Tweets Polarity Predictions (TFIDF).ipynb

{"cells":[{"cell_type":"code","execution_count":7,"id":"3b799d17","metadata":{"id":"3b799d17","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1721138863076,"user_tz":-60,"elapsed":3990,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"outputId":"4ce7f596-9825-4433-f6aa-957cf5ace099"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import imblearn\n","from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n","from sklearn.model_selection import train_test_split\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.metrics import accuracy_score , ConfusionMatrixDisplay,RocCurveDisplay\n","from sklearn.model_selection import GridSearchCV\n","from google.colab import drive\n","\n","drive.mount('/content/drive', force_remount=True)\n","\n","# Loading the training and test datasets\n","train_df = pd.read_csv(\"/content/drive/MyDrive/Projet Intégré S8 (Tweets Réchauffement Climatique)/train_preprocessed.csv\")\n","test_df = pd.read_csv(\"/content/drive/MyDrive/Projet Intégré S8 (Tweets Réchauffement Climatique)/test_preprocessed.csv\")"]},{"cell_type":"code","execution_count":8,"id":"a1f58dfc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a1f58dfc","executionInfo":{"status":"ok","timestamp":1721138863076,"user_tz":-60,"elapsed":4,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"outputId":"f8d86471-5bac-4c64-f3d5-e4d8e65cdf43"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Polarity\n"," 1.0    0.649852\n","-1.0    0.350148\n","Name: count, dtype: float64"]},"metadata":{},"execution_count":8}],"source":["train_df['Polarity'].value_counts()/len(train_df)"]},{"cell_type":"code","execution_count":9,"id":"3e932a45","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3e932a45","executionInfo":{"status":"ok","timestamp":1721138863516,"user_tz":-60,"elapsed":2,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"outputId":"d27d515f-6bf1-43fb-c96b-ada9cdc08d62"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Polarity\n"," 1.0    0.5\n","-1.0    0.5\n","Name: count, dtype: float64"]},"metadata":{},"execution_count":9}],"source":["from imblearn.over_sampling import RandomOverSampler\n","\n","# Separate input features (X) and target variable (y)\n","X = train_df.drop('Polarity', axis=1)\n","y = train_df['Polarity']\n","\n","# Apply oversampling\n","oversample = RandomOverSampler(sampling_strategy='minority',random_state=42)\n","X_oversampled, y_oversampled = oversample.fit_resample(X, y)\n","\n","# Combine oversampled X and y into a new balanced DataFrame\n","train_df = pd.concat([X_oversampled, y_oversampled], axis=1)\n","\n","# Shuffle the dataset\n","train_df = train_df.sample(frac=1, random_state=42)\n","\n","# Check the distribution of the target variable\n","train_df['Polarity'].value_counts()/len(train_df)"]},{"cell_type":"code","source":["train_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"P4RYGW6O2EXe","executionInfo":{"status":"ok","timestamp":1721138865456,"user_tz":-60,"elapsed":310,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"outputId":"ae951055-324b-49a6-896e-009dc5c108c2"},"id":"P4RYGW6O2EXe","execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          Date             User  \\\n","365  2023-03-30 22:44:08+00:00      montreal110   \n","655  2023-03-30 07:30:04+00:00      mawzideilan   \n","828  2023-03-31 14:21:05+00:00         Shuyaftw   \n","692  2023-03-30 19:11:32+00:00   Machinequisait   \n","213  2023-03-31 10:24:48+00:00        DuGalissa   \n","..                         ...              ...   \n","106  2023-03-31 15:02:24+00:00       thoemmalex   \n","270  2023-03-31 08:33:43+00:00        MonniauxD   \n","860  2023-03-30 20:37:43+00:00      LascauxJose   \n","435  2023-03-30 19:06:04+00:00  Rigautnicosslas   \n","102  2023-03-31 15:10:12+00:00      KateYasmina   \n","\n","                                                 Tweet  \\\n","365  Neuf espèces animales pourraient aider à limit...   \n","655  Le mépris dans les yeux de Brunel est à vomir\\...   \n","828  @Mediavenir Après le covid, la variole du sing...   \n","692  #macron qui parle de sécheresse et de réchauff...   \n","213  @ndiayes12 Possible que l’explication ne soit ...   \n","..                                                 ...   \n","106  Eh ouais, le réchauffement climatique c'est pa...   \n","270  Je vois un parlementaire affirmer vouloir lutt...   \n","860  @MaryMbois Arnaque pénuries arnaque !!!!!!! Co...   \n","435  @clairemado06 @silvano_trotta oui le sud-est a...   \n","102  @clem16dums @Tyler3141618 @silvano_trotta Et ?...   \n","\n","                                    Preprocessed_Tweet  Polarity  \n","365  neuf espèce animal pouvoir aider limiter récha...       1.0  \n","655  mépris oeil brunel vomir \\r\\n  ce monsieur con...       1.0  \n","828  après covid variole sing guerre ukrain réchauf...      -1.0  \n","692  macron parle sécheress réchauffement climatiqu...      -1.0  \n","213  possible explication bien étayer rareter poiss...       1.0  \n","..                                                 ...       ...  \n","106  eh ouai réchauffement climatique     bretagne ...       1.0  \n","270  voir parlementaire affirmer vouloir lutter con...      -1.0  \n","860  arnaqu pénurie arnaqu     covid ukrain réchauf...      -1.0  \n","435      oui sud habitud     part cela gèl couille ...      -1.0  \n","102          comment pouvoir être accord dire compl...       1.0  \n","\n","[876 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-e25f5f70-2cc1-4c62-8ca7-b4e83a23ccc1\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>User</th>\n","      <th>Tweet</th>\n","      <th>Preprocessed_Tweet</th>\n","      <th>Polarity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>365</th>\n","      <td>2023-03-30 22:44:08+00:00</td>\n","      <td>montreal110</td>\n","      <td>Neuf espèces animales pourraient aider à limit...</td>\n","      <td>neuf espèce animal pouvoir aider limiter récha...</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>655</th>\n","      <td>2023-03-30 07:30:04+00:00</td>\n","      <td>mawzideilan</td>\n","      <td>Le mépris dans les yeux de Brunel est à vomir\\...</td>\n","      <td>mépris oeil brunel vomir \\r\\n  ce monsieur con...</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>828</th>\n","      <td>2023-03-31 14:21:05+00:00</td>\n","      <td>Shuyaftw</td>\n","      <td>@Mediavenir Après le covid, la variole du sing...</td>\n","      <td>après covid variole sing guerre ukrain réchauf...</td>\n","      <td>-1.0</td>\n","    </tr>\n","    <tr>\n","      <th>692</th>\n","      <td>2023-03-30 19:11:32+00:00</td>\n","      <td>Machinequisait</td>\n","      <td>#macron qui parle de sécheresse et de réchauff...</td>\n","      <td>macron parle sécheress réchauffement climatiqu...</td>\n","      <td>-1.0</td>\n","    </tr>\n","    <tr>\n","      <th>213</th>\n","      <td>2023-03-31 10:24:48+00:00</td>\n","      <td>DuGalissa</td>\n","      <td>@ndiayes12 Possible que l’explication ne soit ...</td>\n","      <td>possible explication bien étayer rareter poiss...</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>2023-03-31 15:02:24+00:00</td>\n","      <td>thoemmalex</td>\n","      <td>Eh ouais, le réchauffement climatique c'est pa...</td>\n","      <td>eh ouai réchauffement climatique     bretagne ...</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>270</th>\n","      <td>2023-03-31 08:33:43+00:00</td>\n","      <td>MonniauxD</td>\n","      <td>Je vois un parlementaire affirmer vouloir lutt...</td>\n","      <td>voir parlementaire affirmer vouloir lutter con...</td>\n","      <td>-1.0</td>\n","    </tr>\n","    <tr>\n","      <th>860</th>\n","      <td>2023-03-30 20:37:43+00:00</td>\n","      <td>LascauxJose</td>\n","      <td>@MaryMbois Arnaque pénuries arnaque !!!!!!! Co...</td>\n","      <td>arnaqu pénurie arnaqu     covid ukrain réchauf...</td>\n","      <td>-1.0</td>\n","    </tr>\n","    <tr>\n","      <th>435</th>\n","      <td>2023-03-30 19:06:04+00:00</td>\n","      <td>Rigautnicosslas</td>\n","      <td>@clairemado06 @silvano_trotta oui le sud-est a...</td>\n","      <td>oui sud habitud     part cela gèl couille ...</td>\n","      <td>-1.0</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>2023-03-31 15:10:12+00:00</td>\n","      <td>KateYasmina</td>\n","      <td>@clem16dums @Tyler3141618 @silvano_trotta Et ?...</td>\n","      <td>comment pouvoir être accord dire compl...</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>876 rows × 5 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e25f5f70-2cc1-4c62-8ca7-b4e83a23ccc1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-e25f5f70-2cc1-4c62-8ca7-b4e83a23ccc1 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Et on a les repr\\u00e9sentants qu'on m\\u00e9rite. M\\u00eame l'\\u00e9cologie semble avoir plus vocation \\u00e0 se donner bonne conscience qu'\\u00e0 prot\\u00e9ger la plan\\u00e8te.\",\n          \"\\ud83d\\udca5\\ud83d\\udca5Le r\\u00e9chauffement climatique est une escroquerie!!!\\ud83d\\udca5\\ud83d\\udca5\\r\\nLA PREUVE !!!\\r\\nA d\\u00e9rouler.\",\n          \"Le secteur de la gestion des d\\u00e9chets est le troisi\\u00e8me plus grand \\u00e9metteur de m\\u00e9thane, contribuant de mani\\u00e8re significative au r\\u00e9chauffement climatique \\u00e0 court terme.\\r\\nSource : PNUE\\r\\nD'o\\u00f9 l'importance de multiplier les actions \\r\\n#ZeroWasteDay \\r\\n#BeatWastePollution https://t.co/wPt7qHuach\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Preprocessed_Tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 654,\n        \"samples\": [\n          \"rappelon saloperie bassine ce ce politique privatisation eau grand partie origin incendie australie cela r\\u00e9chauffement climatique \\u00e9videmment\",\n          \"inic faire plaisir annoncer lancement inic spac lieu     avril occasion journer femme ha\\u00eftien inic space espac d\\u00e9bat divers sujet relatif \\u00e9conomie d\\u00e9mocratie r\\u00e9chauffement climatique\",\n          \"emmanuel macron annoncer jeudi plan investissement adapter r\\u00e9chauffement climatique central nucl\\u00e9aire\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Polarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005712653993504,\n        \"min\": -1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -1.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":10}]},{"cell_type":"code","execution_count":11,"id":"2fb16b4b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2fb16b4b","executionInfo":{"status":"ok","timestamp":1721138867320,"user_tz":-60,"elapsed":293,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"outputId":"ab1cae7f-6a37-40ce-ae93-2e763d616f89"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Polarity\n"," 1.0    0.5\n","-1.0    0.5\n","Name: count, dtype: float64"]},"metadata":{},"execution_count":11}],"source":["# Splitting the training data into training and validation sets\n","X_train, X_val, y_train, y_val = train_test_split(train_df[\"Preprocessed_Tweet\"],\n","                                                  train_df[\"Polarity\"], test_size=0.15,\n","                                                  random_state=42,stratify=train_df[\"Polarity\"])\n","#When stratify is set to a dataframe column, in this case train_df[\"Polarity\"],\n","#it means that the data will be split in a way that maintains the same proportion\n","#of the different column classes in both the training and validation sets as in the original dataset.\n","\n","# Checking the distribution of the target variable in the training and validation sets\n","y_train.value_counts()/len(y_train)"]},{"cell_type":"code","execution_count":12,"id":"313bde74","metadata":{"id":"313bde74","executionInfo":{"status":"ok","timestamp":1721138873359,"user_tz":-60,"elapsed":272,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"78bbb855-3e81-40ab-9997-bcfed182e27d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Polarity\n","-1.0    0.5\n"," 1.0    0.5\n","Name: count, dtype: float64"]},"metadata":{},"execution_count":12}],"source":["y_val.value_counts()/len(y_val)"]},{"cell_type":"code","execution_count":14,"id":"370fcbfb","metadata":{"id":"370fcbfb","executionInfo":{"status":"ok","timestamp":1721138898975,"user_tz":-60,"elapsed":2095,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}}},"outputs":[],"source":["from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","# Vectorizing the text data\n","vectorizer = TfidfVectorizer()\n","X_train_vec = vectorizer.fit_transform(X_train)\n","X_val_vec = vectorizer.transform(X_val)\n","X_test_vec = vectorizer.transform(test_df[\"Tweet\"])"]},{"cell_type":"markdown","id":"15c06fd0","metadata":{"id":"15c06fd0"},"source":["# Trying Multinomial Naive Bayes"]},{"cell_type":"code","execution_count":15,"id":"0e2010ee","metadata":{"id":"0e2010ee","executionInfo":{"status":"ok","timestamp":1721139005488,"user_tz":-60,"elapsed":10447,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"ae920d7b-c812-4777-f144-dd9b19b93ae0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Validation accuracy: 0.8484848484848485\n"]}],"source":["# Create a Multinomial Naive Bayes classifier object\n","NB = MultinomialNB()\n","\n","# Define the range of alpha values to test\n","alpha_values = [0.00001, 0.0001, 0.001, 0.1, 1, 10, 100, 1000, 10000]\n","\n","# Set up grid search to find the best alpha value\n","grid_search = GridSearchCV(NB, {'alpha': alpha_values}, cv=150, n_jobs=4)\n","\n","# Train the grid search object on the training set\n","grid_search.fit(X_train_vec, y_train)\n","\n","# Retrieve the best model from the grid search\n","best_NB = grid_search.best_estimator_\n","\n","# Predict on the validation set using the best model\n","y_val_pred = best_NB.predict(X_val_vec)\n","\n","# Calculate and print the validation accuracy\n","val_acc = accuracy_score(y_val, y_val_pred)\n","print(\"Validation accuracy:\", val_acc)"]},{"cell_type":"code","execution_count":16,"id":"cb2a7dc4","metadata":{"id":"cb2a7dc4","executionInfo":{"status":"ok","timestamp":1721139006044,"user_tz":-60,"elapsed":573,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/","height":467},"outputId":"324f5f8a-344f-4f4c-ac03-f97812953baf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x79c01d4dd270>"]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAgQAAAGwCAYAAADWsX1oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwdklEQVR4nO3deXxU9b3/8fdkmwSSSUiEhEDAYISAbEJ7Ia3K0khELhKhtXCxBgX7sCKyiAvXAiJq1NYi1AgulJQWLgUXriyVS7FsEqgg8arFVDCYQEjsLSYhkSzMnN8fkfE3suXkzGSG8fV8PM7jds4533M+6U3JZz6f7/kem2EYhgAAwHdaiL8DAAAA/kdCAAAASAgAAAAJAQAAEAkBAAAQCQEAABAJAQAAkBTm7wACgcvlUllZmWJiYmSz2fwdDgDAJMMwdOrUKSUnJyskxHffdevq6tTQ0GD5OhEREYqMjPRCRN5DQiCprKxMKSkp/g4DAGBRaWmpOnfu7JNr19XVKbVrtMq/cFq+VlJSkoqLiwMqKSAhkBQTEyNJ+vz9K+WIpouC4HRr9z7+DgHwmTNq1G5tdv977gsNDQ0q/8Kpzw9cKUdMy/9WVJ9yqevAo2poaCAhCDRn2wSO6BBL/08GAlmYLdzfIQC+8/Ui/K3R9o2OsSk6puX3cSkwW9MkBAAAmOA0XHJaeAuQ03B5LxgvIiEAAMAElwy51PKMwMpYX6I+DgAAqBAAAGCGSy5ZKfpbG+07JAQAAJjgNAw5jZaX/a2M9SVaBgAAgAoBAABmBOukQhICAABMcMmQMwgTAloGAACACgEAAGbQMgAAADxlAAAAghcVAgAATHB9vVkZH4hICAAAMMFp8SkDK2N9iYQAAAATnIYsvu3Qe7F4E3MIAAAAFQIAAMxgDgEAAJBLNjllszQ+ENEyAAAAVAgAADDDZTRtVsYHIhICAABMcFpsGVgZ60u0DAAAABUCAADMCNYKAQkBAAAmuAybXIaFpwwsjPUlWgYAAIAKAQAAZtAyAAAAcipETgsFdqcXY/EmEgIAAEwwLM4hMJhDAAAAAhUVAgAATGAOAQAAkNMIkdOwMIcgQJcupmUAAACoEAAAYIZLNrksfJ92KTBLBCQEAACYEKxzCGgZAAAAKgQAAJhhfVIhLQMAAC57TXMILLzciJYBAAAIVFQIAAAwwWXxXQY8ZQAAQBBgDgEAAJBLIUG5DgFzCAAAABUCAADMcBo2OS28wtjKWF8iIQAAwASnxUmFTloGAAAgUFEhAADABJcRIpeFpwxcPGUAAMDlj5YBAAAIWlQIAAAwwSVrTwq4vBeKV5EQAABggvWFiQKzOB+YUQEAgFZFhQAAABOsv8sgML+LkxAAAGCCSza5ZGUOASsVAgBw2QvWCkFgRgUAAFoVFQIAAEywvjBRYH4XJyEAAMAEl2GTy8o6BAH6tsPATFMAAIAk6bHHHpPNZvPY0tPT3cfr6uo0depUJSQkKDo6WuPGjVNFRYXp+5AQAABgguvrlkFLt5YsTHTNNdfoxIkT7m337t3uYzNnztSGDRu0bt067dixQ2VlZRo7dqzpe9AyAADABOtvOzQ/NiwsTElJSefsr6qq0vLly7V69WoNHz5ckrRixQr17NlTe/fu1eDBg5t9DyoEAAD4QXV1tcdWX19/wXM//fRTJScnq1u3bpo4caJKSkokSQcOHFBjY6MyMzPd56anp6tLly4qKCgwFQ8JAQAAJjhls7xJUkpKimJjY91bbm7uee83aNAg5efn6+2339bSpUtVXFys66+/XqdOnVJ5ebkiIiIUFxfnMSYxMVHl5eWmfi5aBgAAmOCtlkFpaakcDod7v91uP+/5I0eOdP/nvn37atCgQeratavWrl2rqKioFsfxbVQIAADwA4fD4bFdKCH4tri4OHXv3l2HDx9WUlKSGhoaVFlZ6XFORUXFeeccXAwJAQAAJjhltW1gTU1NjY4cOaKOHTtq4MCBCg8P17Zt29zHi4qKVFJSooyMDFPXpWUAAIAJrf2UwezZszV69Gh17dpVZWVlmj9/vkJDQzVhwgTFxsZq8uTJmjVrluLj4+VwODRt2jRlZGSYesJAIiEAAMCU1n650bFjxzRhwgT961//Uvv27XXddddp7969at++vSRp0aJFCgkJ0bhx41RfX6+srCy9+OKLpuMiIQAAIICtWbPmoscjIyOVl5envLw8S/chIQAAwARDNrnU8vcRGBbG+hIJAQAAJrR2y6C1BGZUAACgVVEhAADAhGB9/TEJAQAAJpx9a6GV8YEoMKMCAACtigoBAAAm0DIAAAByKUQuCwV2K2N9KTCjAgAArYoKAQAAJjgNm5wWyv5WxvoSCQEAACYwhwAAAMiw+LZDg5UKAQBAoKJCAACACU7Z5LTwgiIrY32JhAAAABNchrV5AC7Di8F4ES0DAABAhQC+84dfJ+mPv0ny2Nf5qjot3/WJJKnsaIReeTxZH/8tWo0NNg0cVq2pTxxXu/Zn/BEuYNnv9/1dSSmN5+x/Kz9Bef/Z2Q8RwRdcFicVWhnrSyQE8KmuPU7r6T8dcX8ODW2qldV9FaL/nHCVuvU6rWfWHZYk/f7ZjpqXk6rFGz9VSGD+7wW4qPtHdldI6Df14CvT6/T0nz7Trg1x/gsKXueSTS4L8wCsjPWlgPpn94033tCIESOUkJAgm82mwsLCZo1bt26d0tPTFRkZqT59+mjz5s2+DRTNFhoqxXc4495iE5ySpI//1lYVpRF64PkSpfasU2rPOj24+HN9+kEbFe6O9nPUQMtUnQzTl/8Md2+DMqtVVhyh/y1o6+/QgEsKqISgtrZW1113nZ555plmj9mzZ48mTJigyZMn6+DBg8rOzlZ2drY++ugjH0aK5jpeHKEJ116jnME99fTULvriWLgkqbHBJtmk8Ihvvk2F2w3ZQqSP/0ZCgMtfWLhLw8d9qS1r4qUA/UaIljm7UqGVLRAFVMvgZz/7mSTp6NGjzR6zePFi3XTTTXrwwQclSQsXLtTWrVv1wgsvaNmyZb4IE82UPqBWs58/rc5X1evkF+H643NJeuDWq/XSXz9R+sBaRbZxafmTybrzkTJJNi1/sqNcTptOfhFQv5ZAi/zgpmpFO5z6n7Xx/g4FXhascwgCMyoTCgoKlJmZ6bEvKytLBQUFFxxTX1+v6upqjw3e9/3hp3TD6Cp161Wn7w09pSf++JlqqkO18604xSU49cuXjmrfVoeyr+6rW3v0UW11qNL6fCXbZf9bCUhZE/6l9/7q0MmKcH+HAjTLZf9VrLy8XImJiR77EhMTVV5efsExubm5WrBgga9Dw7dExzrVuVu9yo7aJUkDh55SfsEhVf0rVKFhTcfH97tGHbvU+zlSwJoOnRp07fU1WjjlSn+HAh9wyeK7DAK0heS372KrVq1SdHS0e9u1a1er3XvOnDmqqqpyb6Wlpa127++y07UhKvs8QvEdPB/Lik1wKjrWqcLd0ar8vzANHkHFBpe3EeNPqvL/wrTvLw5/hwIfML5+yqClmxGgCYHfKgS33HKLBg0a5P7cqVOnFl0nKSlJFRUVHvsqKiqUlJR0gRGS3W6X3W5v0f3QfC8vSNbgEVXq0LlR/yoP0x9+3VGhIdLQW7+UJG1ZE68uV9cpNuGMDh1oq6XzOunWn/9TKWlUCHD5stkMjfjpSf1lXTu5nIH5Dz+s4W2HXhYTE6OYmBjL18nIyNC2bds0Y8YM976tW7cqIyPD8rVhzf+dCFfuvVfq1Jehik04o2u+X6vnN/5DcV8/enjsiF0rcjvqVGWoElMaNOH+Co39+T/9HDVgzbU31Cixc6O2rEnwdyiAKQE1h+DkyZMqKSlRWVmZJKmoqEhSUxXg7Df+O+64Q506dVJubq4kafr06RoyZIiee+45jRo1SmvWrNH+/fv18ssv++eHgNt/Lvv8oscnP3pCkx890UrRAK3j/R0xykru5+8w4EM8ZdAK3nrrLV177bUaNWqUJGn8+PG69tprPR4fLCkp0YkT3/wR+cEPfqDVq1fr5ZdfVr9+/fTaa69p/fr16t27d6vHDwAIfmdbBla2QBRQFYJJkyZp0qRJFz1n+/bt5+z7yU9+op/85Ce+CQoAgO+AgEoIAAAIdMH6LgMSAgAATAjWpwwCag4BAADwDyoEAACYEKwVAhICAABMCNaEgJYBAACgQgAAgBnBWiEgIQAAwARD1h4dNLwXileREAAAYEKwVgiYQwAAAKgQAABgRrBWCEgIAAAwIVgTAloGAACACgEAAGYEa4WAhAAAABMMwybDwh91K2N9iZYBAACgQgAAgBku2SwtTGRlrC+REAAAYEKwziGgZQAAAKgQAABgRrBOKiQhAADAhGBtGZAQAABgQrBWCJhDAAAAqBAAAGCGYbFlEKgVAhICAABMMCQZhrXxgYiWAQAAoEIAAIAZLtlkY6VCAAC+23jKAAAABC0qBAAAmOAybLKxMBEAAN9thmHxKYMAfcyAlgEAACAhAADAjLOTCq1sLfX000/LZrNpxowZ7n11dXWaOnWqEhISFB0drXHjxqmiosL0tUkIAAAwwV8JwXvvvaeXXnpJffv29dg/c+ZMbdiwQevWrdOOHTtUVlamsWPHmr4+CQEAACacfduhlU2SqqurPbb6+voL3rOmpkYTJ07UK6+8onbt2rn3V1VVafny5frNb36j4cOHa+DAgVqxYoX27NmjvXv3mvq5SAgAAPCDlJQUxcbGurfc3NwLnjt16lSNGjVKmZmZHvsPHDigxsZGj/3p6enq0qWLCgoKTMXDUwYAAJjgracMSktL5XA43Pvtdvt5z1+zZo3ef/99vffee+ccKy8vV0REhOLi4jz2JyYmqry83FRcJAQAAJjQlBBYWamw6f86HA6PhOB8SktLNX36dG3dulWRkZEtvmdz0DIAACBAHThwQF988YUGDBigsLAwhYWFaceOHVqyZInCwsKUmJiohoYGVVZWeoyrqKhQUlKSqXtRIQAAwITWfJfBj370I3344Yce++68806lp6fr4YcfVkpKisLDw7Vt2zaNGzdOklRUVKSSkhJlZGSYiouEAAAAE4yvNyvjmysmJka9e/f22Ne2bVslJCS490+ePFmzZs1SfHy8HA6Hpk2bpoyMDA0ePNhUXCQEAABcxhYtWqSQkBCNGzdO9fX1ysrK0osvvmj6OiQEAACY4O/XH2/fvt3jc2RkpPLy8pSXl2fpuiQEAACY0Zo9g1ZEQgAAgBkWKwQK0Ncf89ghAACgQgAAgBneWqkw0JAQAABggr8nFfoKLQMAAECFAAAAUwybtYmBAVohICEAAMCEYJ1DQMsAAABQIQAAwBQWJgIAAMH6lEGzEoK33nqr2Re85ZZbWhwMAADwj2YlBNnZ2c26mM1mk9PptBIPAACBL0DL/lY0KyFwuVy+jgMAgMtCsLYMLD1lUFdX5604AAC4PBhe2AKQ6YTA6XRq4cKF6tSpk6Kjo/XZZ59JkubOnavly5d7PUAAAOB7phOCJ598Uvn5+Xr22WcVERHh3t+7d2+9+uqrXg0OAIDAY/PCFnhMJwQrV67Uyy+/rIkTJyo0NNS9v1+/fvrkk0+8GhwAAAGHlkGT48ePKy0t7Zz9LpdLjY2NXgkKAAC0LtMJQa9evbRr165z9r/22mu69tprvRIUAAABK0grBKZXKpw3b55ycnJ0/PhxuVwuvfHGGyoqKtLKlSu1ceNGX8QIAEDgCNK3HZquEIwZM0YbNmzQX/7yF7Vt21bz5s3ToUOHtGHDBt14442+iBEAAPhYi95lcP3112vr1q3ejgUAgIAXrK8/bvHLjfbv369Dhw5JappXMHDgQK8FBQBAwOJth02OHTumCRMm6N1331VcXJwkqbKyUj/4wQ+0Zs0ade7c2dsxAgAAHzM9h2DKlClqbGzUoUOHdPLkSZ08eVKHDh2Sy+XSlClTfBEjAACB4+ykQitbADJdIdixY4f27NmjHj16uPf16NFDv/3tb3X99dd7NTgAAAKNzWjarIwPRKYTgpSUlPMuQOR0OpWcnOyVoAAACFhBOofAdMvgV7/6laZNm6b9+/e79+3fv1/Tp0/Xr3/9a68GBwAAWkezKgTt2rWTzfZNz6O2tlaDBg1SWFjT8DNnzigsLEx33XWXsrOzfRIoAAABIUgXJmpWQvD888/7OAwAAC4TQdoyaFZCkJOT4+s4AACAH7V4YSJJqqurU0NDg8c+h8NhKSAAAAJakFYITE8qrK2t1X333acOHTqobdu2ateunccGAEBQC9K3HZpOCB566CG98847Wrp0qex2u1599VUtWLBAycnJWrlypS9iBAAAPma6ZbBhwwatXLlSQ4cO1Z133qnrr79eaWlp6tq1q1atWqWJEyf6Ik4AAAJDkD5lYLpCcPLkSXXr1k1S03yBkydPSpKuu+467dy507vRAQAQYM6uVGhlC0SmE4Ju3bqpuLhYkpSenq61a9dKaqocnH3ZEQAAuLyYTgjuvPNOffDBB5KkRx55RHl5eYqMjNTMmTP14IMPej1AAAACSpBOKjQ9h2DmzJnu/5yZmalPPvlEBw4cUFpamvr27evV4AAAQOuwtA6BJHXt2lVdu3b1RiwAAAQ8myy+7dBrkXhXsxKCJUuWNPuC999/f4uDAQAA/tGshGDRokXNupjNZrusE4IfjxqjsFC7v8MAfOL+wxv9HQLgM1+dcmp7/1a6WZA+dtishODsUwUAAHznsXQxAAAIVpYnFQIA8J0SpBUCEgIAAEywutpg0KxUCAAAgg8VAgAAzAjSlkGLKgS7du3S7bffroyMDB0/flyS9Ic//EG7d+/2anAAAAScIF262HRC8PrrrysrK0tRUVE6ePCg6uvrJUlVVVV66qmnvB4gAADwPdMJwRNPPKFly5bplVdeUXh4uHv/D3/4Q73//vteDQ4AgEATrK8/Nj2HoKioSDfccMM5+2NjY1VZWemNmAAACFxBulKh6QpBUlKSDh8+fM7+3bt3q1u3bl4JCgCAgMUcgiZ33323pk+frn379slms6msrEyrVq3S7Nmz9Ytf/MIXMQIAAB8z3TJ45JFH5HK59KMf/UhfffWVbrjhBtntds2ePVvTpk3zRYwAAASMYF2YyHRCYLPZ9Oijj+rBBx/U4cOHVVNTo169eik6OtoX8QEAEFiCdB2CFi9MFBERoV69enkzFgAA4CemE4Jhw4bJZrvwDMl33nnHUkAAAAQ0q48OBkuFoH///h6fGxsbVVhYqI8++kg5OTneigsAgMBEy6DJokWLzrv/scceU01NjeWAAADAN5YuXaqlS5fq6NGjkqRrrrlG8+bN08iRIyVJdXV1euCBB7RmzRrV19crKytLL774ohITE03dx2tvO7z99tv1u9/9zluXAwAgMLXyOgSdO3fW008/rQMHDmj//v0aPny4xowZo48//liSNHPmTG3YsEHr1q3Tjh07VFZWprFjx5r+sbz2tsOCggJFRkZ663IAAAQkbz12WF1d7bHfbrfLbrefc/7o0aM9Pj/55JNaunSp9u7dq86dO2v58uVavXq1hg8fLklasWKFevbsqb1792rw4MHNjst0QvDtrMMwDJ04cUL79+/X3LlzzV4OAIDvpJSUFI/P8+fP12OPPXbRMU6nU+vWrVNtba0yMjJ04MABNTY2KjMz031Oenq6unTpooKCAt8mBLGxsR6fQ0JC1KNHDz3++OMaMWKE2csBAPCdVFpaKofD4f58vurAWR9++KEyMjJUV1en6Ohovfnmm+rVq5cKCwsVERGhuLg4j/MTExNVXl5uKh5TCYHT6dSdd96pPn36qF27dqZuBABAUPDSUwYOh8MjIbiYHj16qLCwUFVVVXrttdeUk5OjHTt2WAjiXKYSgtDQUI0YMUKHDh0iIQAAfCf5Y+niiIgIpaWlSZIGDhyo9957T4sXL9ZPf/pTNTQ0qLKy0qNKUFFRoaSkJFP3MP2UQe/evfXZZ5+ZHQYAALzE5XKpvr5eAwcOVHh4uLZt2+Y+VlRUpJKSEmVkZJi6puk5BE888YRmz56thQsXauDAgWrbtq3H8eaWPwAAuGy14uJCc+bM0ciRI9WlSxedOnVKq1ev1vbt27VlyxbFxsZq8uTJmjVrluLj4+VwODRt2jRlZGSYmlAomUgIHn/8cT3wwAO6+eabJUm33HKLxxLGhmHIZrPJ6XSaCgAAgMtKK69U+MUXX+iOO+7QiRMnFBsbq759+2rLli268cYbJTUtGBgSEqJx48Z5LExkVrMTggULFuiee+7RX//6V9M3AQAALbN8+fKLHo+MjFReXp7y8vIs3afZCYFhNKU0Q4YMsXRDAAAuZ/6YVNgaTM0huNhbDgEA+E7g5UZS9+7dL5kUnDx50lJAAACg9ZlKCBYsWHDOSoUAAHyX0DKQNH78eHXo0MFXsQAAEPiCtGXQ7IWJmD8AAEDwMv2UAQAA32lBWiFodkLgcrl8GQcAAJcF5hAAAICgrRCYfrkRAAAIPlQIAAAwI0grBCQEAACYEKxzCGgZAAAAKgQAAJhCywAAANAyAAAAQYsKAQAAZtAyAAAAwZoQ0DIAAABUCAAAMMP29WZlfCAiIQAAwIwgbRmQEAAAYAKPHQIAgKBFhQAAADNoGQAAAEkB+0fdCloGAACACgEAAGYE66RCEgIAAMwI0jkEtAwAAAAVAgAAzKBlAAAAaBkAAIDgRYUAAAATaBkAAICgbRmQEAAAYEaQJgTMIQAAAFQIAAAwgzkEAACAlgEAAAheVAgAADDBZhiyGS3/mm9lrC+REAAAYAYtAwAAEKyoEAAAYAJPGQAAAFoGAAAgeFEhAADABFoGAAAgaFsGJAQAAJgQrBUC5hAAAAAqBAAAmELLAAAASIFb9reClgEAAKBCAACAKYbRtFkZH4BICAAAMIGnDAAAQNCiQgAAgBk8ZQAAAGyups3K+EBEywAAAFAhgO/07vtPjfvpP5TWvVIJV9Rp4S8Hq+DdTu7jE3P+rhuGl6p9+9NqPBOiw/+I08rlvVV0KN6PUQPNt3fxFfrbb6/w2NeuW71+9j/FkqR3fpmoknfbqvaLMIW3canjgNP64UP/VPxVDf4IF95CywAwJzLSqeIjcfqfP1+puQv3nnP8+LFoLV3cX+Un2irC7tKtP/5UTzy7S5Nvv0nVVXY/RAyYF391vW5dWeL+HBL6zbEOvevU45ZqxSSfUV1liPYtuULrJ6Vo0vYjHufh8sJTBq1k586dGj16tJKTk2Wz2bR+/fpLjtm+fbsGDBggu92utLQ05efn+zxOXNr+vyVp5e+uUcHuTuc9vn1bFxW+n6jyE9EqOerQyy/2VdvoM0q9qqqVIwVaLiTMUNv2TvcWFe90H+s9vkqd/u20HJ0b1aF3vTJm/Z9qToSr+li4HyOGZWfXIbCymZCbm6vvf//7iomJUYcOHZSdna2ioiKPc+rq6jR16lQlJCQoOjpa48aNU0VFhan7BFxCUFtbq379+ikvL69Z5xcXF2vUqFEaNmyYCgsLNWPGDE2ZMkVbtmzxcaTwprAwl0b+e7FqasJVfDjW3+EAzVZ5NELLf3CV8od105ZZHXWq7PyF18avbPr7a7FypDQopmNjK0eJy9mOHTs0depU7d27V1u3blVjY6NGjBih2tpa9zkzZ87Uhg0btG7dOu3YsUNlZWUaO3asqfsEXMtg5MiRGjlyZLPPX7ZsmVJTU/Xcc89Jknr27Kndu3dr0aJFysrKOu+Y+vp61dfXuz9XV1dbCxot9m+DT+jheftktzt18l+RenT2daqupl2Ay0NS/9O68ZkTatetQbVfhGnfbxP02viumri5WBHRTVPJ//ePcXr32Q5q/CpE7brVKzu/VKERfg4clnirZfDtvz12u112+7n//r399tsen/Pz89WhQwcdOHBAN9xwg6qqqrR8+XKtXr1aw4cPlyStWLFCPXv21N69ezV48OBmxRVwFQKzCgoKlJmZ6bEvKytLBQUFFxyTm5ur2NhY95aSkuLrMHEBHxS2131TMvXAfUN14L0kzZm/T7Fxdf4OC2iWK4fU6uqbT+mK9Hp1vaFWY5YfU311iD7dHOM+p8eYak14q1jjVn+uuCsb9Of7O+lMvc2PUcMywwubpJSUFI+/Rbm5uc26fVVVU1s1Pr5pAvaBAwfU2Njo8bcwPT1dXbp0uejfwm+77BOC8vJyJSYmeuxLTExUdXW1Tp8+fd4xc+bMUVVVlXsrLS1tjVBxHvV1YTpRFq2iQwla/KuBcjptyrr5qL/DAlrE7nApLrVBlZ9/UwKwx7gUd2WjOv3bad38wnF9+VmEjvxPtB+jRKAoLS31+Fs0Z86cS45xuVyaMWOGfvjDH6p3796Smv4ORkREKC4uzuPcxMRElZeXNzuegGsZtIYLlWXgfyE2KTw8QFftAC6hodamqpIIpWefvw1pGDbJkJwNl/13se80b7UMHA6HHA6HqbFTp07VRx99pN27d7c8gAu47BOCpKSkc2ZSVlRUyOFwKCoqyk9RQZIiI88ouVON+3Nix6/U7apKnToVoerqCI2//RPtfbejvjwZKUdsg/49+4gS2p/Wrh2d/Rg10Hy7ctsrdXiNHJ3OqPaLMO1dfIVsIYa6/3u1qkrC9Y9NMep6fa2i4p2qKQ/X/mXxCos0dOXQmktfHIHLT287vO+++7Rx40bt3LlTnTt/8+9kUlKSGhoaVFlZ6VElqKioUFJSUrOvf9knBBkZGdq8ebPHvq1btyojI8NPEeGsq3t8qWee3+n+/POp/ytJ2vp2V73wm2vVOeWUHl3wuWJjG1RdHaF/FLXTg/cPUclRcxkz4C815eHaMjNZp78MVVS8U8nfO63bXvtcbRKcqjljU9n+NirMj1d9dajaJJxRp3/7Sj9Z23QcaC7DMDRt2jS9+eab2r59u1JTUz2ODxw4UOHh4dq2bZvGjRsnSSoqKlJJSYmpv4UBlxDU1NTo8OHD7s/FxcUqLCxUfHy8unTpojlz5uj48eNauXKlJOmee+7RCy+8oIceekh33XWX3nnnHa1du1abNm3y14+Ar334QXvdPGzcBY8/OZ+kDZe3kYvLLngsOvGMxiw/1orRoLW09sJEU6dO1erVq/Xf//3fiomJcc8LiI2NVVRUlGJjYzV58mTNmjVL8fHxcjgcmjZtmjIyMpr9hIEUgAnB/v37NWzYMPfnWbNmSZJycnKUn5+vEydOqKTkm1XBUlNTtWnTJs2cOVOLFy9W586d9eqrr17wkUMAACxp5aWLly5dKkkaOnSox/4VK1Zo0qRJkqRFixYpJCRE48aNU319vbKysvTiiy+auk/AJQRDhw6VcZH+yvlWIRw6dKgOHjzow6gAAPCPi/1NPCsyMlJ5eXnNXtTvfAIuIQAAIJAF67sMSAgAADDDZTRtVsYHIBICAADMCNLXH7M6BgAAoEIAAIAZNlmcQ+C1SLyLhAAAADP8tFKhr9EyAAAAVAgAADCDxw4BAABPGQAAgOBFhQAAABNshiGbhYmBVsb6EgkBAABmuL7erIwPQLQMAAAAFQIAAMygZQAAAIL2KQMSAgAAzGClQgAAEKyoEAAAYAIrFQIAAFoGAAAgeFEhAADABJurabMyPhCREAAAYAYtAwAAEKyoEAAAYAYLEwEAgGBdupiWAQAAoEIAAIApQTqpkIQAAAAzDElWHh0MzHyAhAAAADOYQwAAAIIWFQIAAMwwZHEOgdci8SoSAgAAzAjSSYW0DAAAABUCAABMcUmyWRwfgEgIAAAwgacMAABA0KJCAACAGUE6qZCEAAAAM4I0IaBlAAAAqBAAAGBKkFYISAgAADCDxw4BAACPHQIAgKBFhQAAADOYQwAAAOQyJJuFP+quwEwIaBkAAAAqBAAAmELLAAAASBYTAgVmQkDLAAAAUCEAAMAUWgYAAKDpKQGeMgAAAEGICgEAAGYYrqbNyvgAREIAAIAZzCEAAADMIQAAAEGLCgEAAGbQMgAAADJkMSHwWiReRcsAAABQIQAAwJQgbRlQIQAAwAyXy/pmws6dOzV69GglJyfLZrNp/fr1HscNw9C8efPUsWNHRUVFKTMzU59++qnpH4uEAACAAFZbW6t+/fopLy/vvMefffZZLVmyRMuWLdO+ffvUtm1bZWVlqa6uztR9aBkAAGCGl1oG1dXVHrvtdrvsdvs5p48cOVIjR468wKUMPf/88/rlL3+pMWPGSJJWrlypxMRErV+/XuPHj292WFQIAAAw42xCYGWTlJKSotjYWPeWm5trOpTi4mKVl5crMzPTvS82NlaDBg1SQUGBqWtRIQAAwA9KS0vlcDjcn89XHbiU8vJySVJiYqLH/sTERPex5iIhAADADC8tXexwODwSAn+jZQAAgAmG4bK8eUtSUpIkqaKiwmN/RUWF+1hzkRAAAGCGYTR9y2/p5sV1CFJTU5WUlKRt27a591VXV2vfvn3KyMgwdS1aBgAABLCamhodPnzY/bm4uFiFhYWKj49Xly5dNGPGDD3xxBO6+uqrlZqaqrlz5yo5OVnZ2dmm7kNCAACAGYbFOQQmKwT79+/XsGHD3J9nzZolScrJyVF+fr4eeugh1dbW6uc//7kqKyt13XXX6e2331ZkZKSp+5AQAABghssl2SzMAzA5h2Do0KEyLpJE2Gw2Pf7443r88cdbHpOYQwAAAESFAAAAc1q5ZdBaSAgAADDBcLlkWGgZePOxQ2+iZQAAAKgQAABgCi0DAAAglyHZgi8hoGUAAACoEAAAYIphSLKyDkFgVghICAAAMMFwGTIstAwutsiQP5EQAABghuGStQoBjx0CAIAARYUAAAATaBkAAICgbRmQEOibbO2Ms97PkQC+89Upp79DAHzmq5qm3+/W+PZ9Ro2W1iU6o0bvBeNFNiNQaxet6NixY0pJSfF3GAAAi0pLS9W5c2efXLuurk6pqakqLy+3fK2kpCQVFxcrMjLSC5F5BwmBJJfLpbKyMsXExMhms/k7nO+E6upqpaSkqLS0VA6Hw9/hAF7F73frMwxDp06dUnJyskJCfDdfvq6uTg0NDZavExEREVDJgETLQJIUEhLis4wSF+dwOPgHE0GL3+/WFRsb6/N7REZGBtwfcm/hsUMAAEBCAAAASAjgJ3a7XfPnz5fdbvd3KIDX8fuNyxGTCgEAABUCAABAQgAAAERCAAAAREIAAABEQgAfeuONNzRixAglJCTIZrOpsLCwWePWrVun9PR0RUZGqk+fPtq8ebNvAwVM2rlzp0aPHq3k5GTZbDatX7/+kmO2b9+uAQMGyG63Ky0tTfn5+T6PEzCDhAA+U1tbq+uuu07PPPNMs8fs2bNHEyZM0OTJk3Xw4EFlZ2crOztbH330kQ8jBcypra1Vv379lJeX16zzi4uLNWrUKA0bNkyFhYWaMWOGpkyZoi1btvg4UqD5eOwQPnf06FGlpqbq4MGD6t+//0XP/elPf6ra2lpt3LjRvW/w4MHq37+/li1b5uNIAfNsNpvefPNNZWdnX/Cchx9+WJs2bfJIbMePH6/Kykq9/fbbrRAlcGlUCBBQCgoKlJmZ6bEvKytLBQUFfooIsI7fa1wOSAgQUMrLy5WYmOixLzEx0SuvGwX85UK/19XV1Tp9+rSfogI8kRDAK1atWqXo6Gj3tmvXLn+HBAAwgdcfwytuueUWDRo0yP25U6dOLbpOUlKSKioqPPZVVFQoKSnJUnyAP13o99rhcCgqKspPUQGeqBDAK2JiYpSWlubeWvqPXEZGhrZt2+axb+vWrcrIyPBGmIBf8HuNywEVAvjMyZMnVVJSorKyMklSUVGRpKZvS2e/8d9xxx3q1KmTcnNzJUnTp0/XkCFD9Nxzz2nUqFFas2aN9u/fr5dfftk/PwRwHjU1NTp8+LD7c3FxsQoLCxUfH68uXbpozpw5On78uFauXClJuueee/TCCy/ooYce0l133aV33nlHa9eu1aZNm/z1IwDnMgAfWbFihSHpnG3+/Pnuc4YMGWLk5OR4jFu7dq3RvXt3IyIiwrjmmmuMTZs2tW7gwCX89a9/Pe/v9tnf5ZycHGPIkCHnjOnfv78RERFhdOvWzVixYkWrxw1cDOsQAAAA5hAAAAASAgAAIBICAAAgEgIAACASAgAAIBICAAAgEgIAACASAgAAIBICIGBMmjRJ2dnZ7s9Dhw7VjBkzWj2O7du3y2azqbKy8oLn2Gw2rV+/vtnXfOyxx9S/f39LcR09elQ2m02FhYWWrgPg/EgIgIuYNGmSbDabbDabIiIilJaWpscff1xnzpzx+b3feOMNLVy4sFnnNuePOABcDC83Ai7hpptu0ooVK1RfX6/Nmzdr6tSpCg8P15w5c845t6GhQREREV65b3x8vFeuAwDNQYUAuAS73a6kpCR17dpVv/jFL5SZmam33npL0jdl/ieffFLJycnq0aOHJKm0tFS33Xab4uLiFB8frzFjxujo0aPuazqdTs2aNUtxcXFKSEjQQw89pG+/VuTbLYP6+no9/PDDSklJkd1uV1pampYvX66jR49q2LBhkqR27drJZrNp0qRJkiSXy6Xc3FylpqYqKipK/fr102uvveZxn82bN6t79+6KiorSsGHDPOJsrocffljdu3dXmzZt1K1bN82dO1eNjY3nnPfSSy8pJSVFbdq00W233aaqqiqP46+++qp69uypyMhIpaen68UXXzQdC4CWISEATIqKilJDQ4P787Zt21RUVKStW7dq48aNamxsVFZWlmJiYrRr1y69++67io6O1k033eQe99xzzyk/P1+/+93vtHv3bp08eVJvvvnmRe97xx136L/+67+0ZMkSHTp0SC+99JKio6OVkpKi119/XVLTK6ZPnDihxYsXS5Jyc3O1cuVKLVu2TB9//LFmzpyp22+/XTt27JDUlLiMHTtWo0ePVmFhoaZMmaJHHnnE9H8nMTExys/P19///nctXrxYr7zyihYtWuRxzuHDh7V27Vpt2LBBb7/9tg4ePKh7773XfXzVqlWaN2+ennzySR06dEhPPfWU5s6dq9///vem4wHQAn5+2yIQ0HJycowxY8YYhmEYLpfL2Lp1q2G3243Zs2e7jycmJhr19fXuMX/4wx+MHj16GC6Xy72vvr7eiIqKMrZs2WIYhmF07NjRePbZZ93HGxsbjc6dO7vvZRhNr4aePn26YRiGUVRUZEgytm7det44z76O98svv3Tvq6urM9q0aWPs2bPH49zJkycbEyZMMAzDMObMmWP06tXL4/jDDz98zrW+TZLx5ptvXvD4r371K2PgwIHuz/PnzzdCQ0ONY8eOuff9+c9/NkJCQowTJ04YhmEYV111lbF69WqP6yxcuNDIyMgwDMMwiouLDUnGwYMHL3hfAC3HHALgEjZu3Kjo6Gg1NjbK5XLpP/7jP/TYY4+5j/fp08dj3sAHH3ygw4cPKyYmxuM6dXV1OnLkiKqqqnTixAkNGjTIfSwsLEzf+973zmkbnFVYWKjQ0FANGTKk2XEfPnxYX331lW688UaP/Q0NDbr22mslSYcOHfKIQ5IyMjKafY+z/vSnP2nJkiU6cuSIampqdObMGTkcDo9zunTpok6dOnncx+VyqaioSDExMTpy5IgmT56su+++233OmTNnFBsbazoeAOaREACXMGzYMC1dulQRERFKTk5WWJjn/2zatm3r8bmmpkYDBw7UqlWrzrlW+/btWxRDVFSU6TE1NTWSpE2bNnn8IZaa5kV4S0FBgSZOnKgFCxYoKytLsbGxWrNmjZ577jnTsb7yyivnJCihoaFeixXAhZEQAJfQtm1bpaWlNfv8AQMG6E9/+pM6dOhwzrfkszp27Kh9+/bphhtukNT0TfjAgQMaMGDAec/v06ePXC6XduzYoczMzHOOn61QOJ1O975evXrJbrerpKTkgpWFnj17uidInrV3795L/5D/nz179qhr16569NFH3fs+//zzc84rKSlRWVmZkpOT3fcJCQlRjx49lJiYqOTkZH322WeaOHGiqfsD8A4mFQJeNnHiRF1xxRUaM2aMdu3apeLiYm3fvl3333+/jh07JkmaPn26nn76aa1fv16ffPKJ7r333ouuIXDllVcqJydHd911l9avX+++5tq1ayVJXbt2lc1m08aNG/XPf/5TNTU1iomJ0ezZszVz5kz9/ve/15EjR/T+++/rt7/9rXui3j333KNPP/1UDz74oIqKirR69Wrl5+eb+nmvvvpqlZSUaM2aNTpy5IiWLFly3gmSkZGRysnJ0QcffKBdu3bp/vvv12233aakpCRJ0oIFC5Sbm6slS5boH//4hz788EOtWLFCv/nNb0zFA6BlSAgAL2vTpo127typLl26aOzYserZs6cmT56suro6d8XggQce0M9+9jPl5OQoIyNDMTExuvXWWy963aVLl+rHP/6x7r33XqWnp+vuu+9WbW2tJKlTp05asGCBHnnkESUmJuq+++6TJC1cuFBz585Vbm6uevbsqZtuukmbNm1SamqqpKa+/uuvv67169erX79+WrZsmZ566ilTP+8tt9yimTNn6r777lP//v21Z88ezZ0795zz0tLSNHbsWN18880aMWKE+vbt6/FY4ZQpU/Tqq69qxYoV6tOnj4YMGaL8/Hx3rAB8y2ZcaBYTAAD4zqBCAAAASAgAAAAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQCQEAAJD0/wB0rFYaeYxjUAAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["ConfusionMatrixDisplay.from_predictions(y_val,y_val_pred)"]},{"cell_type":"code","execution_count":17,"id":"b958ba4a","metadata":{"id":"b958ba4a","executionInfo":{"status":"ok","timestamp":1721139006478,"user_tz":-60,"elapsed":442,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/","height":467},"outputId":"ea0d77f9-5f99-45bd-cf20-f80f884d42ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x79c01d4de4a0>"]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}],"source":["RocCurveDisplay.from_predictions(y_val, y_val_pred)"]},{"cell_type":"markdown","id":"cad1f965","metadata":{"id":"cad1f965"},"source":["# Trying SVM"]},{"cell_type":"code","execution_count":18,"id":"3fb4e46a","metadata":{"id":"3fb4e46a","executionInfo":{"status":"ok","timestamp":1721139118915,"user_tz":-60,"elapsed":112443,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"07088a7d-b34c-408f-d33b-dafbb23ea65f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Validation accuracy: 0.8636363636363636\n"]}],"source":["from sklearn.svm import SVC\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.metrics import accuracy_score\n","\n","# Create an SVM classifier object\n","svm = SVC()\n","\n","# Define the parameter grid for grid search\n","param_grid = {\n","    'C': [0.1, 1, 10],\n","    'gamma': [0.1, 1, 10]\n","}\n","\n","# Set up grid search to find the best hyperparameters\n","grid_search = GridSearchCV(svm, param_grid, cv=150, n_jobs=4)\n","\n","# Train the grid search object on the training set\n","grid_search.fit(X_train_vec, y_train)\n","\n","# Retrieve the best model from the grid search\n","best_svm = grid_search.best_estimator_\n","\n","# Predict on the validation set using the best model\n","y_val_pred = best_svm.predict(X_val_vec)\n","\n","# Calculate and print the validation accuracy\n","val_acc = accuracy_score(y_val, y_val_pred)\n","print(\"Validation accuracy:\", val_acc)"]},{"cell_type":"code","execution_count":19,"id":"44d340ee","metadata":{"scrolled":false,"id":"44d340ee","executionInfo":{"status":"ok","timestamp":1721139119317,"user_tz":-60,"elapsed":422,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/","height":467},"outputId":"e9595747-40dd-4e85-ae67-e1c5c2c147e4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x79bfd6967b50>"]},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["ConfusionMatrixDisplay.from_predictions(y_val,y_val_pred)"]},{"cell_type":"code","execution_count":20,"id":"183f7f05","metadata":{"scrolled":false,"id":"183f7f05","executionInfo":{"status":"ok","timestamp":1721139119317,"user_tz":-60,"elapsed":26,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/","height":467},"outputId":"4d061fcd-3d08-4961-cf9e-0d6533a759d8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x79bfd69ae2c0>"]},"metadata":{},"execution_count":20},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}],"source":["RocCurveDisplay.from_predictions(y_val, y_val_pred)"]},{"cell_type":"markdown","id":"bbe2de29","metadata":{"id":"bbe2de29"},"source":["# Trying RandomForestClassifier"]},{"cell_type":"code","execution_count":21,"id":"60974043","metadata":{"id":"60974043","executionInfo":{"status":"ok","timestamp":1721139543619,"user_tz":-60,"elapsed":424325,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"5bc54608-af2e-4314-88c6-5a5979e97403"},"outputs":[{"output_type":"stream","name":"stdout","text":["Validation accuracy: 0.8560606060606061\n"]}],"source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.metrics import accuracy_score\n","\n","# Create a Random Forest classifier object\n","rf = RandomForestClassifier()\n","\n","# Define the parameter grid for grid search\n","param_grid = {\n","    'n_estimators': [100, 500, 1000],\n","    'max_depth': [None, 5, 10],\n","    'min_samples_split': [2, 5, 10]\n","}\n","\n","# Set up grid search to find the best hyperparameters\n","grid_search = GridSearchCV(rf, param_grid, cv=10, n_jobs=4)\n","\n","# Train the grid search object on the training set\n","grid_search.fit(X_train_vec, y_train)\n","\n","# Retrieve the best model from the grid search\n","best_rf = grid_search.best_estimator_\n","\n","# Predict on the validation set using the best model\n","y_val_pred = best_rf.predict(X_val_vec)\n","\n","# Calculate and print the validation accuracy\n","val_acc = accuracy_score(y_val, y_val_pred)\n","print(\"Validation accuracy:\", val_acc)"]},{"cell_type":"code","execution_count":22,"id":"322ee4e7","metadata":{"id":"322ee4e7","executionInfo":{"status":"ok","timestamp":1721139544098,"user_tz":-60,"elapsed":497,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"195b18ea-a8d2-4bbd-b417-d53212a98ef0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x79c007e2e200>"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["ConfusionMatrixDisplay.from_predictions(y_val,y_val_pred)"]},{"cell_type":"code","execution_count":23,"id":"5d423528","metadata":{"id":"5d423528","executionInfo":{"status":"ok","timestamp":1721139544425,"user_tz":-60,"elapsed":335,"user":{"displayName":"Chenguiti El Mehdi","userId":"15234694394806801596"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"a8492af2-3830-4ff0-f5f6-b363f34e8a62"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x79c01ccfcd90>"]},"metadata":{},"execution_count":23},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}],"source":["RocCurveDisplay.from_predictions(y_val, y_val_pred)"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}