:39.771427Z",
+     "iopub.status.idle": "2023-12-26T12:26:39.777733Z",
+     "shell.execute_reply.started": "2023-12-26T12:26:39.771400Z",
+     "shell.execute_reply": "2023-12-26T12:26:39.776818Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:41:47.978828500Z",
+     "start_time": "2023-12-26T21:41:47.843310100Z"
+    }
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "((112, 180), (112,))"
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "genetic_algo_svm.fit(train_data_flatten, y_train)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:26:40.337046Z",
+     "iopub.execute_input": "2023-12-26T12:26:40.338023Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.124921900Z",
+     "start_time": "2023-12-26T21:41:47.898760100Z"
+    }
+   },
+   "execution_count": 36,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gen\tnevals\tfitness \tfitness_std\tfitness_max\tfitness_min\n",
+      "0  \t50    \t0.144286\t0.0269258  \t0.205357   \t0.0714286  \n",
+      "1  \t100   \t0.161786\t0.0185267  \t0.205357   \t0.133929   \n",
+      "2  \t100   \t0.172679\t0.0188653  \t0.205357   \t0.142857   \n",
+      "3  \t100   \t0.188214\t0.0175727  \t0.232143   \t0.151786   \n",
+      "4  \t100   \t0.199643\t0.0163429  \t0.232143   \t0.151786   \n",
+      "5  \t100   \t0.209464\t0.0129029  \t0.232143   \t0.1875     \n",
+      "6  \t100   \t0.217857\t0.0139469  \t0.232143   \t0.196429   \n",
+      "7  \t100   \t0.223571\t0.0126219  \t0.232143   \t0.196429   \n",
+      "8  \t100   \t0.22875 \t0.00925992 \t0.232143   \t0.196429   \n",
+      "9  \t100   \t0.23    \t0.00726678 \t0.232143   \t0.205357   \n",
+      "10 \t100   \t0.229464\t0.011044   \t0.232143   \t0.178571   \n",
+      "11 \t100   \t0.231429\t0.005      \t0.232143   \t0.196429   \n",
+      "12 \t100   \t0.231786\t0.0025     \t0.232143   \t0.214286   \n",
+      "13 \t100   \t0.229643\t0.0127576  \t0.232143   \t0.151786   \n",
+      "14 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "15 \t100   \t0.23125 \t0.00625    \t0.232143   \t0.1875     \n",
+      "16 \t100   \t0.230357\t0.0061859  \t0.232143   \t0.205357   \n",
+      "17 \t100   \t0.231429\t0.00349927 \t0.232143   \t0.214286   \n",
+      "18 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "19 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "20 \t100   \t0.231607\t0.00375    \t0.232143   \t0.205357   \n",
+      "21 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "22 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "23 \t100   \t0.22875 \t0.0116947  \t0.232143   \t0.178571   \n",
+      "24 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "25 \t100   \t0.231786\t0.0025     \t0.232143   \t0.214286   \n",
+      "26 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "27 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "28 \t100   \t0.231429\t0.005      \t0.232143   \t0.196429   \n",
+      "29 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "30 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "31 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "32 \t100   \t0.231429\t0.005      \t0.232143   \t0.196429   \n",
+      "33 \t100   \t0.229464\t0.0107514  \t0.232143   \t0.178571   \n",
+      "34 \t100   \t0.231607\t0.00375    \t0.232143   \t0.205357   \n",
+      "35 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "36 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "37 \t100   \t0.23125 \t0.00625    \t0.232143   \t0.1875     \n",
+      "38 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n",
+      "39 \t100   \t0.231964\t0.00125    \t0.232143   \t0.223214   \n",
+      "40 \t100   \t0.232143\t2.77556e-17\t0.232143   \t0.232143   \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "GASearchCV(cv=2,\n           estimator=SVC(C=5.793920520543272, coef0=0.5246447499164322,\n                         degree=4, gamma='auto', kernel='sigmoid',\n                         probability=True, shrinking=False,\n                         tol=6.638660631993536e-05),\n           generations=40, n_jobs=-1,\n           param_grid={'C': <sklearn_genetic.space.space.Continuous object at 0x7ff2e4192dd0>,\n                       'coef0': <sklearn_genetic.space.space.Continuous object at 0x7ff2e4161f50>,\n                       '...\n                       'gamma': <sklearn_genetic.space.space.Categorical object at 0x7ff20f565250>,\n                       'kernel': <sklearn_genetic.space.space.Categorical object at 0x7ff2d4247d90>,\n                       'probability': <sklearn_genetic.space.space.Categorical object at 0x7ff20f4cc310>,\n                       'shrinking': <sklearn_genetic.space.space.Categorical object at 0x7ff2e413d250>,\n                       'tol': <sklearn_genetic.space.space.Continuous object at 0x7ff2e419a450>},\n           return_train_score=True, scoring='accuracy')",
+      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GASearchCV(cv=2,\n           estimator=SVC(C=5.793920520543272, coef0=0.5246447499164322,\n                         degree=4, gamma=&#x27;auto&#x27;, kernel=&#x27;sigmoid&#x27;,\n                         probability=True, shrinking=False,\n                         tol=6.638660631993536e-05),\n           generations=40, n_jobs=-1,\n           param_grid={&#x27;C&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4192dd0&gt;,\n                       &#x27;coef0&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4161f50&gt;,\n                       &#x27;...\n                       &#x27;gamma&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff20f565250&gt;,\n                       &#x27;kernel&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff2d4247d90&gt;,\n                       &#x27;probability&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff20f4cc310&gt;,\n                       &#x27;shrinking&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff2e413d250&gt;,\n                       &#x27;tol&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e419a450&gt;},\n           return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GASearchCV</label><div class=\"sk-toggleable__content\"><pre>GASearchCV(cv=2,\n           estimator=SVC(C=5.793920520543272, coef0=0.5246447499164322,\n                         degree=4, gamma=&#x27;auto&#x27;, kernel=&#x27;sigmoid&#x27;,\n                         probability=True, shrinking=False,\n                         tol=6.638660631993536e-05),\n           generations=40, n_jobs=-1,\n           param_grid={&#x27;C&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4192dd0&gt;,\n                       &#x27;coef0&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4161f50&gt;,\n                       &#x27;...\n                       &#x27;gamma&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff20f565250&gt;,\n                       &#x27;kernel&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff2d4247d90&gt;,\n                       &#x27;probability&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff20f4cc310&gt;,\n                       &#x27;shrinking&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x7ff2e413d250&gt;,\n                       &#x27;tol&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e419a450&gt;},\n           return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5.793920520543272, coef0=0.5246447499164322, degree=4, gamma=&#x27;auto&#x27;,\n    kernel=&#x27;sigmoid&#x27;, probability=True, shrinking=False,\n    tol=6.638660631993536e-05)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5.793920520543272, coef0=0.5246447499164322, degree=4, gamma=&#x27;auto&#x27;,\n    kernel=&#x27;sigmoid&#x27;, probability=True, shrinking=False,\n    tol=6.638660631993536e-05)</pre></div></div></div></div></div></div></div></div></div></div>"
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "best_model_svm = genetic_algo_svm.best_estimator_\n",
+    "best_params_svm = genetic_algo_svm.best_params_"
+   ],
+   "metadata": {
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.143015600Z",
+     "start_time": "2023-12-26T21:42:13.125927200Z"
+    }
+   },
+   "execution_count": 37,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'C': 5.793920520543272,\n 'gamma': 'auto',\n 'kernel': 'sigmoid',\n 'degree': 4,\n 'coef0': 0.5246447499164322,\n 'shrinking': False,\n 'probability': True,\n 'tol': 6.638660631993536e-05}"
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_params_svm"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.144798100Z",
+     "start_time": "2023-12-26T21:42:13.127926700Z"
+    }
+   },
+   "execution_count": 38
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "accuracy_svm = best_model_svm.score(test_data_flatten, y_test)\n",
+    "print(\"Accuracy:\", accuracy_svm)"
+   ],
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2023-12-26T12:24:46.124029Z",
+     "iopub.status.idle": "2023-12-26T12:24:46.124411Z",
+     "shell.execute_reply.started": "2023-12-26T12:24:46.124212Z",
+     "shell.execute_reply": "2023-12-26T12:24:46.124229Z"
+    },
+    "trusted": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.200879800Z",
+     "start_time": "2023-12-26T21:42:13.141016Z"
+    }
+   },
+   "execution_count": 39,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.15\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# XGBoost"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "xgb_model = XGBClassifier(\n",
+    "    objective='multi:softmax',\n",
+    "    num_class=len(np.unique(y_train)),\n",
+    "    max_depth=10,\n",
+    "    learning_rate=0.1,\n",
+    "    n_estimators=100,\n",
+    "    subsample=0.8,\n",
+    "    colsample_bytree=0.8,\n",
+    "    reg_alpha=1e-5,\n",
+    "    gamma=0.1,\n",
+    "    random_state=42\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.227325800Z",
+     "start_time": "2023-12-26T21:42:13.151817Z"
+    }
+   },
+   "execution_count": 40
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=0.1, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=10, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None, num_class=8,\n              num_parallel_tree=None, ...)",
+      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=0.1, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=10, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None, num_class=8,\n              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=0.1, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=10, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None, num_class=8,\n              num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xgb_model.fit(train_data_flatten, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.499981400Z",
+     "start_time": "2023-12-26T21:42:13.159866500Z"
+    }
+   },
+   "execution_count": 41
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.05\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_xgb = xgb_model.score(test_data_flatten, y_test)\n",
+    "print(\"Accuracy:\", accuracy_xgb)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:13.860263Z",
+     "start_time": "2023-12-26T21:42:13.485807Z"
+    }
+   },
+   "execution_count": 42
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "# Define the search space for hyperparameters\n",
+    "search_space_xgb = {\n",
+    "    'max_depth': Integer(1, 10),\n",
+    "    'learning_rate': Continuous(0.01, 1),\n",
+    "    'n_estimators': Integer(10, 100),\n",
+    "    'subsample': Continuous(0.1, 1),\n",
+    "    'colsample_bytree': Continuous(0.1, 1),\n",
+    "    'reg_alpha': Continuous(1e-5, 1e-2),\n",
+    "    'gamma': Continuous(0, 1),\n",
+    "}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:14.155933900Z",
+     "start_time": "2023-12-26T21:42:13.513091100Z"
+    }
+   },
+   "execution_count": 43
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "# Use the genetic algorithm to search for the best hyperparameters\n",
+    "genetic_algo_xgb = GASearchCV(\n",
+    "    estimator=xgb_model,\n",
+    "    param_grid=search_space_xgb,\n",
+    "    scoring='accuracy',\n",
+    "    cv=10,\n",
+    "    verbose=True,\n",
+    "    generations=40,\n",
+    "    n_jobs=-1\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:42:14.158451600Z",
+     "start_time": "2023-12-26T21:42:13.731963100Z"
+    }
+   },
+   "execution_count": 44
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gen\tnevals\tfitness \tfitness_std\tfitness_max\tfitness_min\n",
+      "0  \t50    \t0.131136\t0.0298328  \t0.195455   \t0.0795455  \n",
+      "1  \t100   \t0.151848\t0.020167   \t0.195455   \t0.0977273  \n",
+      "2  \t100   \t0.162803\t0.0167129  \t0.196212   \t0.115909   \n",
+      "3  \t100   \t0.169091\t0.0161141  \t0.203788   \t0.124242   \n",
+      "4  \t100   \t0.174182\t0.0134805  \t0.206061   \t0.123485   \n",
+      "5  \t100   \t0.182576\t0.0187034  \t0.240152   \t0.150758   \n",
+      "6  \t100   \t0.181773\t0.0216245  \t0.240152   \t0.139394   \n",
+      "7  \t100   \t0.187652\t0.0158927  \t0.240152   \t0.141667   \n",
+      "8  \t100   \t0.194   \t0.0147926  \t0.222727   \t0.159091   \n",
+      "9  \t100   \t0.198742\t0.0127125  \t0.222727   \t0.140909   \n",
+      "10 \t100   \t0.205394\t0.00977436 \t0.230303   \t0.17803    \n",
+      "11 \t100   \t0.210379\t0.0099727  \t0.230303   \t0.194697   \n",
+      "12 \t100   \t0.209939\t0.0147754  \t0.230303   \t0.160606   \n",
+      "13 \t100   \t0.214091\t0.012142   \t0.230303   \t0.176515   \n",
+      "14 \t100   \t0.21597 \t0.0112007  \t0.230303   \t0.186364   \n",
+      "15 \t100   \t0.214591\t0.0163059  \t0.230303   \t0.150758   \n",
+      "16 \t100   \t0.216136\t0.0162984  \t0.230303   \t0.150758   \n",
+      "17 \t100   \t0.218045\t0.0149053  \t0.230303   \t0.168939   \n",
+      "18 \t100   \t0.220848\t0.0137589  \t0.230303   \t0.175758   \n",
+      "19 \t100   \t0.222121\t0.0148554  \t0.230303   \t0.158333   \n",
+      "20 \t100   \t0.226879\t0.00646073 \t0.230303   \t0.212121   \n",
+      "21 \t100   \t0.226364\t0.0106934  \t0.238636   \t0.177273   \n",
+      "22 \t100   \t0.228   \t0.00730887 \t0.230303   \t0.194697   \n",
+      "23 \t100   \t0.228667\t0.0053103  \t0.238636   \t0.212121   \n",
+      "24 \t100   \t0.229348\t0.00762832 \t0.238636   \t0.193939   \n",
+      "25 \t100   \t0.229045\t0.0127809  \t0.238636   \t0.176515   \n",
+      "26 \t100   \t0.233985\t0.00428284 \t0.24697    \t0.229545   \n",
+      "27 \t100   \t0.233545\t0.0133213  \t0.24697    \t0.161364   \n",
+      "28 \t100   \t0.233591\t0.0122105  \t0.24697    \t0.186364   \n",
+      "29 \t100   \t0.233227\t0.0135663  \t0.24697    \t0.177273   \n",
+      "30 \t100   \t0.236076\t0.00931281 \t0.24697    \t0.204545   \n",
+      "31 \t100   \t0.239924\t0.00486562 \t0.24697    \t0.229545   \n",
+      "32 \t100   \t0.240152\t0.00677427 \t0.24697    \t0.211364   \n",
+      "33 \t100   \t0.23847 \t0.0132073  \t0.247727   \t0.177273   \n",
+      "34 \t100   \t0.238439\t0.00874704 \t0.247727   \t0.20303    \n",
+      "35 \t100   \t0.237106\t0.0174022  \t0.24697    \t0.132576   \n",
+      "36 \t100   \t0.243076\t0.00904408 \t0.256061   \t0.212121   \n",
+      "37 \t100   \t0.243833\t0.0102807  \t0.256061   \t0.195455   \n",
+      "38 \t100   \t0.238833\t0.019791   \t0.256061   \t0.166667   \n",
+      "39 \t100   \t0.243788\t0.0125666  \t0.256061   \t0.193182   \n",
+      "40 \t100   \t0.246227\t0.0140758  \t0.256061   \t0.158333   \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "GASearchCV(cv=10,\n           estimator=XGBClassifier(base_score=None, booster=None,\n                                   callbacks=None, colsample_bylevel=None,\n                                   colsample_bynode=None,\n                                   colsample_bytree=0.9253012137227039,\n                                   device=None, early_stopping_rounds=None,\n                                   enable_categorical=False, eval_metric=None,\n                                   feature_types=None, gamma=0.0939275647857335,\n                                   grow_policy=None, importance_type=None,\n                                   interaction_constrain...\n                       'learning_rate': <sklearn_genetic.space.space.Continuous object at 0x7ff22dfc8dd0>,\n                       'max_depth': <sklearn_genetic.space.space.Integer object at 0x7ff2e41edd10>,\n                       'n_estimators': <sklearn_genetic.space.space.Integer object at 0x7ff22d9fec50>,\n                       'reg_alpha': <sklearn_genetic.space.space.Continuous object at 0x7ff2e4175090>,\n                       'subsample': <sklearn_genetic.space.space.Continuous object at 0x7ff2e4199890>},\n           return_train_score=True, scoring='accuracy')",
+      "text/html": "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GASearchCV(cv=10,\n           estimator=XGBClassifier(base_score=None, booster=None,\n                                   callbacks=None, colsample_bylevel=None,\n                                   colsample_bynode=None,\n                                   colsample_bytree=0.9253012137227039,\n                                   device=None, early_stopping_rounds=None,\n                                   enable_categorical=False, eval_metric=None,\n                                   feature_types=None, gamma=0.0939275647857335,\n                                   grow_policy=None, importance_type=None,\n                                   interaction_constrain...\n                       &#x27;learning_rate&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff22dfc8dd0&gt;,\n                       &#x27;max_depth&#x27;: &lt;sklearn_genetic.space.space.Integer object at 0x7ff2e41edd10&gt;,\n                       &#x27;n_estimators&#x27;: &lt;sklearn_genetic.space.space.Integer object at 0x7ff22d9fec50&gt;,\n                       &#x27;reg_alpha&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4175090&gt;,\n                       &#x27;subsample&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4199890&gt;},\n           return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GASearchCV</label><div class=\"sk-toggleable__content\"><pre>GASearchCV(cv=10,\n           estimator=XGBClassifier(base_score=None, booster=None,\n                                   callbacks=None, colsample_bylevel=None,\n                                   colsample_bynode=None,\n                                   colsample_bytree=0.9253012137227039,\n                                   device=None, early_stopping_rounds=None,\n                                   enable_categorical=False, eval_metric=None,\n                                   feature_types=None, gamma=0.0939275647857335,\n                                   grow_policy=None, importance_type=None,\n                                   interaction_constrain...\n                       &#x27;learning_rate&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff22dfc8dd0&gt;,\n                       &#x27;max_depth&#x27;: &lt;sklearn_genetic.space.space.Integer object at 0x7ff2e41edd10&gt;,\n                       &#x27;n_estimators&#x27;: &lt;sklearn_genetic.space.space.Integer object at 0x7ff22d9fec50&gt;,\n                       &#x27;reg_alpha&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4175090&gt;,\n                       &#x27;subsample&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x7ff2e4199890&gt;},\n           return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=0.9253012137227039, device=None,\n              early_stopping_rounds=None, enable_categorical=False,\n              eval_metric=None, feature_types=None, gamma=0.0939275647857335,\n              grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.447118977728237,\n              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=7, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=14, n_jobs=None, num_class=8,\n              num_parallel_tree=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=0.9253012137227039, device=None,\n              early_stopping_rounds=None, enable_categorical=False,\n              eval_metric=None, feature_types=None, gamma=0.0939275647857335,\n              grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.447118977728237,\n              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=7, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=14, n_jobs=None, num_class=8,\n              num_parallel_tree=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "genetic_algo_xgb.fit(train_data_flatten, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:47:54.465594200Z",
+     "start_time": "2023-12-26T21:42:13.731963100Z"
+    }
+   },
+   "execution_count": 45
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "best_model_xgb = genetic_algo_xgb.best_estimator_\n",
+    "best_params_xgb = genetic_algo_xgb.best_params_"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:47:54.469108500Z",
+     "start_time": "2023-12-26T21:47:54.463586800Z"
+    }
+   },
+   "execution_count": 46
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'max_depth': 7,\n 'learning_rate': 0.447118977728237,\n 'n_estimators': 14,\n 'subsample': 0.9907399136014624,\n 'colsample_bytree': 0.9253012137227039,\n 'reg_alpha': 0.005898077418318591,\n 'gamma': 0.0939275647857335}"
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_params_xgb"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:47:54.511907100Z",
+     "start_time": "2023-12-26T21:47:54.465594200Z"
+    }
+   },
+   "execution_count": 47
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.05\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_xgb = best_model_xgb.score(test_data_flatten, y_test)\n",
+    "print(\"Accuracy:\", accuracy_xgb)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:47:54.541634300Z",
+     "start_time": "2023-12-26T21:47:54.485719100Z"
+    }
+   },
+   "execution_count": 48
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "class AntColonyOptimization:\n",
+    "    def __init__(self, num_ants, max_iter=10, Model=XGBClassifier()):\n",
+    "        self.model = Model\n",
+    "        self.best_selected_features = None\n",
+    "        self.best_fitness = None\n",
+    "        self.final_report = None\n",
+    "        self.final_f1 = None\n",
+    "        self.final_recall = None\n",
+    "        self.final_precision = None\n",
+    "        self.final_accuracy = None\n",
+    "        self.final_confusion_mat = None\n",
+    "        self.num_ants = num_ants\n",
+    "        self.max_iter = max_iter\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def _initialize_pheromones(num_features):\n",
+    "        return np.ones(num_features)\n",
+    "\n",
+    "    def _select_features(self, pheromones):\n",
+    "        probabilities = pheromones / np.sum(pheromones)\n",
+    "        selected_features = np.random.choice(len(pheromones), size=self.num_ants, p=probabilities)\n",
+    "        return selected_features\n",
+    "\n",
+    "    def _fitness(self, selected_features, X_train, Y_train, X_test, Y_test):\n",
+    "        if len(selected_features) == 0:\n",
+    "            return 0  # Penalize if no features are selected\n",
+    "        self.model.fit(X_train[:, selected_features], Y_train)\n",
+    "        y_predict = self.model.predict(X_test[:, selected_features])\n",
+    "        Accuracy = accuracy_score(Y_test, y_predict)\n",
+    "        precision = precision_score(Y_test, y_predict, average='weighted')\n",
+    "        recall = recall_score(Y_test, y_predict, average='weighted')\n",
+    "        f1 = f1_score(Y_test, y_predict, average='weighted')\n",
+    "        report = classification_report(Y_test, y_predict)\n",
+    "        confusion_mat = confusion_matrix(Y_test, y_predict)\n",
+    "        return Accuracy, precision, recall, f1, report, confusion_mat\n",
+    "\n",
+    "    def run(self, X_train, Y_train, X_test, Y_test):\n",
+    "        num_features = X_train.shape[1]\n",
+    "        pheromones = self._initialize_pheromones(num_features)\n",
+    "        self.best_fitness = 0\n",
+    "\n",
+    "        for iteration in range(self.max_iter):\n",
+    "            # Initialize ant colony\n",
+    "            selected_features = self._select_features(pheromones)\n",
+    "\n",
+    "            # Evaluate fitness\n",
+    "            fitness_value = self._fitness(selected_features, X_train, Y_train, X_test, Y_test)\n",
+    "\n",
+    "            # Update pheromones based on fitness values\n",
+    "            pheromones[selected_features] += fitness_value[0]  # Update based on accuracy (you can choose other metrics)\n",
+    "\n",
+    "            print(f\"Iteration: {iteration + 1},\\tBest Fitness: {self.best_fitness:.4f}\")\n",
+    "\n",
+    "            # Update the best solution if a better one is found\n",
+    "            if fitness_value[0] > self.best_fitness:  # Update based on accuracy (you can choose other metrics)\n",
+    "                self.best_fitness = fitness_value[0]\n",
+    "                self.best_selected_features = selected_features\n",
+    "\n",
+    "        # print(\"Best Selected Features:\", self.best_selected_features)\n",
+    "        print(\"\\nBest Fitness (Accuracy):\", self.best_fitness)\n",
+    "\n",
+    "        # Use the best-selected features for prediction on the test set\n",
+    "\n",
+    "        self.model.fit(X_train[:, self.best_selected_features], Y_train)\n",
+    "        y_predict = self.model.predict(X_test[:, self.best_selected_features])\n",
+    "\n",
+    "        # Calculate metrics for the final model\n",
+    "        self.final_accuracy = accuracy_score(Y_test, y_predict)\n",
+    "        self.final_precision = precision_score(Y_test, y_predict, average='weighted')\n",
+    "        self.final_recall = recall_score(Y_test, y_predict, average='weighted')\n",
+    "        self.final_f1 = f1_score(Y_test, y_predict, average='weighted')\n",
+    "        self.final_report = classification_report(Y_test, y_predict)\n",
+    "        self.final_confusion_mat = confusion_matrix(Y_test, y_predict)\n",
+    "\n",
+    "        return self.best_selected_features\n",
+    "\n",
+    "    def results(self):\n",
+    "        # Plot the confusion matrix using Seaborn\n",
+    "        print('The Confusion Matrix is: ')\n",
+    "        plt.figure(figsize=(8, 6))\n",
+    "        sns.heatmap(self.final_confusion_mat, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y_test),\n",
+    "                    yticklabels=np.unique(y_test))\n",
+    "        plt.title('Confusion Matrix')\n",
+    "        plt.xlabel('Predicted')\n",
+    "        plt.ylabel('Actual')\n",
+    "        plt.show()\n",
+    "\n",
+    "        # Print the classification report\n",
+    "        print('The Classification Report is: ')\n",
+    "        print(self.final_report)\n",
+    "\n",
+    "        # Print the accuracy, precision, recall, and f1_score\n",
+    "        print(f'Accuracy: {self.final_accuracy: .4f}')\n",
+    "        print(f'Best Fitness: {self.best_fitness: .4f}')\n",
+    "        print(f'Precision: {self.final_precision: .4f}')\n",
+    "        print(f'Recall: {self.final_recall: .4f}')\n",
+    "        print(f'F1-Score: {self.final_f1: .4f}')\n",
+    "\n",
+    "        # Plot the search space\n",
+    "        plot_search_space(self.model.search_space_)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:47:54.594586200Z",
+     "start_time": "2023-12-26T21:47:54.523981900Z"
+    }
+   },
+   "execution_count": 49
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration: 1,\tBest Fitness: 0.0000\n",
+      "Iteration: 2,\tBest Fitness: 0.0500\n",
+      "Iteration: 3,\tBest Fitness: 0.0500\n",
+      "Iteration: 4,\tBest Fitness: 0.0500\n",
+      "Iteration: 5,\tBest Fitness: 0.0500\n",
+      "Iteration: 6,\tBest Fitness: 0.0500\n",
+      "Iteration: 7,\tBest Fitness: 0.0750\n",
+      "Iteration: 8,\tBest Fitness: 0.0750\n",
+      "Iteration: 9,\tBest Fitness: 0.0750\n",
+      "Iteration: 10,\tBest Fitness: 0.1000\n",
+      "Iteration: 11,\tBest Fitness: 0.1000\n",
+      "Iteration: 12,\tBest Fitness: 0.1000\n",
+      "Iteration: 13,\tBest Fitness: 0.1000\n",
+      "Iteration: 14,\tBest Fitness: 0.1000\n",
+      "Iteration: 15,\tBest Fitness: 0.1000\n",
+      "Iteration: 16,\tBest Fitness: 0.1000\n",
+      "Iteration: 17,\tBest Fitness: 0.1000\n",
+      "Iteration: 18,\tBest Fitness: 0.1000\n",
+      "Iteration: 19,\tBest Fitness: 0.1000\n",
+      "Iteration: 20,\tBest Fitness: 0.1000\n",
+      "Iteration: 21,\tBest Fitness: 0.1000\n",
+      "Iteration: 22,\tBest Fitness: 0.1000\n",
+      "Iteration: 23,\tBest Fitness: 0.1000\n",
+      "Iteration: 24,\tBest Fitness: 0.1000\n",
+      "Iteration: 25,\tBest Fitness: 0.1000\n",
+      "Iteration: 26,\tBest Fitness: 0.1000\n",
+      "Iteration: 27,\tBest Fitness: 0.1000\n",
+      "Iteration: 28,\tBest Fitness: 0.1000\n",
+      "Iteration: 29,\tBest Fitness: 0.1000\n",
+      "Iteration: 30,\tBest Fitness: 0.1000\n",
+      "Iteration: 31,\tBest Fitness: 0.1000\n",
+      "Iteration: 32,\tBest Fitness: 0.1000\n",
+      "Iteration: 33,\tBest Fitness: 0.1000\n",
+      "Iteration: 34,\tBest Fitness: 0.1000\n",
+      "Iteration: 35,\tBest Fitness: 0.1000\n",
+      "Iteration: 36,\tBest Fitness: 0.1000\n",
+      "Iteration: 37,\tBest Fitness: 0.1250\n",
+      "Iteration: 38,\tBest Fitness: 0.1250\n",
+      "Iteration: 39,\tBest Fitness: 0.1250\n",
+      "Iteration: 40,\tBest Fitness: 0.1250\n",
+      "Iteration: 41,\tBest Fitness: 0.1250\n",
+      "Iteration: 42,\tBest Fitness: 0.1250\n",
+      "Iteration: 43,\tBest Fitness: 0.1250\n",
+      "Iteration: 44,\tBest Fitness: 0.1250\n",
+      "Iteration: 45,\tBest Fitness: 0.1250\n",
+      "Iteration: 46,\tBest Fitness: 0.1250\n",
+      "Iteration: 47,\tBest Fitness: 0.1250\n",
+      "Iteration: 48,\tBest Fitness: 0.1250\n",
+      "Iteration: 49,\tBest Fitness: 0.1250\n",
+      "Iteration: 50,\tBest Fitness: 0.1250\n",
+      "Iteration: 51,\tBest Fitness: 0.1250\n",
+      "Iteration: 52,\tBest Fitness: 0.1250\n",
+      "Iteration: 53,\tBest Fitness: 0.1250\n",
+      "Iteration: 54,\tBest Fitness: 0.1250\n",
+      "Iteration: 55,\tBest Fitness: 0.1250\n",
+      "Iteration: 56,\tBest Fitness: 0.1250\n",
+      "Iteration: 57,\tBest Fitness: 0.1250\n",
+      "Iteration: 58,\tBest Fitness: 0.1250\n",
+      "Iteration: 59,\tBest Fitness: 0.1250\n",
+      "Iteration: 60,\tBest Fitness: 0.1250\n",
+      "Iteration: 61,\tBest Fitness: 0.1250\n",
+      "Iteration: 62,\tBest Fitness: 0.1250\n",
+      "Iteration: 63,\tBest Fitness: 0.1250\n",
+      "Iteration: 64,\tBest Fitness: 0.1250\n",
+      "Iteration: 65,\tBest Fitness: 0.1250\n",
+      "Iteration: 66,\tBest Fitness: 0.1250\n",
+      "Iteration: 67,\tBest Fitness: 0.1250\n",
+      "Iteration: 68,\tBest Fitness: 0.1250\n",
+      "Iteration: 69,\tBest Fitness: 0.1250\n",
+      "Iteration: 70,\tBest Fitness: 0.1250\n",
+      "Iteration: 71,\tBest Fitness: 0.1250\n",
+      "Iteration: 72,\tBest Fitness: 0.1250\n",
+      "Iteration: 73,\tBest Fitness: 0.1250\n",
+      "Iteration: 74,\tBest Fitness: 0.1250\n",
+      "Iteration: 75,\tBest Fitness: 0.1250\n",
+      "Iteration: 76,\tBest Fitness: 0.1250\n",
+      "Iteration: 77,\tBest Fitness: 0.1250\n",
+      "Iteration: 78,\tBest Fitness: 0.1250\n",
+      "Iteration: 79,\tBest Fitness: 0.1250\n",
+      "Iteration: 80,\tBest Fitness: 0.1250\n",
+      "Iteration: 81,\tBest Fitness: 0.1250\n",
+      "Iteration: 82,\tBest Fitness: 0.1250\n",
+      "Iteration: 83,\tBest Fitness: 0.1250\n",
+      "Iteration: 84,\tBest Fitness: 0.1250\n",
+      "Iteration: 85,\tBest Fitness: 0.1250\n",
+      "Iteration: 86,\tBest Fitness: 0.1250\n",
+      "Iteration: 87,\tBest Fitness: 0.1250\n",
+      "Iteration: 88,\tBest Fitness: 0.1250\n",
+      "Iteration: 89,\tBest Fitness: 0.1250\n",
+      "Iteration: 90,\tBest Fitness: 0.1250\n",
+      "Iteration: 91,\tBest Fitness: 0.1250\n",
+      "Iteration: 92,\tBest Fitness: 0.1250\n",
+      "Iteration: 93,\tBest Fitness: 0.1250\n",
+      "Iteration: 94,\tBest Fitness: 0.1250\n",
+      "Iteration: 95,\tBest Fitness: 0.1250\n",
+      "Iteration: 96,\tBest Fitness: 0.1250\n",
+      "Iteration: 97,\tBest Fitness: 0.1250\n",
+      "Iteration: 98,\tBest Fitness: 0.1250\n",
+      "Iteration: 99,\tBest Fitness: 0.1250\n",
+      "Iteration: 100,\tBest Fitness: 0.1250\n",
+      "Iteration: 101,\tBest Fitness: 0.1250\n",
+      "Iteration: 102,\tBest Fitness: 0.1250\n",
+      "Iteration: 103,\tBest Fitness: 0.1250\n",
+      "Iteration: 104,\tBest Fitness: 0.1250\n",
+      "Iteration: 105,\tBest Fitness: 0.1250\n",
+      "Iteration: 106,\tBest Fitness: 0.1250\n",
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+      "Iteration: 110,\tBest Fitness: 0.1250\n",
+      "Iteration: 111,\tBest Fitness: 0.1250\n",
+      "Iteration: 112,\tBest Fitness: 0.1250\n",
+      "Iteration: 113,\tBest Fitness: 0.1250\n",
+      "Iteration: 114,\tBest Fitness: 0.1250\n",
+      "Iteration: 115,\tBest Fitness: 0.1250\n",
+      "Iteration: 116,\tBest Fitness: 0.1250\n",
+      "Iteration: 117,\tBest Fitness: 0.1250\n",
+      "Iteration: 118,\tBest Fitness: 0.1250\n",
+      "Iteration: 119,\tBest Fitness: 0.1250\n",
+      "Iteration: 120,\tBest Fitness: 0.1250\n",
+      "Iteration: 121,\tBest Fitness: 0.1250\n",
+      "Iteration: 122,\tBest Fitness: 0.1250\n",
+      "Iteration: 123,\tBest Fitness: 0.1250\n",
+      "Iteration: 124,\tBest Fitness: 0.1250\n",
+      "Iteration: 125,\tBest Fitness: 0.1250\n",
+      "Iteration: 126,\tBest Fitness: 0.1250\n",
+      "Iteration: 127,\tBest Fitness: 0.1250\n",
+      "Iteration: 128,\tBest Fitness: 0.1250\n",
+      "Iteration: 129,\tBest Fitness: 0.1250\n",
+      "Iteration: 130,\tBest Fitness: 0.1250\n",
+      "Iteration: 131,\tBest Fitness: 0.1250\n",
+      "Iteration: 132,\tBest Fitness: 0.1250\n",
+      "Iteration: 133,\tBest Fitness: 0.1250\n",
+      "Iteration: 134,\tBest Fitness: 0.1250\n",
+      "Iteration: 135,\tBest Fitness: 0.1250\n",
+      "Iteration: 136,\tBest Fitness: 0.1250\n",
+      "Iteration: 137,\tBest Fitness: 0.1250\n",
+      "Iteration: 138,\tBest Fitness: 0.1250\n",
+      "Iteration: 139,\tBest Fitness: 0.1250\n",
+      "Iteration: 140,\tBest Fitness: 0.1250\n",
+      "Iteration: 141,\tBest Fitness: 0.1250\n",
+      "Iteration: 142,\tBest Fitness: 0.1250\n",
+      "Iteration: 143,\tBest Fitness: 0.1250\n",
+      "Iteration: 144,\tBest Fitness: 0.1250\n",
+      "Iteration: 145,\tBest Fitness: 0.1250\n",
+      "Iteration: 146,\tBest Fitness: 0.1250\n",
+      "Iteration: 147,\tBest Fitness: 0.1250\n",
+      "Iteration: 148,\tBest Fitness: 0.1250\n",
+      "Iteration: 149,\tBest Fitness: 0.1250\n",
+      "Iteration: 150,\tBest Fitness: 0.1250\n",
+      "Iteration: 151,\tBest Fitness: 0.1250\n",
+      "Iteration: 152,\tBest Fitness: 0.1250\n",
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+      "Iteration: 1298,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1300,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1302,\tBest Fitness: 0.1500\n",
+      "Iteration: 1303,\tBest Fitness: 0.1500\n",
+      "Iteration: 1304,\tBest Fitness: 0.1500\n",
+      "Iteration: 1305,\tBest Fitness: 0.1500\n",
+      "Iteration: 1306,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1310,\tBest Fitness: 0.1500\n",
+      "Iteration: 1311,\tBest Fitness: 0.1500\n",
+      "Iteration: 1312,\tBest Fitness: 0.1500\n",
+      "Iteration: 1313,\tBest Fitness: 0.1500\n",
+      "Iteration: 1314,\tBest Fitness: 0.1500\n",
+      "Iteration: 1315,\tBest Fitness: 0.1500\n",
+      "Iteration: 1316,\tBest Fitness: 0.1500\n",
+      "Iteration: 1317,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1320,\tBest Fitness: 0.1500\n",
+      "Iteration: 1321,\tBest Fitness: 0.1500\n",
+      "Iteration: 1322,\tBest Fitness: 0.1500\n",
+      "Iteration: 1323,\tBest Fitness: 0.1500\n",
+      "Iteration: 1324,\tBest Fitness: 0.1500\n",
+      "Iteration: 1325,\tBest Fitness: 0.1500\n",
+      "Iteration: 1326,\tBest Fitness: 0.1500\n",
+      "Iteration: 1327,\tBest Fitness: 0.1500\n",
+      "Iteration: 1328,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1330,\tBest Fitness: 0.1500\n",
+      "Iteration: 1331,\tBest Fitness: 0.1500\n",
+      "Iteration: 1332,\tBest Fitness: 0.1500\n",
+      "Iteration: 1333,\tBest Fitness: 0.1500\n",
+      "Iteration: 1334,\tBest Fitness: 0.1500\n",
+      "Iteration: 1335,\tBest Fitness: 0.1500\n",
+      "Iteration: 1336,\tBest Fitness: 0.1500\n",
+      "Iteration: 1337,\tBest Fitness: 0.1500\n",
+      "Iteration: 1338,\tBest Fitness: 0.1500\n",
+      "Iteration: 1339,\tBest Fitness: 0.1500\n",
+      "Iteration: 1340,\tBest Fitness: 0.1500\n",
+      "Iteration: 1341,\tBest Fitness: 0.1500\n",
+      "Iteration: 1342,\tBest Fitness: 0.1500\n",
+      "Iteration: 1343,\tBest Fitness: 0.1500\n",
+      "Iteration: 1344,\tBest Fitness: 0.1500\n",
+      "Iteration: 1345,\tBest Fitness: 0.1500\n",
+      "Iteration: 1346,\tBest Fitness: 0.1500\n",
+      "Iteration: 1347,\tBest Fitness: 0.1500\n",
+      "Iteration: 1348,\tBest Fitness: 0.1500\n",
+      "Iteration: 1349,\tBest Fitness: 0.1500\n",
+      "Iteration: 1350,\tBest Fitness: 0.1500\n",
+      "Iteration: 1351,\tBest Fitness: 0.1500\n",
+      "Iteration: 1352,\tBest Fitness: 0.1500\n",
+      "Iteration: 1353,\tBest Fitness: 0.1500\n",
+      "Iteration: 1354,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1361,\tBest Fitness: 0.1500\n",
+      "Iteration: 1362,\tBest Fitness: 0.1500\n",
+      "Iteration: 1363,\tBest Fitness: 0.1500\n",
+      "Iteration: 1364,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1367,\tBest Fitness: 0.1500\n",
+      "Iteration: 1368,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1370,\tBest Fitness: 0.1500\n",
+      "Iteration: 1371,\tBest Fitness: 0.1500\n",
+      "Iteration: 1372,\tBest Fitness: 0.1500\n",
+      "Iteration: 1373,\tBest Fitness: 0.1500\n",
+      "Iteration: 1374,\tBest Fitness: 0.1500\n",
+      "Iteration: 1375,\tBest Fitness: 0.1500\n",
+      "Iteration: 1376,\tBest Fitness: 0.1500\n",
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+      "Iteration: 1378,\tBest Fitness: 0.1500\n",
+      "Iteration: 1379,\tBest Fitness: 0.1500\n",
+      "Iteration: 1380,\tBest Fitness: 0.1500\n",
+      "Iteration: 1381,\tBest Fitness: 0.1500\n",
+      "Iteration: 1382,\tBest Fitness: 0.1500\n",
+      "Iteration: 1383,\tBest Fitness: 0.1500\n",
+      "Iteration: 1384,\tBest Fitness: 0.1500\n",
+      "Iteration: 1385,\tBest Fitness: 0.1500\n",
+      "Iteration: 1386,\tBest Fitness: 0.1500\n",
+      "Iteration: 1387,\tBest Fitness: 0.1500\n",
+      "Iteration: 1388,\tBest Fitness: 0.1500\n",
+      "Iteration: 1389,\tBest Fitness: 0.1500\n",
+      "Iteration: 1390,\tBest Fitness: 0.1500\n",
+      "Iteration: 1391,\tBest Fitness: 0.1500\n",
+      "Iteration: 1392,\tBest Fitness: 0.1500\n",
+      "Iteration: 1393,\tBest Fitness: 0.1500\n",
+      "Iteration: 1394,\tBest Fitness: 0.1500\n",
+      "Iteration: 1395,\tBest Fitness: 0.1500\n",
+      "Iteration: 1396,\tBest Fitness: 0.1500\n",
+      "Iteration: 1397,\tBest Fitness: 0.1500\n",
+      "Iteration: 1398,\tBest Fitness: 0.1500\n",
+      "Iteration: 1399,\tBest Fitness: 0.1500\n",
+      "Iteration: 1400,\tBest Fitness: 0.1500\n",
+      "Iteration: 1401,\tBest Fitness: 0.1500\n",
+      "Iteration: 1402,\tBest Fitness: 0.1500\n",
+      "Iteration: 1403,\tBest Fitness: 0.1500\n",
+      "Iteration: 1404,\tBest Fitness: 0.1500\n",
+      "Iteration: 1405,\tBest Fitness: 0.1500\n",
+      "Iteration: 1406,\tBest Fitness: 0.1500\n",
+      "Iteration: 1407,\tBest Fitness: 0.1500\n",
+      "Iteration: 1408,\tBest Fitness: 0.1500\n",
+      "Iteration: 1409,\tBest Fitness: 0.1500\n",
+      "Iteration: 1410,\tBest Fitness: 0.1500\n",
+      "Iteration: 1411,\tBest Fitness: 0.1500\n",
+      "Iteration: 1412,\tBest Fitness: 0.1500\n",
+      "Iteration: 1413,\tBest Fitness: 0.1500\n",
+      "Iteration: 1414,\tBest Fitness: 0.1500\n",
+      "Iteration: 1415,\tBest Fitness: 0.1500\n",
+      "Iteration: 1416,\tBest Fitness: 0.1500\n",
+      "Iteration: 1417,\tBest Fitness: 0.1500\n",
+      "Iteration: 1418,\tBest Fitness: 0.1500\n",
+      "Iteration: 1419,\tBest Fitness: 0.1500\n",
+      "Iteration: 1420,\tBest Fitness: 0.1500\n",
+      "Iteration: 1421,\tBest Fitness: 0.1500\n",
+      "Iteration: 1422,\tBest Fitness: 0.1500\n",
+      "Iteration: 1423,\tBest Fitness: 0.1500\n",
+      "Iteration: 1424,\tBest Fitness: 0.1500\n",
+      "Iteration: 1425,\tBest Fitness: 0.1500\n",
+      "Iteration: 1426,\tBest Fitness: 0.1500\n",
+      "Iteration: 1427,\tBest Fitness: 0.1500\n",
+      "Iteration: 1428,\tBest Fitness: 0.1500\n",
+      "Iteration: 1429,\tBest Fitness: 0.1500\n",
+      "Iteration: 1430,\tBest Fitness: 0.1500\n",
+      "Iteration: 1431,\tBest Fitness: 0.1500\n",
+      "Iteration: 1432,\tBest Fitness: 0.1500\n",
+      "Iteration: 1433,\tBest Fitness: 0.1500\n",
+      "Iteration: 1434,\tBest Fitness: 0.1500\n",
+      "Iteration: 1435,\tBest Fitness: 0.1500\n",
+      "Iteration: 1436,\tBest Fitness: 0.1500\n",
+      "Iteration: 1437,\tBest Fitness: 0.1500\n",
+      "Iteration: 1438,\tBest Fitness: 0.1500\n",
+      "Iteration: 1439,\tBest Fitness: 0.1500\n",
+      "Iteration: 1440,\tBest Fitness: 0.1500\n",
+      "Iteration: 1441,\tBest Fitness: 0.1500\n",
+      "Iteration: 1442,\tBest Fitness: 0.1500\n",
+      "Iteration: 1443,\tBest Fitness: 0.1500\n",
+      "Iteration: 1444,\tBest Fitness: 0.1500\n",
+      "Iteration: 1445,\tBest Fitness: 0.1500\n",
+      "Iteration: 1446,\tBest Fitness: 0.1500\n",
+      "Iteration: 1447,\tBest Fitness: 0.1500\n",
+      "Iteration: 1448,\tBest Fitness: 0.1500\n",
+      "Iteration: 1449,\tBest Fitness: 0.1500\n",
+      "Iteration: 1450,\tBest Fitness: 0.1500\n",
+      "Iteration: 1451,\tBest Fitness: 0.1500\n",
+      "Iteration: 1452,\tBest Fitness: 0.1500\n",
+      "Iteration: 1453,\tBest Fitness: 0.1500\n",
+      "Iteration: 1454,\tBest Fitness: 0.1500\n",
+      "Iteration: 1455,\tBest Fitness: 0.1500\n",
+      "Iteration: 1456,\tBest Fitness: 0.1500\n",
+      "Iteration: 1457,\tBest Fitness: 0.1500\n",
+      "Iteration: 1458,\tBest Fitness: 0.1500\n",
+      "Iteration: 1459,\tBest Fitness: 0.1500\n",
+      "Iteration: 1460,\tBest Fitness: 0.1500\n",
+      "Iteration: 1461,\tBest Fitness: 0.1500\n",
+      "Iteration: 1462,\tBest Fitness: 0.1500\n",
+      "Iteration: 1463,\tBest Fitness: 0.1500\n",
+      "Iteration: 1464,\tBest Fitness: 0.1500\n",
+      "Iteration: 1465,\tBest Fitness: 0.1500\n",
+      "Iteration: 1466,\tBest Fitness: 0.1500\n",
+      "Iteration: 1467,\tBest Fitness: 0.1500\n",
+      "Iteration: 1468,\tBest Fitness: 0.1500\n",
+      "Iteration: 1469,\tBest Fitness: 0.1500\n",
+      "Iteration: 1470,\tBest Fitness: 0.1500\n",
+      "Iteration: 1471,\tBest Fitness: 0.1500\n",
+      "Iteration: 1472,\tBest Fitness: 0.1500\n",
+      "Iteration: 1473,\tBest Fitness: 0.1500\n",
+      "Iteration: 1474,\tBest Fitness: 0.1500\n",
+      "Iteration: 1475,\tBest Fitness: 0.1500\n",
+      "Iteration: 1476,\tBest Fitness: 0.1500\n",
+      "Iteration: 1477,\tBest Fitness: 0.1500\n",
+      "Iteration: 1478,\tBest Fitness: 0.1500\n",
+      "Iteration: 1479,\tBest Fitness: 0.1500\n",
+      "Iteration: 1480,\tBest Fitness: 0.1500\n",
+      "Iteration: 1481,\tBest Fitness: 0.1500\n",
+      "Iteration: 1482,\tBest Fitness: 0.1500\n",
+      "Iteration: 1483,\tBest Fitness: 0.1500\n",
+      "Iteration: 1484,\tBest Fitness: 0.1500\n",
+      "Iteration: 1485,\tBest Fitness: 0.1500\n",
+      "Iteration: 1486,\tBest Fitness: 0.1500\n",
+      "Iteration: 1487,\tBest Fitness: 0.1500\n",
+      "Iteration: 1488,\tBest Fitness: 0.1500\n",
+      "Iteration: 1489,\tBest Fitness: 0.1500\n",
+      "Iteration: 1490,\tBest Fitness: 0.1500\n",
+      "Iteration: 1491,\tBest Fitness: 0.1500\n",
+      "Iteration: 1492,\tBest Fitness: 0.1500\n",
+      "Iteration: 1493,\tBest Fitness: 0.1500\n",
+      "Iteration: 1494,\tBest Fitness: 0.1500\n",
+      "Iteration: 1495,\tBest Fitness: 0.1500\n",
+      "Iteration: 1496,\tBest Fitness: 0.1500\n",
+      "Iteration: 1497,\tBest Fitness: 0.1500\n",
+      "Iteration: 1498,\tBest Fitness: 0.1500\n",
+      "Iteration: 1499,\tBest Fitness: 0.1500\n",
+      "Iteration: 1500,\tBest Fitness: 0.1500\n",
+      "\n",
+      "Best Fitness (Accuracy): 0.15\n"
+     ]
+    }
+   ],
+   "source": [
+    "aco_optimizer_xgb = AntColonyOptimization(num_ants=500, max_iter=1500, Model=xgb_model)\n",
+    "selected_features_aco = aco_optimizer_xgb.run(train_data_flatten, y_train, test_data_flatten, y_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:50:26.932529400Z",
+     "start_time": "2023-12-26T21:47:54.556761500Z"
+    }
+   },
+   "execution_count": 50
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([ 11,  89, 142, 152,  82,   3, 135,  47, 160,  24,   4,  37, 161,\n        13,  41, 134,  98,   7, 111,  22, 141, 100, 115, 157,  91,  92,\n        61,  55,  26,  50,  29,  50, 112, 126, 106,  33,  32, 144,  95,\n       126, 154, 140, 120,  57, 129,   3,  82, 151, 141, 147, 118, 172,\n       159,  71,  19, 175,  79, 143,  13,  97, 165, 100,  33,  11,  77,\n        15, 148, 174, 109, 118, 162, 147, 103, 104, 164,  92, 100, 104,\n        77,  49,  55, 178, 122,  66, 157, 127, 151, 132, 106,  71,  21,\n       144, 110, 120,  12, 170, 166,  11, 148,  87, 109, 153,  27,  91,\n         4, 114,  79, 144,  81,  31, 162, 160,  61, 101,  16,  21,  72,\n       149, 151,  45,   7, 175, 173,   7,   4,  10,   6,   7,  76,  82,\n        38, 177,  10,  79, 176,  99, 122,  21, 156,  84, 168, 150, 105,\n       130,  50,  42,  22, 166,  83,  54, 124,  38,  48,  58, 140, 160,\n        74,  17,  80, 106,  59, 145, 104, 129, 144,   1,  41,  20,  51,\n       170,  98,  81,  10,  64,  44, 111, 104, 158,  81,  33, 122,  35,\n        95,  33,  26, 148,  50, 163, 122, 110,  89,  41, 174,  36,  80,\n        71,  89, 104,  37, 116,  14,  35,  24, 136, 160, 172, 166,  77,\n        88,  28, 103, 126,  25,  43, 132,  98,  67, 116,  14, 179, 129,\n        64,  36, 176,   9,  79,  15, 132,  85,  27, 105, 168,  59, 141,\n        96, 174, 119,  27, 105, 172,  81, 102, 117, 152,  92,  98,  56,\n        56, 146, 105, 161,  30, 102,  73, 113, 146, 107, 167, 131,  82,\n         8, 134, 122,  84,  38, 137, 116, 105,  24,   1,  66,  42, 157,\n       112,  70,  51,   7,  12,  47,  79,  38, 153,  78, 104, 144,  40,\n       148,  30,  96, 134,  53,  74,  69, 101,  56,  32,   3,  99,  80,\n        22,  50,  40, 114, 165,  63, 172, 110,  17,  23, 164,  54, 159,\n       138, 127,  59, 174, 136,  44,  35,  34,  94, 111,  87,  39,  67,\n       168,  21,  87,   8,  32, 125,   9, 122, 106,  36,  24,  36, 132,\n        13,  60,  36,  30,  42, 121, 150, 133,  47, 162, 156, 155, 145,\n        16, 154,  10, 103,  23, 118,  31, 145,  34, 103,  60, 125,  71,\n       143, 112,  93, 105, 125, 107, 122, 121,  67,  54,  19,  44,  23,\n       149, 103, 158,  63,  12,   1,  28,  80, 151,  90, 141, 173, 167,\n       133,   4, 110,  49,  57,  71,  42, 113,  22, 128, 178,  26,  10,\n       150,  66,   5, 156, 138,  42,  92, 137, 153,  81,  69,  57, 143,\n       134,  65,  37,  61, 150, 130,  15,  97, 176,  87,  62,  29, 138,\n        80, 126, 157,  14, 152, 165, 173, 143,   9,  79,  32,  16, 104,\n        79, 179, 124,  96,  57, 112, 175, 130,  88,  14, 114,  34,  77,\n       126, 128, 173,  57, 145,  13,  25, 165,  21, 122,  52,   7, 159,\n        63, 157, 149, 110,  90,  92,  14, 121,  11,  25, 158,  90,   6,\n        72, 164,   5,  32,  42, 152,  23, 125,   9, 152,  19, 163,  79,\n        96, 112, 100, 102,  72,  82])"
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_features_aco"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:50:26.956217900Z",
+     "start_time": "2023-12-26T21:50:26.932529400Z"
+    }
+   },
+   "execution_count": 51
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Confusion Matrix is: \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 800x600 with 2 Axes>",
+      "image/png": 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UEueOnzfskRQWk+XvLlHXbt0U85dYBQUHa9SYBAUEBmjN6iSjQ3MqnpvndofnlqRKFcxaMqWPhkxK0tlfLhkdTolwx8/7oSYt1a33IDVp9bDRoZQ4d/y87VApJCksDnlWqzIOHlBUy9Z27VEtW+m/6XsMisr5eG6eWyr9z33d/43pri2f79enuw4ZHUqJcPfP293weUMyePXxiRMnlJSUpP/85z/KycmRyWSSj4+PGjdurLi4OAUGBhoZXpHlns1Vfn6+fHx87Np9fHyVk5NtUFTOx3Pz3FLpf25Jiu0YqfAHa6p1z2lGh1Ji3Pnzdkd83pLKsPrYsKRw9+7d6t+/vwIDA9WqVSv5+vrKZrPp9OnT+vjjj7V8+XItWLBAkZGRRoXoMNPvlrPbbLYb2kojnvtXPHfpVONeL735Ujd1HjJXV6xXjQ6nxLnb5+3u+Lzdm2FJocViUWxsrF555ZVC358yZYqmTJmilJSUEo7McdW8qsnDw0M5OTl27WfOnJaPj69BUTkfz81zS6X/uSPq1dK9Pvdo54pRBW1ly3qodeNgDereVlWb/13XrtkMjNA53PXzdld83rqr5v45i2E/gcOHDysuLu6m73fv3l2HDx8uwYjuXDmzWfXqN9CXO3fYtX+5c6cahUcYFJXz8dw8t1T6n/vTtEOK/MtkNY+bWvD694EjWrV5t5rHTS2VCaHkvp+3u+Lz1q+bVzvrdZcwrFLo5+enPXv2KCgoqND309PT5efnV8JR3blez8Yr4eVRqt+woRo1ilDK2tXKyspSbPebJ76lAc/Nc5f25z5/8YoOZmbZtV24ZNWZny/c0F7auOPnffnSRZ08/r8th3JOHNeRzG9Uuco98vEPMDAy53PHzxv2DEsK+/btq3HjxunAgQNq2bKlfH19ZTKZlJ2drZ07d2rt2rU3HVp2RY8/0Uk/n81V4tvzlJ19SiF16mruO4mqXv0+o0NzKp6b53aH53ZX7vh5f384Q2+MGVJwnLTw/yRJrTo8qf4vvGZQVCXDHT9vOwwfy2Sz2Qwb+9i8ebOWLl2qAwcOKD8/X5Lk4eGhBg0aqE+fPurUqdMd3fey+80FB9xGtabDjA7BELlfzTE6BEPs+eGs0SEYIqK2l9EhGMLTwD1RKjwy1Wn3vvTxy067d3EydEuaTp06qVOnTsrLy1Nubq4kqVq1aipXrpyRYQEAAHdzF839cxZDk8LrypUrJ39/f6PDAAAAcFsukRQCAAAYijmFfM0dAAAAqBQCAAAwp1AkhQAAAAwfi+FjAAAAiEohAAAAw8eiUggAAABRKQQAAGBOoagUAgAAQFQKAQAAmFMoKoUAAAAQlUIAAADmFIqkEAAAgKRQDB8DAABAVAoBAABYaCIqhQAAABCVQgAAAOYUikohAAAARKUQAADAZeYUrly5UklJSfrpp58kSXXq1NGQIUMUHR1d6Pm7du1S7969b2jfvHmzgoODHeqbpBAAAMBFBAQEaOTIkapVq5YkKTU1VUOHDtW6detUp06dm163ZcsWVa5cueDY29vb4b5JCgEAAJw4p9Bqtcpqtdq1mc1mmc3mG85t37693fGIESOUlJSk9PT0WyaFPj4+uueee/5QnKUyKXxxY4bRIRiiZ1ig0SEYIqK2l9EhoARtW/u60SEYYs8PZ40OwRD8fqPEOHH4eP78+ZozZ45d27BhwzR8+PBbXpefn68tW7bo4sWLioiIuOW5Xbp0kdVqVXBwsAYPHqwWLVo4HGepTAoBAABcxcCBAxUfH2/XVliV8LpDhw4pLi5OV65cUcWKFTV37lyFhIQUeq6fn58mTZqkBg0ayGq1av369erTp4+WL1+upk2bOhQnSSEAAHB7JidWCm82VHwzDzzwgFJTU/XLL7/oww8/1OjRo/Xee+8VmhgGBQUpKCio4DgiIkInTpzQokWLHE4K2ZIGAADAhZjNZt1///0KCwvTiy++qAcffFDLli0r8vWNGjXSkSNHHO6XSiEAAHB7zqwU/lE2m+2GhSq3kpGRIT8/P4f7ISkEAABwETNmzFDbtm0VEBCgCxcuaPPmzUpLS9PChQslSdOnT9fJkyc1bdo0SdLSpUtVo0YNhYSEKC8vTxs2bNDWrVs1e/Zsh/smKQQAAHCRQmFOTo5GjRqlU6dOqUqVKgoNDdXChQvVqlUrSVJ2draysrIKzs/Ly9Mbb7yhkydPytPTUyEhIUpMTLzpZte3YrLZbLZiexIXMXQdW9K4E7ascC/uujWLu+L32714GliqqhS7xGn3vrA2/vYnuQAqhQAAwO258pzCkkJSCAAA3B5JIVvSAAAAQFQKAQAAqBSKSiEAAABEpRAAAIBKoagUAgAAQFQKAQAAXGbzaiNRKQQAAACVQgAAAOYUUikEAACAqBQCAABQKRRJIQAAAEmhGD4GAACAqBQCAABQKRSVQgAAAIhKIQAAAJtXi0ohAAAARKUQAACAOYWiUggAAAC5eFKYlZWlMWPGGB0GAAAo5Uwmk9NedwuXTgp//vlnpaamGh0GAAAo5UgKDZ5T+Mknn9zy/aNHj5ZQJAAAAO7N0KRw6NChMplMstlsNz3nbsqwAQDAXYp0w9ik0M/PT+PGjdMjjzxS6PsZGRmKiYkp4agAAADcj6FzChs0aKADBw7c9P3bVREBAACKA3MKDa4UPvfcc7p48eJN369Vq5aWLVtWghEBAAC4J0OTwiZNmtzy/YoVK6pZs2YlFA0AAHBXd1NFz1lceksaAAAAlAy+5g4AALg9KoUkhQAAACSFYvgYAAAAolIIAADA5tWiUggAAABRKQQAAGBOoagUAgAAQFQKAQAAqBSKSiEAAABEpRAAAIBKoUgKAQAA2JJGDB8DAABAVAoBAAAYPhaVQgAAAIhKIQAAAJVCUSkEAACAqBQCAABQKRSVQgAAAJexcuVKde7cWY0bN1bjxo3VvXt3ffbZZ7e8Ji0tTTExMQoLC1OHDh2UlJR0R31TKQQAAG7PVSqFAQEBGjlypGrVqiVJSk1N1dChQ7Vu3TrVqVPnhvOPHj2qAQMGKDY2Vm+++ab+85//aMKECfL29lbHjh0d6pukEAAAwDVyQrVv397ueMSIEUpKSlJ6enqhSeGqVasUGBiohIQESVJwcLD27dunxYsXO5wUMnwMAADgRFarVefPn7d7Wa3W216Xn5+vTZs26eLFi4qIiCj0nPT0dLVq1cqurU2bNtq/f7/y8vIcirNUVgp7hgUaHYIhImp7GR0CACdx19/vPT+cNToEQ7y3L8voEAwxt2s9w/p25vDx/PnzNWfOHLu2YcOGafjw4YWef+jQIcXFxenKlSuqWLGi5s6dq5CQkELPzcnJka+vr12bj4+Prl69qtzcXPn7+xc5zlKZFAIAALiKgQMHKj4+3q7NbDbf9PwHHnhAqamp+uWXX/Thhx9q9OjReu+9926aGP4+obXZbIW23w5JIQAAcHvOrBSazeZbJoGFnX///fdLksLCwrRv3z4tW7ZMEydOvOFcX19fZWdn27WdOXNGZcuWlZeXl0NxMqcQAADAhdlstpvOQQwPD9fOnTvt2rZv366GDRuqXLlyDvVDUggAANyeyeS8lyNmzJih3bt369ixYzp06JBmzpyptLQ0de7cWZI0ffp0jRo1quD8uLg4HT9+XBaLRZmZmUpOTlZKSor69u3r8M+A4WMAAAAXkZOTo1GjRunUqVOqUqWKQkNDtXDhwoIVxtnZ2crK+t9CpJo1ayoxMVEWi0UrVqyQv7+/EhISHN6ORiIpBAAAcJnNq6dMmXLL96dOnXpDW7NmzbRu3bo/3DdJIQAAcHsukhMaijmFAAAAoFIIAADgKsPHRqJSCAAAACqFAAAAFAqpFAIAAEBUCgEAAFSmDKVCKoUAAACgUggAAMCcQpJCAAAAtqQRw8cAAAAQlUIAAACGj0WlEAAAAKJSCAAAwJxCUSkEAACAqBQCAABQKRSVQgAAAIhKIQAAAKuPRVIIAADA8LEYPgYAAICoFAIAADB8LBeoFF6+fFm7d+/Wt99+e8N7V65cUWpqaskHBQAA4GYMTQq///57derUST179lTnzp3Vq1cvnTp1quD9c+fOacyYMQZGCAAA3IHJZHLa625haFL41ltvqW7dutq5c6e2bNmiSpUqqUePHjp+/LiRYQEAALgdQ5PCPXv2aMSIEfL29tb999+vd955R23bttUzzzyjo0ePGhkaAABwIyaT8153C0OTwsuXL6tsWfu1LuPGjVP79u3Vs2dP/fDDD8YEBgAA4GYMXX0cFBSkffv2KTg42K597NixstlsGjx4sEGRAQAAd3I3zf1zFkMrhY8++qg2bdpU6HuvvfaannzySdlsthKOCgAAwP0YmhQOHDhQCxYsuOn748eP19dff12CEQEAAHfEnEI2rwYAAGD4WC6weTUAAACMR6UQAAC4PQqFVAoBAAAgKoUAAADMKRSVQgAAAIhKIQAAAHMKRaUQAAAAolIIAADAnEKRFAIAADB8LIaPAQAAICqFAAAADB+LSiEAAABEpRAAAIBKoagUAgAAQFQKAQAAWH0sKoUAAAAQlUIAAADmFIqkEAAAwGWGj+fPn68PP/xQ3333nTw9PRUREaGRI0cqKCjoptfs2rVLvXv3vqF98+bNCg4OLnLfJIUAAAAuIi0tTc8884zCwsKUn5+vmTNnql+/ftq0aZMqVqx4y2u3bNmiypUrFxx7e3s71DdJIQAAcHuuMny8aNEiu2OLxaKoqCgdOHBATZs2veW1Pj4+uueee+64b5JCAAAAJ7JarbJarXZtZrNZZrP5tteeO3dOklS1atXbntulSxdZrVYFBwdr8ODBatGihUNxkhTirrfnh7NGh4ASFFHby+gQUILc9fN+b1+W0SG4HWcWCufPn685c+bYtQ0bNkzDhw+/5XU2m00Wi0WRkZGqW7fuTc/z8/PTpEmT1KBBA1mtVq1fv159+vTR8uXLb1td/C2SQgAAACcaOHCg4uPj7dqKUiWcOHGivvnmG61cufKW5wUFBdktRImIiNCJEye0aNEikkIAAABHlHFiqbCoQ8W/NWnSJG3btk3vvfeeAgICHO6zUaNG2rBhg0PXkBQCAAC4CJvNpkmTJumjjz7S8uXLVbNmzTu6T0ZGhvz8/By6hqQQAAC4PRdZfKwJEybogw8+0Lx581SpUiVlZ2dLkqpUqSJPT09J0vTp03Xy5ElNmzZNkrR06VLVqFFDISEhysvL04YNG7R161bNnj3bob5JCgEAgNtzlS1pkpKSJEm9evWya7dYLIqJiZEkZWdnKyvrf4uR8vLy9MYbb+jkyZPy9PRUSEiIEhMTFR0d7VDfJIUAAAAu4tChQ7c9Z+rUqXbH/fv3V//+/f9w3ySFAADA7ZVxjUKhocoYHQAAAACMR6UQAAC4PVeZU2gkKoUAAACgUggAAEChkEohAAAARKUQAABAJlEqJCkEAABujy1pGD4GAACAqBQCAACwJY2oFAIAAEBUCgEAANiSRlQKAQAAICqFAAAAKkOpkEohAAAAqBQCAAAwp1AkhQAAAGxJI4aPAQAAICqFAAAADB+LSiEAAABEpRAAAIAtaeQCSWFmZqbS09MVHh6u4OBgZWZmatmyZbJarfrzn/+sqKgoo0MEAAAo9QxNCv/f//t/GjJkiCpVqqRLly5pzpw5Gj16tB588EHZbDY999xzWrhwIYkhAABwKuqEBs8pnDdvnvr166ddu3bJYrFo5MiRevrpp7VkyRItXbpUzz33nBYsWGBkiAAAAG7B0KTw8OHDiomJkSQ98cQTunDhgh577LGC95988kkdOnTIqPAAAICbMJlMTnvdLQyfU3hdmTJlZDabdc899xS0VapUSefOnTMwKgAA4A7K3D25m9MYWim87777dOTIkYLj1atXKzAwsOA4KytLfn5+RoQGAADgVgytFPbo0UPXrl0rOK5bt67d+59//rlatGhR0mEBAAA3czcN8zqL4UnhrYwYMaKEIgEAAHBvLjOnEAAAwCgUCvmaOwAAAIhKIQAAAHMKVcSk8JNPPinyDTt06HDHwQAAAMAYRUoKhw4dWqSbmUwmZWRk/KGAAAAAShr7FBYxKfz666+dHQcAAIBhGD5moQkAAAB0hwtNLl68qK+++krHjx9XXl6e3Xu9e/culsAAAABKCnXCO0gKDx48qAEDBujSpUu6dOmSqlatqtzcXFWoUEHe3t4khQAAAHchh4ePLRaLHn74YaWlpal8+fJas2aNPv30UzVo0ECjR492RowAAABOVcZkctrrbuFwUpiRkaH4+Hh5eHjIw8NDVqtVgYGBeumllzRjxgxnxAgAAAAnczgpLFu2bMEKHR8fHx0/flySVLlyZWVlZRVvdAAAACXAZHLe627h8JzC+vXra//+/XrggQfUvHlzzZo1S7m5uVq/fr3q1q3rjBgBAADgZA5XCkeMGCE/Pz9J0t///nd5eXlp/PjxOnPmjCZNmlTsAQIAADibyWRy2utu4XClMCwsrODv3t7eWrBgQbEGBAAAgJJ3R/sUAgAAlCZ3UUHPaRxOCtu3b3/LUugnn3zyhwICAAAoaXfT1jHO4nBS+Oyzz9odX716VQcPHtT27dvVr1+/YgsMAAAAJecPJ4XXrVixQvv37//DAQEAAJQ0VykUzp8/Xx9++KG+++47eXp6KiIiQiNHjlRQUNAtr0tLS9PUqVN1+PBh+fv767nnnlOPHj0c6tvh1cc307ZtW23durW4bgcAAOB20tLS9Mwzz2jNmjVasmSJ8vPz1a9fP128ePGm1xw9elQDBgxQZGSkUlNTNWjQIE2ePNnhvKzYFpps2bJFXl5exXU7AACAEuMqW8csWrTI7thisSgqKkoHDhxQ06ZNC71m1apVCgwMVEJCgiQpODhY+/bt0+LFi9WxY8ci9+1wUtilSxe7H5zNZlNOTo7OnDmjcePGOXo7AACAUs1qtcpqtdq1mc1mmc3m21577tw5SVLVqlVvek56erpatWpl19amTRulpKQoLy9P5cqVK1KcDieFHTp0sEsKTSaTvL291axZMwUHBzt6O6eIqO1ldAgoQe76eb+4McPoEAzhrp/3nh/OGh2CIdz180bJK7b5dIWYP3++5syZY9c2bNgwDR8+/JbX2Ww2WSwWRUZG3vJb43JycuTr62vX5uPjo6tXryo3N1f+/v5FitPhpPB2DwAAAID/GThwoOLj4+3ailIlnDhxor755hutXLnytuf+fvjbZrMV2n4rDifG9erV0+nTp29oz83NVb169Ry9HQAAgOGc+TV3ZrNZlStXtnvdLimcNGmStm3bpnfffVcBAQG3PNfX11fZ2dl2bWfOnFHZsmUdWu/hcKXweub5e1artchj1gAAAK6kjGusM5HNZtOkSZP00Ucfafny5apZs+ZtrwkPD9enn35q17Z9+3Y1bNjQodysyEnhsmXLJP2aSa9du1YVK1YseO/atWv66quvbruHDgAAAG5uwoQJ+uCDDzRv3jxVqlSpoAJYpUoVeXp6SpKmT5+ukydPatq0aZKkuLg4rVixQhaLRU8//bT27NmjlJQUTZ8+3aG+i5wULl26VNKvGeyqVatUpsz/Rp7LlSunGjVqaMKECQ51DgAA4ApcpVKYlJQkSerVq5ddu8ViUUxMjCQpOztbWVlZBe/VrFlTiYmJslgsWrFihfz9/ZWQkODQdjSSA0nhtm3bCoKcM2fOLZdGAwAAwHGHDh267TlTp069oa1Zs2Zat27dH+rb4TmFy5cv/0MdAgAAuBpX2bzaSA6vPn7++eeVmJh4Q/vChQv1/PPPF0tQAAAAKFkOJ4VpaWmKjo6+ob1NmzbavXt3sQQFAABQksqYnPe6WzicFF68eLHQ5c1ly5bV+fPniyUoAAAAlCyHk8I6depo8+bNN7Rv3rxZISEhxRIUAABASTKZnPe6Wzi80GTIkCF6/vnndfToUbVo0UKS9MUXX+iDDz7QrFmzij1AAAAAZytzN2VvTuJwUtihQwfNnTtX77zzjrZu3ary5cvrwQcf1LvvvqvKlSs7I0YAAAA4mcNJoSS1a9dO7dq1kyT98ssv2rhxo6ZMmaKvv/5aGRkZxRkfAACA0zk8n64UuqOkUPp1yDglJUUfffSRqlevrscee0yTJ08uztgAAABQQhxKCk+cOKH3339fKSkpunTpkp544gldvXpVs2fPZpEJAAC4azGl0IGksH///vr3v/+thx9+WGPHjlWbNm3k4eGhVatWOTM+AAAAlIAiJ4U7duxQr1691KNHD9WuXduJIQEAAJQsVh87MK9yxYoVunDhgrp166bY2Fi99957OnPmjDNjAwAAQAkpclIYERGh119/Xdu3b1f37t21adMmtW3bVteuXdOOHTv4NhMAAHDXYvPqO1iBXaFCBf3lL39RUlKSNmzYoPj4eC1YsEAtW7bUoEGDnBEjAACAU/Hdx39wW56goCCNGjVKn332mWbMmFFcMQEAAKCE3fE+hb/l4eGhRx55RI888khx3A4AAKBEsdCEDbwBAACgYqoUAgAA3M0oFLpgpdBmsxkdAgAAgNtxuaQwLCxMmZmZRocBAADcCKuPDRw+tlgshbbn5+crMTFRXl5ekqQxY8aUYFQAAADuybCk8N1339WDDz6oKlWq2LXbbDZlZmaqQoUKMjHADwAASoBJ5ByGJYUjRozQmjVrNHr0aEVFRRW0N2jQQFOnTlVISIhRoQEAADdzNw3zOothSeHAgQMVFRWll156Se3bt9cLL7ygcuXKGRUOAACAWzN0oclDDz2k999/X2fOnFG3bt106NAhhowBAECJY6GJC+xTWKlSJb3xxhvatGmT+vbtq/z8fKNDAgAAcDuGJ4XXPfnkk4qMjNT+/ftVvXp1o8MBAABuhJFKF0oKJSkgIEABAQFGhwEAAOB2XCopBAAAMMLdNPfPWVzuG00AAABQ8qgUAgAAt8eUQpJCAAAAlSErZPgYAAAAVAoBAABYaCIqhQAAABCVQgAAABaaiEohAAAARKUQAABAZUSpkEohAAAAqBQCAAAwp5CkEAAAgC1pxPAxAAAARKUQAACAr7kTlUIAAACISiEAAAALTUSlEAAAACIpBAAAUBmTyWkvR3311VcaNGiQWrdurdDQUH388ce3PH/Xrl0KDQ294ZWZmelQvwwfAwAAuJCLFy8qNDRUMTExGj58eJGv27JliypXrlxw7O3t7VC/JIUAAMDtOXNOodVqldVqtWszm80ym82Fnh8dHa3o6GiH+/Hx8dE999xzRzFKpTQp3PPDWaNDMEREbS+jQ0AJmt65ntEhGMJdf7/f25dldAgoQe76+20kZ86nmz9/vubMmWPXNmzYMIeqgEXRpUsXWa1WBQcHa/DgwWrRooVD15fKpBAAAMBVDBw4UPHx8XZtN6sS3gk/Pz9NmjRJDRo0kNVq1fr169WnTx8tX75cTZs2LfJ9SAoBAIDbMzlx/PhWQ8XFISgoSEFBQQXHEREROnHihBYtWuRQUsjqYwAAgFKmUaNGOnLkiEPXUCkEAABur7TtXZ2RkSE/Pz+HriEpBAAAcCEXLlzQjz/+WHB87NgxZWRkqGrVqqpevbqmT5+ukydPatq0aZKkpUuXqkaNGgoJCVFeXp42bNigrVu3avbs2Q71S1IIAADc3p1sMu0s+/fvV+/evQuOLRaLJKlr166aOnWqsrOzlZX1vx0J8vLy9MYbb+jkyZPy9PRUSEiIEhMTHd7WxmSz2WzF8wiu44tvzxodgiHYkgbugC1p3EvPsECjQzCEu/577mlgqeq9fx9z2r17RtZw2r2LE5VCAADg9lynTmgckkIAAOD2XGj02DBsSQMAAAAqhQAAAM7cvPpuQaUQAAAAVAoBAACokvEzAAAAgKgUAgAAMKdQVAoBAAAgKoUAAABsXi0qhQAAABCVQgAAAOYUiqQQAACAoVPxMwAAAICoFAIAADB8LCqFAAAAEJVCAAAAtqQRlUIAAACISiEAAICYUkilEAAAAHKxSuHPP/+sdevW6ciRI/Lz81PXrl0VGBhodFgAAKCUK8OsQmMrha1bt1Zubq4k6ejRo3ryySe1cOFCHTlyRKtXr1bnzp2VmZlpZIgAAMANmEzOe90tDE0Kc3JydO3aNUnSzJkz9cADD+ijjz7S4sWL9dFHH6lJkyb6xz/+YWSIAAAAbsFl5hT+97//1ZAhQ1ShQgVJktls1uDBg/Xf//7X4MgAAEBpZ3Lin7uF4Unh9R3ErVarfH197d7z8fHRmTNnjAgLAADArRi+0OTZZ59V2bJldf78ef3www+qU6dOwXtZWVmqVq2agdEBAAB3cDfN/XMWQ5PCYcOG2R17enraHW/btk1NmjQpyZAAAADckkslhb83evToEooEAAC4M7akcYE5hQAAADCe4XMKAQAAjMacQpJCAAAAkkIxfAwAAABRKQQAALirNpl2FiqFAAAAoFIIAABQhkIhlUIAAABQKQQAAGBOoagUAgAAQFQKAQAA2KdQJIUAAAAMH4vhYwAAAIhKIQAAAFvSiEohAAAARKUQAACAOYWiUggAAABRKQQAAGBLGlEpBAAAcClfffWVBg0apNatWys0NFQff/zxba9JS0tTTEyMwsLC1KFDByUlJTncL0khAABweyYnvhx18eJFhYaG6rXXXivS+UePHtWAAQMUGRmp1NRUDRo0SJMnT9bWrVsd6pfhYwAA4PbKuND4cXR0tKKjo4t8/qpVqxQYGKiEhARJUnBwsPbt26fFixerY8eORb4PlUIAAAAnslqtOn/+vN3LarUW2/3T09PVqlUru7Y2bdpo//79ysvLK/J9qBQCwF2gZ1ig0SGgBO354azRIRgiKsTLsL6dWSecP3++5syZY9c2bNgwDR8+vFjun5OTI19fX7s2Hx8fXb16Vbm5ufL39y/SfUgKAQAAnGjgwIGKj4+3azObzcXah+l3w982m63Q9lshKQQAAHBiqdBsNhd7Evhbvr6+ys7Otms7c+aMypYtKy8vryLfhzmFAAAAd7Hw8HDt3LnTrm379u1q2LChypUrV+T7kBQCAAC3Z3LiH0dduHBBGRkZysjIkCQdO3ZMGRkZOn78uCRp+vTpGjVqVMH5cXFxOn78uCwWizIzM5WcnKyUlBT17dvXoX4ZPgYAAHAh+/fvV+/evQuOLRaLJKlr166aOnWqsrOzlZWVVfB+zZo1lZiYKIvFohUrVsjf318JCQkObUcjSSbb9ZmIpcgX3541OgRDRNT2MjoEwOncdVUm4A6MXH2c9t3PTrt3s6CqTrt3caJSCAAA3J7rbF1tHOYUAgAAgEohAAAApUIqhQAAABCVQgAAgDvaOqa0oVIIAAAAKoUAAAAOfEVwqUWlEAAAAFQKAQAAKBSSFAIAAJAViuFjAAAAiEohAAAAW9KISiEAAABEpRAAAIAtaUSlEAAAAKJSCAAAwIxCUSkEAACAqBQCAABQKhRJIQAAAFvSiOFjAAAAiEohAAAAW9LI4ErhwYMHdfTo0YLj9evXKy4uTtHR0erRo4c2bdpkYHQAAADuw9CkMCEhQT/99JMkae3atXrttdfUsGFDDRo0SGFhYXr11VeVnJxsZIgAAMANmJz4ulsYOnz8/fffq1atWpKklStX6pVXXlH37t0L3g8LC9M777yjv/zlL0aFCAAA4BYMrRSWL19eZ86ckSSdPHlSjRo1snu/UaNGOnbsmBGhAQAAd0Kp0NiksG3btkpKSpIkNW3aVFu2bLF7/5///GdBJREAAADOY+jw8ciRI9WjRw/17NlTDRs21JIlS5SWlqbg4GB9//33Sk9P19y5c40MEQAAuAH2KTS4UnjvvfcqNTVV4eHh+vzzz2Wz2bR3717t2LFD9957r5KSkhQdHW1kiAAAAG7BZLPZbEYHUdy++Pas0SEYIqK2l9EhAE6354ezRocAwEmiQrwM6/vg8QtOu3f96pWcdu/ixObVAADA7TF4zNfcAQAAQFQKAQAAKBWKSiEAAABEpRAAAIAtaUSlEAAAAKJSCAAAIBOFQiqFAAAAoFIIAADAjEKRFAIAAJAViuFjAAAAiEohAAAAW9KISiEAAABEpRAAAIAtaUSlEAAAAKJSCAAAwIxCUSkEAACAqBQCAABQKhSVQgAAAJmc+OdOrFixQu3bt1dYWJhiYmK0e/fum567a9cuhYaG3vDKzMx0qE8qhQAAAC5k8+bNslgsGjdunBo3bqxVq1apf//+2rRpk6pXr37T67Zs2aLKlSsXHHt7ezvUL5VCAADg9kwm570ctWTJEnXr1k2xsbEKDg5WQkKCAgIClJSUdMvrfHx85OfnV/Dy8PBwqF8qhQAAAE5ktVpltVrt2sxms8xmc6HnHjhwQAMGDLBrb9Wqlfbs2XPLfrp06SKr1arg4GANHjxYLVq0cCjOUpkUvrcvy+gQDBFR28voEACnc9ff7+md6xkdAkrQnh/OGh2C23HmOpP58+drzpw5dm3Dhg3T8OHDbzg3NzdX+fn58vHxsWv39fVVdnZ2off38/PTpEmT1KBBA1mtVq1fv159+vTR8uXL1bRp0yLHWSqTQgAAAFcxcOBAxcfH27UVViX8LdPvxp1tNtsNbdcFBQUpKCio4DgiIkInTpzQokWLSAoBAAAc4sRS4c2GigtTrVo1eXh4KCcnx6799OnT8vX1LXKfjRo10oYNGxyKk4UmAAAALsJsNqtBgwbasWOHXfvOnTsVERFR5PtkZGTIz8/Pob6pFAIAALd3p/sJOkN8fLxGjRqlhg0bKiIiQqtXr1ZWVpbi4uIkSdOnT9fJkyc1bdo0SdLSpUtVo0YNhYSEKC8vTxs2bNDWrVs1e/Zsh/olKQQAAG7vTraOcZZOnTopNzdX8+bN06lTp1S3bl0lJibqvvvukyRlZ2crK+t/i+7y8vL0xhtv6OTJk/L09FRISIgSExMVHR3tUL8mm81mK9YncQFD12UYHYIhWJ0Id/DiRn6/Ufq56+rjqBAvw/r+8cwVp927lnd5p927OFEpBAAAbs+FCoWGYaEJAAAAqBQCAAC40pxCo1ApBAAAAJVCAAAAZhVSKQQAAICoFAIAADCnUCSFAAAADB6L4WMAAACISiEAAADDx6JSCAAAAFEpBAAAkIlZhVQKAQAAQKUQAACA5ceiUggAAABRKQQAAKBQKJJCAAAAtqQRw8cAAAAQlUIAAAC2pBGVQgAAAIhKIQAAACtNZHClcNKkSdq9e7eRIQAAAEAGJ4UrVqxQr1691LFjRyUmJio7O9vIcAAAgJsyOfF1tzB8TuHixYvVtm1bLV68WA8//LAGDx6sTz/9VNeuXTM6NAAAALdheFJYt25dJSQk6PPPP9e0adNktVo1dOhQRUdHa+bMmTpy5IjRIQIAgFLOZHLe627hMgtNypUrp06dOqlTp046fvy4UlJStG7dOiUmJiojI8Po8AAAQCnGljQulBT+VvXq1TV8+HANGzZMO3fuNDocAACAUs/QpLB69eoqU+bmI9gmk0mtWrUqwYgAAIA7upuGeZ3F0KRw27ZtRnYPAACA/5/hC00AAABgPJJCAAAAuOZCEwAAgJLEnEIqhQAAABCVQgAAAPYpFEkhAAAAw8di+BgAAACiUggAAMDgsagUAgAAQFQKAQAAKBWKSiEAAABEpRAAAIAtaUSlEAAAAKJSCAAAwD6FolIIAAAAUSkEAABgRqFICgEAAMgKxfAxAAAARFIIAAAgkxP/3IkVK1aoffv2CgsLU0xMjHbv3n3L89PS0hQTE6OwsDB16NBBSUlJDvdJUggAAOBCNm/eLIvFosGDBys1NVWRkZHq37+/jh8/Xuj5R48e1YABAxQZGanU1FQNGjRIkydP1tatWx3ql6QQAAC4PZPJeS9HLVmyRN26dVNsbKyCg4OVkJCggICAm1b/Vq1apcDAQCUkJCg4OFixsbGKiYnR4sWLHeqXpBAAAMCJrFarzp8/b/eyWq03PffAgQNq3bq1XXurVq20Z8+eQq9JT09Xq1at7NratGmj/fv3Ky8vr8hxlsrVx3O71jM6BABOwu833EFUiJfRIbgdTydmRLNnz9ecOXPs2oYNG6bhw4ffcG5ubq7y8/Pl4+Nj1+7r66vs7OxC75+TkyNfX1+7Nh8fH129elW5ubny9/cvUpylMikEAABwFQMHDlR8fLxdm9lsvuU1pt+NO9tsthvabnd+Ye23QlIIAADgRGaz+bZJ4HXVqlWTh4eHcnJy7NpPnz59QzXwusKqiGfOnFHZsmXl5eVV5DiZUwgAAOAizGazGjRooB07dti179y5UxEREYVeEx4erp07d9q1bd++XQ0bNlS5cuWK3DdJIQAAgAuJj49XcnKykpOTlZmZqSlTpigrK0txcXGSpOnTp2vUqFEF58fFxen48eOyWCzKzMxUcnKyUlJS1LdvX4f6ZfgYAADAhXTq1Em5ubmaN2+eTp06pbp16yoxMVH33XefJCk7O1tZWVkF59esWVOJiYmyWCxasWKF/P39lZCQoI4dOzrUr8l2fSYiAAAA3BbDxwAAACApBAAAAEkhAAAARFIIAAAAkRQWqxUrVqh9+/YKCwtTTEyMdu/ebXRITvXVV19p0KBBat26tUJDQ/Xxxx8bHVKJmD9/vrp166aIiAhFRUVpyJAh+u6774wOy+lWrlypzp07q3HjxmrcuLG6d++uzz77zOiwStz8+fMVGhqqyZMnGx2KU82ePVuhoaF2r99/t2ppdfLkSY0cOVLNmzdXo0aN9NRTT2n//v1Gh+VU7du3v+HzDg0N1YQJE4wODSWILWmKyebNm2WxWDRu3Dg1btxYq1atUv/+/bVp0yZVr17d6PCc4uLFiwoNDVVMTEyh399YWqWlpemZZ55RWFiY8vPzNXPmTPXr10+bNm1SxYoVjQ7PaQICAjRy5EjVqlVLkpSamqqhQ4dq3bp1qlOnjsHRlYy9e/dq9erVCg0NNTqUElGnTh0tWbKk4NjDw8PAaErGzz//rB49eqh58+ZasGCBvL29dfToUd1zzz1Gh+ZUycnJys/PLzg+fPiw4uPj9fjjjxsYFUoaSWExWbJkibp166bY2FhJUkJCgrZv366kpCS9+OKLBkfnHNHR0YqOjjY6jBK3aNEiu2OLxaKoqCgdOHBATZs2NSgq52vfvr3d8YgRI5SUlKT09HS3SAovXLigl156Sa+//rrefvtto8MpER4eHvLz8zM6jBK1YMECBQQEyGKxFLTVqFHDwIhKhre3t91xYmKiatWqpWbNmhkUEYzA8HExsFqtOnDggFq3bm3X3qpVK+3Zs8egqFBSzp07J0mqWrWqwZGUnPz8fG3atEkXL1686dculTYTJ05UdHS0WrZsaXQoJebIkSNq3bq12rdvrxEjRujo0aNGh+R027ZtU8OGDfX8888rKipKXbp00Zo1a4wOq0RZrVZt2LBB3bp1k8lkMjoclCAqhcUgNzdX+fn58vHxsWsv7AuqUbrYbDZZLBZFRkaqbt26RofjdIcOHVJcXJyuXLmiihUrau7cuQoJCTE6LKfbtGmTDh48qOTkZKNDKTEPPfSQ3njjDdWuXVunT5/W22+/rbi4OH3wwQeqVq2a0eE5zdGjR5WUlKT4+HgNGjRIe/fu1euvvy6z2awuXboYHV6J+Pjjj3Xu3Dl17drV6FBQwkgKi9Hv/0dls9n4X1YpN3HiRH3zzTdauXKl0aGUiAceeECpqan65Zdf9OGHH2r06NF67733SnVimJWVpcmTJ2vx4sUqX7680eGUmN9PDQkPD9ejjz6q1NRUxcfHGxSV89lsNjVs2FAvvPCCJKl+/fr69ttvlZSU5DZJYUpKitq2bat7773X6FBQwhg+LgbVqlWTh4eHcnJy7NpPnz4tX19fg6KCs02aNEnbtm3Tu+++q4CAAKPDKRFms1n333+/wsLC9OKLL+rBBx/UsmXLjA7LqQ4cOKDTp08rJiZG9evXV/369ZWWlqbly5erfv36dpPzS7OKFSuqbt26+uGHH4wOxan8/PwUHBxs1xYUFKTjx48bFFHJ+umnn7Rz50795S9/MToUGIBKYTEwm81q0KCBduzYoUcffbSgfefOnerQoYOBkcEZbDabJk2apI8++kjLly9XzZo1jQ7JMDabTVar1egwnKpFixbauHGjXduYMWMUFBSk/v37u8WKXOnXeWaZmZmKjIw0OhSnaty4sb7//nu7th9++EH33XefQRGVrPfff18+Pj5q166d0aHAACSFxSQ+Pl6jRo1Sw4YNFRERodWrVysrK0txcXFGh+Y0Fy5c0I8//lhwfOzYMWVkZKhq1aqldhseSZowYYI++OADzZs3T5UqVSqYN1qlShV5enoaHJ3zzJgxQ23btlVAQIAuXLigzZs3Ky0tTQsXLjQ6NKeqXLnyDfNFK1asKC8vr1I9j/SNN97Qww8/rMDAQJ05c0Zvv/22zp8/X+rnmT377LPq0aOH3nnnHT3xxBPau3ev1qxZo4kTJxodmtNdu3ZN77//vrp06aKyZUkP3BGfejHp1KmTcnNzNW/ePJ06dUp169ZVYmJiqf7f5f79+9W7d++C4+tbOHTt2lVTp041KiynS0pKkiT16tXLrt1isSgmJsaIkEpETk6ORo0apVOnTqlKlSoKDQ3VwoUL3WZDY3dz4sQJvfDCCzp79qyqVaum8PBwrVmzplT/myb9usBmzpw5mjFjhubOnasaNWrolVde0Z///GejQ3O6nTt36vjx4+rWrZvRocAgJpvNZjM6CAAAABiLhSYAAAAgKQQAAABJIQAAAERSCAAAAJEUAgAAQCSFAAAAEEkhAAAARFIIAAAAkRQCcGGzZ8/WU089VXD88ssva8iQISUex7FjxxQaGqqMjIwS7xsASgpfcwfAYS+//LLWrVsnSSpbtqwCAgL02GOPafjw4apYsaLT+k1ISFBRv4Tp2LFj6tChg1JTU1WvXj2nxQQApQVJIYA70qZNG1ksFl29elW7d+/Wq6++qosXL2rChAl25+Xl5alcuXLF0meVKlWK5T4AgBsxfAzgjpjNZvn5+SkwMFCdO3dW586d9cknnxQM+SYnJ6tDhw4KCwuTzWbTuXPnNHbsWEVFRalx48bq3bu3vv76a7t7JiYmqmXLloqIiNArr7yiK1eu2L3/++Hja9euKTExUY8++qgaNmyodu3a6e2335YkdejQQZLUpUsXhYaGqlevXgXXpaSk6IknnlBYWJgef/xxrVixwq6fvXv3qkuXLgoLC1NMTAzDxgDcApVCAMXC09NTeXl5kqQff/xR//znPzV79myVKfPr/z0HDBigqlWrKjExUVWqVNHq1av17LPPauvWrfLy8tLmzZs1a9YsjRs3TpGRkVq/fr2WL1+umjVr3rTP6dOna+3atRozZowiIyN16tQpff/995KktWvXKjY2VkuXLlVISEhBtXLNmjWaNWuWXnvtNdWrV08ZGRkaO3asKlasqK5du+rixYsaOHCgWrRooTfffFPHjh3T5MmTnfzTAwDjkRQC+MP27t2rjRs3KioqStKvQ8ZvvvmmvL29JUlffPGFvvnmG33xxRcym82SpNGjR+vjjz/W1q1b1b17dy1btkzdunVTbGysJGnEiBH64osvbqgWXnf+/HktW7ZMr732mrp27SpJqlWrlpo0aSJJBX17eXnJz8+v4Lp58+bp5Zdf1mOPPSZJqlmzpr799lutXr1aXbt21caNG3Xt2jVNmTJFFSpUUJ06dXTixAmNHz++mH9qAOBaSAoB3JF//etfioiI0NWrV3X16lV16NBBY8eO1cqVK1W9evWCpEySDhw4oIsXL6p58+Z297h8+bJ+/PFHSVJmZqbi4uLs3g8PD9euXbsK7f+7776T1WpVixYtihzzmTNnlJWVpYSEBI0dO7ag/erVqwXzFTMzMxUaGqoKFSoUvB8REVHkPgDgbkVSCOCONG/eXOPHj1fZsmXl7+9vt5jktwmV9OvcPz8/Py1fvvyG+9zp4pHy5cs7fM21a9ckSZMmTVKjRo3s3rs+zF3U1c0AUNqQFAK4IxUqVND9999fpHMbNGignJwceXh4qEaNGoWeExwcrPT0dHXp0qWg7b///e9N71m7dm15enrqyy+/LHTe4fUkNT8/v6DN19dX9957r44ePao///nPhd43JCREGzZs0OXLl+Xp6SlJSk9Pv90jAsBdj9XHAJyuZcuWCg8P19ChQ/X555/r2LFj+s9//qOZM2dq3759kqTevXsrJSVFycnJ+v777zVr1iwdPnz4pvcsX768+vfvrzfffFOpqan68ccflZ6errVr10qSfHx85Onpqc8//1w5OTk6d+6cJGn48OFKTEzUu+++q++//16HDh1SSkqKlixZIkn605/+JJPJpISEBH377bf67LPPtHjxYif/hADAeFQKATidyWRSYmKi/u///k+vvPKKcnNz5evrqyZNmsjX11eS1KlTJ/3444966623dOXKFXXs2FE9evTQ9u3bb3rfIUOGyMPDQ7NmzdKpU6fk5+dXMC+xbNmyevXVVzV37lzNmjVLTZo00fLlyxUbGytPT08tWrRIb775pipWrKi6devq2WeflSRVqlRJ77zzjsaNG6cuXbooJCREI0eO1PDhw53/gwIAA5lsTKABAABwewwfAwAAgKQQAAAAJIUAAAAQSSEAAABEUggAAACRFAIAAEAkhQAAABBJIQAAAERSCAAAAJEUAgAAQCSFAAAAkPT/Aa86gjDf7vo8AAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Classification Report is: \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.00      0.00      0.00         5\n",
+      "           1       1.00      0.20      0.33         5\n",
+      "           2       0.00      0.00      0.00         5\n",
+      "           3       0.33      0.20      0.25         5\n",
+      "           4       0.00      0.00      0.00         5\n",
+      "           5       0.40      0.40      0.40         5\n",
+      "           6       0.25      0.20      0.22         5\n",
+      "           7       0.12      0.20      0.15         5\n",
+      "\n",
+      "    accuracy                           0.15        40\n",
+      "   macro avg       0.26      0.15      0.17        40\n",
+      "weighted avg       0.26      0.15      0.17        40\n",
+      "\n",
+      "Accuracy:  0.1500\n",
+      "Best Fitness:  0.1500\n",
+      "Precision:  0.2635\n",
+      "Recall:  0.1500\n",
+      "F1-Score:  0.1699\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'XGBClassifier' object has no attribute 'search_space_'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[52], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m aco_optimizer_xgb\u001B[38;5;241m.\u001B[39mresults()\n",
+      "Cell \u001B[0;32mIn[49], line 99\u001B[0m, in \u001B[0;36mAntColonyOptimization.results\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m     96\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mF1-Score: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfinal_f1\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m .4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m     98\u001B[0m \u001B[38;5;66;03m# Plot the search space\u001B[39;00m\n\u001B[0;32m---> 99\u001B[0m plot_search_space(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel\u001B[38;5;241m.\u001B[39msearch_space_)\n",
+      "\u001B[0;31mAttributeError\u001B[0m: 'XGBClassifier' object has no attribute 'search_space_'"
+     ]
+    }
+   ],
+   "source": [
+    "aco_optimizer_xgb.results()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-12-26T21:50:27.248045300Z",
+     "start_time": "2023-12-26T21:50:26.941102500Z"
+    }
+   },
+   "execution_count": 52
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "aco_optimizer_svm = AntColonyOptimization(num_ants=500, max_iter=1500, Model=svm_model)\n",
+    "selected_features_aco = aco_optimizer_svm.run(train_data_flatten, y_train, test_data_flatten, y_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-12-26T21:50:27.203483700Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "selected_features_aco"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-12-26T21:50:27.203483700Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "aco_optimizer_svm.results()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-12-26T21:50:27.204484200Z"
+    }
+   }
+  }
+ ]
+}